esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of the Biological Potential and Chemical Composition of Brazilian and Mexican Propolis
## 3 Discussion
### Antibacterial Activity
In the case of Mexican propolisis extracts, it is verified that ethanol extracts have a greater concentration spectrum for inhibition of both classes of bacteria, and the concentration range from 0 to 3750 \(\mu\)g/mL presents a higher frequency of inhibition. Aqueous extracts are the least efficient in terms of MICs. As expected, the extracts from both Brazilian and Mexican propolis samples showed higher activity against the Gram-positive strains than against the Gram-negative strains. These results following those from several authors, which can easily be explained by the structural differences of the bacterial cell wall [91; 92; 93; 94; 95; 96; 97]. Compared to the extraction method, EtOH extracts showed the best antimicrobial activities, and as previously shown, these extracts also had the best antioxidant activities and the highest content of total phenolic acids and flavonoids. Although the mechanism of action of these compounds in the antimicrobial function of propolis is poorly understood, some studies suggest that certain isolated constituent compounds have antimicrobial activity [98]. Among the more than 300 compounds already identified in different propolis, the following are examples. (1) Gallic acid and ferulic acid can cause irreversible changes in membrane properties and consequently the occurrence of local rupture or formation of pores in cell membranes, causing leakage of essential intracellular constituent substances. Furthermore, ferulic acid enhances the antibacterial activity of quinolone antibiotics against _A. baumannii_[99; 100]. (2) Artepillin C has bacteriostatic activity with membrane blebbing [101]. (3) Cinnamic acid and its derivatives inhibit bacteria by division of the cell membrane, inhibiting ATPases, cell and biofilm formation [102]. (4) Catechins in vitro studies have demonstrated antimicrobial effects in bacteria (G+ and G\(-\)) and have been reported as effective antivivalence agents [103].
## 2 Experimental Setup
### AC-DBD Plasma Actuator  
The linear Single Dielectric-Barrier Discharge (SDBD) plasma actuator used was flush-mounted around the perimeter at the rear end of the trailer model as shown in Figure 1. Figure 2 illustrates the construction of the SDBD plasma actuator used in this study. Copper tape, supplied by RS UK with a thickness of 0.06 mm was used to construct the exposed and embedded electrodes of the actuator. The widths of these two electrodes were 8 and 20 mm, respectively. Kapton tape, also supplied by RS UK, was used to separate the two electrodes and its width and overall thickness were 25 mm and 0.195 mm, respectively. The exposed electrode was placed 17 mm upstream of the rear end of the trailer model and the two electrodes were overlapped by 3 mm as shown in Figure 2.  
A set of Minipuls-4 high-voltage generator, manufactured by GBS Elektronik GmbH, Germany, was used to maintain the potential difference between the exposed and embedded electrodes of the SDBD plasma actuator. A Voltrcraft (VSP2410, Germany) variable laboratory Direct-Current (DC) power supply was used to power the high voltage generator, and its voltage setting was maintained constant at 20 V DC. The actuation frequency of the high voltage generator used varied from 7 to 11 kHz. A sample voltage and current waveform captured at 8 kHz is shown in Figure 3. The peak-to-peak voltage output from the high voltage generator was in the range of 14.9 to 18.6 kV while the input voltage remained at 20 V DC throughout. The output voltage signals were measured by an Agilent  
Figure 1: Schematic of the articulate lorry model (adopted and modified from [30]). Note: all units are in mm. (**a**) Front view and (**b**) side view.  
Figure 2: Schematic of the linear Single Dielectric Barrier Discharge (SDBD) plasma actuator.  
(DSO1014A, USA) digital storage oscilloscope at various actuation frequencies and are presented in Figure 4. The bandwidth, maximum sampling rate and data writing speed of the digital oscilloscope are 100 MHz, 1 GSa/s, and 10 kpts, respectively.
## 2 Materials and Methods
### Flux Data Processing and Gap-Filling  
We calculated the net ecosystem exchange (NEE) for each 30-min period using the Eddypro software version 6.1.0 (LI-COR Inc., Lincoln, NE, USA). We used the standard quality checks and corrections for the following: spike detection [57], planar fit coordinate rotation of wind vectors [58], time lag corrections, and air density fluctuation [59], and high [60] and low pass filtering [61]. Flux outputs were flagged as 0 (high quality), 1 (medium), and 2 (low quality) when deviations in steady state and integral turbulence characteristics are \(<\)30%, \(>\)30% to \(<\)100%, and \(>\)100%, respectively [62].  
