esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Luminescence Study
In fact, the PL lifetime increases from 0.173 ms for Eu\({}^{3+}\) monodoped ZnO to 0.366 ms after 1%Na\({}^{+}\) addition (Table 4). Then, it increases considerably with further Na\({}^{+}\) addition and reaches 0.67 ms for the ZnEuNa4 sample, indicating the important role of the sodium ions as activator of Eu\({}^{3+}\) ones. The high mobility of Na\({}^{+}\) ions and their interactions with Eu\({}^{3+}\) lead to an important charge transfer that is reflected by the considerable improvement of the luminescence properties. It is known that Na incorporation in ZnO leads to the formation of Na\({}_{\rm Zn}\) complexes, acting as acceptor levels [10]. The transitions from the ZnO conduction band to such Na\({}_{\rm Zn}\) levels are at the origin of the violet emission which is close to the energy of the \({}^{5}\)\(D_{2}\)\(\rightarrow\)\({}^{7}\)\(F2\) transition of Eu\({}^{3+}\) (405 nm). Thus, an easy and resonant excitation transfer from these levels to Eu\({}^{3+}\) is evidenced. Figure 9: PL spectra of undoped and Eu, Na codoped ZnO NCs for \(\lambda_{\rm exc}\) = 405 nm. Further, we think that the achievement of relatively longer recombination times is promising for possible application of the present NCs in several fields such as optoelectronics, photovoltaics and photocatalysis. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Samples** & **ZnO** & **ZnEu** & **ZnEuNa1** & **ZnEuNa2** & **ZnEuNa3** & **ZnEuNa4** & **ZnEuNa5** \\ \hline PL lifetime \(\tau\) (ms) & - & 0.173 & 0.366 & 0.467 & 0.527 & 0.673 & 0.842 \\ \hline Kinetic constant (10\({}^{-3}\) min\({}^{-1}\)) & 4.14 & 4.16 & 8.61 & 9.54 & 11.63 & 21.16 & 14.49 \\ \hline photodegradation percentage (\%) & 40 & 40 & 64 & 68 & 75 & 93 & 82 \\ \hline \end{tabular}
\end{table} 
Table 4: Variations of PL lifetime and kinetic constant with the doping concentration. Figure 10: PL decays relatives to \({}^{5}D_{0}\rightarrow{}^{7}F2\) transition (\(\lambda_{\text{exc}}\) = 615 nm).
## 2 Materials and Methods
### bAuNPs and Ampicillin Effect on Bacteria Growth  
The influence of gold nanoparticles in the kinetics growth curve was tested on two strains of _S. aureus_. For the present study, it was used a methicillin-resistant _S. aureus_ (MRSA, ATCC 43300) and a methicillin-susceptible _S. aureus_ (MSSA, ATCC 25923). All strains were cultured in Muller-Hinton Broth (MHB) and adjusted to 0.1 (OD600).  
The effect of bAuNPs and ampicillin in bacterial growth was first tested separately and using the serial dilution method. Final concentrations of bAuNPs ranging from 0.5 to 0.0001 mM and final concentrations of antibiotic ranging from 10 to 0.00001 mg/L were added to the cultures. Cultures were placed in 96 well plates adding 200 uL of the final solution in each well and maintaining at 37 \({}^{\circ}\)C for 24 h. Absorbances (OD600) were measured every 30 min using the Multiskan SkyHigh spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) over a 24 h period. Control wells containing only medium, and bacteria were monitored in the same conditions throughout the experiment.  
bAuNPs were tested again under the same conditions but adding only 0.1 mM of solution concentration in both bacteria. Ampicillin was tested at a concentration of 0.01 mg/L for MSSA and 0.1 mg/L for MRSA. Cultures were also done with the addition of a mixture of bAuNPs and ampicillin in the same concentrations. The growth was again monitored for 24 h using the spectrophotometer.  
The area under the curve (AUC) was calculated using GraphPad prism 9.0.0 (GraphPad Software, Inc., San Diego, CA, USA), which was used as cumulative measure of the effect of the bAuNPs in the total growth of bacteria.
