esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Functional Group Characteristics of ACM-Stabilized Soya Milk Emulsion  
The ATR-FTIR spectra of soya milk emulsion stabilized with freeze-dried ACM showing the wavelength peaks in the range 4000-400 cm\({}^{-1}\) with the focal functional groups shown in Figure 2b. Also Table 2 gives the wavelength peak changes noted in ACM-stabilized soya milk emulsions. The shift in the amide I at 3336 cm\({}^{-1}\) is similar to the stretching vibrations of hydroxyl groups of hydrophilic amino in the spectra of raw ACM however; the peak is much wider and profound (Table 2). This might result from interactions between the hydroxyl groups of hydrophilic amino of ACM and the soya milk. Similarly, the peak at 2924 cm\({}^{-1}\) in ACM which was ascribed to CH\({}_{2}\) symmetric and anti-symmetric stretching vibrations shifted to a peak 2963 cm\({}^{-1}\) in the ATR-FTIR spectra of soya milk emulsion stabilized with freeze-dried ACM (Table 2). In addition, shifts were observed for amide I at 1638 cm\({}^{-1}\), C-O of carboxyl COO group accepting zero or one H-bond at 1480 cm\({}^{-1}\) and anti-symmetric C-O-C glycoside bond, C-O strong bond stretch at 1183 cm\({}^{-1}\) (Table 2). This phenomenon was described by [32] in the study of changes in the chemical composition of aloe vera and snail mucus composite scaffold using ATR-FTIR spectroscopy. The higher wavelength bands for functional groups of amide A, amide I, C-O of carboxyl, COO group accepting zero or one H-bond, CH\({}_{2}\) symmetric and anti-symmetric stretching vibrations and anti-symmetric C-O-C glycoside bond observed in the spectra of ACM-stabilized soya milk emulsions (Figure 3 and Table 2) might suggest a type of bonding that leads to a stable structure. This is consistent with results observed in TEM images of ACM-stabilized soya milk emulsion (Figure 1c-f).  
Figure 3: Changes in stability of ACM-stabilized 50 mL (_w/w_ soya milk emulsions (**a**) 1 g _w/w;_ (**b**) 3 g _w/w;_ (**c**) 5 g _w/w_ African catfish mucus.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wave Number** & **Wave Number Soya** & & \\
**ACM (CM-1)** & **Milk Stabilized** & **Band Assignments** & **References** \\  & **Emulsion (CM-1)** & & \\ \hline
301 & 3336 & Possible broad peak stretching vibrations of hydroxyl groups of & \\  & & hydrophilic amino acids & [32; 64] \\
2958 & 2963 & Possible mucus/glycopotic CH2 out of plane vibration & [32; 66; 67] \\
2924 & 2963 & Possible CH2 symmetric and out of plane vibration & [32] \\
1634 & 1638 & Possible Amide stretch, \(\beta\)-sheets absorption bands; Amide I & [32; 64] \\
1414 & 1480 & Possible C-O of carbonyl COO group accepting zero or & [29; 32] \\  & & one H-bond & \\
1136 & 1183 & Anti-symmetric C-O-C glycoside bond; C-O strong bond stretch; & [32; 68] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Interactions observed in the functional groups ACM-stabilized soya milk emulsion.
## 2 GNSS Signal Characteristics
### Carrier-to-Noise Ratio  
The Huawei Mate30 sky plot and _C/N\({}_{0}\)_ range of GPS, BDS, and GLONASS carrier L1 in static data collection are shown in Figure 2a. It should be noted that the number and quality of Galileo observations received by the Huawei Mate30 were lower than expected, and even the number of Galileo satellites could not meet the requirements of standard single point positioning (SPP) for some time. Therefore, the Galileo observations were not used in the experiment. The _C/N\({}_{0}\)_ values of GNSS satellites received by the geodetic receiver and Huawei Mate30 vary with elevation ranges from 10 to 90 degrees, as shown in Figure 2b.  
