esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Telemetric data collection has been widely used in applications where human participation is cumbersome, dangerous or unfeasible. Many examples exist in the arena of spaceflight and planetary exploration, but the application to biological payloads has thus far been restricted to the collection of standard white-light photography that is limited to reporting the surface characteristics of the specimens involved. The incorporation of imaging technologies that enables the excitation and collection of specialized wavelengths greatly expands the range of scientific information that can be returned from a payload telemetrically. Organisms engineered with GFP reporter genes used in conjunction with fluorescent imaging systems can create a system of biological sensors that telemetrically report on the patterns of gene expression occurring in real time while adapting to a novel environment. The insights obtained with the integration of TIS-II into the ACMG have provided initial proof of concept for telemetric collection of fluorescent biosensor images. While the ultimate goal is to create a fully integrated system that can be deployed in any environment to collect real-time data on the physiological and molecular biological responses, intermediate development areas include improvement of fluorescent image data processing, reducing the form and power requirements for imaging, and producing engineering designs that address specific monitoring needs.
## 2 Materials and Methods
### Testing of Various Instruments  
We tested the performance of both GPR and AML systems at the certified underground utility detection test site operated by Sungkyunkwan University. This site provides a standardized environment for evaluating whether detection systems comply with South Korea's regulatory standards, including minimum detection depth, horizontal and vertical accuracy, and object classification capability.  
GPR (Ground-Penetrating Radar) is a non-destructive testing method that transmits high-frequency electromagnetic waves through the ground using transmitting and receiving antennas. These waves interact with subsurface structures, allowing for the detection of buried objects based on signal reflection and time delay. GPR has been widely adopted for applications such as utility mapping, tunnel inspection, and structural health monitoring due to its rapid data acquisition, high precision, ease of deployment, and cost efficiency [9; 13].  
The Sungkyunkwan University Geospatial Technology Center has operated its certified performance evaluation site since 2010. The 25 m by 25 m testbed is divided into two dedicated zones: one for metallic conduits and another for non-metallic conduits, including PVC and PE pipes. This configuration enables comprehensive testing under realistic underground conditions [13].  
In accordance with national standards, the standard depth for conduit installation is 3 m. To simulate varying site conditions, additional conduits have been installed at a depth of 2 m. All pipelines were constructed in compliance with legal specifications, including pipe diameter, wall thickness, and material type. The backfill material consists of a 30 cm layer of compacted decomposed granite soil, known for its favorable electromag  
Figure 5: Underground utility performance test site and the locations of the installed utilities.  
netic and acoustic transmission characteristics. A 5 cm asphalt overlay simulates urban pavement environments.  
To ensure the accurate spatial positioning of test targets, a total station was used to record the horizontal and vertical coordinates of each conduit, generating a 3D reference model. This allows for reliable comparison between the different types of detection equipment and facilitates precise accuracy evaluation.  
Figure 6 presents the experimental flowchart for subsurface utility detection using AML measurements and subsequent data analysis.
## 2 Practical Considerations on the Implementation of Matrix Converters
Another typical concern when designing matrix converters is the protection circuit. In the particular case of direct matrix converters if there is a switch malfunction and an inductive load current path is suddenly interrupted, it may result in overvoltages. Several techniques have been proposed to address this problem. The most usual is the use of a clamper, consisting of a set of two diode rectifiers, one connected to the matrix converter input phases while the second is connected to the matrix converter output phases, featuring a common DC link capacitor to mitigate the transient overvoltages. Another solution is the use of input and output varistors. Also, the use of dedicated semiconductor firing sequences can ultimately lead the converter to a safe state, for example, a vector leading to zero output voltage or a different modulation strategy [5; 6; 7].
## 3 Results and Discussion
### Thermal Properties of Nanocomposite Films  
The weaker thermal stability of biopolymers, particularly natural ones, compared to petroleum-based polymers is one of the main reasons for their limited application in the packaging sector [1; 8]. Therefore, to improve their thermal behavior, it is possible to blend them with different polymers and/or to add suitable fillers. The acquired thermal properties of neat and starch-PVA films are compared in Table 1.  
