esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models
## 2 Materials and Methods
### Hydrological Loading Computations  
We computed surface displacements due to continental water storage variations using two state-of-the-art global hydrological models, GLDAS-2.1 (Global Land Data Assimilation System)/Noah [61] and MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications) [62] land component, and estimates derived from the latest (RL06v1.0) GRACE and GRACE Follow-On iterated global mascons from the NASA Goddard Space Flight Center [63]. Among other differences, the GLDAS-2.1 model includes soil moisture and snow and canopy water, whereas MERRA2 only includes soil moisture and snow.  
We used the classical Green's function approach [64], assuming a spherically symmetric nonrotating elastic isotropic (SNREI) Earth model, using PREM [65] rheological parameters. More details of the loading computations can be found in [9; 66]. In particular, we ensured the total water mass conservation of the hydrological models by adding/removing a uniform ocean layer to compensate for any lack/excess of water over land.  
All the loading time series are available at the EOST loading service ([[http://loading.u-strasbg.fr](http://loading.u-strasbg.fr)]([http://loading.u-strasbg.fr](http://loading.u-strasbg.fr)) (accessed on 7 November 2021)).
# Freshness Monitoring of Packaged Vegetables
## 4 Intelligent Packaging (IP)
### Indicators
#### 4.2.3 Freshness Indicators  
Freshness indicators have the target of detecting the chemical changes or the microbial growth inside the packages based on reactions with the growth metabolites such as carbon dioxide, oxygen, ethanol, lactic acid, glucose, and the other volatile organic compounds. They require a direct contact of indicator with the food. Thus, all the materials used together with the output of these reactions should comply with food regulations. A commercial example, Food FreshTM[13; 183], shown in Figure 15a, is a timer for the safe consumption period that ranges between few days to several months. It is made of polyethylene terephthalate (PET) that changes its color with time. Typically, this product is used for meat and jars like mayonnaise. However, it can be modified depending on the products monitoring time, and for vegetable packages, it normally lasts for several days.  
The Insignia Technologies integrity indicators [184] are used to indicate the freshness of packaged perishable food products. These indicators use sensing films made from proprietary smart plastics and inks that show color changes as a response to carbon dioxide level, leakage, temperature variation, or shelf life. For carbon dioxide monitoring, light yellow color will turn to dark purple to indicate freshness of a packaged product as shown in Figure 15b. This indicator was initially for meat, but it is claimed that it can also be used on any product as long as it was flushed with carbon dioxide while processing for modified atmosphere packaging (MAP). This is the case for bagged salads, as flushing with carbon dioxide is used to inhibit the growth of aerobic bacteria or molds [185; 186].
# A Versatile Shared-Aperture Antenna for Vehicle Communications
###### Abstract  
This communication introduces a versatile, multi-service, shared-aperture antenna system for multiple vehicle applications. The design comprises three antenna elements: a rotatable microstrip antenna for global positioning system (GPS) communication, a cross-dipole circularly polarized antenna for satellite communication in the S-band, and a pattern reconfigurable antenna for V2V (vehicle-to-vehicle) communication. These antennas collectively support GPS, satellite communication (Satcom), and V2V communication in a single, shared-aperture design. This shared-aperture antenna system offers cost savings and occupies less space compared to using separate antennas for each service. The microstrip antenna covers the 1575 MHz frequency band used for GPS communication. The cross-dipole circularly polarized antenna provides continuous wideband coverage for S-band satellite communication. The pattern reconfigurable antenna, tailored for the specific application scenario, covers the 5.9 GHz V2V working frequency band (5.855-5.925 GHz). Practical testing and simulation results confirm the effectiveness of this antenna system for the intended applications. In summary, the microstrip antenna has a bandwidth of 1.565-1.578 GHz and a realized gain of 7 dBi with radiation efficiency of 81%, the cross-dipole antenna has a bandwidth of 2.2-3.8 GHz (53.3%) and a realized gain of 8.3 dBi with radiation efficiency of 90%, and the pattern reconfigurable antenna has a 5.8-6 GHz bandwidth and a realized gain of 3.7 dBi with radiation efficiency of 85%, and the isolation between antennas with different frequencies is 25 dB, 20 dB, and 30 dB in three frequency bands.  