In the Figure 2b, the changing trends for GNSS satellite _C/N\({}_{0}\)_ values of the Huawei Mate30 and geodetic receiver are almost opposite. As the elevation increased, the _C/N\({}_{0}\)_ values of the smartphone were actually decreased. [PERSON] et al. [8] showed the _C/N\({}_{0}\)_ values of the Huawei P20 decrease significantly while the elevation is below 30 degrees, which is even below 10 dB-Hz. However, it seems  
Figure 2: Huawei Mate30 sky plot and the GNSS satellites _C/N\({}_{0}\)_ values of the geodetic receiver and Huawei Mate30. (**a**) Each color corresponds to the _C/N\({}_{0}\)_ range of GNSS L1 band signal. (**b**) The same color on the top and bottom represents the same GNSS satellite. The G, C, and R denote GPS, BeiDou (BDS), and GLONASS, respectively.  
to be improved on the Huawei Mate30; the C/N\({}_{0}\) values of the GNSS satellite are never below 10 dB-Hz in this experiment. Moreover, the C/N\({}_{0}\) values of the Huawei Mate30 do not decrease sharply as shown in [4] when the elevation is above 45 degrees. With the elevation in Figure 2b divided into eight parts on a scale of 10 degrees from low to high, the average difference of C/N\({}_{0}\) values between the geodetic receiver and Huawei Mate30 are \(-\)2.92, \(-\)0.70, 2.86, 9.90, 13.78, 13.95, 16.85, and 15.23 dB-Hz. The overall average difference is 8.62 dB-Hz, which is approximately in line with prior studies [7; 8; 19].
## 2 Related Work & Background
### Dynamic Data Acquisition
#### 2.2.1 Comparison with Existing Architectures and Systems  
To contextualize the proposed system architecture, we reviewed existing industrial solutions and reference frameworks such as RAMI 4.0, the Industrial Internet Reference Architecture (IIRA), and vendor-specific systems by Siemens, Beckhoff, and B&R. While these systems offer robust integration and automation capabilities, many rely on proprietary ecosystems or are primarily geared toward either high-level enterprise integration or low-level control tasks. As such, they often lack the flexibility for integrating heterogeneous data sources--especially when combining legacy controllers, smart sensors, and third-party analytics platforms.  
During our prototyping phase, we evaluated multiple approaches and identified the ibaPDA system as a pragmatic and extensible foundation. The iba platform provides native support for protocols like Profibus, Modbus TCP, and OPC UA, and allows the seamlessintegration of live data streams through external interfaces (e.g., UDP, OPC UA Server). However, our implementation goes beyond the default iba system by introducing semantic abstraction layers, modular edge-to-cloud orchestration, and continuous knowledge modeling using AI-enabled extensions.  
Therefore, our contribution lies not in developing a new vendor system but in architectecting an interoperable, lightweight, and scalable approach tailored to the demands of real-time data acquisition and domain-centric knowledge modeling. This bridges the gap between existing frameworks and practical industrial needs in heterogeneous production environments.
## 2 Rotating Bending Fatigue Test
### Specimens  
The shape and dimensions of a rotating bending fatigue test specimen are shown in Figure 1. A commercial round bar 13 mm in diameter was used and turned into an hourglass shape with a grip diameter of 10 mm, a tip diameter of 4 mm, and a radius of curvature of 10 mm. The surface was then polished with #800, #1200, and #2000 emery paper to remove the turning marks on the tip surface and buffed with 3 \(\upmu\)m abrasive alumina to finish the surface. The elastic stress concentration factor in rotating bending fatigue is \(K_{t}=1.055\).
## 2 Related Work
### Room Segmentation  
Room segmentation is key semantic information used in model reconstruction. [PERSON] et al. [10] proposed the segmentation method of rooms, which repeated k-medoids algorithm to cluster sub-sampled pixels, the distance metric for clustering is based on a binary visibility vector of scanning center. [PERSON] et al. [12] presented an approach that established a global affinity measure between cells by diffusion maps, and partitioned rooms by clustering 2D cells iteratively. [PERSON] et al. [13] proposed a method that segmented indoor point clouds into individual rooms by the visibility-based and class-conditional probabilities, which based on initial knowledge of scans and scan positions; however, the over-segmentation occurs in long corridors. [PERSON] et al. [14] proposed an approach that triangulated a 2D sampling of wall positions and separated these triangles into interior and exterior domains. The room segmentation can be obtained by Graph-cut in the triangulated map. However, these methods of room partition are limited to depend on TLS the scanning position, but not for MLS point clouds. [PERSON] et al. [17] employed the hierarchical clustering method for partitioning rooms, which established diffusion maps to merge the over-segmented spaces. The method uses scan trajectories instead of scanner positions. [PERSON] et al. [18] proposed a method that used the timestamp information to determine the visible point clouds of each trajectory point and constructed the energy minimization function for global spatial optimization to complete individual room segmentation. Their method relies heavily on data quality and integrity and has been shown effective in simple scenarios. [PERSON] et al. [19] proposed a comprehensive segmentation method that is created by a morphological erosion and connectivity analysis methods on the floor space, which overcomes over-segmentation in long corridors. Similarly, [PERSON] et al. [22] proposed a fully automatic room segmentation that performed visibility tests by the ray casting between point patches on surfaces to build visibility graph, and then the nodes of this graph are clustered by the Markov Clustering method [21].