Figure 3: XRD patterns of (**a**) starch-PVA film, (**b**) starch-PVA loaded with 1% CNPs, (**c**) starch-PVA film loaded with 3% CNPs, and (**d**) starch-PVA film loaded with 5% CNPs.  
A progressive decrease of the glass transition temperature was revealed when increasing the CNP loading amount, in agreement with previous works related to polyethylene (PE)- and polylatcide acid (PLA)-based nanocomposites [8; 32] and to CNP-loaded gelatin or starch [29; 33]. This experimental evidence has been ascribed to the microstructure of the films and, in particular, to the nanofillers' positioning in free intramolecular voids of the polymer matrix [8], as well as to the higher mobility of their polymer chains [34]. The melting point (T\({}_{\text{m}}\)) and melting enthalpy (\(\Delta\)H\({}_{\text{m}}\)) also strongly influence the thermal behaviour. As seen in Table 1, the films' melting temperature is reduced by the addition of the CNPs, in particular, from 180.2 to 173.9 \({}^{\circ}\)C in the case of neat and 5% loaded starch-PVA films. Concerning the melting enthalpy, a gradual increase was recorded with the increment of the CNP amount. This finding was associated with a higher crystallinity degree and is in agreement with XRD results (Figure 3), suggesting a good filler dispersion [31], as supported by the related SEM micrographs (Figure 2).
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 5 Discussion
### Detection Accuracy Performance  
Accuracy is an important indicator for a pallet recognition algorithm. The best algorithms should be useful at long detection distances and large observable angles. A static experiment was performed to test the relationship between accuracy and pallet pose parameters. A dynamic experiment was performed to explore the influence of changing distance and angle on the algorithm.  
In the static and dynamic accuracy experiments, the detection accuracy was associated with the detection distance and the angle. The error had a positive correlation with angle of the pallet. As the detection distance was near the edge of the depth sensor's effective range, the depth sensor's detection mistakes affected the detection accuracy.  
One reason for the increasing amount of detection errors as the pallet angle increased could have been the length change in pallets in the image with the angle change. Only one shape template was created to match the rotation of pallets. The length of the template was longer than that of the pallets in the image according to the projection principle. This led to the pallet foot measure-points moving, and the measurement error became larger as the rotation angle increased.  
In the dynamic accuracy experiment, the detection error fluctuated with the angle change. The performance in the dynamic test matched the tendency shown in the static test. There may have been three major reasons for the error in the dynamic test being larger than that in the static experiment.  
The first reason was the size error between the template and pallet in images with the change in distance and angle. The second reason was the error in the transformation matrix between the RGB-D sensor and the location sensor, which caused the error to increase when the distance was longer. The final reason was the communication delay between the RGB-D sensor and location sensor, during which location data with noise affected the detection results.
## 4 Steady Flow Force Compensation Optimization
### Flow Field Analysis of Convex Shoulder Compensation Structures  
The convex shoulder structure is widely used in hydrodynamic compensation. This section analyzes the flow field of a slide valve that utilizes only the convex shoulder structure. Considering that the jet angle of the fluid in the ideal slide valve model is \(69^{\circ}\), a shoulder with an additional \(21^{\circ}\) is added at the non-throttling valve port for this study. Figure 19 illustrates the flow field velocity contours for the three-shoulder compensation structure at different openings. Compared to the original structure, the added shoulder significantly guides the fluid within the chamber, resulting in a concentrated exit jet. As the valve opening increases, the jet velocity increases while the jet angle decreases.  
Figure 20 presents the simulated flow force values for the slide valve with the three-stage shoulder compensation structure and the original structure, compared with the empirical formula values. The three-shoulder design reduces steady flow forces in both the inlet throttling and outlet throttling modes. To further understand the compensation mechanism of the shoulder structure, Figure 21 illustrates the jet angle and axial velocity of  
Figure 19: Velocity clouds of three shoulder-compensated structures at different openings \(x_{y}\), from left to right: \(x_{y}\) = 0.2, 0.6, 1, 1.4, respectively.  