V2V; GPS; shared-aperture; satellite communication; pattern reconfigurable antenna +
Footnote †: journal: Electronics Electronics  
_et al._ Article_
## 3 The Optimal Selection Model of the Backhaul Links
### A Heuristic Algorithm for the Optimization Problem  
Due to the mobility of both the UAV and the satellite, it is necessary to get the location data and the topological graph during a period of time. Generally, the use of the snapshot method on LEO satellites refers to a specific imaging technique employed for capturing images of the Earth's surface. Moreover, UAVs can be programmed to follow specific flight paths and to capture images at predetermined locations or on demand. Unlike ordinary aircraft, UAVs have a limited range of motion and can remain still for dozens of seconds. Therefore, UAVs can also use the snapshot method to fix a location or capture images of specific areas on the Earth's surface. Thus, at each snapshot of a period of time \(\Delta t\), the objective function can be solved with a general operation. A heuristic algorithm is presented for adjustable association for UAV-LEO as shown in Algorithm 1. The process is worked as a flowchart in Figure 3. Such a process can be divided into four steps:  
Step (1): Transfer the candidate satellite array \(H\) to the candidate UAV array \(H^{\prime}\). Herein, the array \(H^{\prime}=H^{\prime}_{1},H^{\prime}_{2},\ldots,H^{\prime}_{M}\) represents the UAV set that is in the visible area of each satellite \(S_{j}\), where \(H^{\prime}_{j}\) are the visible UAVs corresponding to \(S_{j}\). This process is described in Line 1 in Algorithm 1. Step (2): Find the subsets of the visible set \(H^{\prime}_{j}\): that is, \(St^{l}_{j}\). The subscript of \(Su\) means the \(j\)th satellite, and the superscript denotes the index of the subsets. Specifically, the size of the subset is less than the constraint number of the connected UAVs: that is, \(p_{j}\leq P_{max}\). This process is described in Lines 2 to 8 in Algorithm 1 and is used as the constraint in Equation (9). Step (3): Get all the feasible solutions \(F^{k}\). This process is described in Lines 9 to 29. Herein, a feasible set consists of a series of UAVs associated with satellites. We create an iterative function \(\mathrm{FESSET}(\cdot)\) to search for all the feasible sets in Lines 9 to 22. In the function, a temporary variable \(fe\)s is set to store the associated UAVs. If \(fe\)s should satisfy the two constraints in Line 17, the function will be stopped and \(fe\)s will be added to a feasible solution \(F^{k}\). One of the constraints is that \(fe\)s has to compose the whole UAV set. The other is that a UAV should be associated with only one satellite at the same time. In other words, the elements in the feasible solution should not be duplicated. We use a function \(\mathrm{Uni}(\cdot)\)to delete the repeated elements in \(fes\). If \(\text{Uni}(fes)\) is equal to \(fes\), there is no repetition in \(fes\). In this way, we can get many feasible solutions \(F^{k}\). Step (4): Calculate the quality of the system \(Q\) and get the maximum value. This step is described in Lines 30 to 35 in Algorithm 1.
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
## 3 Data and Methods
### Method
#### 3.2.2 GRACE-Based Drought Severity Index (GRACE-DSI)  
The influence of human activities on TWSC is mainly concentrated in the long-term trend term and the seasonal term reflects the climatological state of TWSC [49; 50]. Thus, we used multiple linear regression fitting to decompose the original signal into a long-term trend signal, a seasonal signal, and a reference signal. The expression is as follows:  
\[TWSC(t)=a_{0}+a_{1}t+a_{2}\cos(2\pi t)+a_{3}\sin(2\pi t)+a_{4}\cos(4\pi t)+a_{ 5}\sin(4\pi t)+\varepsilon \tag{13}\]  
where \(TWSC(t)\) denotes the original signal, \(t\) denotes the time, \(\varepsilon\) denotes the residual signal, \(a_{0},a_{1},a_{2},a_{3},a_{4},a_{5}\) denote the pending parameters, \(a_{0}+a_{1}t\) denotes the long-term trend signal, and \(a_{2}\cos(2\pi t)+a_{3}\sin(2\pi t)+a_{4}\cos(4\pi t)+a_{5}\sin(4\pi t)\) denotes the seasonal signal. The results of the time series decomposition are shown in Figure 11.  