# Ras Isoforms from Lab Benches to Lives--What Are We Missing and How Far Are We?
## 5 Missing Pieces in the Ras Puzzle
### One Ras and Many Locations
#### 5.6.2 Ras and Endoplasmic Reticulum  
The Endoplasmic reticulum forms another platform for Ras activation. Ras GRF, a GEF, is present in the ER. Immunofluorescence studies show that Ras GRF1 and RasGRF2 and SOS localize to the ER but not to the Golgi and activate H-Ras [140]. As mentioned in the earlier section, RasGRP mainly activated Ras in the Golgi. A transmembrane signal that retains Ras in the ER, activates MAPK cascade through Raf1 and leads to transformation. In addition to ERK, JNK and AKT are also activated by endomembrane Ras [140]. The kinetics of activation of Ras in Golgi and ER is more or less similar [70]. Signaling through Golgi and the ER regulates Ras signaling through MAPK scaffold. [PERSON] et al. found that in response to oxidative stress and subsequent accumulation of misfolded protein, K-Ras in the ER stimulates unfolded protein response (UPR) and autophagy in human umbilical vein endothelial cells (HUVECs) [144].
# NO Separation Characteristics in Integrated Electromigration Membrane Reactor
## 3 Results and Discussion
### Effect of Absorbent on no Separation Efficiency and Total Mass Transfer Coefficient  
The effect of two different absorbents, NaClO\({}_{2}\) and KMnO\({}_{4}\)/NaOH, on the separation efficiency and total mass transfer coefficient is shown in Figures 8 and 9. It can be seen from Figures 8 and 9 that the corona discharge could improve the NO separation efficiency and total transfer coefficient, and the separation efficiency and total transfer coefficient with NaClO\({}_{2}\) as the absorbent were higher than those with KMnO\({}_{4}\)/NaOH as the absorbent in the condition of discharge or no discharge. This may be due to three reasons: (i) the migration of NO negative ions improved the absorption efficiency in the electric field; (ii) the reaction rate of NaClO\({}_{2}\) with NO was higher than that of KMnO\({}_{4}\)/NaOH with NO [23; 29; 31; 32; 33] ([PERSON] et al. 2000; [PERSON] et al. 2013; [PERSON] et al. 1997; [PERSON] et al. 2009; [PERSON] et al. 2005); (iii) a higher absorption rate can reduce the mass transfer resistance in the liquid phase when the gas flow rate remains fixed and the other experimental conditions are the same.  
was about 57%. It increased by 48.7%, and the total mass transfer coefficient of NO was raised 9.7 times compared with the one in membrane absorption process without discharge.
2. The electromigration of NO negative ions can enhance NO separation and mass transfer. \"When the applied voltage is higher onset voltage, higher discharge voltage has greater separation efficiency and total mass transfer coefficient of NO under experimental\" condition.
3. Regardless of discharge or not, the separation efficiency of NO continuously declined with the increase of gas flow rate and inlet concentration of NO. The total mass transfer coefficient of NO increased first and then reduced with the increase of gas flow rate, while it decreased with the increase of NO inlet concentration.  
Z.W.: Conceptualization, Supervision, Project administration, Writing--Reviewing and Editing, G.S.: Investigation, Data curation, Writing--Original draft preparation. All authors have read and agreed to the published version of the manuscript.  
The work was supported by the National Natural Science Foundation of China (nsfc) under grant (No. 50278072).  
The work described has not been submitted elsewhere for publication, and no conflict of interest exists in the submission of this manuscript.
## 3 Simulation Experiments
### Experimental Results and Analysis
The final loss values of each model, obtained from 20 identical experimental runs, were paired for comparison, and the corresponding _p_-values were calculated to determine the statistical significance of the differences.The experimental results are shown in Table 6.  