Figure 17: Variation in steady flow force and flow rate with respect to \(\lambda_{1}\).  
Figure 18: Variation in steady flow forces with \(\lambda_{1}\) for throttling and non-throttling valve ports.  
the fluid at the non-throttling outlet in the inlet throttling mode. From the figure, it can be observed that the newly added shoulder structure shortens the axial length of the jet at the non-throttling outlet, significantly increasing the fluid velocity along this axial length. In the original structure, the jet angle of the fluid only elevates near the end of the chamber flow path where the velocity is lower (close to the chamber wall). The three-shoulder structure enables a rapid increase in the jet angle of the fluid in the axial direction, thereby enhancing the momentum of the fluid at the non-throttling outlet and effectively compensating for the steady flow forces.
## 3 Proposed Method
### Loss Function  
In order to help the network to better improve performance, it is very important to design a good loss function. Our network utilizes the L1 loss function, which has good convergence properties and high robustness, to optimize the network. For a reconstructed depth map \(I^{SR}\) obtained through network reconstruction and an original high-resolution image \(I^{GR}\), the loss function expression is as follows:  
\[L(\theta)=\frac{1}{N}\sum_{i=1}^{N}\
orm{I^{GR}_{i}-I^{SR}_{i}}_{1} \tag{11}\]  
Figure 3: Multi-scale feature optimization module. The multi-scale features of the feature map are extracted through the interweaving and fusion of different paths.  
where \(\theta\) represents the network parameters that need to be optimized, \(\left\|{{{l}_{i}^{HR}}-{{l}_{i}^{SR}}}\right\|_{1}\) denotes the loss of the i-th original high-resolution image and the image reconstructed by the network.
# Metal-Organic Frameworks Toward Electrocatalytic Applications
## 4 Recent Progress in Utilizing MOFs for Electrocatalysis and Relevant Applications
### Oxygen Reduction  
Electrochemical oxygen reduction plays an important role in electrochemical energy conversion in fuel cells. The kinetics of oxygen reduction that involves four-electron transfer is usually very slow, therefore, efficient electrocatalysts are required on the cathodes of fuel cells [6]. Platinum is the most common electrocatalyst for oxygen reduction; other less price alternatives include metal oxides, metal sulfides, metal nitrides, and nitrogen-doped carbons [7; 102]. As the oxygen can also get reduced through the two-electron pathway to produce the undesired hydrogen peroxide, in addition to achieving the low overpotential and high catalytic current density, the number of electrons transferred is also crucial for these electrocatalysts. In 2012, [PERSON] et al. demonstrated the use of a composite material composed of an iron-based MOF and graphene for electrochemical oxygen reduction, and a number of electrons transferred of 3.82 was achieved in alkaline electrolytes [103]. A range of copper-based, cobalt-based, iron-based, and manganese-based MOFs have been directly applied for electrochemical oxygen reduction in alkaline electrolytes and found to show remarkable activity [104; 105; 106; 107; 108]. In 2017, [PERSON] et al. showed that an aluminum-based MOF constructed from cobalt (III) porphyrinic linkers exhibits significant activity for electrochemical oxygen reduction in acidic solutions, and the observed activity is originated from the cobalt (III) porphyrinic centers [109]. [PERSON] and coworkers also demonstrated the use of porphyrinic MOFs for electrochemical oxygen reduction (see Figure 7) [110]. With the Zr-MOF constructed from electrocatalystally active iron (III) porphyrinic linkers, a number of electrons transferred of 3.8 can be achieved in the acetic acid-based electrolyte. On the other hand, [PERSON] et al. utilized the 2D electrically conductive MOF, Ni\({}_{3}\)(HITP)\({}_{2}\) (see Figure 3a for the structure), as the electrocatalyst for oxygen reduction [111; 112]. The conductive MOF was found to reveal chemical stability in alkaline electrolytes and show remarkable electrocatalytic for oxygen reduction, with an onset potential of +0.82 V vs. reversible hydrogen electrode (RHE) and a number of electrons transferred of 2.25.  