During the post-processing of the 30-min data, we filtered the data for low signal strength, integral turbulence characteristics, despiking, and low friction velocity [63].  
Figure 1: Location of the eddy covariance flux tower established at the Alligator River National Wildlife Refuge. (ARNWR), Dare Country, North Carlina, USA. Map taken from Google Earth version 9.166.0.1 (accessed on 25 May 2022).  
Missing NEE\({}_{30\text{-min}}\) were gap-filled following the Max Planck Institute of Biogeochemistry method in Germany, which is an online protocol ReddyProc: Eddy covariance data processing tool ([[https://www.bgcjena.mpg.de/bgi/index.php/Services/REddyProcWeb](https://www.bgcjena.mpg.de/bgi/index.php/Services/REddyProcWeb)]([https://www.bgcjena.mpg.de/bgi/index.php/Services/REddyProcWeb](https://www.bgcjena.mpg.de/bgi/index.php/Services/REddyProcWeb))) accessed on 20 February 2022. This web tool automatically partitioned the 30-min NEE into 30-min gross primary production (GPP) and ecosystem respiration (RE). The few remaining gaps (2%-6%) left after gap-filling and flux partitioning using ReddyProc [64] were filled by means of linear interpolation from the corresponding NEE values of the previous and succeeding years. Finally, daily NEE was computed as the sum of NEE\({}_{30\text{min}}\) values over a 24-h period. Monthly averages were also obtained. The seasonality includes winter (January-March), spring (April-June), summer (July-September), and fall (October-December). The growing season usually occurs from April to October, while the non-growing season covers November to March. We present the annual C balance in atmospheric sign convention where positive NEE values connote C loss by the ecosystem, and negative values correspond to C gain [50].
# Retrofitting of Steel Structures with CFRP: Literature Review and Research Needs
## 2 Bond Behavior between CFRP and Steel
### Bond Length  
Research findings show that the bond strength, as well as the failure modes of CFRP bonded steel elements, can be affected by the bond length [85]. The bond strength of the CFRP/steel interface tends to increase at greater bond lengths. The strain level experienced by the applied adhesive was seen to drop significantly when a longer bond length was used [85]. Therefore, it is more likely for specimens with shorter bond lengths to undergo debonding failure, whereas steel components retrofitted with greater CFRP bond lengths are more likely to fail due to CFRP rupture. However, increasing the bond length from a certain threshold, the so-called effective bond length, does not yield a further increase in the bond strength [86; 87; 88; 89; 90]. Moreover, increasing either the CFRP elastic modulus or CFRP thickness results in an increase in CFRP/steel bond strength, although applying CFRP with higher axial stiffness seems to require a greater bond length [91; 90]. Furthermore, the type of adhesive has a significant effect on the effective bond length [92]. Applying the nonlinear adhesive can increase the bond strength and the effective bond length compared to the linear adhesive as a result of its ductile behavior and larger elongation at break [60]. It was also found that the rate of impact loading has a trivial effect on the effective bond length; however, it has a notable effect on the bond strength and failure modes [93; 94; 95; 46; 90]. Although the effect of bond length on the stiffness of the retrofitted elements is negligible, it can increase the load-carrying capacity of the components [12]. The proposed formulas to calculate the effective bond length are provided in Table 3. As observed, the ultimate load is not influenced by the adhesive thickness. This issue is also the same for calculating the ultimate load in CFRP/concrete joints [96].
## 3 Results and Discussion
### Texture Features  
Hyperspectral images were a combination of image and spectral techniques that reflect not only the spectral information of the target but also the image information [32]. Grayscale images at R, G, and B bands were obtained by measuring the brightness of each pixel within a single electromagnetic spectrum [33], and the nitrogen affected the absorption of light by the corn leaves. It led to different grayscale distributions in the pictures of corn obtained by the camera with different leaf nitrogen content. Therefore, there was an internal link between the picture characteristics of corn and their leaf nitrogen content.  
