esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Preliminary Analysis
### Preliminary Analysis
#### 3.2.3 Comparison and Analysis of Mechanical Properties between Longitudinal and Transverse Specim  
Limited by GEM production process, the maximum single membrane width ranges generally from 6 m to 8 m, and adjacent membranes rely chiefly on the welding splicing technology to form an impervious body of the entire dam surface. There is a longitudinal/transverse connection in welding adjacent GEM, and difference in mechanical properties between longitudinal and transverse may affect welding quality due to improper temperature control, resulting in the shorter service life of the structure. Therefore, it is necessary to study the difference in mechanical properties.  
Four testing temperatures (\(-40\), \(-30\), 4 and 20 \({}^{\circ}\)C) were selected to analyze the mechanical properties of longitudinal/transverse specimen in axial tension, and the comparison results are shown in Figure 7. As can be seen, the stress of longitudinal specimen is higher than that of the transverse specimen. The higher the temperature, the greater the difference in stress between longitudinal and transverse at 4 \({}^{\circ}\)C and 20 \({}^{\circ}\)C (higher than glass transition temperature), the difference is remarkable at the initial strain and increases gradually with the strain; the difference in stress follows similar patterns at \(-40\)\({}^{\circ}\)C and \(-30\)\({}^{\circ}\)C, but it is not significant at the initial strain and mainly occurs after the yield point.  
Figure 7 shows that the longitudinal specimen differs from the transverse in mechanical properties significantly under small strain conditions, and a striking difference is noted with the increasing strain. The GEM is susceptible to tensile failure at the peripheral joints and the uneven settlement on surface, at which the design of GEM impervious structure should consider the directionality of GEM to improve the safety of structure.  
Figure 7: Comparison of mechanical properties between longitudinal and transverse specimen at (a) 4 and 20 \({}^{\circ}\)C; (b) \(-40\) and \(-30\)\({}^{\circ}\)C.
# Design and Application of New Aeration Device Based on Recirculating Aquaculture System
## 2 Materials and Methods
### Analysis of Outdoor Pond Conditions  
Since the test site was outdoors in a freshwater aquaculture pond, we had to consider that the dissolved oxygen level in outdoor water is affected by many factors, including physical, chemical, biological, and anthropogenic factors [21; 22]. To minimize the measurement error due to outdoor conditions on the values of dissolved oxygen and other water indicators of the aquaculture tanks, we chose to conduct the tests on 15 days that presented similar conditions (i.e., the factors affecting the dissolved oxygen levels, pH value, water temperature, and air pressure, were similar). The experiment was conducted in January 2023, abstaining from the activation of any oxygenation devices, to maintain consistency in culture management practices. It is worth noting that fish were not farmed in this pond. DO levels in the pond were monitored using a dissolved oxygen meter (YSI-58), with measurements recorded every five minutes. Each measurement was replicated three times under identical conditions to ensure accuracy and reliability.  
Figure 3 shows the changes in the natural dissolved oxygen level in the test pond over 24 h. It can be found that the lowest daily DO level was at 6:00-10:00 [23], and that, over 24 h, there were significant changes in the magnitude of this parameter, which was mainly due to light intensity. Therefore, in all of the oxygenation tests, devices were operated in the range of 7:00-10:00, which effectively reduced the interference of light intensity with the experimental results, allowing us to better verify the performance of the NAD.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Parameter Value** & **Unit** \\ \hline Calibre & 500 & mm \\ Individual impeller diameter & 380 & mm \\ Blade width & 100 & mm \\ Number of blades & 8 & / \\ Shaft assembly diameter & 50 & mm \\ Total motor power & 1.5 & kW \\ Standard oxygen transfer rate (SOTR) & 2.25 & Kg/h \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main design parameters of impeller aeration component.
## 2 Data and Methods
### Methods  
#### 2.2.1 Wavelet Spectral Analysis  
In this study, we examine the validity and advantages of the 1D-CNN filter by applying it to extract QBWO and ISO signals from the HKO observation station as an example. Thus, before applying the filter, a wavelet spectral analysis [38; 39] is applied to identify the dominant periods of the QBWO and ISO signals in our input data, i.e., daily mean and maximum and minimum surface air temperatures.  
