esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Plant Material and LEM Infestation  
Lychee plants (var. 'Maurtius') were grown from seed in pots 3.7 L filled with ProMix (ProMix BX Mycorrhizae, Denver, CO, USA) and kept in a climate room (25 \(\pm\) 2 \({}^{\circ}\)C, 50% RH, and a 12:12 h (L:D) photoperiod) in the biocontainment facility of the Tropical Research and Education Center (TREC), University of Florida, Homestead, FL (25.50\({}^{\circ}\) N, 80.49\({}^{\circ}\) W) under the Florida Department of Agriculture and Consumer Services, Division of Plant Industry permit 2018-029. The plants were watered twice per week and fertilized every 15 d with 24-8-16 (N-P-K) (Miracle-Gro(r), The Scotts Company, Marysville, OH, USA) and 138 chelated EDDHIA iron (Sequestrene(r), Syngenta, Wilmington, DE, USA). LEM-infested leaflets were detached from the infested lychee plants collected from the FruitScapes Nursery (Bokeelia, FL, USA; 26.38\({}^{\circ}\) N 82.07\({}^{\circ}\) W) and placed on 4-month-old pest-free lychee seedlings showing new leaf shoots. Mite establishment was confirmed by appearance of erineum which varied per plant from 6 to 30 days. Erineum development was assessed by the change of color and trichome density. The density of the erineum and its color seem to be two important factors shaping mite population sizes among stages, varying from zero, in the initial infestation, to thousands, in the heavy infestation stage ([PERSON] et al. in preparation). To obtain erinea of different colors available for the experiments, the lychee plant infestation was repeated four times using plants of the same age. Infested plants were separated into four groups, covering four erinea stages, hereafter called initial infestation, intermediate infestation, heavy infestation, and overexploitation. Thus, five treatments were conducted for potted (infested) lychee plants (A): initial infestation (A1), intermediate infestation (A2), heavy infestation (A3), plant overexploitation (A4), and healthy, uninfested plant (A5) (Figure S1). In addition, uninfested lychee branches were cut from fully grown lychee trees 'Maurtius' at TREC and USDA-ARS Subtropical Horticulture Research Station, Miami, FL (25.64\({}^{\circ}\) N, 80.29\({}^{\circ}\) W). From these floral buds, open flowers, leaf buds and new leaf shoots were obtained. Thus, four treatments were conducted for field-collected (non-infested) lychee tissues (B): floral buds (B1), open flowers (B2), leaf buds (B3), and new leaf shoots (B4). LEM dual-choice bioassays and volatile collections were carried out at TREC's biocontainment facility using potted plants and field-collected tissues, separately (see below).
# Actuation Mechanisms and Applications for Soft Robots: A Comprehensive Review
## 2 Actuation Materials and Mechanisms for Soft Robots
### Fluid
#### 2.2.4 Pneumatic-Hydraulic  
The combined pneumatic-hydraulic actuation mechanism has been mostly used for underwater robots. By taking the changes of the internal and external air pressures, a soft robot can absorb or discharge water to make the robot dive or float up. Utilising the elasticity of the dielectric elastomer and pneumatic-hydraulic combination, [PERSON] et al. designed a gas-liquid joint-driven soft jellyfish robot [103]. The overall weight of the robot is 261 g, and the load capacity is 241 g, so it is equivalent to its own weight. The robot has two chambers. The internal chamber is made of a dielectric elastomer membrane (actuation pressure is 38.97 kPa). When the air pressure in the internal chamber decreases, the water enters the external chamber and the robot descends. When the air pressure in the internal chamber increases, the water is discharged and the robot rises. Through the descending-rising actions of the robot, specific functions are fulfilled. In 20 s, the robot can generate a maximum buoyancy force of about 0.15 N and rise to 18 cm with an average speed of 9 mm/s.  