As seen in Figure 12, the correlation coefficients of these four feature values with the nitrogen content were all above 0.8 and highly correlated.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Algorithm**} & **Feature** & \multicolumn{2}{c}{**Calibration Set (\(n=64\))**} & \multicolumn{2}{c}{**Validation Set (\(n=18\))**} \\ \cline{3-6}  & **Types** & \(\mathbf{R}^{2}\)**c** & **RMSEC** & \(\mathbf{R}^{2}\text{P}\) & **RMSEP** \\ \hline \multirow{3}{*}{SVR} & Ref & 0.979 & 0.063 & 0.704 & 0.248 \\ \cline{2-6}  & CARS-SPA & 0.906 & 0.132 & 0.807 & 0.192 \\ \hline \multirow{3}{*}{PLSR} & Ref & 0.882 & 0.149 & 0.876 & 0.143 \\ \cline{2-6}  & CARS-SPA & 0.905 & 0.134 & 0.877 & 0.142 \\ \hline \end{tabular}
\end{table}
Table 4: Training results of various models based on full waveband and characteristic wavelength.  
Figure 12: Correlation coefficient between corn leaf texture attributes and nitrogen values.
# OQPSK Synchronization Parameter Estimation Based on Burst Signal Detection
## 1 Introduction  
The importance of satellite communication is not only reflected in the ordinary communication transmission, but it also has a profound impact on production safety and economic development. With the commercialization of the Fifth-Generation (5G) mobile networks in recent years, the integration of satellite communication and ground 5G has become a new hot topic in the industry. Therefore, satellite communication has ushered in a new round of development upsure in the world, and the development of its technology has also been paid much attention [1].  
As a common communication system in satellites, Time Division Multiple Access (TDMA) shares the transmission medium or network, which is conducive to the transmission of burst data. TDMA works by allowing multiple users to use the same frequency in different time slots, where each user occupies one particular slot. Therefore, TDMA has the advantages of high signal quality, good security, and large system capacity [2].  
Offset Quadrature Phase Shift Keying (OQPSK) as a modulation method is widely used in satellite communication because of its constant envelope characteristics, good spectral efficiency, and power efficiency. The OQPSK signal is an improved version of the Quadrature Phase Shift Keying (QPSK) signal, which can be obtained by staggering the code streams of the in-phase component and quadrature component of the QPSK signal by all a symbol period in time, such that its phase can only jump 90 degrees at most.  
In satellite communications, due to the burstiness and shortness of the burst signal and the huge influence of synchronization parameters on the performance of signal demodulation [3], it is very important to quickly and accurately capture the starting position of the burst signal and estimate its synchronization parameters. In addition, because of the limitation of hardware resources in practical application, the complexity of the algorithmis required to be as low as possible to reduce resource consumption. On the other hand, the accuracy of the algorithm should be guaranteed to meet the actual needs.
## 4 Design of Position Output-Constrained Robust Adaptive Control  
The control problem of a constrained system is one of the most studied fields in control theory and engineering application [27]. In FAO, in order to ensure the safety of multiple train tracking operation and station fixed-point parking, it is necessary to make strict restrictions on the upper and lower bounds of the train position output value. The theoretical basis for designing the position output-constrained robust adaptive controller is as follows:  
**Lemma 1** ([28]).: _Consider the following error system:_  
\[\hat{\mathbf{e}}=\mathbf{f}(t,\mathbf{e}), \tag{18}\]  
_where \(\mathbf{e}=[e_{1}\ \ e_{2}]^{\mathrm{T}}\), \(e_{1}\) and \(e_{2}\) are the position error and speed error._  
These are functions with continuous differentiable and positive definite \(V_{1}\) and \(V_{2}\), \(k>0\), where \(x_{1}\) is position output, and \(x_{d}\) is desired position output. The position error \(e_{1}=x_{1}-x_{d}\) is defined, when the following conditions are satisfied:  
1. When \(e_{1}\rightarrow-k\) or \(e_{1}\to k\), \(V_{1}(e_{1})\rightarrow\infty\);
2. \(\chi_{1}(\|e_{2}\|)\leq V_{2}(e_{2})\leq\chi_{2}(\|e_{2}\|)\), where \(\chi_{1}\) and \(\chi_{2}\) are \(K_{\infty}\) class functions. It is assumed that \(|e_{1}(0)|<k\), taking \(V(\mathbf{e})=V_{1}(e_{1})+V_{2}(e_{2})\), if \(V(\mathbf{e})\) can satisfy  
\[\dot{V}=\frac{\partial V}{\partial\mathbf{x}}f\leq-\mu V+l, \tag{19}\]  
where \(l>0\) and is bounded. Thus, \(|e_{1}(t)|<k\), \(\forall t\in[0,\infty)\).  