## 3 Results and Discussions
### Sensory Evaluation  
Figure 5 (CK1)-(C4) displays the results of sensory evaluation results for the HTS fillets including color, odor, tissue morphology, elasticity, and mucus, and the appearance of the HTS fillets stored after 12 days are displayed in Figure 5 \(<\)CK1\(>\)\(-\)\(<\)C4\(>\). At day 0, all samples had the highest sensory scores, indicating they were fresh and of excellent quality. The sensory scores of all HTS fillets samples declined gradually with storage. However, the results showed that the treated HTS fillets (C1, C2, C3, and C4) had significant higher sensory scores than that of the controls (CK1 and CK2) (\(p<\)0.05). In addition, C4 treated with higher level of \(\varepsilon\)-PL (0.20% (\(w\)/\(v\))) consistently demonstrated better sensory qualities. Considering different \(\varepsilon\)-PL additions, their efficiencies for shelf-life extension were quite comparable. In the present research, the sensory score of CK1 and CK2 were close to the threshold of sensory rejection (score of 3.0) after six and nine days of storage, respectively. However, the treated samples were on the verge of unacceptability after 12 days. Sensory deterioration was caused oxidation reactions and microbial spoilage which produced off-odor compounds, such as  
Figure 5: Changes in microstructure (CK1–C4), sensory evaluation results ((CK1)–(C4)), and appearance (\(<\)CK1\(>\)\(-\)\(<\)C4\(>\)) of half-smooth tongue sole fillets during refrigerated storage (CK1: Chitosan coating without rosmarific acid or \(\varepsilon\)-PL; CK2: Chitosan coating with 30 mg/kg rosmarific acid; C1/C2/C3/C4: Chitosan coating with 30 mg/kg rosmarific acid and 0.05%/0.10%/0.15%/0.20% \(\varepsilon\)-PL).  
TVB-N, TMA, hypoxanthine, and aldehydes. Higher sensory scores in the treated samples compared with CK1 and CK2 indicated that chitosan-based coatings containing \(\varepsilon\)-PL and RA could contribute to maintain the sensory quality in HTS fillets samples because of antioxidant, antimicrobial, and gas barrier effects of the coatings.
###### Abstract  
This research paper presents the design of a wireless power transfer (WPT) circuit integrated with magnetic resonance coupling (MRC) and harvested radio frequency (RF) energy to wirelessly charge the battery of a mobile device. A capacitor (100 uF, 16 V) in the RF energy harvesting circuit stored the converted power, and the accumulated voltage stored in the capacitor was 9.46 V. The foundation of the proposed WPT prototype circuit included two coils (28 AWG)--a transmitter coil, and a receiver coil. The transmitter coil was energized by the alternating current (AC), which produced a magnetic field, which in turn induced a current in the receiver coil. The harvested RF energy (9.46 V) was converted into AC, which energized the transmitter coil and generated a magnetic field. The electronics in the receiver coil then converted the AC into direct current (DC), which became usable power to charge the battery of a mobile device. The experimental setup based on mathematical modeling and simulation displayed successful charging capabilities of MRC, with the alternate power source being the harvested RF energy. Mathematical formulae were applied to calculate the amount of power generated from the prototype circuit. LTSpice simulation software was applied to demonstrate the behavior of the different components in the circuit layout for effective WPT transfer.  
b  
battery; coupling; energy; frequency; magnetic; mobile; power; radio; receiver; resonance; transmitter
## 3 Indoor Localization Architecture and Methodology
### Indoor Localization Architecture  
The basic flow of the algorithm starts from sending a ping command to the IP address of the AP at the receiving end to collect data packets. After collecting the data packets, the algorithm imports the data into MATLAB for processing and obtains the original RSSI value and original CSI value on each antenna of the MIMO system. The original RSSI value is averaged and the obtained CSI matrix is averaged with weights to obtain the effective CSI value. After that, the EKF is used to fuse the nonlinear RSSI ranging model and the nonlinear CSI ranging model to obtain the distance between the receiver and the transmitter. When using RSSI and CSI values to measure the access point or mobile terminal, the ranging model Equations (3) and (11) are usually used. The unknown parameters in the formulausually need to be trained to obtain. For different environments, the parameters will be different. In order to eliminate the influence of parameter changes on the ranking results, the propagation model formula is linearized. The architecture of the ranging algorithm is shown in Figure 5.
# Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing
## 3 System Model and Problem Description
### Channel Model
#### 3.3.2 UAV-LEO Channel  
The geometric distance from the UAV to the LEO satellite, neglecting other factors, is such that the satellite enters the communication window, and data can be transmitted  
Figure 1: An illustration of task offloading scenario.  
only when \(\alpha\geq 20\). The coordinates of the UAVs are denoted as \((X_{j},Y_{j},H_{j})\). The geocentric angle \(\theta_{mj}\) between ground-based IoT devices and satellites can be expressed as [16]  
\[\theta_{mj}=arccos\left(\frac{R+H_{m}}{R+H_{j}}cos\alpha_{j}\right)-\alpha_{j} \tag{5}\]  
where \(R\) denotes the Earth's radius, \(H_{m}\) represents the UAV's height, \(H_{j}\) denotes the satellite altitude, and \(\alpha_{j}\) indicates the horizontal angle between the UAV and the satellite. The maximum value of the communication window \(\theta\) is obtained when \(\alpha=20\). The distance is given by  
\[H_{mj}=\sqrt{\left(R+H_{m}\right)^{2}+\left(R+H_{j}\right)^{2}-2R\left(R+H_{j }\right)cos\theta_{mj}} \tag{6}\]  
According to 3 GPP Release15, an additional Doppler shift due to satellite motion should be taken into account according to the following formula:  
\[f_{j,m}=(v_{sat}\ /\ c)\times\left(\frac{R+H_{m}}{R+H_{j}}\cos\alpha\right) \times f_{m,j} \tag{7}\]  
where \(v_{sat}\) denotes the satellite speed, \(c\) denotes the speed of light, and \(f_{m,j}\) is the carrier frequency at the transmitter. The drone antenna transmitting gain and receiving antenna gain of the satellite are given by the following formula [34]:  
\[g_{m}=\phi\left(\frac{\pi f_{m,j}\Omega_{m}}{c}\right)^{2} \tag{8}\]  
\[g_{j}=\phi\left(\frac{\pi f_{j,m}\Omega_{j}}{c}\right)^{2} \tag{9}\]  
where \(\phi\) denotes the effectiveness of the antenna, and \(\Omega_{m}\) and \(\Omega_{j}\) are the antenna radii on the reflective surfaces of the UAV and satellite, respectively. \(c\) is the speed of light. According to the work that has been finished [30], the channel coefficient of the UAV-LEO channel is modelled as  
\[h_{mj}=u_{mj}l_{mj} \tag{10}\]  
where \(u_{mj}\) and \(l_{mj}\) represent the path loss factor and the small range fading, respectively. In particular, the path loss factor can be written as \(u_{mj}=\left(\frac{4\pi H_{mj}}{\lambda_{mj}}\right)^{2},\lambda_{mj}=c/f_{m,j}\). The small-scale fading is given by  
\[l_{mj}=\sqrt{\frac{Q}{Q+1}\overline{l}_{mj}+\frac{1}{Q+1}\widetilde{l}_{mj}} \tag{11}\]  
where Q is the Rician fading factor, \(\overline{l}_{mj}\) denotes the LoS component satisfying \(\ |\overline{l}_{mj}|=\)1, and \(\widetilde{l}_{mj}\) denotes the non-line-of-sight (NLA) component following \(\widetilde{l}_{mj}\sim\mathcal{CN}(0,1)\). According to the Shannon formula, the following equation gives the data rate of the uplink  
\[R_{mj}=B\log\left(1+\frac{P_{mj}|l_{mj}|^{2}u_{mj}}{N_{0}B}\right) \tag{12}\]  
where \(B\) refers to the bandwidth used to link the UAV and the satellite, \(P_{mj}\) denotes the UAV uplink transmission power, and \(N_{0}\) denotes the noise power spectral density.
# Teleolocation--Remotely Operated Legged Robots
## 2 Methods
### Teleoperated Platform
#### 2.1.1 Hexapod Legged Mechanism  
The PhantomX hexapod is comprised of 18 joints: six kinematically identical limbs with three joints each. The robot exhibits sagittal symmetry, with each symmetrical half consisting of three legs. These legs are denoted the prothoracic, mesothoracic and metathoracic. In this work they are enumerated as Legs 0-5, as depicted in Figure 1a.  
