esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Modeling Languages for Applications Hosted on Cloud-Edge Computing Environments
###### Abstract  
In the field of edge-cloud computing environments, there is a continuous quest for new and simplified methods to automate the deployment and runtime adaptation to application lifecycle changes. Towards that end, cloud providers promote their own service description languages to describe deployment and adaptation processes, whereas application developers opt for cloud-agnostic open standards capable of modeling applications. However, not all open standards are able to capture concepts that relate to the adaptation of the underlying computing environment to changes in the application lifecycle. In our quest for a formal approach to encapsulate these concepts, this study presents various Cloud Modeling Languages (CMLs). In this study, when referring to CMLs, we are discussing service description languages, domain-specific languages, and open standards. The output of this study is a review that performs a classification on CMLs based on their effectiveness in describing deployment and adaptation of applications in both cloud and edge environments. According to our findings, approximately 90.9% of the examined languages offer support for deployment descriptions overall. In contrast, only around 27.2% of examined languages allow developers the choice to specify whether their application components should be deployed on the edge or in a cloud environment. Regarding runtime adaptation descriptions, approximately 54.5% of the languages provide support in general.  
cloud computing; edge computing; domain-specific languages; cloud modeling languages; service description languages +
Footnote †: journal: _Applied Sciences_
## 3 Results  
### Data Analysis for Pnl1  
The differential strain for panel PNL1 after impact is reported in Figure 8. The damage index is evaluated according to Equation (3) and is provided in Figure 9. The damage index is the normalized cross-correlation applied to the differential strain vector (Figure 8) and its spatial transition (one sensor gauge). The threshold line (red line in Figure 9) is estimated according to Equation (4), that is, the mean value of the cross-correlation distribution.  
The TL is applied to select eligible sensors, assuming that all the sensors (along the x-axis) of which the DI value (along the y-axis) exceeds the TL are associated to a damage event. The estimation of the damage extension is provided by the clustering of the eligible sensors position along the fiber optic.  
A simple graphical user interface is realized to sketch the top view panel geometry and visualize the fiber optic layout (yellow line) and the damage position and extension (red dots) according to the SHM methodology (Figure 10).  
Figure 7: Sensor layout dotted line: on PNL1 all along the stringer perimeter (**a)**; on PNL2 along the inner line of left stringer and on the skin (**b**). The red arrow indicates the light insertion origin.  
Figure 8: Differential strain after impact for PNL1.  
Figure 10: Graphical user interface of the panel geometry and fiber optic layout (yellow dotted line). The red dots are the sensors with higher DI value.  
Figure 9: Damage index and threshold level (red line) estimated from the Damage Index (DI).  
The SHM estimations are validated by C-scan analysis. The C-scan signal amplitude and depth data are collected within gated regions of interest and plotted with each focal law progression, using the programmed beam aperture. Amplitude shows the presence of wide intense skin/stringer interface damage (Figures 11 and 12).  
In particular, it is possible to see a wide surface stringer disbond which affects both the feet of the two stiffeners, (black dashed line areas) with analogous detachment length, 80 mm for the left foot and 90 mm for the right foot, also confirmed by the B-scan section views. Similarly, there are further little disbonded areas at the tip of the stiffeners probably attributable to bending phenomena during impact.
# Kalman Filter and Wavelet Cross-Correlation for VHF Broadband Interferometer Lightning Mapping
## 2 Materials and Methods
### Broadband Interferometer (ITF)
#### 2.1.1 Preprocessing  
Prior to capturing the lightning electric field (E-field) signal at the VHF-ITF system, the lightning electric field signal travels from its origin in the clouds and usually propagates through open space. The signal is affected by reflection, refraction, absorption, and other interference means. To remove the unwanted noise from the recorded lightning, signals are usually filtered using bandpass filters with different cut-off frequencies [9; 21; 22]. In this paper, we investigate three types of noise filtering approaches including bandpass filter (BPF), Kalman filter (KF) [23; 24], and wavelet transform (WT) denoising [25]. The key difference between these filters is that the BPF is the simplest and fastest approach of filtering in-band noises; however, it implicitly assumes that the noise occupies a specific frequency band (e.g., 20 to 100 MHz). Hence, the BPF fails if the noise occupies different frequency bands. The WT denoising solves this issue by decomposing the signal into different frequency sub-bands and apply thresholding rules to eliminate the unwanted frequency contents from different sub-bands. However, WT requires prior information of the specific bands when the noise exists. The Kalman filter can explicitly model the signal as a stochastic process given prior knowledge of the noise statistical parameters which make Kalman filter superior in cases where the noise frequency band is unknown. The main limitation of the Kalman filter is that it assumes the signal has only Gaussian noise.