Consider the following symmetric Barrier Lyapunov function [29]:  
\[V_{1}(e_{1})=\frac{1}{2}\log\frac{k^{2}}{k^{2}-e_{1}^{2}}, \tag{20}\]  
where \(\log(\cdot)\) is natural logarithm.  
It can be seen that the Lyapunov function satisfies the \(V(0)=0\), \(V(x)>0\) (\(x\
eq 0\)) Lyapunov design principle.  
It can be seen from \(\dot{V}\leq-\mu V+\lambda\) that \(V\) is a bounded. Due to \(V\) being a continuous function, \(V_{1}=\frac{1}{2}\log\frac{k^{2}}{k^{2}-e_{1}^{2}}\). The initial state satisfies \(|e_{1}(0)|<k\), and \(e_{1}^{2}\
eq k^{2}\); that is, \(\forall t\in[0,\infty)\), \(|e_{1}(t)|<k\).  
In this paper, the maximum position error is constrained as \(e_{1}(\mathrm{t})=0.2\), due to \(|e_{1}(t)|<k\); taking \(k=0.21\), the input and output results of the symmetric Barrier Lyapunov function are shown in Figure 2.  
Figure 2: Symmetric barrier Lyapunov function.  
**Lemma 2** ([28]).: _For \(k>0\), if \(|e_{1}(t)|<k\), \(\forall t\in[0,\infty)\), then_  
\[\log\frac{k^{2}}{k^{2}-e_{1}^{2}}<\frac{e_{1}^{2}}{k^{2}-e_{1}^{2}}. \tag{21}\]
# Object Detection for Industrial Applications: Training Strategies for AI-Based Depalletizer
## 2 Materials and Methods
### The Employed Object Detection Model as Part of the Depalletization System  
The depalletizer solution considered in this work leverages 2D images and 3D point clouds. In this specific case, both types of data were acquired by exploiting the same sensor, i.e., the PhoXi 3D Scanner XL [31]. The depalletization process starts with a 2D and 3D scan of the environment. The image acquired is processed by an object detection model, which is in charge of detecting all the visible stock-keeping units, i.e., paper boxes, in the image. For each paper box that has been found, a bounding box that fully inscribes it is computed. Exploiting the mapping between the 2D image and 3D point cloud, the bounding boxes will then be employed to extract the portion of the point cloud containing the box. The extracted 3D portion of point cloud is supposed to be further processed in order to estimate the pose of the specific paper box to grasp and manipulate. It is worth noting that the object detection step is really important in order to extract the majority of the 3D points that belong to the specific paper box, minimizing, at the same time, the number of 3D points of other boxes. Thus, the quality of the 2D RoI resulting from the object detection step has a big impact on the 3D pose estimation accuracy and on the complexity of the estimation procedures.
# Harnessing the Potential of Emerging Technologies to Break Down Barriers in Tactical Communications
## 5 Discussion and Future Trends
### From a Technological Point of View  
The technologies mentioned in this work are mostly part of 5G and future generation 6G. For this reason, as is analyzed in this paper, the integration of these technologies, which entails the integration of 5G and 6G in tactical networks, opens up many possibilities to improve performance, security, and reliability in military environments. Below, we highlight these possibilities for further investigation.  
1. Network virtualization: Implement virtualized networks using 5G technology to provide greater flexibility, scalability, and security. Explore ways to optimize network slicing, which allows the creation of dedicated virtual networks tailored to specific tactical communication requirements.
2. Software-defined networks: Deploy SDN solutions in tactical communications to enable centralized control and management of network resources. SDN can enhance network agility, optimize bandwidth allocation, and improve network resilience in dynamic tactical environments.
3. Internet of things: Utilize IoT sensors and devices to enhance situational awareness, collect real-time data, and enable seamless connectivity among military assets. Explore the integration of IoT with tactical communication networks to enable efficient data exchange and decision making.
