esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Clinical Liver Ultrasound Images
It can be noted that from the Frost, Median, and Kuan filtered data displayed in Figure 10d-f, an unclear mass (blood clot) and texture feature are blurred and over smoothed. Figure 10h,i show that the Weiner and SRAD filters are not much more effective on speckle reduction. These filters also greatly reduce the contrast, making images more indistinguishable from the background. This effect is especially noticeable in the case of the Wavelet filter as shown in Figure 10g. It was found that the anatomical structure was more clearly visible in Figure 10b,c obtained using the SR framework, where the speckle is reduced around the unclear mass without removing its features such as edges and texture. These results were comparatively better than those of Figure 10d-i of the standard despeckling methods. Thus, the proposed algorithm has various advantages for use in CAD systems based on image analysis, such as segmentation and edge detection. Future work will include extensive laboratory and clinical testing on diseased and healthy subjects for a more rigorous validation of the system. In conclusion, our approach reconstructs the detailed information in real ultrasound images, not only by preserving edge information but also by eliminating artifacts and reducing speckle noise. Figure 8: Comparison of PSNRs obtained by different methods. SRAD: speckle reducing anisotropic diffusion. Figure 10: _Cont._  
Figure 9: (**a**) Ultrasound image of the thrombus in the left ventricle. LV: left ventricle, RA: right atrium and RV: right ventricle and (**b**) noisy image. Despeckled ultrasound images of proposed method using (**c**) Dictionary 1 and (**d**) Dictionary 2. Results using the (**e**) Frost, (**f**) median, (**g**) Kuan, (**h**) wavelet, (**i**) Weiner, and (**j**) SRAD filters. The dashed white box indicates the region of image showing visual enhancement owing to despeckling.
## 3 Biosynthesis and Chemical Synthesis of Camphor  
The biosynthesis of camphor was extensively investigated and elucidated by [PERSON] _et al_. [25; 26; 27] in their work on _Salvia officinalis_[25; 27; 26]. After several elegant experiments they determined the basic biosynthesis of camphor (Scheme 1) and the enzymes involved to be as follows: Camphor is biosynthetically produced in plants by way of the biotransformation of the starting material geranyl diphosphate (GPP) which is the preferred substrate. Cyclisation of geranyl diphosphate, by the enzyme (+)-bornyl diphosphate synthase yields (+)-bornyl diphosphate. (+)-Bornyl diphosphate is then hydrolys to (+)-borneol through the action of bornyl-diphosphate diphosphatase. The last step is catalysed by (+)-borneol dehydrogenase as it oxidises (+)-borneol to (+)-camphor [25; 26; 27].  
**Scheme 1.** The biosynthesis of camphor (adapted from [28]).  
The synthetic production of camphor involves using turpentine oil as a starting material. Turpentine is used as the source of \(\alpha\)-pinene through a distillation process; \(\alpha\)-pinene is converted into camphene through the catalysis of a strong acid with acetic acid as the solvent; the camphene then undergoes Wagner-Meerwein rearrangement into the isobornyl cation, which is captured by acetate; the isobornyl acetate subsequently formed is hydrolysed to isoborneol, which is finally converted to camphor through dehydrogenation [20] (Scheme 2). The synthetic route from \(\alpha\)-pinene produces a racemic mixture, _i.e._, a 1:1 ratio of (-) and (+)-camphor.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 4 Proposed Scheme
### Key Generation
#### 4.1.1 Raw Key Exchange  
The RTU generates a string of raw qubits with the help of either of the two basis, rectilinear and diagonal, randomly. The rectilinear basis polarizes the 0-bit into a 0\({}^{\circ}\) polarized photon, and the diagonal basis polarizes the 1-bit into a +45\({}^{\circ}\) polarized photon. The RTU sends the sequence of superposed state qubits, called raw qubits, to the MTU. Furthermore, the raw key exchange utilizes the quantum channel, and the other sub-phases use the public channel.