# Static and Moving Target Imaging Using Harmonic Radar
## 4 Synthetic Aperture Radar (SAR) Imaging
### SAR Imaging with Fixed 16-Channel Receiver
#### 4.3.3 SAR Imaging Results  
Data were collected with the targets positioned at nine different locations. The targets were placed at each location one at a time while data were collected. The targets were placed directly on the ground, thereby providing a more realistic scenario. Top down and side view diagrams of the SAR scene are illustrated in Figure 18.  
All frequencies were transmitted and received on one antenna, before switching to the next antenna. The 16:1 switching network allowed for a SAR image to be collected every 20 min. In comparison, the same data collection using a moving aperture would take >2 h. The increased speed in data collection achieved by using a fixed aperture, however, comes at a cost. Since the time delay through each of the 16 channels is not exactly the same, a complex calibration matrix is needed during data processing, of size \(M\times N\), where \(M\) is the number of antennas and \(N\) is the number of frequency steps.  
\begin{table}
\begin{tabular}{c c} \hline
**Target** & **Description** \\ \hline
1 & Hand Held Radio 1 \\
2 & Hand Held Radio 2 \\
3 & Hand Held Radio 3 \\
4 & Commercial RF amplifier, powered on using batteries \\
5 & Commercial RF amplifier, no power \\ \hline \end{tabular}
\end{table}
Table 3: Target Descriptions for Switched Aperture SAR Imaging.  
As an example, harmonic SAR images of Target 2 at four different locations are shown in in Figure 19.  
Figure 19: SAR images of Target 2 at different locations in the scene: (**a**) at \(x=-1.22\) m = \(-4\) feet, \(y=7.92\) m = \(26\) feet; (**b**) at \(x=1.22\) m = \(4\) feet, \(y=7.92\) m = \(26\) feet; (**c**) at \(x=-1.22\) m = \(-4\) feet, \(y=5.79\) m = \(19\) feet; (**d**) at \(x=-1.22\) m = \(-4\) feet, \(y=5.79\) m = \(19\) feet.  
Figure 18: Geometry of the scene used for SAR imaging. (**a**) Top view; (**b**) Side view.  
Figure 19 shows that as the target is moved around the imaging space, the radar is clearly able to track it in down range and cross range. Figure 19 also shows ground bounce from the target and multipath from the wooden bench.
# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
## 2 System Design
### Edge Tier
In the downlink phase, the received signal at the \(k\)-th UAV is expressed as  
\[y_{k}=\sum_{m=1}^{M}\overline{\mathbf{h}}_{k,m}^{H}\left(\sum_{i\in\mathcal{A }_{m}}\mathbf{w}_{i,m}x_{i}\right)+z_{k}, \tag{4}\]  
where \(\mathbf{w}_{i,m}\) denotes the precoding vector for the \(k\)-th UAV at the \(m\)-th EDU. Similarly, the downlink data stream for same UAV is exclusively transmitted from the UCDU to multiple EDUs, enabling a downlink coherent joint transmission (CJT) through coordinated beamforming across distributed EDUs [44].