Figure 6: The use of a Zr-MOF (metal–organic framework) incorporated with ruthenium-based complex for electrocatalytic oxygen evolution [41]. Reproduced with permission from [41], Copyright John Wiley and Sons, 2017.
# Study on Microwave Deicing of Carbon-Fiber-Modified Concrete under Multi-Factor Coupling Effect
## 1 Introduction
### Research Program  
In this study, carbon fiber concrete with different dosages (0, 0.1%, 0.3% and 0.6%) and lengths (3 mm and 6 mm) was prepared, and the height of a microwave transmitter was used to set the test control group (20 mm, 40 mm and 60 mm). Under the coupling of the two parameters of the ice layer on the surface of the concrete and the initial temperature, a multi-factor coupling test was carried out with carbon fiber, ice, microwave height factor and initial temperature as variables, and the overall temperature of the concrete surface was analyzed. COMSOL Multiphysics software was used to simulate and verify the accuracy and regularity of the experiments.
## 2 Materials and Methods
### Determination of Total Carotenoid Content (TCC) by UV-Vis Spectrophotometry  
Freeze-dried pulp was ground and transferred (100 mg) to a 10 mL Falcon tube. An extractive solvent mixture (1 mL of hexane:acetone in a 1:1 ratio) (Merck, USA) was added. Then, the mixture was shaken in a vortex for 20 s and centrifuged at 14,000\(\times\)\(g\) rpm for 5 min at 4 \({}^{\circ}\)C. This procedure was done several times until the disappearance of the color in the solvent mixture; then the extracts were filtered with Whatman 4 filter paper. Finally, the extractive solvent was added to complete 10 mL in volumetric flasks. The absorbance of the extracts was read in a spectrophotometer Genesys 10S (Thermo Scientific, USA) at 454 nm.  
For the calibration curve, the following procedure was carried out: for the preparation of the stock solution, 0.005 g of the \(\beta\)-carotene standard (Sigma-Aldrich, 99%) was weighed, transferred to a 50 mL volumetric flask, and adjusted with the solvent mixture hexane:acetone (1:1 ratio). Standard solutions of concentration 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5 and 7.5 \(\mu\)g/mL were prepared, and their absorbance was read at 454 nm. The calibration curve, from 13 replicates, was calculated by a least square regression: 0.0222 u.a. intercept; 0.1592 u.a./\(\mu\)g/mL slope; \(R^{2}>0.99\); TCC was expressed as \(\mu\)g/g (\(\mu\)g of \(\beta\)-carotene equivalents/g), calculated according to the following equation:  
\[Total\ carotenoid\ content\ (TCC)\left(\frac{\mu g\ \beta-carotene_{eq}}{g}\right)\ =\ \left(\frac{Abs_{454\ nm}-b}{m}\right)\ast(V/W)\ast\ DF \tag{1}\]  
where \(Abs_{454\ nm}\) is the measured absorbance at 454 nm wavelength; \(b\) and \(m\) are the intercept and slope of the calibration curve, respectively; \(V\) is the extract volume (mL); \(W\) is the weight of the freeze-dried sample (g), and DF is the corresponding dilution factor. Total carotenoid content was corrected to a dry basis or wet basis, according to the moisture content of the sample (freeze-dried or fresh), determined as described in Section 2.3.
## 4 Discussion
### Application Prospects of SPH in Simulating Crack Propagation in Porous Rocks  
In the field of rock crack propagation simulation, the SPH method has shown significant application potential and advantages. By discretizing the continuous medium into particles, SPH can naturally handle complex boundary conditions and material discontinuities, which is crucial for simulating crack propagation in rocks with random pores. Its kernel function approximation and particle interaction mechanism can effectively capture the local mechanical behavior changes during stress concentration around pores, crack initiation, and propagation. Compared with traditional grid-based methods, it can more accurately reflect the complex mechanical response inside the rock when simulating the pore-crack interaction.  