Each of the six legs consists of three links, the coxa, femur and tibia, as depicted in Figure 2a. The servo-driven joints that actuate these links afford 3 DOF rotary motion for each leg. These joints are the thorax-coxa, coxa-trochanter, and femur-tibia joints, as illustrated in Figure 2b. Joint 0, the thorax-coxa, has a vertical axis of rotation, while joints 1 and 2, the coxa-trochanter and femur-tibia respectively, present parallel horizontal axes of rotation.  
The PhantomX ROS package comes prepackaged with a general position plus yaw controller. The gait mechanism allows for forward and backward translation as well as pivoting about the vertical z-axis around its center. These predefined gait trajectories are well-studied alternating tripod gait sequences, similar to that of the common cockroach. More explicitly for forward and backward translation, from Figure 1a, odd enumerated legs move in concert while even enumerated legs contact points remain fixed for balance and vice versa.
# Comparative Study of Natural Fibres to Improve Insulation in Wooden Beehives Using Sensor Networks
## 2 Materials and Methods
### Sensor Network
#### 2.3.2 Software  
The node and the gateway are programmed in C using the Arduino Integrated Development Environment (IDE). The code facilitates encrypted communication between nodes and the gateway. Each node possesses a unique address, enabling the gateway to identify each one and synchronise their transmissions.  
In the developed workflow, the gateway anticipates the data transmission from the node. Subsequently, the node gathers sensor information, saves it to the SD card, and relays this information to the gateway. If the transmission is successful, the gateway communicates a new wake-up time to the node. The node then sets a new wake-up alarm and powers down.  
This process aims to (1) conserve power in the nodes by shutting them off, (2) synchronise data transmissions, and (3) allow the gateway to control the sampling rate. If a specific node fails to respond, the gateway recursively attempts communication for a set number of cycles (defaulted to 2). If subsequent communication efforts remain unsuccessful, the gateway initiates a management alarm and proceeds with other nodes (Figure 4). Once the gateway collects a sample from each node, it stores the data locally and also on an online server (using HTTPS queries + MySQL 8.0.36) for long-term assessment (Figure 4). The developed architecture supports the integration of alarms in the gateway through the  
Figure 3: Architecture of the nodes implemented for remote monitoring of beehives and main components. The node supports a wide range of sensors.  
programming of data assessment scripts or specific algorithms. However, this was outside the scope of our study and is reserved for future field deployment of the network.
## 2 Materials and Methods
### Methods
#### 2.3.5 Construction of Urban Synergy Index  
Spatial variations in ecological quality result from the complex interplay of numerous influencing factors. Notably, substantial differences in natural factors, such as climatic conditions, may overshadow the positive impact of human policies on ecological quality in the face of continuous urbanization. Acknowledging the pivotal role of land transitions in the nexus between urbanization and ecological quality, we formulated an Urban Synergistic Index (USI) to compare urbanization intensity with changes in ecological quality. Cities should be seen as complex systems that gather various elements such as land management, population aggregation, and transportation [88]. Measuring the level of urbanization and urban scaling requires considering the adaptability after numerous interactions [89]. For example, one city may bring more ecological deterioration and aggregating population than two cities of the half expansion area together. For the convenience of comparing different cities, we propose a hypothesis that the expansion of the same area in different cities might lead to comparable degrees of ecological degradation. In instances where two cities exhibit similar levels of urbanization intensity, the influence of human policies and protective practices may significantly contribute to discernible differences in ecological quality changes between them. This insight can serve as a basis for diverse cities to formulate targeted coordination and sustainable development policies.  