Figure 1 presents the wavelet power spectra of the daily average temperatures at the HKO, HKA, CCH, and TKL stations. It is evident that most QBWO and ISO signals stay shorter than 25 days, while some individual events are featured with longer periodic signals of up to 60 days. On this basis, we determine that the primary periods of the QBWO and ISO signals in Hong Kong range from 10 to 60 days.  
Figure 1: The wavelet energy spectra of daily average temperatures at the (**a**) HKO, (**b**) HKA, (**c**) CCH, and (**d**) TKL stations. Black contours denote results that are statistically significant at the 95% confidence level for the red noise-based significance test. The significance test was performed under the red noise assumption because time series of meteorological variables are mostly serially correlated in time, in which background noise does not obey the white noise assumption [40; 41].
# Investigation of Open Air Stability of CsPbBr3 Thin-Film Growth on Different Substrates
## 3 Results and Discussion
### UV-Visible Light Absorption Spectra  
The optical properties of the films were monitored as a function of degradation time by means of UV-Vis absorption measurements. Figure 5 depicts the normalized reflectance as a function of the wavelength for each sample. To normalize the data, the minimum value was subtracted from each reflectivity value, and the result was divided by the maximum, as reported in Equation (1).  
\[R_{norm.}=\frac{R_{0}-R_{min}}{R_{max}}. \tag{1}\]  
Figure 4: SEM images of the aged samples: (**a**) quartz glass; (**b**) ITO; (**c**) soda-line glass; (**d**) gold. Insets display zoomed views of the same images.  
The spectra collected on soda-time and quartz glass closely resembled each other, whereas those collected on ITO and gold differed notably. It is also worth noting that the spectra recorded on pristine samples (green lines in Figure 5) did not present the peak at 520 nm, attributable to lead bromide, which is a typical byproduct of moisture degradation of lead bromide perovskites. However, this peak appeared in the spectra collected after just 1 day and became more and more intense as the aging test proceeded (Figure 5a,b).  
The evidently different aspects of the spectra collected on gold-deposited samples can be attributed to the reflectivity of the substrate.  
In the UV-Vis spectra, an edge of the reflectivity is typically visible, which allows calculating the bandgap value. However, in our case, this calculation was made difficult by the relatively modest thickness of the film.
## 2 Emittance Growth Issues on LPA  
### Theoretical Approach  
The peak brightness [70], a proper figure of merit for most LPA based applications, is defined as:  
\[B=\frac{2*I}{\pi^{2}\epsilon_{g,x}\epsilon_{g,x}}, \tag{1}\]  
where the peak current \(I\) and the beam transverse geometrical emittance \(\epsilon_{g,z}\) and \(\epsilon_{g,x}\) appear as key characteristics. Calculating the propagation on a simple model composed of a drift of length \(L\), followed by a quadrupole of strength \(k=1/L\) to compensate the divergence, and tilted by \(\theta\), gives for the emittance:  
\[\epsilon_{g,x}^{2}\approx\epsilon_{g,x,i}^{2}+4\theta^{2}\sigma_{x,i}^{\prime 2 }\sigma_{z,i}^{\prime 2}L^{2}+L^{2}\sigma_{x,i}^{\prime 4}\sigma_{\gamma}^{2}, \tag{2}\]  
where \(\epsilon_{g,x,i}^{2}=(<x^{2}><x^{\prime 2}>-<xx^{\prime}>^{2})\)[42] is the initial emittance and \(\sigma_{x,i}\), \(\sigma_{z,i}\) and \(\sigma_{\gamma}^{\prime}\) the horizontal and vertical divergence and energy spread, respectively. The second and third term are the tilt and chromatic [71] emittance, respectively, both largely amplified by the beam divergence. For \(\theta\), \(\sigma_{x,i}^{\prime}\), \(\sigma_{z,i}^{\prime}=1\) mrad, \(\sigma_{\gamma}=1\%\), \(\epsilon_{g,x,i}=0.2\) mm.mrad, and \(L=200\) mm, the usual distance between source and the quadrupole, the tilt term gives a value of 0.14 mm.mrad and the chromatic term 0.7 mm.mrad, which shows the how these two terms dominate the emittance evolution for mrad divergences.We define the normalized emittance, which is most commonly used, as the emittance normalized by the mean relativistic factor \(<\gamma>\) according to relation 15 in [42].