However, this study is based on a two-dimensional model. The rock structures in actual engineering are three-dimensional, and the crack propagation paths and pore distributions in three-dimensional space have more complex geometric shapes and mechanical properties. Therefore, it is of great significance to focus on the development of a three-dimensional SPH high-performance program in future research. During the development process, it is necessary to optimize the particle search algorithm and calculation efficiency to deal with the large increase in the number of particles and calculation complexity in the three-dimensional model. By using advanced computer technology and parallel computing architectures, fast and accurate numerical simulations can be achieved to ensure high-precision results are obtained within a reasonable time. In simulating the crack propagation of real-shaped rocks, the three-dimensional SPH program should be able to accurately characterize the heterogeneity and anisotropy of the rock. By combining geological survey data and rock microstructure analysis, a more realistic rock model can be constructed, considering the influences of different mineral compositions, particle size distributions, and cementation conditions on crack propagation. By introducing multi-scale modeling techniques, the microscopic pore structure can be linked with the macroscopic crack propagation behavior to deeply explore the failure mechanism of the rock under different loading conditions and environmental factors, providing more reliable stability assessment and disaster prediction means for rock engineering, such as underground mining, slope stability, and water conservancy project foundations, and promoting the development and application of rock mechanics in engineering practice. Therefore, future research should focus on developing a high-performance, three-dimensional parallel SPH program.
## 2 Principle
### Propagation Characteristics of Elastic Stress Wave in Cylindrical Bar
#### 2.1.2 Group Velocity Curve  
The phase velocity curve shows the propagation velocity of the equal phase plane of each frequency component of the stress wave. The elastic stress wave excited by an explosion shock wave at the pressure bearing end face of the pressure bar contains rich frequency components. The components with similar frequency will form a wave packet when they propagate. The propagation velocity is called group velocity, which describes the process of energy transmission. The relationship between group velocity and phase velocity is  
\[C_{g}=C+\gamma\frac{dC}{d\gamma} \tag{2}\]  
The group velocity is dimensionless to the elementary theoretical wave velocity, and formula (2) is transformed into  
\[\frac{C_{g}}{C_{0}}=\frac{C}{C_{0}}+\frac{D}{L}\frac{d(C/C_{0})}{d(D/L)} \tag{3}\]  
According to formula (3), the group velocity curve can be calculated from the phase velocity curve shown in Figure 3. It can be seen from Figure 4 that the group velocity curve is non monotonic, and the stress waves of different wavelengths may have the same group velocity. The first-order group velocity curve shows a character of decrease followed by slow increase, which can be summarized as low-frequency fast, medium-frequency slow, and high-frequency approximating constant velocity. In the case of low frequency, \(C_{g}/C_{0}=1\), that is, the wave packet propagation velocity is consistent with the elementary theoretical wave velocity. When the intermediate frequency part is \(D/L=0.857\), the group velocity decreases to the minimum value of \(C_{g}/C_{0}=0.378\). With further decrease of wavelength, the group velocity gradually converges to \(C_{g}/C_{0}=0.576\).  
The phase velocity of the low frequency component of the second-order and above propagation modes is more than \(C_{0}\), but its group velocity is always less than \(C_{0}\). In the low frequency part of the second-order group velocity curve \(D/L<0.334\), \(C_{g}/C_{0}<0\), the wave packet cannot forward propagation along the bar, that is, there is a low-frequency cutoff phenomenon. \(D/L\in(0.334,0.891]\) part increases monotonically, and the peak group velocity is \(C_{g}/C_{0}\)= 0.850. The \(D/L\in(0.891,1.825]\) part monotonically decreases, and the minimum group velocity is \(C_{g}/C_{0}\)= 0.452. Part of \(D/L\in(1.825,10]\) increases monotonously slowly to \(C_{g}/C_{0}\)= 0.616.  
Table 2 is a summary of the peak, valley, and asymptotic values of the first to fourth order group velocity curves. The frequency corresponding to the peak value and valley value of each order group velocity increases in turn.  
Figure 4: The first to fourth order group velocity curves of Poisson’s ratio of 0.287.