The intensity of urban expansion was computed by assessing the interannual changes in impervious surface area within each city. Simultaneously, the intensity of EQI change was determined by evaluating the city's interannual average EQI. Cities exhibiting positive USI values have demonstrated certain success in ecological recovery and protection within the context of urbanization. A USI value less than 0 indicates a decline in urban ecological quality. However, if the absolute value of USI is very small, it suggests that these cities have recognized the value of ecological protection and implemented certain measures for its preservation. The formula for calculating USI is as follows:  
\[\text{USI}=\frac{\left(EQI_{mean}^{t_{2}}-EQI_{mean}^{t_{1}}\right)/EQI_{mean} ^{t_{1}}}{\left(1 mp_{area}^{t_{2}}-1 mp_{area}^{t_{1}}\right)/1 mp_{area}^{t_{1}}} \tag{13}\]  
where \(EQI_{mean}^{t_{2}}\), \(EQI_{mean}^{t_{1}}\), \(Imp_{area}^{t_{2}}\), and \(Imp_{area}^{t_{1}}\) refers to the mean value of all pixels' EQI value and impervious surface area in a city at the end and beginning of the period, respectively.
# Inundation Extent Mapping by Synthetic Aperture Radar: A Review
## 3 Relative Strengths and Limitations of Existing Techniques
### Approaches
#### 3.1.1 Supervised Versus Unsupervised Methods  
Mapping water extent is a matter of classification using supervised or unsupervised approaches. Supervised classification methods require a training set consisting of labeled inundated areas or core water locations and their corresponding pixels extracted from SAR data. Using training sets, mappers can achieve better accuracy without deeply understanding the physics of the data signal before designing an algorithm [40; 41; 42; 43; 44; 45; 46]. Drawbacks are at least twofold, however, the generation of training sets cannot be automated, and the algorithm has local dependence--that is, a classifier well-trained over one area may not work well in another.  
[PERSON] et al. [47] developed an \"almost automatic\" fuzzy logic classifier, which used fixed thresholds determined by a theoretical scattering model to classify pixels automatically or to use human-labeled samples. This method has proved reasonably accurate, but the automatic classification requires a microwave scattering model and introduces modeling uncertainty, while the human labeling of samples is time consuming. These drawbacks of supervised methods may be circumvented through the use of three unsupervised methods: threshold determination, segmentation, and change detection.
## 6 Conclusions  
Unmanned ground vehicles (UGVs) that aim to travel over obstacles are typically classified into legged, legged-wheeled, wheeled, and tanked forms. It is known that the former types can move over higher obstacles, but have more degrees of freedom. Legged vehicles can significantly shift their centers of gravity (COG) by positioning their multi-articulated legs at appropriate trajectories while stepping over a high obstacle. To realize a COG movable mechanism with a small number of joints on conventional wheeled and tracked vehicles, some vehicles have been developed that can travel over a high step by explicitly shifting a heavy mass with a single or two degrees of freedom to change their COG position. However, these UGVs tend to shift the mass at a high position, causing instability.  
Figure 15: Prospective image of teleoperation of “_Dyjob_”. (**a**) teleoperation and (**b**) remote field.  
To address this issue, we proposed a mechanism for simple tracked vehicles in which the COG can be moved forward and backward at a lower level. Based on this mechanism, we developed a prototype called \"_Dyjob_\", in which the movable mass contained all the heavy geared motors as well as electric devices and batteries. Experimental results confirmed that _Dyjob_ with the significant movement of COG could successfully traverse a step, stairs, and a gap.  
Since _Dyjob_ is still a prototype, further improvements are being considered. For example, lightening the parts except for the movable mass will increase the weight ratio of the movable mass to the whole body. In addition, increasing the moving distance of the movable mass will enhance the performance. This proposed mechanism is also available for conventional four-wheel vehicles.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app12010525/s1](https://www.mdpi.com/article/10.3390/app12010525/s1)]([https://www.mdpi.com/article/10.3390/app12010525/s1](https://www.mdpi.com/article/10.3390/app12010525/s1)), Video S1: _Dyjob_ moving over a step. Video S2: _Dyjob_ climbing up stairs. Video S3: _Dyjob_ moving over a gap. Video S4: _Dyjob_ climbing a slope.  
Conceptualization, Y.F.; methodology, Y.F.; software, K.O.; validation, Y.F. and K.O.; formal analysis, Y.F. and K.O.; investigation, Y.F. and K.O.; resources, Y.F. and K.O.; data curation, Y.F. and K.O.; writing--original draft preparation, Y.F., K.O., and A.N.I.; writing--review and editing, Y.F. and A.N.I.; visualization, Y.F. and K.O.; supervision, Y.F.; project administration, Y.F.; funding acquisition, Y.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.