# A 0.18 um CMOS Millimeter Wave Antenna-on-Chip with Artificial Magnetic Conductor Design
## 2 Materials and Methods
### Parameter Analysis of Antenna Branches  
Then, we conducted a simulation analysis for the inverted C branch of the antenna, which was divided into four types. The evolution diagram of the antenna branch structure and the simulation results are shown in Figures 10a-d and 11. The reflection coefficient was also less than \(-\)10 dB, as the parameter standard. In Figure 10a, when no branch was added, the bandwidth of this type was 32.6 GHz-69.4 GHz. Moreover, there was resonance at 44 GHz and 54 GHz, which were \(-\)33.2 dB and \(-\)28.1 dB, respectively. Figure 10b added a single transverse branch beside the feeder line. The frequency width was 33.5 GHz-71.6 GHz, and there was resonance at 42 GHz and 59 GHz. The individual values were \(-\)19.6 dB and \(-\)38.3 dB. Compared with Figure 10a, the frequency width was 1.3 GHz more. Figure 10c added a straight line branch to the horizontal branch, which could obtain 33.2 GHz-44.4 GHz and 62 GHz-101.5 GHz dual-frequency results. The resonance point was \(-\)15.1 dB at 39 GHz and \(-\)14.8 dB at 75 GHz. Compared with Figure 10b, the  
Figure 8: Evolution of antenna slot: (**a**) Type 1; (**b**) Type 2; (**c**) Type 3.  
Figure 9: Simulated results of antenna slot evolution.  
bandwidth was 9.8 GHz more. Figure 10d added a horizontal branch to the straight branch to form an inverted C-type branch. The frequency bandwidth could be 30.1 GHz-78.8 GHz, with resonance at 39 GHz and 65 GHz, and the resonance point was \(-41.9\) dB and \(-38.6\) dB. Compared with Figure 10c, it could cover the frequency range not covered by Figure 10c. Therefore, Figure 10d is the suggested choice.
# Initialize GroupKFold for cross-validation group_kfold = GroupKFold(n_splits = 5)
## 5 Results
### Comparative Analysis  
Comparing the results across real data, synthetic data, and verification provided valuable insights into the consistency and robustness of our findings.  
Spread probability (p_spread) emerged as a consistently important factor across all analyses. In real data, it was the most important feature (importance = 0.321), while in synthetic data, it remained implicitly important through its influence on peak_speed_time. The optimal spread probability was consistently high (0.3-0.4) across all datasets.  
Our findings suggest that temporal dynamics might be more influential than static network properties in certain contexts, though further research is needed to establish broader generalizability.  
The observed patterns suggest a potential research question: could network structural effects be more context-dependent than temporal dynamics? Our study provided initial evidence that while network properties showed variable influence across contexts, the importance of peak diffusion timing appeared to be more consistent, but this hypothesis requires validation through additional studies with diverse network types. The optimal configuration for maximizing information coverage was consistently as follows (Figure 35):  
* Random network structure (highest average coverage);
* Constant delay mechanism (slightly better than alternatives);
* High spread probability (0.4);
* High average degree (approximately 46 in synthetic networks).  
These findings demonstrate that while specific feature importances may vary across datasets, the fundamental patterns and optimal configurations for information diffusion remain relatively consistent.  
Figure 35: Summary of optimal information diffusion configuration.