In comparison with other types of actuators, fluid actuators can generally output larger forces and respond to external factors much faster. Nevertheless, fluid actuators require external devices for control and execution. The external devices are large in size and heavy in weight, so the fluid-driven soft robots are difficult for miniaturisation. At present, fluidic actuators are mainly used in semi-automated soft robots like grippers, crawlers, or underwater robots [104; 105; 106; 107], and most of them are pneumatically driven. A trend is to make fluidic actuators to achieve a centimetre level, with which soft robots can be developed to be fully automated.
# Leveraging Artificial Intelligence in Blockchain-Based E-Health for Safer Decision Making Framework
## 5 Results and Discussion
### Overall Performance Comparison  
This section discusses the proposed methodology's overall performance by examining three essential perspectives, security, cost efficiency, and run-time efficiency.  
* Security Perspective: No system can be 100% secured, and smart contracts are not an exception to that rule. Smart contracts can contain numerous security vulnerabilities: re-entrancy, unhandled exceptions, Integer Overflow, and unrestricted action [28]. Many reports and literature have discussed and studied such vulnerabilities. For instance, a study of [29] that evaluated 1.2 million smart contracts found that only 4% of real-world smart contracts are affected by the integer bug vulnerability. Authors of [28] argued that, even if the smart contract contained reported vulnerabilities, it does not mean it can be exploited in practice. They analyzed 23,327 vulnerable contracts and found that only 1.98% are exploited. By remarking on this, the proposed smart contract-based system can preserve the safety of execution by 96-98%.
* Cost Efficiency Perspective: The proposed methodology of integrating ML into Blockchain by only deploying the decision function of the ML model preserves the cost of storing large ML libraries and the execution cost of training the ML model entirely on-chain. Training the ML model off-chain and only deploying the decision process on-chain enhances the ML-integrated Blockchain cost efficiency.
* Run-time Efficiency Perspective: The run-time of the proposed system depends on EVM execution abilities. EVM is a run-time virtual machine with limited resources, which causes it to take longer to execute basic programming procedures, such as loops. Although the proposed system can preserve higher security and better cost efficiency, it has low run-time efficiency, which is believed to be increased with EVM development.
# Air-Water Bubbly Flow by Multiple Vents on a Hydrofoil in a Steady Free-Stream
## 2 Experimental Method  
The experiments on air injection through multiple vents on an inclined NACA 0010 hydrofoil in a steady free-stream were performed in Chungnam National UniversityCavitation Tunnel (CNU-CT), and it was geometrically analogous to the air injection through the hull surface of a ship [1]. The NACA 0010 hydrofoil hd a chord length of 100 mm and had a maximum thickness of 10 mm. The test section of the cavitation tunnel was 100 mm in width and 100 mm in breadth and it was 1.4 m long. The contraction ratio of the converging section ahead of the test section was 5:1. The pressure within the tunnel could vary from 0.1 bar to 3 bar. The maximum attainable tunnel velocity was 20 m/s. There were provisions for the removal of air bubbles suspended in water ahead of the test section. The drawing of the cavitation tunnel is given in Nagarathinam [23]. The current experimental setup consisted of a hydrofoil placed in the test section of CNU-CT in a steady free-stream flow at the angle of inclination of \(\alpha=3.5^{\circ}\). Air was injected through an array of vent holes on the hydrofoil surface. The air injection vents of 1 mm diameter were arrayed at \(x/c=0.3\), which was the point of maximum thickness for NACA 0010 hydrofoil. The schematic of the experimental setup used in the current experiments is shown in Figure 1. The hydrofoil with multiple air vents placed in a uniform stream at the angle of inclination of \(3.5^{\circ}\) was shown in the schematic. The flow was imaged using high-speed cameras and the air injection rate was metered through a calibrated air injection pump, having an accuracy of \(\pm 0.8\%\) at the calibration conditions, in terms of Standard Liter Per Minute (SLPM). The high-speed cameras were capable of imaging the flow field at the rate of 8000 frames per second, at the resolution of 1920 \(\times\) 1080 pixels on a CMOS sensor. The minimum shutter speed of the camera was 3.9 \(\mu\)s. The control variables in the present problem were air injection rate, \(Q\), free-stream velocity, \(V_{\infty}\), with angle of inclination of the hydrofoil, \(\alpha\), held constant at \(3.5^{\circ}\) and the tunnel ambient pressure was held constant at 1 bar. The non-dimensional air injection rate was defined as \(C_{q}=V_{\infty}c^{2}/Q\) and the Froude number, \(Fn\), was defined as \(Fn=V_{\infty}/\sqrt{gd_{v}}\), where \(d_{v}\) was the air injection vent diameter; Reynolds number was defined as \(Re_{air}=\rho_{air}\,V_{inj}c/\,\mu_{air}\), where \(\rho_{air}\) was the density of air, \(\mu_{air}\) was the viscosity of air, injection velocity, \(V_{inj}=Q/\,d\,n\,d_{v}\), where \(d\) was the thickness of the hydrofoil; \(n\) was the number of injection vents, which was equal to forty-one in the current experiments.
