esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Structural and Morphological Properties of the Power Particles
#### 3.1.3 Scanning Electron Microscopy  
Figure 5 shows the SEM images of C, Mo, and W elemental powder particles. The average particle size calculations and the Gaussian fit distribution for the raw materials were carried out using the \begin{table}
\begin{tabular}{c c c c c c} \hline
**Element** & _a (V)_ & _n_*(_a.u. **)*(_a_) & \(G\) (10\({}^{10}\) N m\({}^{-2}\)) & \(K\) (10\({}^{10}\) N m\({}^{-2}\)) & _V_**(cm\({}^{3}\) mol\({}^{-1}\))** & _T (K)_ \\ \hline Mo & 4.65 & 1.77 & 2 & 23 & 9.33 & 2896 \\ W & 4.80 & 1.81 & 1.61 & 31 & 9.47 & 3695 \\ \hline \end{tabular}
**density units.  
\end{table} 
Table 2: Parameters for thermodynamic calculations in the MoW system [32; 33]. Figure 4: Gibbs free energy change for the formation of the solid solution and amorphous phase in the MoW system during mechanical alloying (MA) as calculated by Equations (9)–(11). Imagej and Origin software. The C particles were flake-like and their average size was approximately 6.029 \(\upmu\)m with a standard deviation (Std. Dev (\(\sigma\))) of 2.8583. Mo and W showed a semispherical morphology with particle diameter sizes of about 15.18 \(\upmu\)m (\(\sigma=4.7581\)) and 19.41 \(\upmu\)m (\(\sigma=5.1682\)), respectively. Table 3 shows that the average size of the C, Mo, and W powder particles at 0 h of milling was 13.54 \(\upmu\)m.  
Figure 6 shows the SEM micrographs of the powders milled at 75, 125, and 150 h. The average particle size calculations for the different milling times were made through the ImageJ software. At 75 h of milling (Figure 6a,b), there was a wide distribution of particle sizes. The largest particles with flake shape corresponded to the graphite phase, surrounded by smaller particles with irregular shapes corresponding to the metals. The micrographs in Figure 6a,b showed no particles with equiaxial grains, which is a characteristic morphology of the raw materials; these particles are fractured powder particles caused by the milling process of ductile metal particles, which tends to flatten the shape of this material. At this stage of milling, the average particle size was approximately 9.32 \(\upmu\)m. At 125 h of milling (Figure 6c,d), there was a heterogeneous size particle distribution. This type of distribution was characteristic of fracture, and this was the dominant mechanism at this stage. During MA, fracture and excision are widespread processes. The plate shapes shown in Figure 5 indicated a strong dependence of Mo's mechanical behavior and how graphite was introduced into the MoW matrix;  
Figure 5: Scanning electron microscopy (SEM) micrographs of un-milled powders particles: (**a**) C, (**b**) Mo, and (**c**) W. Particle size distribution histograms with Gaussian fit of (**d**) C flakes, (**e**) Mo, and (**f**) W particles.
## 6 Future Research Directions
### Improvement of the Computational Efficiency of InSAR  
In the context of dynamic LSM and landslide prediction, the processing and analysis of InSAR data must meet higher demands for computational efficiency. The processing of InSAR data, particularly when integrating observations from multiple platforms, can be computationally intensive due to the large volumes of data, the need for precise phase unwrapping, and the challenges associated with maintaining coherence over time. Enhancing the computational efficiency of InSAR processing allows for timely and accurate landslide susceptibility assessments and facilitates near real-time monitoring and early warning systems. In scenarios where rapid response is critical, such as in landslide-prone areas, enhancing computational efficiency can significantly improve the effectiveness of InSAR-based monitoring.  
One promising direction is the development of advanced algorithms that optimize the processing of large-scale InSAR datasets. For instance, parallel computing and distributed processing techniques can significantly reduce the time required for InSAR data analysis [152; 153]. By leveraging high-performance computing (HPC) resources, scholars can process extensive datasets more quickly, enabling real-time monitoring and more frequent updates of susceptibility maps. In addition, an optimized allocation of computational resources may also serve to enhance the efficiency of InSAR processing. Developing adaptive algorithms that dynamically adjust their processing strategies based on the data characteristics may also improve efficiency. These algorithms could prioritize areas with significant deformation or a higher risk of landslides, reducing the computational load by focusing resources where they are most needed.  
Another issue for future research is the development of more efficient data compression and storage solutions. Given the high volume of InSAR data, effective compression techniques that maintain data integrity while reducing storage requirements can enhance both processing efficiency and accessibility. Additionally, cloud-based platforms for InSAR data managing and processing can provide extensible solutions that accommodate the increasing data volumes associated with dynamic landslide studies [154].