After decomposing TWSC using this method, the residual signal was obtained by subtracting the long-term trend signal and the seasonal signal from the original signal. We denoted this residual signal \(TWSC_{Residuals}\). GRACE-DSI is an index that can be used to quantify droughts, whose expression is as follows [51]:  
\[GRACE-DSI=\frac{TWSC_{Residuals}}{\sigma_{TWSC_{Residuals}}} \tag{14}\]  
where \(\sigma_{TWSC_{Residuals}}\) is the standard deviation of \(TWSC_{Residuals}\). In our study, GRACE-DSI was used for monitoring and assessing the 2023 extreme drought in the ARB. We categorized drought into five classes based on GRACE-DSI values to quantify the degree of drought, and the details are shown in Table 2[52]
## 4 Results
### Sensitivity Analysis
#### 4.1.3 Carbon Emission Reduction  
The simulation results of PBIPs on carbon emission reduction are shown in Figure 8. The order of impact of PBIPs on carbon emission reduction is R&DSP \(>\) PTP \(>\) FP \(>\) LP \(>\) FSP \(>\) CPMP \(>\) PRRP. The promotion effect of R&DSP on carbon emission reduction is the most obvious and improving R&DSP will continue to promote carbon emission reduction. The impact of PTP on carbon emission reduction is also undeniable, and the increase of 10% can produce a significant promotion effect. Still, the further improvement of PTP makes it challenging to increase the proportion of carbon emission reduction. A slight increase in FP and LP does not impact reducing carbon emissions. It will only have a significant promotion effect when they are increased to 20% or more. CPMP and FSP have little impact on the reduction of carbon emissions. The difference is that the increase of CPMP has a continuous positive effect on reducing carbon emissions. However, FSP needs to be increased by 30% to impact the reduction of carbon emissions significantly. The impact of PRRP on carbon emission reduction is small, and the promotion effect of improving PRRP on carbon emission reduction is not significant.  
Figure 6: Simulation results of the impact of PBIP on the newly added area of PB. (**a**) Simulation results of LP, (**b**) Simulation results of PRRP, (**c**) Simulation results of FP, (**d**) Simulation results of FSP, (**e**) Simulation results of PTP, (**f**) Simulation results of R&DSP, (**g**) Simulation results of CPMP.  
Figure 7: Simulation results of the impact of PBIPs on the supply of PBs. (**a**) Simulation results of LP, (**b**) Simulation results of PRRP, (**c**) Simulation results of FP, (**d**) Simulation results of FSP, (**e**) Simulation results of PTP, (**f**) Simulation results of R&DSP, (**g**) Simulation results of CPMP.  
Figure 8: Simulation results of PBIPs on carbon emission reduction. (**a**) Simulation results of LP, (**b**) Simulation results of PRRP, (**c**) Simulation results of FP, (**d**) Simulation results of FSP, (**e**) Simulation results of PTP, (**f**) Simulation results of R&DSP, (**g**) Simulation results of CPMP.