## 3 Results
### Optimisation of the Synthesis of Monometallic Pd Catalysts-Catalyst Characterisation
It was evaluated at 50 \({}^{\circ}\)C, 750 rpm, 0.5 M HCOOH, substrate/metal molar ratio of 2000 and 4 h reaction time, typical reaction conditions we had optimised in previous reports and showing the reaction is taking place in chemical kinetic regime [10]. Figure 2 illustrates the catalytic performance of the catalysts prepared by varying the PVA/Pd weight ratio from 0 to 1.2. The highest catalytic performance in terms of conversion was obtained with the catalyst Pd/C-e (PVA/Pd weight ratio = 0.3). As previously commented, this catalyst presents the highest atomic content of Pd on the surface, increasing the probability of formic acid to reach the active sites. Comparing the final conversion achieved and the Pd content on the surface versus the PVA/Pd weight ratio it is possible to observe the typical volcano trend (Figure 3). Regarding particle size influence, the catalyst presenting the higher activity (Pd/C-e) had an mean particle size of 4.8 nm, while catalysts with smaller mean particle size (approximately 3.0 nm) as observed in the catalysts with PVA/Pd weight ratio of 0.65 and 1.2 presented a much lower catalyst activity. For this reason, the influence of particle size in catalyst activity for this series of catalysts was negligible compared to that of Pd exposure on the surface. Figure 2: Formic acid dehydrogenation reaction. Effect of the stabiliser. Reaction conditions: 0.5 M HCOOH, substrate/metal molar ratio: 2000:1, 50 \({}^{\circ}\)C, 750 rpm, 4 h reaction time. We could conclude that equilibrium must be reached. TEM micrographs presented zones with a high agglomeration of particles when the stabiliser was not present. On the contrary, when PVA/Pd is increased above a certain ratio, it probably tends to form a thick layer around the nanoparticle occupying active sites. Regarding NaBH\({}_{4}\)/Pd molar ratio, an increment in the catalytic activity was observed when it increased from 5 to 10 (Figure 4). This change can be explained by both the higher content of Pd on the surface and the higher percentage of Pd\({}^{0}\) species for the catalysts synthesised using NaBH\({}_{4}\)/Pd molar ratio of 10. When comparing the activity observing the used support, a slight increment was observed for the catalysts supported on activated charcoal. As previously explained, activated charcoal has a smaller pore size than titania. This could prevent nanoparticles from entering into the pores in activated charcoal, being those preferentially deposited on the external surface of the catalyst. Moreover, The amount of PVA (stabiliser used during the preparation method), that can block active sites, and the time requested to be removed from the surface during reaction, since it is soluble in water and during the progress of the reaction it can be removed from the surface can explain the induction-like trend observed. Furthermore, typically nanoparticles supported on TiO\({}_{2}\) are hemispherical due to the wetting effect of the metal nanoparticle onto metal oxide. Therefore, reducing the active surface area and decreasing catalyst activity.
## 5 State-of-the-Art
### Containerization  
A lightweight container using Wasm that offers flexible deployment and live code migration in isomorphic IoT systems was proposed in [86] with the goal of supporting developers in creating liquid IoT applications that enable seamless and hassle-free use of multiple devices. Another study [87] investigated the use of Wasm for containers in IoT devices and compared it with Docker, the current leader in container technology. The findings showed that while Docker is more efficient in most cases, Wasm excels in scenarios involving sporadic execution of simple programs, suggesting its suitability for serverless computing at the edge. Another study found that performance of Wasm runtimes (e.g., Wasmer and Wasmtime) in terms of execution speed, memory footprint, and maturity, could outperform similar Docker solutions in certain instances, especially for short tasks on IoT devices [88]. In [89; 90], attempts were made to replace Docker with Wasm. The results indicated that Wasm containers can achieve faster cold starts and higher throughput, suggesting their suitability for low-latency serverless edge computing with improved security and fault tolerance. An isomorphic microservice deployment architecture for IoT-based systems was proposed in [91]. It uses Wasm to achieve a uniform computing environment to simplify software deployment on future heterogeneous devices. Another study introduced WASMICO, a full-cycle management solution for containers built on the Wasm3 interpreter and the FreeRTOS operating system [92]. The platform allows developers to write programs in various programming languages, compile them to Wasm, and remotely manage tasks on low-end resource-limited IoT devices through an HTTP API and command line interface. It enables access to device capabilities through ready-to-use abstractions in higher-level specifications. The authors found that WASMICO performed better than other solutions in terms of efficiency, computation, and memory usage. By facilitating firmware development similar to software development, it can bridge the gap between these two computing paradigms.