## 3 Results and Discussion
### Stability of Grain Structure  
The steep drop in stress range of P91 during the first 10 cycles (Figure 3a,b) does not correlate to any visible sub-grain activity (cf. Figure 4a: 0 cycles to Figure 4b: 10 cycles) within the martensite lath structure. The decrease in stress range for this reason is caused by recovery of excess dislocations at the hot ends of the TMF cycles (cf. Figure 3c) alone. The start of degradation of the martensite lath structure appears in transition from the initial to the stable stress range section of the fatigue curve and becomes progressive with an increasing number of cycles (cf. Figure 4c: 250 cycles and 4d: 1250 cycles). Cyclic plasticization causes polygonization of the martensite lath structure. The resulting grain size distribution rather approximates that of the initial martensite lath width distribution (cf. Figure 4a-d) than the prior austenite grain size, which is in accordance with the results of [4, 11, 12, 13, 14] acquired at other AFM steels under TMF loading.  
The iron- and chromium-containing mixed oxides within the crack do not play a role in microstructural degradation of the bulk material and the initiation of short fatigue cracks. A possible effect on the propagation of long fatigue cracks and residual lifetime is the subject of ongoing research.  
The evolution of grain aspect ratio AR (ratio of grain length to grain width, Figure 5) of the martensite laths is the most suitable microstructural feature for quantification of this phenomenon. Elongated martensite laths of AR \(\geq\) 3 (clearly observable after 0, 10 and 250 cycles, Figure 4a-c or  
Figure 4: Binarized images (**a**–**e**) from post-processed electron backscatter diffraction band contrast images (misorientation angle used to define fatigue induced sub-grain boundaries: \(>\)3\({}^{\circ}\), step size: 0.2191 \(\upmu\)m) for the quantitative analysis of martensite lath degradation and complete polygonization of ferritic-martensitic grade 91 steel after 0 (**a**), 10 (**b**), 250 (**c**), 1250 (**d**) and 3500 (**e**) TMF cycles. (**f**): Sub-grain orientation added to (**e**).  
Figure 5) gradually disappear with an increasing number of cycles and become substituted by smaller, almost globular sub-grains of AR \(\to\) 1 (Figure 4d,e: 1250, 3500 cycles, Figure 5). This phenomenon is not restricted to the strongly deformed areas in front of crack tips or at crack edges (Figure 4e), where large plastic contortions exist. Typically the entire gauge length is found to present polygonization, beginning in the stable stress range phase already, even without the presence of strain concentrators like crack tips [(15; 16)].  
In contrast to this, grain structure and size of the ferritic Crofer(r)22 H remain stable over the entire lifespan (Figure 6), because Laves phase particles effectively hinder dislocation activity. The governing mechanisms will be outlined in detail in the upcoming sections.
## 4 Applications
### Enzyme-Free Biosensors
#### 4.2.4 Gallic and Sialic Acids  
OECTs were applied to quantify less investigated analytes such as total polyphenol content (TPC) from real tea samples. For example, [PERSON] et al. [109] developed a high sensitive OECT sensor for the detection of gallic acid (3,4,5-trihydroxybenzoic acid) where the Au gate electrode was modified with poly(diallydimethylammonium) chloride (PDDA) and multiple carbon nanomaterials (MWNTs, graphene, and graphite oxide). The functionalization of the gate electrode with PDDA-carbon  
Figure 15: Schematic diagram of an OECT cross-section and the wiring system for device operation (**right**). Channel transconductance (gm) to additions of dopamine with different concentrations (**left**). Reprinted with permission from [143]. Copyright 2017 American Chemical Society.  
nanomaterials composites improved the sensitivity of the OECTs through the enhancement of the electron transfer and the electrostatic attraction between gallic acid and PDDA (Figure 17). While OECTs with either the graphene-PDDA or GO-PDDA modified gate exhibited a detection limit of 30 nM, MWNT-PDDA modified gate showed the best detection limit of 10 nM, 10 times better than that of the OECT with the bare Au gate.
# Brillouin Frequency Shift Sensing Technology Used in Railway Strain and Temperature Measurement
## 3 Methods of Test and Verification
### Description of the Outline  
We installed a distributed fiber optic sensing system (DFOS) in Pingtung Rail Station that was 2 km away from the tested rail (not shown in Figure 2). The DFOS contains a commercial Brillouin optical time domain interrogator, and its measurement parameters were set as: a spatial resolution of 50 cm, a measurement accuracy of 7 \(\mu\epsilon\), a repeatability of 3 \(\mu\epsilon\), and a test speed of 60 s. In addition, the DFOS has a computer responsible for numerical calculations, a computer responsible for large amounts of test data storage and management, and an operating platform for presenting measurement results.  