###### Abstract  
Identifying agricultural fields that grow cabbage in the highlands of South Korea is critical for accurate crop yield estimation. Only grown for a limited time during the summer, highland cabbage accounts for a significant proportion of South Korea's annual cabbage production. Thus, it has a profound effect on the formation of cabbage prices. Traditionally, labor-extensive and time-consuming field surveys are manually carried out to derive agricultural field maps of the highlands. Recently, high-resolution overhead images of the highlands have become readily available with the rapid development of unmanned aerial vehicles (UAV) and remote sensing technology. In addition, deep learning-based semantic segmentation models have quickly advanced by recent improvements in algorithms and computational resources. In this study, we propose a semantic segmentation framework based on state-of-the-art deep learning techniques to automate the process of identifying cabbage cultivation fields. We operated UAVs and collected 2010 multispectral images under different spatiotemporal conditions to measure how well semantic segmentation models generalize. Next, we manually labeled these images at a pixel-level to obtain ground truth labels for training. Our results demonstrate that our framework performs well in detecting cabbage fields not only in areas included in the training data but also in unseen areas not included in the training data. Moreover, we analyzed the effects of infrared wavelengths on the performance of identifying cabbage fields. Based on the results of our framework, we expect agricultural officials to reduce time and manpower when identifying information about highlands cabbage fields by replacing field surveys.  
land-cover classification; semantic segmentation; unmanned aerial vehicles +
Footnote †: journal: Medical Imaging
## 3 Analysis of Examples
### Statistical Result Analysis
The analysis conducted above indicates that the PCI, RQI, RDI, and SRIes do not follow a normal distribution entirely, and the probability distribution of pavement performance evaluation indexes varies. The gradual decay of pavement performance over time is influenced by various factors such as climate, geology, traffic load, material properties, and structural geometry parameters. Consequently, the probability distribution of pavement performance evaluation indexes will also change throughout different stages of the pavement's life cycle. The current deterministic evaluation method, which employs fixed weights, is insufficient to meet the practical requirements of the project. To ensure accurate and reliable pavement performance evaluation, it is crucial to consider the probabilistic nature of these indexes and incorporate appropriate statistical models or methods for analysis. Figure 3: Comparison of measured values and fitting values of pavement performance indexes: (**a**) PCI; (**b**) RQI; (**c**) RDI; (**d**) SRI.
## 3 Considering the ALS Workflow and Its Impact on the Efficacy of Data Products
### Planning and Data Collection
#### 3.1.4 Data Collection  
During the collection phase the LiDAR operator must often make real-time adjustments to the equipment configuration to account for conditions encountered during flying that might cause the final result to deviate from what was planned--conditions such as having to fly lower or higher to avoid clouds or dangerous terrain and/or obstructions, higher than expected ground speeds due to winds, or determining that the vegetation cover is not as anticipated.  
The data collected by the ALS system on the aircraft are useless if the GNSS data for a set of reference stations is not collected simultaneously with the airborne data. The data from these reference stations will be used in post-processing to derive the aircraft trajectory in differential mode, which means that instead of treating the observations from the aircraft and the base station individually a differential vector is computed between the reference station and the aircraft (see Figure 2). Because the coordinates of the reference station are known very accurately (mm level) the differential vector processing produces a trajectory accurate sub-decimeter level in both the horizontal and vertical components [46]. It is important to note that this uncertainty might be as high as 30 cm in some situations. As a rule of thumb the baseline (distance between the aircraft and base station) has to remain less than a 100 km in order to maintain the above mentioned trajectory accuracy values [47]. Thus the placement of these base stations is also of crucial importance and determined by other factors that include having a clear sky view, ease of access, safety, access to power and communication services.
## 3 Results and Discussion
### Ftir  
Figure 1 shows the FTIR spectra for alginate, FHO, and FHO-A before and after the sorption of Cu(II) or phosphate. In Figure 1a, the broad peak from 3500-3000 cm\({}^{-1}\) represents the H-O stretching bond from H\({}_{2}\)O [23]. The peaks at 426 and 743 cm\({}^{-1}\) appeared only for FHO, which was assigned to the Fe-O-Fe stretching vibration [24] and would be the Fe-OH band, respectively [25]. A peak at 1021 cm\({}^{-1}\) was detected for alginate, FHO, and FHO-A which can be assigned to O-H bending [26]. Compared with FHO, some small peaks at 1522 and 1631 cm\({}^{-1}\) were not apparent for FHO-A, instead, two new peaks appeared at 1398 and 1593 cm\({}^{-1}\) for FHO-A which were also shown in the alginate spectra. In general, the peaks ranging from 1400-1600 cm\({}^{-1}\) represent the O-C-O stretching bond [27] and then the peak at 1409 and 1585 cm\({}^{-1}\) are the symmetrical and asymmetrical stretching C-O of the carboxylate group from alginate [28]. Therefore, there is evidence that the alginate polymer was firmly coated on the FHO.  