The experimental results demonstrate that the _p_-values for the performance comparisons between the TCN-BiLSTM model and all other comparative models were consistently less than 0.05. According to statistical standards, when the _p_-value is smaller than the significance level (commonly set at 0.05), it can be concluded that the observed differences between the two groups are unlikely due to random variations and are statistically significant. This indicates that the TCN-BiLSTM model outperforms all other comparative models in terms of performance.
# Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine
## 2 Materials and Methods
### Data Collection  
The data processing flowchart for this study is presented in Figure 2. First, the cloudless composite Landsat images for 1990, 2000, 2007, and 2019 (Section 2.2.1) and the maximum value of NDVI [19] in the given year were calculated in GEE (Section 2.4). The administrative boundary, forest inventory data, and 1:10,000 topographic map of Pingtan County were obtained from Fujian Forestry Bureau, and socio-economic data were collected from Fuzhou Statistical Yearbook. Second, the classification and accuracy assessment (Section 2.3) were performed in GEE. Except for the field survey data as additional validation in 2007, other samples were all collected in Google Earth Pro by visual interpretation. Finally, the major land cover conversion between 1990 and 2019 was mapped to identify the spatial pattern and process, using maximum NDVI to understand the influence of LULC changes on Haitan Island.  
Figure 2: The flowchart of data processing in this study. SR = Surface reflectance; NDVI = Normalized Difference Vegetation Index; NDBI = Normalized Difference Built-up Index; MNDWI = Modified Normalized Difference Water Index; LULC = Land use and land cover.
## 2 Materials and Methods
### Analysis of Grape Volatile Compounds by HS-SPME-GC-Ms  
Determination of volatile compounds in the musts was carried out by head space solid-phase microextraction (HS-SPME) and their subsequent analysis by gas chromatography (GC) coupled to mass spectrometry (MS), according to the method described by [PERSON] et al., 2018 [34]. The SPME fiber used was divinylbenzene/carboxen/polydime thylsiloxane (DVB/CAR/PDMS, 50/30 \(\mu\)m) (Supelco, Bellenfonte, PA, USA). In 20 mL vials (Supelco), 9 mL of sample, 2.5 g NaCl and 10 \(\mu\)L of internal standard (2-octanol) were added. After adding a stir bar, the vial was closed and placed in the GC-MS (Agilent, Palo Alto, CA, USA). Sample conditioning was done at 60 \({}^{\circ}\)C, for 15 min and with stirring. After this step, the fiber was automatically inserted into the headspace in order to the extraction of the volatile compounds could take place, for 105 min, with agitation.  
After the extraction process was completed, the fiber was immediately introduced into the GC injection port at 250 \({}^{\circ}\)C and held for 15 min for desorption of the compounds of interest. The capillary column used for analyte separation is SPB\({}^{\text{TM}}\)-20 (30 m \(\times\) 0.25 mm I.D. \(\times\) 0.25 \(\mu\)m film thickness) (Supelco). Helium was used as the carrier gas at a flow rate of 1.2 mL/min. The chromatographic conditions used were: initial temperature, 40 \({}^{\circ}\)C for 5 min, a temperature gradient of 2 \({}^{\circ}\)C/min, up to a final temperature of 220 \({}^{\circ}\)C, to be maintained for 20 min (total time = 115 min). The ionization of the volatile compounds was performed at 70 eV. The detector worked at full scan (35--300 \(m/z\)). Identification was carried out using the NIST library and comparing with mass spectra and retention time of chromatographic standards, when available, as well as with data found in the literature. Semi-quantification was performed by relating the areas of each compound to the area and known concentration of the internal standard.  
Since the treatments were performed in triplicate, the results of grape volatile compounds are expressed as the mean concentration and standard deviation of the three replicates (\(n\) = 3).