4. Blockchain technology: Enhance the integrity, security, and trustworthiness of tactical communications. Implement decentralized architectures that provide secure peer-to-peer communication, identity management, and tamper-proof data storage.
5. Semantic communications: Enable intelligent information exchange and interoperability among different tactical communication systems. Develop standardized ontologies and protocols for seamless communication and data sharing.
6. Neuromorphic processors: Explore using neuromorphic processors and cognitive computing in tactical communications. Investigate their potential for advanced signal processing, pattern recognition, and cognitive decision making in resource-constrained environments.
7. Resilient communication architectures: Design resilient communication architectures that can withstand disruptions, jamming and cyber threats. Explore redundancy mechanisms, dynamic routing protocols, and adaptive communication strategies to ensure uninterrupted connectivity in challenging tactical scenarios.
8. Human-machine interfaces with AINAI: Develop intuitive and user-friendly interfaces that enable efficient interaction between military personnel and advanced communication technologies. Focus on designing interfaces that reduce cognitive load, enhance situational awareness, and facilitate rapid decision making.  
These lines of action can help drive the renewal of tactical communications by leveraging the capabilities of 5G technologies and emerging technologies with 6G, ultimately enhancing the efficiency, security, and performance of military communications in tactical scenarios.
## 5 3D BEM-DMST Code Experimental Validation
### Analysis of the Tip Loss Model  
One of the novel aspects of the present tool is the spanwise distribution of aerodynamic loss resulting from the finite length of the blades. This section aims at presenting a detailed analysis of the distribution of the tangential forces along the blade semi-span in order to evaluate the effect of the tip-loss model implemented in the 3D BEM code. The discussion covers the case of H-shaped 200 W and Troposkein 450 W VAWTs, as exemplary configurations of two different rotor shapes.  
First considering the micro H-shaped rotor, tangential force coefficient \(C_{T}\) distribution along normalized semi-span is shown in Figure 9 for the upstream and downstream region for \(TSR=2.4\) (close to \(C_{P}\) peak). The \(x\) axis corresponds to the transversal coordinate, made dimensionless using the rotor radius (\(y/R=1\) corresponds to azimuthal angle \(\theta=0^{\circ}\) and \(y/R=-1\) to \(\theta=180^{\circ}\)). First of all, the effect of the tip loss factor \(F\) is observable as force coefficients tend to zero approaching the tip. Tangential force is predominantly generated by the first half of the rotation in the upstream disc and in the range \(-1<y/R<-0.5\) in the downstream one (\(180^{\circ}<\theta<235^{\circ}\)). Contour plot in Figure 9 conveys the information about the sector where the turbine blade is producing useful power: the tangential force originates torque and power, nonetheless the link is 'weighted' by the relative velocity on the discs. Even though high values of \(C_{T}\) are found in the downstream disc, the output torque is halved compared to torque coefficient of the blade in Figure 5d since the blade in the leeward phase is subjected to low relative velocities.  
An example of \(C_{T}\) distribution is provided also for Troposkein shape. The \(C_{T}\) decrease due to finite blade length is barely visible since aerodynamics at tip is challenging and forces strongly depend on the vertical coordinate because velocity triangles change continuously along the span. A similar trend discussed for H-shaped rotor is valid at equatorial sections, while towards the blade tips angles of attack increase abruptly resulting in high drag forces. Moreover, dynamic stall activation originates non-physical oscillations in \(C_{T}\) values around \(\theta=180^{\circ}\). Therefore, local minima and maxima at the blade tips are not trustworthy. High fidelity models are only capable of capturing flow physics in such complex regions. Particularly for Troposkein rotors, the tangential coefficient is mostly provided in the upstream windward phase. As discussed, conclusions drawn for Troposkein 450 W rotor are also applicable to the DeepWind Demonstrator case, which features a Troposkein shape as well.
## 3 Network Handover Algorithm Based on Location Prediction  
The network algorithm based on location prediction is divided into six parts, as shown in Figure 3. The indoor trajectory grid sequence is formed based on the mobile trajectory data matched by the central controller during signal acquisition. The historical mobility data are analyzed using data mining techniques to form trajectory clusters, and trajectory location prediction is conducted with the HMM. The mobility prediction module predictsthe subsequent trajectory based on the user's current position. The network at the predicted locations prepares in advance to minimize the handover failures caused by handover peaks and adjusts the handover management parameters accordingly.