Figure 1b compared FTIR data after the adsorption for Cu(II) and phosphate for FHO and FHO-A. The peak at 426 and 743 cm\({}^{-1}\) was still detected after both Cu(II) and phosphate adsorption for FHO-A. The peak of 1582 cm\({}^{-1}\) shifted from 1593 cm\({}^{-1}\) (Figure 1a) and observed for FHO-A-C and FHO-A-P. The slightly declined peak at 1593 cm\({}^{-1}\) to 1588 cm\({}^{-1}\) was also observed by [PERSON] et al. [29] and was due to the formation of a Cu-carboxylate complex. It is obvious that the metal-carboxylate complex (M-O) coordinates in the bidentate chelating form [30; 31]. However, the Cu-ligand chelating interaction may happen with weaker intensity for phosphate than for carboxylate, even though phosphate was negatively charged in the solution. For FHO without alginate, the peak after Cu(II) or phosphate  
\begin{table}
\begin{tabular}{c c c c} \hline  & **A-60 mg/L** & **A-120 mg/L** & **A-180 mg/L** \\ \hline Initial alginate concentration in solution, mg/L & 60 & 120 & 180 \\ \hline Final alginate concentration in solution, mg/L & 5.40 & 4.16 & 38.2 \\ \hline Unused alginate, \% & 9.00 & 3.47 & 21.4 \\ \hline Amount of alginate on FHO, mg & 27.3 & 57.9 & 70.7 \\ \hline \end{tabular}
\end{table}
Table 1: Concentration of alginate before and after ferric hydroxide-alginate (FHO-A) synthesis.  
adsorption decreased from 1582 cm\({}^{-1}\) to 1567 cm\({}^{-1}\) or increased from 1582 to 1634 cm\({}^{-1}\), respectively, indicating that there were other interactions occurring. Namely, phosphate ions and iron oxide are implied to form inner-sphere surface complexes [32; 33; 34].
## 4 Discussion
### Thermal Trend-Differential Scanning Calorimetry  
The DSC analysis measures the heat quantity radiated or absorbed by each sample and compares it to a reference material, and the outcome is depicted in Figure 4. A key finding from the DSC analysis in the study is the observed decreasing trend in glass transition temperature (\(T_{\text{g}}\)) with increasing boron substitution. This trend has implications for potential applications in coating and provides valuable insights into the thermodynamic behavior of the glass series, and it is crucial for predicting suitable parameters for future coating applications. Lowering the glass transition temperature can enhance the material's processability and flexibility, making it more suitable for certain coating applications that require heat, such as sintering.  
Boron's ability to lower \(T_{\text{g}}\) lies in its characteristics within the molecular structure. As previous sections have indicated boron's ability to form both three- and fourfold coordinate bonds, the additional networks (Figure 4) replace the original structural backbone, which ultimately influences the thermodynamics of each system by reducing the bond stability and lowering the thermal energy required to loosen or break the bonds within the glass tetrahedra in response to increasing boron. As a result, the glass transition temperature decreases with increasing boron substitution.
## 3 Results and Discussion
### Mechanisms of the Antifungal Activity of Cu(AZPY)-MOF
This result is different from that observed in the constructed MOFs in previous studies, where NOx was generated in PBS treated with MOFs [39; 40]. As shown in the results, H2O2 instead of NOx might play a role in inactivating fungal cells and spores. However, the H2O2 level was not significantly different among the different treated amounts of **Cu(AZPY)-MOF** (Figure 8). This is not sufficient to explain the observed differences in antifungal activity among the applied concentrations of **Cu(AZPY)-MOF**. Interestingly, H2O2 (3-4 mM) and NOx (0-6 mM) were detected in PBS treated with free AZPY (Figure 8b). This result suggests that AZPY intercalated in MOF (although not in free form) may play a role in generating ROS and RNS. Recently, several studies have demonstrated the antibacterial and antifungal effects of Cu associated nanostructures [51; 52; 53]. These studies show that incorporation of additional components, such as lysozyme or plant extract, seems to enhance the antimicrobial efficiency of Cu nanostructures. Similarly in our study, incorporation of AZPY can elevate the effectiveness of fungal inactivation by **Cu(AZPY)-MOF**. Besides fungicidal effect of released Cu ions, shape of nanostructures can cause mechanical damages to fungal cells [52]. This suggests a possibility that structural shape of **Cu(AZPY)-MOF** may be able to cause mechanical damage to fungal cells in our study. Further investigation is needed to clarify this.
