esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Carbon-Fiber UAV Structures as Communication Antennas for Adaptive Relay Applications
## 3 Results
### Antenna Characterization Results
The usable bandwidths (defined by VSWR \(\leq\) 2) around these minima span roughly 500 MHz each, and the overlap between the solid and dashed traces confirms that the MATLAB-derived VSWR accurately predicts the antenna's matching performance. Minor undulations appear in the measured curve above 2 GHz--particularly near 2.5 GHz--where small oscillations, likely arising from connector or cable reflections, introduce slight departures from the smoother simulated response. These deviations, however, remain well within acceptable limits and do not affect the antenna's resonant behavior. Antenna B exhibits equally strong correspondence between measurement and calculation. The two resonance dips occur at the same frequencies, and the square-marker curve closely follows the solid trace throughout the entire 0-3 GHz span. The breadth of the lower-band matching region (VSWR \(\leq\) 2) below 1 GHz is marginally wider in the experimental data, which can be attributed to fabrication tolerances or subtle material variations. Yet, the calculated model still captures this effect with high accuracy. Together, these comparisons demonstrate that the analytical procedure for deriving VSWR from S\({}_{11}\) is both robust and reliable. The excellent agreement across both antennas and over a wide frequency range validates the underlying electromagnetic model and lends confidence to simulation-driven design and optimization in future antenna development. Figure 14: VSWR variation for each antenna individually, (a) Antenna A (b) Antenna B. The juxtaposition between measured and calculated VSWR curves for both Antenna A and Antenna B attests to the accuracy of the theoretical model, with resonance minima and -500 MHz bandwidths in each band reproduced almost identically. The minor oscillations observed above 2 GHz can be attributed to residual laboratory parasitic, connector and cable reflections and instrumentation limits, yet these small deviations do not compromise the overall agreement. Consequently, the experimental results robustly validate the mathematical approach used to derive VSWR from S\({}_{11}\), confirming its efficacy as a predictive tool for future simulation-driven antenna designs.
## 2 Materials and Methods  
### Study Sites  
The study was performed at four selected Agri-Parks in Gauteng province of South Africa, namely Rooiwal, Soshanguve, Tarlton, and Westonaria (Figure 1). An Agri-Park is a market-driven integration of agricultural activities to form a networked innovation system of agro-production, processing, logistics, marketing, training, and extension services. Agri-Parks contribute immensely to food security, economic development, and poverty reduction, especially among women and youth. There are 44 Agri-Parks dotted across South Africa, some of which have collapsed [21]. The four selected Agri-Parks in Gauteng province were active at the time of the study and were easy to access.  
According to the Weather Atlas (www.weather-atlas.com, accessed on 16 November 2022), the climatic characteristics for the four study sites differed significantly. The average precipitation for Soshanguve and Rooiwal, which are in the northern zone of Gauteng, are 583 and 516 mm year\({}^{-1}\), respectively. The average precipitation for Westonaria is 541 mm year\({}^{-1}\). Tarlton is the driest site with average precipitation of 441 mm year\({}^{-1}\). Long-term average temperature also varies greatly with Soshanguve and Rooiwal averaging 18.5 and 18.8 \({}^{\circ}\)C, respectively. The respective average temperatures for Tarlton and Westonaria are 23.8 and 15.9 \({}^{\circ}\)C, respectively. Soil properties also show great variability with Rooiwal and Tarlton exhibiting loamy soils, while Soshanguve has gravelly soil and Westonaria sandy soil.  
The drip irrigation system was the irrigation technology used at the selected sites. It is the most efficient irrigation water application system with a capacity to deliver water and chemicals precisely and uniformly at a higher frequency of application than other systems [5]. It offers many advantages over other irrigation methods, such as reduced hazard of runoff and erosion on steep slopes; leaching of excessive salts and phytotoxins from the root zone; adaptability to remote areas without pressurized water systems; conservation of water where it is costly or scarce; and promotion of deep root growth and better plant development [22]. Conventional methods of irrigation have not been efficient in the use of water, thus, resulting in excessive wastage of water and creation of environmental problems, such as waterlogging and salinity [4]. Tomato and green pepper were the dominant crops at the selected Agri-Parks. However, oriander was also an important crop at Rooiwal.  