## 2 Principles and Methods
### Simulation Scenario  
Following Gao [15], the multi-GNSS signals from GPS, GLONASS, Galileo, and BDS were used as the signal sources for GNSS-R observations in the present simulation. What needs to be mentioned is that the four full operational GNSS constellations were all simulated based on their nominal constellation configurations presented in [24; 25; 26; 27], considering that Galileo and BDS have not yet reached their full planned constellations until now, and the third generation of BDS was used. In the simulation, four steps were followed to determine the available reflected GNSS signals: (1) For each LEO satellite, the potentially available GNSS satellites for reflection observations were selected; (2) The positions of the specular reflection points could be calculated after the positions of the LEO and the GNSS satellites were determined; (3) As shown in Figure 1, only the specular points located within the minimum antenna gain could be contacted; and (4) Furthermore, only the locations of the specular points with 10 highest range corrected gain (RCG) were considered (see the detail of RCG in [16]). In addition, it should be mentioned that the LEO satellites were 3-axis stabilized and the antenna beam widths of the LEO satellites simulated in the present study were the same as that of the TDS-1 satellite, considering that the definitive beam widths of the CYGNSS mission have not been given [15].
## 4 Analysis and Evaluation of the PPP-B2b and Broadcast Ephemeris Performance  
Before applying the PPP-B2b signals in the RT POD of LEO satellites, the statistical characteristics of the PPP-B2b corrections are analyzed and the RT orbit and clock offset accuracy of the PPP-B2b and broadcast ephemeris is evaluated.
## 1 Introduction
In this paper, an analytical approximation model of the QPE introduced by orbit determination errors is proposed. With the a priori probability of the onboard orbit determination system and SAR satellite orbit elements, together with the observing geometry of the spaceborne SAR and appropriate approximations, a series of equations describing the parameters of the QPE probability distribution are presented. This analytical approximation model can provide approximation results at two granularities, i.e., the approximations with the satellite's true anomaly as the independent variable and the approximations for all positions of the satellite during its entire orbit. Following this section, Section 2 describes the QPE analytical approximation model in general. Section 3 derives the key variables in the analytical approximation model and reveals the appropriate approximations in the model. This is followed by the evaluation of the analytical approximation model by comparing it with numerical simulation results in Section 4, and a related discussion is presented in Section 5. Finally, Section 6 provides a brief conclusion regarding the analytical approximation model.
## 3 Results
### Temporal Dynamic  
The temporal evolution of estimated AGB for our study area is shown in Figure 5. From a first analysis, the estimated AGB satisfactorily reproduces the seasonal variation and fits the mean value of the reference map very well. The growing season of the grassland spanned from \(\sim\) DOY 81 to 313. The growing peak appeared on \(\sim\) DOY 193, with value of 365.3 g\(\cdot\)m\({}^{-2}\). Note that the AGB in dormancy was not zero. This is because the grassland in our study area is divided into summer pastures and winter pastures by local people [30], and the winter pastures are adhered to much with Figure 4: Aboveground biomass (AGB) map (DOY 225, 2017) generated from the proposed method.  
Figure 3: Density scatterplots between the estimated and reference aboveground biomass (AGB) in DOY 225, 2017.  
The LAI and Cm used to calculate the AGB can also be retrieved simultaneously. Their temporal evolution was also analyzed (Figure 6). The LAI temporal profile is very similar with that of AGB. This is expected considering the fact that leaves are the only carrier for grassland AGB. A close inspection finds that Cm shows a contrary seasonal variation as that of LAI and AGB. This is because during the dormancy and senescence periods, grassland leaves contain low moisture due to the moisture stress. During the growing peak (DOY 193), the growing environment is favorable, resulting in the highest moisture content (lowest Cm) for leaves. The rational temporal profiles of LAI and Cm further confirm the rationality of the physically-based AGB retrieval method.