The test section is a standard 60 kg/m ballast track. The tested rail starts at the expansion joint (EJ) and extends 100 m in the direction of increasing length. It belongs  
Figure 2: The sensing fiber deployment diagram: the location and length of the tested rail, reference track, and three (3) installed segments of sensing fibers. The sensing fibers were fuse-spliced into one (1) test path.  
The strain measured from the sensing fiber was mainly from the direction of its horizontal axis, such as with the expansion of rail, but the external force was mainly from vertical axis, such as with the weight and rolling of train. It was necessary to attach sensing fibers to three spatial positions of the rail, which were the main data sources for our strain measurement and displacement analysis.  
In this test, we attached sensing fibers at the left foot, right foot, and upper web of the tested rail. In addition to the measured strain in the long axis direction of the rail, the displacement in the horizontal and vertical directions was calculated based on the magnitude of the strain that was measured by each individual fiber and their symmetrical difference in stress. The fiber attachment positions are shown in Figures 3 and 4.  
We set the measurement reference track parallel to the side of mile 26K101 and kept it 2 m from the tested rail. The reference track and the tested rail were at the same ambient temperature and had the same coefficient of thermal expansion, so they can be regarded as having the same temperature. We placed three sensing fibers on the reference rail in parallel to those of the tested rail; the setting of the reference track is shown in Figure 5.  
As shown in Figure 6, the system was separated into two main functions. The first is measurement, where the server sends command through LAN/WAN to BOTDA to start measuring. After recording the measurement, BOTDA sends the measured data back to the server for analysis and saves them in the database. If the calculated values exceed the alarm level, messages are automatically sent to users with LAN/WAN. The second function is the presentation of results. Users can use a PC/pad/phone to obtain the latest measurement results, which are graphically shown on the device.
## 1 Introduction
This investigation builds upon the research program [12] focused on additive drone manufacturing, utilizing a simple desktop printer and commercially available polymeric materials. The selection of PLA and LW-PLA in this study is also based on the findings of the investigation [12], which systematically evaluated the mechanical and fabrication characteristics of various polymeric filaments for drone applications. PLA was identified as a favorable material due to its non-toxicity, ease of processing, and cost-effectiveness, while LW-PLA has demonstrated promising potential for weight-sensitive applications. [PERSON] et al. [12] provided a comparative overview of key material properties, including tensile strength, modulus of elasticity, and thermal behavior. These findings, combined with the materials' compatibility with desktop FFF printers, support the selection of PLA for the current study, which focuses on enhancing deformation energy absorption in drone fuselage components. Recent studies have further confirmed the suitability of PLA-based materials for drones, highlighting their favorable strength-to-weight ratio, printability, and adaptability to complex geometries [31; 32; 33]. Additionally, LW-PLA has been increasingly recognized for its potential in lightweight drone applications, particularly where energy absorption and structural efficiency are crucial [2; 32; 33]. This experimental study assesses the feasibility of utilizing low-density polymers for drone fuselage components. Through controlled fabrication utilizing FFF technology and mechanical testing under monotonic and repeated compression, this research assesses the impact of internal structural modifications on the deformation energy absorption and mechanical resistance. It employs two types of polymeric materials, PLA and LW-PLA. A simplified example showcases the DfAM potential, while theoretical calculations evaluate the energy released during a drone collision. Although this energy is not a design condition, it serves as a comparative benchmark for assessing the performance of various metastructural configurations, combining different density parts to meet the targeted weight while enhancing the energy absorption and mechanical resistance of the FFF fuselage segment.
## 7 Conclusions  
Optical Backscatter Reflectometry OBR was successfully applied to measure strains during curing in large and small blocks of epoxy. New and unexpected curing behavior was found, made possible by the high spatial resolution of this method.  
* The OBR method gives qualitatively the same consistent results compared with point measurements on small samples using fiber Bragg gratings [9; 10].
* The OBR method gives considerably more information than previous point measurements, because it yields the strain along the whole length of the fiber reaching from the edges to the center of the specimen, or wherever the fiber is put.
* The curing strains inside a small and large epoxy block behave differently.
* For small samples, the measured strain along the middle axis of a curing epoxy block behaves as expected from previous studies using other strain measurement methods. The strain profile develops a \"bathtub\" shape, which is preserved during the cool down of the sample.
* For larger specimens (20 to 60 cm long), curing strains do not show a bathtub-shape but they describe a \"W-shape\" during the cooling to room temperature. The strain on the sides behaves similar to the known observations from small samples. However, a strain-plateau develops in the middle approximately 5 cm to 8 cm away from the sides that was not identified before. The strains in the plateau region were notably different from the sides of the large specimens and they also did not correspond to strains measured on small samples.
* The thermal shrinkage in the center of large specimens under curing does not behave as expected from measurements of small volumes of epoxy. This is illustrated by the thermal coefficient of expansion being half an order of magnitude smaller above the glass transition temperature compared to below it. The expected behavior is an increase of the thermal expansion coefficient. A possible explanation of this anomaly is that thermal shrinkage under curing in this area is governed by the of the surrounding steel vessel.