Figure 1: Map of Gauteng province showing locations of Soshanguve, Rooiwal, Tarlton, and Westonaria relative to the cities of Pretoria and Johannesburg. Insert is a map of South Africa showing the location of Gateng Province.
## 2 Materials and Methods
### Correlation Coefficient  
To evaluate the strength of two independent variables, the correlation coefficient plays an important role in statistical analysis. Suppose that \(X=\left\{X_{1},X_{2},\ldots,X_{m}\right\}\) and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Similarity Measures**} & \multicolumn{2}{c}{**The Similarity Measures for Classifying Sample \(B\) in Patterns \(A_{i}\)**} & \multirow{2}{*}{**Classification Results**} \\ \cline{2-3} \cline{5-5}  & \(s(A_{1}B)\) & \(s(A_{2}B)\) & \(s(A_{3}B)\) & \\ \hline Equation (3) & 0.6100 & **0.8000** & 0.7727 & \(A_{2}\) \\ Equation (4) & 0.3000 & **0.6000** & 0.5000 & \(A_{2}\) \\ Equation (5) & 0.5789 & **0.8000** & 0.7727 & \(A_{2}\) \\ Equation (6) & 0.6000 & **0.7750** & 0.7500 & \(A_{2}\) \\ Equation (7) & 0.2308 & 0.2308 & 0.2051 & Null \\ Equation (8) & 0.4074 & **0.6667** & 0.6296 & \(A_{2}\) \\ Equation (9) & 0.5556 & 0.5556 & 0.5556 & Null \\ Equation (10) & 0.4000 & **0.5902** & 0.5630 & \(A_{2}\) \\ Equation (11) & 0.4201 & **0.7138** & 0.6674 & \(A_{2}\) \\ Equation (12) & 0.6930 & **0.8590** & 0.8380 & \(A_{2}\) \\ Equation (13) & 0.5000 & 0.5000 & 0.5000 & Null \\ Equation (14) & 0.4074 & **0.6667** & 0.6296 & \(A_{2}\) \\ Equation (15) & 1 & 1 & Null \\ Equation (16) & 0.4074 & **0.6667** & 0.6296 & \(A_{2}\) \\ Equation (17) & 0.4074 & **0.6667** & 0.6296 & \(A_{2}\) \\ Equation (9) & 0.731 & **0.8618** & 0.8448 & \(A_{2}\) \\ (proposed measure) & & & & \\ \hline \hline \end{tabular} The maximum similarity measures are indicated in bold.  
\end{table}
Table 3: Classification results for Example 2.  
\(Y=\{Y_{1},Y_{2},\ldots,Y_{m}\}\) are two independent observations; then, the correlation coefficient of variables \(X\) and \(Y\) is expressed as follows:  
\[r(X,Y)=\frac{\sum\limits_{i=1}^{m}{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}}{ \sqrt{\sum\limits_{i=1}^{m}{(X_{i}-\overline{X})}^{2}}\sqrt{\sum\limits_{i=1}^{ m}{(Y_{i}-\overline{Y})^{2}}}}, \tag{18}\]  
where \(\overline{X}=\frac{1}{m}\sum\limits_{i=1}^{m}{X_{i}}\), \(\overline{Y}=\frac{1}{m}\sum\limits_{i=1}^{m}{Y_{i}}\), and \(-1\leq r(X,Y)\leq 1\). If \(r(X,Y)\geq 0\), then variables \(X,Y\) increase (decrease) simultaneously in the same direction. If \(r(X,Y)\leq 0\), then variables \(X,Y\) increase (decrease) simultaneously in the opposite direction.
# A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments
## 2 Methodology and Data  
### Characteristics of TEMPO, GEMS, and SENTINEL-4  
Figure 1 shows the expected spatial resolution for TEMPO, GEMS, and SENTINEL-4 imagery, which was determined according to a geostationary projection on the globe from their respective longitudes, listed in Table 1. The table also contains the instruments' fields of regard and spectral ranges. Each instrument observes its' field of regard by scanning from east to west over the course of an hour for TEMPO and SENTINEL-4, and 30 min for GEMS, which will take observations for 30 min every hour. All of the instruments will nominally cover the 300-500 nm spectral range, but SENTINEL-4 will also measure 750-775 nm to gain additional information about cloud heights from the oxygen-A band.  