## 2 Equations of Motion
\begin{array}{l}\ddot{q}_{n}^{*}(t^{*})+\mu_{n}^{*}\dot{q}_{n}^{*}(t)+ \omega_{n}^{*}2q_{n}^{*}(t^{*})+\sum\limits_{i,j,k=1}^{\infty}\Gamma_{nijk}^{ *}q_{i}^{*}(t^{*})q_{j}^{*}(t^{*})q_{k}^{*}(t^{*})\\ \hskip 113.811024 pt=M_{n}^{*}V_{a}^{*}(t^{*})+f_{n}^{*}\cos\Omega^{*}t^{*},\quad n =1,2,...,\infty,\end{array}  \tag{10}\]  
where \(t^{*}=\omega_{1}t,\tau^{*}=\omega_{1}\tau,q_{n}^{*}=q_{n}(t)/l,\omega_{n}^{*}= \omega_{n}/\omega_{1},\mu_{n}^{*}=\mu_{n}/\omega_{1},\Gamma_{nijk}^{*}=\Gamma_ {nijk}l^{2}/\omega_{1}^{2},M_{n}^{*}=M_{n},f_{n}^{*}=f/\omega_{1}l\), and \(\Omega^{*}=\Omega/\omega_{1}\). For convenience, remove the asterisk of the following equation. In this article, the driving voltage of piezoelectric excitation uses the time-delayed displacement feedback strategy, for the following form:  
\[V_{a}(t)=\sum\limits_{m=1}^{\infty}-\bar{k}_{a}\phi_{m}(x_{3})q_{m}(t-\tau), \tag{11}\]  
where \(\bar{k}_{a}\) is control gain and \(\tau\) is time delay. Substituting \(V_{a}(t)\) into Equation (10), we obtain:  
\[\ddot{q}_{n}(t)+\mu_{n}\dot{q}_{n}(t)+\omega_{n}^{2}q_{n}(t)+\sum\limits_{i,j, k=1}^{\infty}\Gamma_{nijk}q_{i}(t)q_{j}(t)q_{k}(t)=-\sum\limits_{m=1}^{\infty}k_{ annm}q_{m}(t-\tau)+f_{n}\cos\Omega t, \tag{12}\]  
where \(M_{n}\bar{k}_{a}\phi_{m}(x_{3})=k_{ann}\). The governing equation expressed in the modal coordinate form is:  
\[\ddot{q_{n}}(t)+\mu_{n}\dot{q}_{n}(t)+\omega_{n}^{2}q_{n}(t)+\Gamma_{nmm}q_{n} ^{3}(t)=-k_{ann}q_{n}(t-\tau)+f_{n}\cos\Omega t. \tag{13}\]
### Evolving Needs and Challenges  
Several factors have guided changes in COMET's approach to designing and developing online learning materials and in-person instruction. New and evolving satellite capabilities and products, combined with the availability of substantially more data, have led to additional requirements and needs for the operational workforces we train. We have consistently incorporated additional interactions and activities that address the evolution in tools and capabilities as well as how they can be applied. Meteorological workforce responsibilities have shifted increasingly toward impact-based decision support services (IDSS) in response to evolving societal needs, particularly when storms or other phenomena threaten lives and livelihoods. In addition to being able to incorporate new data into their forecasts, operational meteorologists and hydrologists must be able to use these data to support partners and stakeholders in decision-making. These changes in our partners' needs have required that we remain adaptable. As this shift has occurred, we have adjusted our instructional approach to incorporate the additional skills that forecasters must possess to effectively perform their work, while remaining considerate of the time constraints of our audience.  
As meteorologists continue to be tasked with forecasting as well as successfully communicating their forecasts to an expanding variety of users, the increased demands of their work have decreased the available time for training. These changes mean that effective training needs to be focused, efficient (i.e., accessible and completable in a short window of time), and impactful. Increasingly, training needs to incorporate practical elements that include examples and exercises relating directly to decision support services and effective communication.  
The evolution in our training deliverables attempts to match the paradigm shift that forecasters are facing. A recent World Meteorological Organization (WMO) survey of the workforces of international meteorological and hydrological services [4] documents identified training priorities (Figure 2). Instrumentation and observation (which includes environmental satellites) and communication and customer interaction scored high as priorities, in line with the new-generation observational data becoming available and the shift in duties to increased focus on IDSS.
## 4 Structure Parameters Evaluation
### Nanoantenna Thickness Sweep  
The nanoantenna thickness sweep results are respectively presented in Figures 7 and 8, for the gold and the aluminum nanoantenna. Here, it is very important to remember that the nanoantenna is inside the dielectric medium. This one has a thickness of 200 nm and so that, when the nanoantenna thickness is varying, it is also varying the air medium thickness. This is an important statement, because all the conclusions that can be drawn will continue to be based on the assumption that the distance between the port, where the electromagnetic wave is generated, and the nanoantenna rear, which is in contact with the substrate, remains constant.  
Figure 5: Hole diameter sweep on a gold nanostructure with a quartz substrate.  