## 2 Results and Discussion
### Optimization of SPME-Arrow Conditions
Since most of the grape varieties are considered to be aromatically neutral and contain similar classes of volatiles to Merlot, the obtained and optimized conditions can be used in the analysis of other red grape varieties. Similarly, Merlot was used in the optimization process developed by [PERSON], et al. (2020), [PERSON], et al. (2020). \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Temperature** (\({}^{\circ}\)C)**} & \multirow{2}{*}{**Incubation Time (min)**} & \multirow{2}{*}{**Exposure Time (min)**} & \multirow{2}{*}{**Desorption Time (min)**} & **Predicted Value (Peak Area \(\times\) 10\({}^{6}\)**)** & **Obtained Value (Peak Area \(\times\) 10\({}^{6}\)**)** & **Obtain Peak Area \(\times\) 10\({}^{6}\)** & **(Peak Area \(\times\) 10\({}^{6}\)**)** & **Mean \(\pm\) SD**)** \\ \hline Aldehydes & & & & & 293.00 & 298.00 \(\pm\) 2.50 \\ Alcohols & & & & & 48.00 & 47.50 \(\pm\) 0.95 \\ Ketones & & & & 4.80 & 4.76 \(\pm\) 0.05 \\ Acids & & & & 8.34 & 8.39 \(\pm\) 0.09 \\ Monoterpenes & & & & 1.32 & 1.37 \(\pm\) 0.02 \\ Sesquiterpenes & & & & 10.30 & 11.00 \(\pm\) 0.31 \\ \hline \end{tabular}
\end{table} 
Table 2: Optimal SPME-Arrow extraction conditions, predicted and experimentally obtained values for individual groups of free volatile compounds. Figure 3: Response surface plot for interaction between extraction temperature and exposure time for free (**A**) aldehydes, (**B**) alcohols, (**C**) monoterpenes and (**D**) sesquiterpenes.
## 4 Storm Surge Induced by Typhoon Jebi in 2018  
To demonstrate the applicability of the calibrated and validated model to a range of extreme events, we conducted a simulation of the storm surge caused by Typhoon Jebi in 2018. Typhoon Jebi was the strongest typhoon to come ashore in this area in the last 25 years [32] and caused significant damage to urban areas [33]. While previous studies have mainly used meteorological and wave models to analyze storm surges [32; 34], we aimed to reproduce the peak sea elevation caused by Typhoon Jebi while considering tidal interactions. In this storm surge application of Typhoon Jebi, we utilized the identical model setups described in Section 2.  
Figure 8a shows the surface elevation distribution throughout one tidal cycle (12 h and 20 min) before the landfall of Typhoon Jebi. At this time, the sea surface elevation was relatively high on the western side of the SIS, as evidenced by time-series data from four tidal stations (Stations d-g in Figure 8a), with the black nabla marks indicating the specific sampling times. At the Osaka tidal station (Figure 8f), the observed sea surface height was 0.19 m, while the model calculated a height of 0.32 m with reference to the mean sea level (M.S.L.).  
One tidal cycle later, when Typhoon Jebi approached Osaka City, the sea surface height reached its peak. The model calculated a storm surge of 2.37 m, whereas the observed  
Figure 8: (**a,b**) Surface elevation one tidal cycle before Typhoon Jebi and during the typhoon passage. The dashed line indicates the typhoon track. (**c**) Elevation difference between (**a**) and (**b**). (**d–g**) Time series of the observed (black lines) and simulated (red lines) sea surface elevations. Locations of tidal stations are indicated in panel (**a**). Wind vectors (blue vectors) were extracted at the position of the Osaka tidal station (**f**). The black (red) nabla indicates the time used in panels (**a,b**).  
elevation reached only 1.95 m. This discrepancy in the peak elevation may have been due to the temporal resolution of the observations. While the hourly records failed to capture the maximum height of the storm surge, the model results with a 20 min interval were able to sufficiently describe the peak.  
The storm surge was concentrated on the eastern side of the SIS, and the peak elevation is not visible in Figure 8e but is present in Figure 8g, indicating that the surge developed along the eastern and northern coasts. The maximum height of the storm surge can be observed at station (f), at the innermost station of Osaka Bay, and among stations (d) to (g).  
In terms of timing, the sea level rise at (g) reached its peak at 1 pm, 1 h and 20 min earlier than the peak recorded at (f). The distance between (g) and (f) was approximately 70 km, yielding a calculated storm surge velocity of approximately 50.5 km/h.
# Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles
## 3 Overview of the SWARMs Architecture
### The SWARMs Architecture  
Figure 1 shows an overview of the SWARMs architecture. At the top layer we find the application layer where two applications were developed:  
* A Mission Management Tool (MMT), used by a human operator to supervise the global mission, to track the situational changes, and to generate the mission plan.
* A Context Awareness Framework (CAF) application, used to provide a visual interface to the context awareness to a human operator.  