Figure 1: Spatial resolution of TEMPO, SENTINEL-4, and GEMS. Pixel sizes were determined according to a geostationary projection on the globe from the satellites’ respective longitudes.
# Self-Sustainable Biomedical Devices Powered by RF Energy: A Review
## 3 RF Energy Harvesting Application in Medical Devices
### RF Energy Harvesting for Implantable Medical Devices
#### 3.2.2 Midfield
Considering these, the work by [191] proposed a novel rectifier that achieves both broadband and wide dynamic input power range; a modified real-frequency (MRF) technology was used in the design of the rectifier circuit to power deep-placed IMDs in the human body. As a numerical technique, the MRF technique achieves optimum performance by generating a bounded input reflection coefficient, and this differentiates it from the traditional methods where the preselection of the network topology is needed to design the matching network. The operating bandwidth of the fabricated rectifier ranged from 0.9-1.7 GHz, whereas the PCE was >50% at the input power level of 0 dBm. The compact size of the circuit, which is 16 \(\times\) 11 mm\({}^{2}\), made it suitable for use with IMDs. The proposed rectifier recorded a measured DC power at load value of around 0.9 mW upon the application of 250 mW to the transmitter source.
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 7 Performance Evaluation
### Large-Scale Trace-Driven Simulations
#### 7.3.1 Settings  
We simulate the performances of LESS in MATLAB in two network topologies--the coast topology and the uniform topology. The coast topology simulates the scenario where sensors are deployed along the coastline in 10 deployment sites. Around 3-8 nodes are randomly deployed at each site, as shown in Figure 14a. The uniform topology in Figure 14b imitates the scenario where the intertidal zone is divided into blocks. A node is randomly deployed in a single block. Each node is assigned with a random altitude value in the network which indicates its deployment height. We exploit real tide datasets which record the continuous changes of water levels to regulate the behaviors of sensor nodes. The tide datasets are from the National Oceanic and Atmospheric Administration [67] and the water level is changing in the range of [0.32 m, 6.602 m]. A node will be submerged when the water level is higher than the altitude of the node.  
We deploy 50 nodes in each network, and the maximum communication distance between nodes is set to 150 m. The link qualities between nodes are derived from the signal propagation model with random Gaussian noise. In the simulation, we utilize the K-Temporal sampling algorithm in LESS. We compare the reconstruction accuracies of link quality, routing path and topology in two network topologies with sampling rates from 0.2 to 0.9. We summarize the simulation settings in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline Topology & Coast topology and uniform topology \\ Network size & 50 nodes in each network \\ Communication range & 150 m \\ Nodes altitude distribution & [2,3], [2,4] or [2,5] (unit: meter) \\ Sampling rate & 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 \\ \hline \end{tabular}
\end{table}
Table 4: The values of simulation parameters.  
Figure 13: Energy consumption comparisons of link quality estimation between the real trace and LESS (RS, K-S, and K-T) with sampling rates from 0.2 to 0.8.
## 2 Distributed Fair Rate Allocation Algorithm
### Proposed Algorithm
#### 2.4.2 Primal Projection
In summary, the primal projection is distributedly found as follows. First, we compute the initial allocation with Equation (17). If \(\hat{y}_{2,ag}\leq\hat{y}_{2,ag}^{th}\), then we allocate \(\hat{y}_{2,ag}\) to sensors 2-4 and we are done. If not, we allocate \(c_{1}-c_{2}\) to sensors 1-2 and \(c_{2}\) to sensors 3-4. Finally, this approach can be scaled to any tree-deployed sensor network. In general, a parent node has to signal at most \(m_{l}\) 4-tuples, where \(m_{l}\) is the number of network levels below it. Each 4-tuple contains an aggregated rate demand, the number of nodes involved in that demand, a threshold value and the rate absorbed once the allocation is above it. Note that this threshold value indicates the maximum possible allocation for a given hypothesis, that is, which channels are congested and which not. Therefore, the absorbed rate must correspond to this specific hypothesis. Once this information is available at the sink node, it can make the first-level allocation and successively, all parent nodes can compute their own allocations.
# Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks
## 4 Results and Discussion
### SNR Prediction
This is most likely because a higher \(f_{Dm}\) creates a more unpredictable channel, and since the NN2 is penalized for overestimating the SNR, it starts to consistently predict much lower values in order to avoid the overestimating. This results in a higher MSE and, therefore, worse performance when compared to the NN approach. Another interesting characteristic worth noting is that the OI has a higher MSE for heavy shadowing as opposed to average shadowing for \(f_{Dm}\)s of 50 Hz and above (75 Hz and 100 Hz) for all SNR values. This is an interesting find, as heavy shadowing is considered a worse scenario than average shadowing. On the other hand, for unpredictable channels, such as those with high \(f_{Dm}\)s values, heavy shadowing actually creates a more predictable pattern; although the SNR values are lower, more frequent shadowing occurrences create more regularity in the pattern and create a correlation between previous samples and future ones. The neural networks, however, compensate for these characteristics, and the same aspect is not prominent in neural networks' performance.
## 5 User Study
### Results and Discussion
#### 5.2.4 Realism  
Questions were asked about the level of realism of haptic feedback and the existence of a similarity between the feelings of sawing using the haptic model with that of the real saw.  
After analyzing the answers in this section (Figure 20), we can say that users were generally satisfied with the haptic feedback of the user. The feeling of cutting was close to the real sensation, but everyone said they could easily identify if they used the real saw or the haptic system.
# Is Microfracture Alone Enough?
## 3 Microfracture Plus Scaffolds and Stem Cells
They found no significant differences in preoperative and postoperative IKDC and KOOS scores between the two groups. Despite that, after 48 weeks, the MFX + group had a higher KOOS quality of life (QOL) score compared with the MFX alone group. Moreover, the T2 value (mapping of magnetic resonance images) showed no significant differences, but the mean magnetic resonance observation of cartilage repair tissue (MOCART) score was significantly higher in the MFX + group [21]. Bone marrow aspirate concentrate is another biologic adjunct. Intra-articular delivery of bone marrow concentrate was shown to improve MFX outcomes in full-thickness cartilage defects in a horse model. BMAC concentrations are higher in the iliac crest compared to tibial or femoral bone marrow blood. This finding has led to the development of BMAC and a protective scaffold that can treat chondral lesions when combined with MFX. This is a single-stage procedure, where a large number of multipotent cells are able to differentiate toward the chondral lineage and improve the defect filling and the rate of hyaline-like repair [22; 23]. [PERSON] et al. divided 101 patients into an MFX group (\(n=52\)) and MFX + BMAC group (\(n=49\)). They compared complications, revision rates, and visual analogue pain scores. They stated that MFX + BMAC appeared to be a safe and effective treatment option for chondral injuries of the talus, decreasing the revision rates without any complications at the donor site [24]. A retrospective study conducted by [PERSON] et al. evaluated MFX alone (group I, 43 cases) and MFX + BMAC (group II, 48 cases). Of these, 64 patients underwent a second-look arthroscopic assessment. Clinical outcomes were similar between the two groups, but the cartilage repair assessment (CRA) grade was significantly higher with BMAC augmentation after two years. Therefore, MFX + BMAC resulted in better cartilage regeneration, although studies with longer follow-ups might be needed to confirm these findings [25].
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 4 Critical Applications
### System Operation and Optimization
#### 4.2.1 Load Balancing and Optimization  
Digital Twin technology plays a crucial role in load balancing and optimization in smart grids. By creating a dynamic digital replica of the physical grid, operators can simulate various load scenarios in real time, allowing for the prediction and mitigation of potential imbalances before they occur. This proactive approach not only ensures a stable and efficient distribution of electrical power but also optimizes the grid's operational efficiency. Advanced analytics applied to the Digital Twin can identify optimal load distribution patterns, reduce peak load pressures, and suggest modifications to improve system resilience and efficiency.
## 3 Experimental Testing  
Model of the pump was prepared in such a way as to enable the connection of the necessary measuring transducers necessary for the measurement of characteristic parameters. A schematic view of the test installation and data acquisition system is shown in Figure 4.  
The following sizes were measured:  
* Pressure \(p_{c}\) in the cylinder as a function of the pump drive shaft angle
* Pressure \(p_{v}\) in the valve chamber as a function of the pump drive shaft angle
* Pressure \(p_{n}\) in the pressure pipeline as a function of the angle of the pump drive shaft
* Amplitude of vibration of the pump casing as a function of the angle of the pump drive shaft.