## 3 Results and Discussion
### Compressive Strength  
Figure 3 shows the compressive strength of the single OPC and OPC-CSA binary blended cement under the effects of NC and AS. It can be seen that the addition of NC slightly decreased the compressive strength of hardened OPC paste at the NC dosages of 1.5% and 3.0% (Figure 3a), which may be related to the ineffective dispersion and increased agglomeration of NC. It is reported that the reactivity and workability of nanoparticles strongly depend on their dispersion state [25]. Moreover, the insufficient nanoparticles could agglomerate and reduce their capacity to act as nucleation sites. Besides, the increased agglomeration could be a defect inside HCP and then decrease the strength [20]. When both NC and AS were added (Figure 3b), enhanced strength was found at an early age (till 3 d) compared to that with only AS. Although the sample with 3% NC and 2% AS still shows a lower strength compared to that without NC at 28 d, the incorporation of AS obviously minimized this gap from 20.1% to 9.2%.  
Regarding the OPC-CSA binary blended cement, an obvious reduction in strength was observed at 1 d and 3 d compared to OPC (Figure 3c), which indicates that the addition of CSA has a strongly negative effect on the development of early strength. It could be caused by the fast hydration of aluminate-contained phases in CSA (for example, ye'elimite), which hinders the normal hydration of the silicate phase [12]. Therefore, the strength development was restricted. Only after the extended curing age of 7 d was a comparable strength found to OPC and OPC-CSA. Moreover, different from the condition with only OPC, the addition of 1.5% NC significantly increased the strength at 7 d and 28 d compared to that without NC. With the combined addition of NC and AS (Figure 3d), the strength was significantly improved after 8 h of hydration, and the OPC-CSA exhibited higher strength compared to that of the OPC at a later age. It indicated the passivation on the silicate  
Figure 3: Compressive strength of the OPC and OPC-CSA binary blended cement with or without the addition of NC and AS; (**a,b**) single OPC; (**c,d**) OPC-CSA.  
phase reaction was eliminated through the addition of NC. The accelerated silicate phase hydration in the presence of NC has also been reported by many researchers [20; 33; 34]. The main underlying mechanism is that the presence of NC can provide more adsorption sites for Ca\({}^{2+}\) and rapidly adsorb Ca\({}^{2+}\) in the pore solution. Thereby, the concentration of Ca\({}^{2+}\) around the silicate phases was reduced, and then the hydration of the silicate phases was further accelerated. In addition, the corresponding setting time in Figure 2 was also significantly shortened compared to that of OPC, which confirms the effectiveness of NC in improving the early strength of OPC-CSA binary blended cement and the possibility of achieving rapid setting and hardening paste with the combination of NC and AS.
### Applications of Machine-Learning Methods to Porous Materials  
Recently, many approaches have been presented in the literature that employ machine-learning techniques to porous media modeling. Some examples are presented here. In [37], an approach is presented to design an acoustic sink, i.e., a duct-shaped structure filled with porous material, with desired acoustic behavior using a convolutional neural network in an encoder-decoder setting. The acoustic sink is modeled using a combination of transfer matrix method and the JCA model. The authors show that the obtained model is able to derive the geometrical parameters of the acoustic sink for a given absorption spectra, and can predict the absorption spectra for a given set of geometry parameters. In [38], a deep-learning approach is pursued to estimate the parameters for water-saturated porous material from ultrasound tomography data. The authors show the accuracy of the approach, even for a wide range of material parameters. A direct prediction of the absorption coefficient of a layered porous material sample by means of a neural network is shown in [39]. Therefore, the neural network requires only the density and flow resistivity of the porous materials and macroscale geometrical parameters, such as thickness and sequence of the layers. The authors obtain a good agreement with measured absorption coefficients. A similar approach with a sparse amount of input data is presented in [40]. The authors show the use of neural networks to predict the absorption coefficient and surface impedance of porous materials based only on the frequency and the flow resistivity. Although obtaining good results, the authors note that the developed models only suffice for a given class of materials, and do not necessarily have the ability to generalize for other material classes. An application of neural networks for predicting the absorption coefficient of natural fibers is presented in [41]. The authors model the porous material using a phenomenological approach and with a neural network, and verify better prediction quality of the neural network. An approach concerned with the characterization of porous materials with inclusions using a Gaussian-process-based machine-learning approach is presented in [42; 43]. The authors model the porous material using both the model of Delany & Bazley (with the Miki improvement) and the JCA model. It is shown that the improvement introduced by using the JCA model compared to the Delany & Bazley model allows the machine-learning model to reach a better prediction quality along with less training effort. The resulting models are used for predicting the acoustic behavior with regard to the transmission loss.