## 2 Materials and Methods  
### Field Experiment  
Flour was obtained from winter wheat cv. 'Julius' in a field experiment conducted during three growing seasons (2018-2021) at the Agricultural Experiment Station (AES) in Balcyny (53 deg35 deg46.4'' N, 19 deg51'19.5'' E, northeastern Poland) owned by the University of Warmia and Mazury in Olsztyn. The experiment involved three factors. The first factor was the sowing date: early (6 September 2018, 5 September 2019, and 3 September 2020), delayed by 14 days (17-20 September), and delayed by 28 days (1-4 October). The second factor was the sowing density: 200, 300, and 400 live grains m\({}^{-2}\). The third factor was the split application of N fertilizer in the spring in the full tillering stage (FT; BBCH 22-25) and in the first node stage (FN; BBCH 30-31) at 40 + 100, 70 + 70, and 100 + 40 kg ha\({}^{-1}\) (Pulan, Grupa Azoty SA, Pulawy, Poland; ammonium nitrate, 34% N). The third split of N fertilizer (40 kg ha\({}^{-1}\)) was applied in the \"flag leaf just visible, but still rolled\" stage (BBCH 37) (Pulan, Grupa Azoty SA, Pulawy, Poland; ammonium nitrate, 34% N). In the spring, N was applied on 6-12 March (FT), 7-10 May (FN), and 21-30 May (flag leaf just visible, but still rolled).  
The experiment had a split-split-plot design (sowing date was assigned to whole plot treatments, sowing density was assigned to subplot treatments, and the split spring application of N fertilizer was assigned to sub-subplot treatments) with three replications. The plot size was 15 m\({}^{2}\) (10 m by 1.5 m). The preceding crop was winter oilseed rape (_Brassica napus_ L.). All field treatments that did not constitute the experimental variables were consistent with the agronomic requirements of winter wheat and good agricultural practices. The experimental conditions (soil type and content of plant-available nutrients) and the production technology of winter wheat were described in detail by [PERSON] and [PERSON] [94] (Tables S1 and S2).
## 4 Analysis of Differential Deformation Control
### Analysis of Differential Deformation Control during Construction
#### 4.1.2 Long Pile and Short Pile Combination Method  
It can be seen from the above that increasing the pile length can significantly reduce the uplift displacement of the foundation, so the uneven settlement of the foundation can be controlled by increasing the pile length (Figures 12 and 13). After the tunnel excavation, the plastic zone extends from the tunnel to the deep surrounding rock. When the pile length is the same, the depth of the plastic zone (red range) at the foot of the wall is about 5 m (Figure 14), and the depth of the plastic zone at the optimized foot of the wall is about 3 m (Figure 15). The differential settlement of the tunnel bottom 3 m away from the lining is extracted for comparative analysis, as shown in Figure 15. Under the combined action of the long pile and short pile, the range of the plastic zone at the depth of 3 m at the tunnel bottom is significantly reduced. The distribution of the plastic zone of the foundation can be obviously reduced and the self-bearing capacity of the surrounding rock can be fully exerted by using long piles and short piles to strengthen the foundation.  
Figure 10: The additional stress of base: (**a**) No pile arrangement; (**b**) The pile length is 6 m; (**c**) The pile length is 11 m; (**d**) The pile length is 15 m.  
Figure 11: Variation law of base settlement under different replacement rates and pile lengths: (a) Replacement rate; (b) Pile length.  
Figure 12: Layout of foundation pile.  
Designers are prone to make mistakes when choosing long and short piles, they deem that the plastic zone of the wall foot is large and the stress is large. Therefore, the piles at the foot of the wall shall be increased appropriately to reduce the plastic zone at the arch foot. The plastic zone range is calculated by the finite element method (Figure 16). To analyze its control effect on uneven settlement, the pile length at the foot of the wall is increased to 12 m (Figure 17), and the original design pile length of other parts of the tunnel bottom is still 6 m.. According to the comparison of the plastic zone (red zone) before and after optimization in Figure 17, increasing the pile length at the foot of the wall cannot effectively reduce the plastic zone range at the foot of the wall, and the local plastic zone range increases, as shown in the black circle area in Figure 18.  
Figure 14: Plastic zone of the original scheme.  
Figure 13: Differential settlement of foundation base under different stiffness.  
Figure 15: Plastic zone of optimization scheme.
## 2 HSI: Basic Concepts  
HSI started to be used in the 80s in remote sensing for agriculture, forestry, geology, environment, ocean, atmosphere, climate change, defense, and security and law enforcement [5,6]. Then, during the 90s, HSI became important also in proximal sensing as a chemical imaging used in food science [7] and postharvest research [8,9] for chemical composition mapping and categorization.