## 7 Results and Discussion
### The Influence of Volume Fraction Nanoparticle Parameter (\(\mathbf{\phi}\))  
The volume concentration and volume fraction are the same in ideal solutions when the volumes of all of the constituents are added together (the volume of the solution is equal to the sum of the volumes of its ingredients). Prior to mixing, the volume fraction of a specific element (metal nanoparticle) is calculated by dividing the volume of that component (metal nanoparticle) by that of the mixture (basic fluid). The effect of the nanoparticle volume fraction parameter on particle velocity is shown in Figure 11. The speed of the fluid dynamics diminishes as the volume fraction of the nanoparticles of both nanofluids increases. This phenomenon occurs as a result of the aggregate fluid viscosity of the amassed nanofluid concentration, increasing the friction. As a result of this increase, the MWCNT-CMC nanofluid has a higher velocity that that of the SWCNT-CMC nanofluid. Furthermore, the system temperature increase, along with \(\phi\), as shown in Figure 12. Liquid velocity plays an active role in HT. When the mobility of the particles in the fluid is slowed, heat accumulates in the system. As a consequence, the system temperature doubles. Additionally, the entropy of the system is expected to intensify, owing to the increased temperature, as shown in Figure 1. The numerical results presented in Figure 13 reveal that the volume percentage of nanoparticles has a substantial influence on the generated entropy. A comparison of the two nanofluids, reveals that the MWCNT-CMC nanofluid creates less entropy than the SWCNT-CMC nanofluid, as shown in Figure 13. This discovery suggests that MWCNT-CMC nanofluids are superior to SWCNT-CMC nanofluids in terms of controlling the system entropy.  
Figure 10: Entropy variation versus K.
## 5 Experimental Verification
### Qualitative Analysis  
We analyze the performance of SiamFCCA and other tracking methods on the OTB and VOT benchmark datasets. The tracking results of some classic video sequences are shown in Figure 11.  
The aforementioned experimental results show that the proposed SiamFCCA performs efficiently in the presence of background clutter, low resolution, and fast-moving objects compared with the original SiamFC and other tracking methods. This mainly relies on SiamFCCA's effective extraction and fusion of object appearance features, which enhance the convolution kernel features that are conducive to expressing the object characteristics  
Figure 11: Qualitative comparison between SiamFCCA and other tracking methods.  
Figure 10: The SiamFCCA object tracking architecture.  
and reduce the response of interfering objects in the cluttered background. At the same time, based on the suppression of other interference peaks in the response map, the object can still correctly respond to the object position, even when it is moving rapidly.
## 4 Efficiency Management
### Model Construction  
The mathematical model constructed in this paper is as follows:  
\[\text{min}z_{1}=\sum_{i\in I}\sum_{j\in J}d_{ij}c_{1}+\sum_{i\in I}a_{i}c_{3}+ \sum_{v\in V}C_{v}N_{v} \tag{2}\]  
\[\text{max}z_{2}=\sum_{i\in I}\sum_{v\in V}G_{i}(T_{iv})\cdot y_{iv}w_{i} \tag{3}\]  
\[\sum_{i\in I}a_{i}\leq\sum_{v\in V}N_{v}\cdot Q_{v} \tag{4}\]  
\[\sum_{i\in I}\sum_{v\in V}x_{ijv}y_{iv}\leq Q_{v} \tag{5}\]  
\[\sum_{v\in V}y_{iv}=1\ \forall i\in I \tag{6}\]  
\[\sum_{j\in I}s_{j}=\sum_{i\in I}\sum_{j\in V}\sum_{v\in V}x_{ijv}\cdot y_{iv} \tag{7}\]  
\[\sum_{v\in V}G_{i}(T_{iv})\cdot y_{iv}\geq 0\ \forall i\in I \tag{8}\]  
\[y_{iv}\in\{0,1\}\ \forall i\in I,\ \forall v\in V \tag{9}\]  
Figure 5: Trapezoidal fuzzy time window function diagram.  
Objective function Equation (2) seeks to optimize cost efficiency, incorporating vehicle driving cost, loss cost, and storage cost while aiming for optimal cost from the perspectives of work efficiency and environmental sustainability. Objective function Equation (3) addresses the security service level, describing the overall risk of the network using the security satisfaction function combined with the risk weight.  
Equation (4) ensures that the total amount of recovered materials does not exceed the total load capacity of all vehicles in the plant. Equation (5) guarantees that the recovery amount of each vehicle does not exceed its load capacity. Equation (6) specifies that only one vehicle can serve each collection point. Equation (7) ensures that the amount of recycled materials received by the processing center in the plant is equal to the amount generated by each collection point. Equation (8) sets the minimum level of safety service to 0. Equation (9) corresponds to the decision variable, with a value of 1 indicating that vehicle \(v\) serves collection point \(i\), and 0 indicating that it does not.