The applications at the application layer interact directly with the SWARMs middleware. Located between the communication network with the vehicles and the application layer, the middleware is responsible for:  
* Centralize the communications.
* Serve as a mission executive, providing execution and control mechanisms.
* Define a common information model.
* Use a semantic management.
* Provide a registry of all available vehicles and services.
* Track and report the progress status of a mission.  
Figure 1: SWARMs architecture overview.  
* Define mechanisms and functionalities for understanding the surrounding environment of the vehicles participating in a mission.
* Define mechanisms for data validation.  
With that into consideration, the SWARMs architecture follows a hierarchical control pattern for decentralized self-adaptive control [23].  
Finally, to make transparent the SWARMs Data Distribution Service (DDS) communication through the acoustic network between the Command and Control Station (CCS) where the middleware will be running and the vehicles, two interface modules are used [39; 40]:  
* The Control Data Terminal (CDT), that provides an interface between the middleware components at the CCS and the acoustic communication system used to communicate with the vehicles.
* The Vehicle Data Terminal (VDT), that provides an interface between the communication system and the vehicle.  
Among all the components in the SWARMs middleware, the proposed mission manager, called here Missions and Tasks Register and Reporter (MTRR), is the component responsible for the treatment and supervision of all the mission related interactions within the middleware. In a general overview, it is responsible for mediating between a MMT application, used by a human operator to generate the mission plan and supervise the mission progress, and a DDS [41] used as the paradigm of communications with the vehicles participating in a mission [21]. In the middleware, the MTRR interacts with the Semantic Query from the high-level services block, with the Publish/Subscription Manager component from the Data Management and with all the three reporter components from the Low-level Services block. It also makes use of the mission plan structure defined in the SWARMs information model [42]. The following subsections give a brief description of the components interacting with the MTRR and the mission plan model being used.
## 4 Results
### Additional Observations on Robustness, Generalizability, and Limitations
#### 4.4.3 Model Stability across Datasets  
Stability in the performance of the AdaBoost model was noted when tested across different sizes and types of datasets. This suggests a lower susceptibility to overfitting.
## 3 Preliminaries  
This section defines the network model, explains the theory of thermal fields and principal formulas, which are applied for DAG creation. The mapping between characteristics of node and the theory of thermal fields is also shown and different characteristics are discussed.  
We assume the network consisting of sensor nodes and multiple static data sinks. All sensor nodes have limited amount of energy given by the capacity of their battery. We assume that data sinks are connected to external, unlimited, source of energy. Sensor nodes are mobile, however, there is no energy dissipation due to mobility (we assume that sensors are carried by humans/animals). Every sensor is periodically generating small amount of data (e.g., heart-beat) and this message is sent to the data sink in multi-hop manner. Thus, some sensor nodes are also involved in the relaying of messages toward the data sink. We also assume that a successful delivery of data messages to the sink does not need to be acknowledged, so there is no need for backward communication from data sink to the sensor nodes. Therefore, end-to-end communication is one-directional, the only acknowledgment is possible on the link level between two directly connected nodes.  
In order to create DAG, the maximum temperature value is in the data sink, \(\phi_{SINK}=MAX\), and every sensor node joining the network has initial temperature set to zero, \(\phi_{i}=0\). After the initial message exchange between nodes, every node calculates its own temperature based on the values of its neighbors and its own conductivity value. The temperature of node can be calculated only from neighbors, which are having higher temperature than the calculating node. Iterative algorithm for calculation of node's temperature is shown in Algorithm 1.  
```
\(\phi_{i}=0\); sort neighbors by temperature descending; foreachNeighbor\(N_{j}\in S_{N}\)do if\(\phi_{i}<\phi_{j}\)then \(\phi_{i}=\phi_{i}+(\phi_{j}-\phi_{i})\cdot\xi_{i}\); end end
```  
**Algorithm 1**Node \(N_{i}\) calculates its own temperature.  
\(S_{N}\) is set of the node's direct neighbors, \(\xi_{i}\) is the thermal conductivity of node \(N_{i}\). \(\phi_{i}\) and \(\phi_{j}\) are the temperatures of nodes \(N_{i}\) and \(N_{j}\), respectively.