* Angle \(\varphi\) of the pump drive shaft
* Pump flow \(Q_{p}\)
* Temperature of the working fluid T
* Number of revolutions n of the drive shaft of the pump.  
Structure of the ADS 2000-CADEX measurement and acquisition system. It is given in Figure 5. VME-bus ADC contains two A/D converters with simultaneous operation of the speed twice 350 kHz at 12 bit, one timer, the start of each conversion is performed via the pulse of the angle encoder or the timer, with hardware registration of the reference brand of the incremental angle encoder in order to 100% control of the correctness of the operation of the angle encoder in real time. Up to 6 A/D cards = 12 A/D converters can be integrated into the system. Two VME-bus ADC modules are installed in the implemented system.  
Figure 4: Testing installation and data acquisition system. 1. Electric motor power 137 kW, 1450 min\({}^{-1}\) with speed and torque regulation, 2. Reducer, 3. Piston-radial pump, 4. Angular marker, 5. Vibration measurement transducer, 6. Pressure measurement transducer in the pressure chamber, 7. Pressure measurement transducer in the cylinder, 8. ADS 2000-CADEX measurement and acquisition system.  
Implementation of VME-bus PGA multiplexer for experiments with hydraulic radial piston pumps is very useful for monitoring and controlling various parameters. Used to handle multiple input signals from sensors that monitor parameters such as pressure, flow rate, and temperature in a hydraulic system. For pressure sensors, pressure transducers are installed to measure the pressure inside the hydraulic system. Flow sensors use flow meters to monitor the flow rate of hydraulic fluid. Temperature sensors include temperature probes to ensure the fluid is operating within safe limits. The PGA will amplify the signals from the sensors, especially if they are emitting low voltage signals, ensuring the data is suitable for processing. An A/D converter connected to the VME is used to acquire the  
Figure 5: Ultra-fast measuring system ADS 2000.  
data. bus to digitize amplified signals. A PGA multiplexer can switch between different sensors based on control signals, allowing efficient data collection from multiple sources.  
The applied measurement system enables simultaneous measurement on four fast analog channels each, with parallel measurement of time intervals from the angle mark to the mark of the incremental angle encoder.
## 3 Results and Discussion
### Experiment 2. Training Time  
The purpose of this experiment is to reduce the number of classifiers to test, by identifying and keeping those yielding the best results for the experimental data set.  
For the next tests, the feature set \(c_{3}\) and the multi-class architecture \(q_{1}\) were used. In the first round, where 10 chromosome images were used, the best classification accuracy, 64.73%, was obtained by the MLP classifier. In order to identify the classifiers yielding an similar accuracy compared to the MLP classifier, the classifiers with an accuracy above 60% were selected to conform the set of classifiers \(q_{2}\), as shown in Figure 6. The set \(q_{2}\) is presented in Table 2. Next, using the set of 119 images, the best accuracy was obtained by the same classifier (MLP): 86.77%. The accuracies obtained in this experiment are reported in Figure 7.  
Figure 5: Accuracy for experiment 1, using the feature group \(c_{1}\) and feature group \(c_{2}\). Labels in the horizontal axis associate the tested algorithms with its corresponding name in Table 2.  
In these experiments, 51 algorithms were tested and 5 of them obtained accuracies above 60%. The selection of the 60% threshold is arbitrary, considering that a 50% accuracy is not better than a random classification. The accuracy obtained in this round of experiments was used to discard the algorithms with worse accuracies for our purpose and to reduce the training time in future experiments. It is worth noting that during the second round of this experiment, more training information was used, rising the execution time, but improving the classification accuracy results, from 67.58% to 86.78% when using the MLP algorithm. Figure 7 shows the classifiers that yielded the best accuracies. Results  
Figure 6: accuracies using 10 Q-band images.  
Figure 7: Comparison of accuracies using 10 and 119 Q-band images.  
of this experiment show that the MLP classifier yielded the best accuracies and could be used in the final classification model.  
Execution time was not studied, but results showed that between 5 and 7 s are needed to classify a whole set of chromosomes once the trained model was obtained.