Figure 6: Hole diameter sweep on an aluminum nanostructure with a quartz substrate.  
Analyzing the values for \(t=50\,\mathrm{nm}\), \(t=100\,\mathrm{nm}\) and \(t=150\,\mathrm{nm}\), it is possible to conclude that the thicker the nanoantenna is, the lower the intensity values. In addition to it, the aluminum response seems to be flatter as the nanoantenna becomes thicker.
## 3 Results and Discussion
### Antibacterial Activities of Ag(II)PRO Nanoparticles and AgNPs (Comparative Study)  
The antibacterial activities of the silver nanoparticles depends on the valence form of the silver ion. Here, we have synthesized high-valence silver nanoparticles and compared the antibacterial activity with zero-valence silver nanoparticles with similar particle size. The TEM and size distribution study confirmed that the both nanoparticles were in same size (~20 nm). The antibacterial activities of the synthesized Ag(II)PRO nanoparticles and AgNPs with similar size were tested against Gram-positive and Gram-negative strains responsible for DFU. Antibacterial activity mainly depends on the size and shape of the nanoparticles. Earlier reports suggested that small nanoparticles in size can enter bacterial cell membrane and prevent its natural metabolism processes [28; 29]. It is observed that the antibacterial activity of AgCHX nanoparticles is far superior to the newly synthesized Ag(II)PRO nanoparticles. This is due to the presence of halogen element (chlorine) in fourth position of phenyl  
Figure 4: UV–Vis absorption spectra and particle size distribution of Ag(II)PRO nanoparticle (**a**,**b**) and AgNPs (**c**,**d**).  
moiety of CHX. The halogenated polydiguanide compounds usually show higher bacteriostatic effect against Gram-positive and Gram strains [23]. The antibacterial activity of AgCHX nanoparticles was compared with AgNO\({}_{3}\) and the gold standard wound healing agent silver sulfadiazine. In the present work, we have compared the antibacterial activity of Ag(II)PRO nanoparticles with AgNPs of similar size.  
The bacteriostatic activity of synthesized nanoparticles were determined by MIC technique. The MICs and MBCs values of the Ag(II)PRO and AgNPs were determined by using agar dilution method and the antibacterial activity of these compound was compared in Table 1. MBCs of Ag(II)PRO nanoparticles with different bacterial strains were shown in Figure S3. From the obtained data, the synthesized Ag(II)PRO nanoparticles has the maximum antibacterial effect against all the tested strains than silver nanoparticles alone. MBCs of AgNPs with different bacterial strains were shown in Figure S4. The MICs of Ag(II)PRO were in the range of 6.25 uM, although the MBCs of Ag(II)PRO showed 6.25 uM. The Ag(II)PRO nanoparticles showed better antibacterial activity than the silver nanoparticles even at very low concentrations. The study showed that MIC of _E. faecalis_ and _S. aureus_ as Gram-positive bacteria is around 6.25 uM. These results indicated that there was no difference in MICs in terms of Gram-positive and Gram-negative bacteria. From the obtained data, the Ag(II)PRO nanoparticles showed antibacterial activity six times higher than AgNPs.
###### Abstract  
One challenging problem is the representation of three-dimensional datasets that vary with time. These datasets can be thought of as a cloud of points that gradually deforms. However, point-wise variations lack information about the overall deformation pattern, and, more importantly, about the extreme deformation locations inside the cloud. This present article applies a technique in computational mechanics to derive the strain-rate state of a time-dependent and three-dimensional data distribution, by which one can characterize its main trends of shift. Indeed, the tensorial analysis methodology is able to determine the global deformation rates in the entire dataset. With the use of this technique, one can characterize the significant fluctuations in a reduced multivariate description of an urban system and identify the possible causes of those changes: calculating the strain-rate state of a PCA-based multivariate description of an urban system, we are able to describe the clustering and divergence patterns between the districts of a city and to characterize the temporal rate in which those variations happen.  
Keywords:dimensionality reduction; pattern identification; three-dimensional data-cloud; strain-rate; Finite Element Method (FEM); trajectory visualization; deformation analysis +
Footnote †: journal: Applied Sciences