## 4 Related Work
### Approach and Design
The application logic comprises different behaviours in data flow programming, whereas user applications are a set of clients that receive and send data to the PoCo with the information originating from the system and the required information to activate the flows. Behaviours comprise the application logic of the resulting applications and will always be up to date with the underlying contexts. User applications are typically smartphone applications which display an up-to-date representation of the behaviours of the underlying physical infrastructure provided by the PoCo to end users. IoT devices are another key part of the architecture, and the most restricted one, since it contains resource-constrained devices. For this reason, AppAdaptivity has adopted CoAP as the application protocol to interact with final devices. In the scope of this work, there will be sensors to be monitored and actuators to be interacted with, therefore a lightweight standard like CoAP with accessible resources represents a suitable protocol. All the wireless sensor networks with CoAP support that can be part of AppAdaptivity will henceforth be referred to as the CoAP network. The CoAP network provides the sensors and actuators and device information which are accessible using CoAP to create IoT applications. The PoCo is responsible for interacting with the CoAP networks and enables the definition of data flows with the obtained CoAP resources that will form different behaviours in the user applications. An overview of the App adaptivity architecture is presented in Figure 3. In this case, the PoCo adopts a cloud deployment, however it can be deployed in other scenarios as we will see later. In real worldwide IoT deployments, CoAP networks along with their sensors and actuators are deployed in different physical locations, for example different cities, such as Malaga and Ghent. Appdaptivity handles the process of portability of the IoT applications to these environments while at the same time it allows the definition of custom behaviours with a unified and intuitive interface. Discovery is responsible for discovering the CoAP networks in different environments where the portability will be carried out. The behaviours comprise the functionalities that can be defined in the PoCo through its data flow framework and define the actions to be taken with the discovered CoAP networks. Behaviours are executed in the PoCo, however some tasks such as a device interaction can be carried out directly by user applications once the corresponding behaviour has been received. Examples of these behaviours are: the creation of charts with the average value of temperature sensors, alert monitoring based on the sensor data and CoAP groups, for interacting with them with just a unique CoAP request. In the last example, end users could directly interact with the group using CoAP. The CoAP interface in PoCo manages all the CoAP interactions in the system such as a group creation and an observation.
###### Abstract
Reference [16] analyzes the application of SORA for a multi-UAS airframe inspection (AFI) operation, which involves the deployment of multiple UAS with autonomous features within an airport. In [17], the authors present the most important risks related to conducting operations with the use of UAS by first responders (FRs), while reference [18] presents the application of the SORA methodology for media production with a small UAS team. In addition, [19] describes the application of SORA to the flight of large remotely piloted aircraft systems (RPAS) in Australian airspace highlighting its distinguishing factors. The paper is structured as follows. First, the current European drone regulation will be explained in detail in Section 2. Materials and methods are described in Section 3, where the SORA methodology will be shown as a basis to assess the risk of drone operation and help categorize the operation. Then, the concept of U-Space will be introduced in Section 4 since it is the framework that will help integrate drone operations into the airspace. Section 5 explains the results of the application of the SORA methodology to some typical mosquito-related operations. Finally, Section 6 closes the paper with a discussion of the analysis.
_Applied_  
_sciences_  
Production of Microalgal Slow-Release Fertilizer by Valorizing Liquid Agricultural Digestate: Growth Experiments with Tomatoes  
**[PERSON] 1\({}^{\copyright}\)**, [PERSON] 2\({}^{\copyright}\), [PERSON] 1,3,4, [PERSON] 3,4,  
**[PERSON] 1 and [PERSON] 1,*  
1 APESA, Plateau Technique, Cap Ecologia, 64230 Lescar, France; [EMAIL_ADDRESS] (R.J.); [PERSON] (C.C.)  
2 Institute of Technology of Agricultural Products, Hellenic Agricultural Organization-Demeter (HAO-Demeter), Leaf. [PERSON] 1, Lykovrysi, 14123 Athens, Greece; [EMAIL_ADDRESS]  
3 IATE, Montpellier University, INRAE, Agro Institut, 34060 Montpellier, France; [EMAIL_ADDRESS] (A.B.); [EMAIL_ADDRESS] (S.T)  
4 School of Agriculture, Fertilizing and Environment, Mohammed VI Polytechnic University (UM6P), 43150 Ben Guerir, Morocco  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +33-688-491-845  
**Abstract:** Anaerobic Digestion (AD) is a process that is well-known and fast-developing in Europe. AD generates large amounts of digestate, especially in livestock-intensive areas. Digestate has potential environmental issues due to nutrients (such as nitrogen) lixiviation or volatilization. Using liquid digestate as a nutrient source for microalgae growth is considered beneficial because digestate could be valorized and upgraded by the production of an added value product. In this work, microalgal biomass produced using liquid digestate from an agricultural biogas plant was investigated as a slow-release fertilizer in tomatoes. _Monoraphidium_ sp. was first cultivated at different dilutions (1:20, 1:30, 1:50), in indoor laboratory-scale trials. The optimum dilution factor was determined to be 1:50, with a specific growth rate of 0.13 d\({}^{-1}\) and a complete nitrogen removal capacity in 25 days of culture. Then, outdoor experiments were conducted in a 110 dm\({}^{3}\) vertical, closed photobioreactors (PBRs) in batch and semi-continuous mode with 1:50 diluted liquid digestate. During the batch mode, the microalgae were able to remove almost all NH\({}_{4}\)\({}^{+}\) and 65 (\(\pm\)13) % of PO\({}_{4}\)\({}^{3-}\), while the microalgal growth rate reached 0.25 d\({}^{-1}\). After the batch mode, the cultures were switched to operate under semi-continuously conditions. The cell densities were maintained at \(1.3\times 10^{7}\) cells mL\({}^{-1}\) and a biomass productivity around 38.3 mg TSS L\({}^{-1}\) d\({}^{-1}\) during three weeks was achieved, where after that it started to decline due to unfavorable weather conditions. Microalgae biomass was further tested as a fertilizer for tomatoes growth, enhancing by 32% plant growth in terms of dry biomass compared with the control trials (without fertilization). Similar performances were achieved in tomato growth using synthetic fertilizer or digestate. Finally, the leaching effect in soils columns without plant was tested and after 25 days, only 7% of N was leached when microalgae were used, against 50% in the case of synthetic fertilizer.  
**Keywords:** circular economy; growth plant tests; nutrients leaching; nutrients recycling; photo-bioreactor; slow released fertilizer
# Development of Wearable Textile MIMO Antenna for Sub-6 GHz Band New Radio 5G Applications
## 4 Results
### Reflection Coefficient Results  
Figure 7a shows the reflection and transmission coefficients of the conventional two-port antenna (i.e., an irregular octagonal antenna) and the proposed two-port irregular octagon-shaped wearable antenna. In Figure 6b, the measured impedance bandwidth is illustrated, showing values of 45.6% (4.35-7 GHz) for the 5G NR mid-band sub-6 GHz. During the HFSS simulation, simulated results achieved an impedance bandwidth of 64.15% (3.6-7 GHz). However, the HFSS simulation indicated some minor variations. Moreover, the obtained results exhibit minor deviations attributed to fabrication errors and conductor loss in cables encountered during the measurement analysis, as depicted in Figure 7b. The simulated and measured isolation parameter provides good isolation between the proposed two-port flexible MIMO antennas. The measured lowest isolation is found at \(-28\) dB and it is maintained over the entire sub-6 GHz band. The wearable jeans textile irregular octagon-shaped two-port MIMO antenna covers sub-6 GHz frequencies includes the N79 band (4.40-5 GHz), the Wi-Fi-5 band (5 GHz to 5.85 GHz), the V2X/DSRC bands (585-590 GHz) and the Wi-Fi-6 band (5.925-7.125 GHz) for 5G communications. Due to the symmetry of this two-port antenna design, only single antenna parameters are shown, and this information applies directly to the other antenna as well. A similar antenna design was tested successfully within [2], further validating its use. Comparing on-body effects with off-body results revealed no deviation from the intended frequency bands, confirming the reliability of this method. HFSS v21 simulation software enabled the thorough examination of wearable antennae's characteristics as well as a precision equivalent to testing conducted on human beings for accurate and scientifically valid results.  
Figure 6: Proposed textile irregular octagon antenna measurement setup. (**a**) VNA 5, (**b**) anechoic chamber.  
Figure 7: _Cont._