## 2 Numerical Methodology
### Darrieus VAWT Numerical Model  
Vertical axis wind turbines (VAWTs) are commonly categorized into two main types: drag-driven turbines, such as the [PERSON] design, and lift-driven turbines, like the Darrieus design. While drag-driven turbines tend to have better initial start-up performance, they exhibit lower power generation efficiency than lift-driven turbines. This investigation evaluates the performance of a Darrieus-type wind turbine operating at a wind speed of 12 m/s. The straight blades of the turbine utilize the NACA 0015 airfoil profile. The specific characteristics of the VAWT employed in the virtual wind tunnel simulation conducted by [15] are as follows: the chord length is 150 mm, the rotor diameter (D) is 740 mm, the length (L) is 600 mm, and the aspect ratio (L/D) is 0.81. The turbine consists of three blades. Figure 2 illustrates a schematic CAD representation of the Darrieus VAWT employed in this study, with dimensions matching those of the experimental research conducted by [15]. The Reynolds number for the given air velocity values at 12 m/s is approximately 120,000.  
Figure 1: The lattice structure of the D3Q27 model. Each colour shown velocity direction is associated with a lattice node, and the arrangement of these nodes forms a 3D grid.  
Figure 2: CAD geometry of Darrieus Vertical Axis Wind Turbine.
###### Abstract  
Sensor failures are common events in wastewater treatment plant (WWTP) operations, resulting in ineffective monitoring and inappropriate plant management. Efficient aeration control is typically achieved by the dissolved oxygen (DO) control, and its associated sensor becomes critical to the whole WWTP's reliable and economical operation. This study presents the Fisher discriminant analysis (FDA) used for fault diagnosis of the DO sensor of a currently operating municipal WWTP. Identification of the bias, drift, wrong gain, loss of accuracy, fixed value, complete failure minimum and maximum types of DO sensor fault was investigated. The FDA-proposed methodology proved efficiency and prompttude in obtaining the diagnosis decision. The consolidated fault identification showed an accuracy of 87.5% correct identification of the seven faulty and normal considered classes. Depending on the fault type, the results of the diagnosing time varied from 2.5 h to 16.5 h during the very first day of the fault appearance and were only based on observation data not included in the training data set. The latter aspect reveals the potential of the methodology to learn from incomplete data describing the faults. The rank of the fault type detection prompttude was: bias, fixed value, complete failure minimum, complete failure maximum, drift, wrong gain and loss of accuracy. Greenhouse gases (GHGs) such as nitrova oxide (N\({}_{2}\)O) and carbon dioxide (CO\({}_{2}\)) emitted during wastewater treatment, electrical energy quantity in association with costs spent in the WWTP water line and clean water effluent quality were ranked and assessed for the normal operation and for each of the DO sensor faulty regimes. Both for CO\({}_{2}\) and N\({}_{2}\)O, the on-site emissions showed the most significant GHG contribution, accounting for about three-quarters of the total emissions. The complete failure maximum, fixed value and loss of accuracy were the DO sensor faults with the highest detrimental impact on GHG-released emissions. The environmental and economic study reveals the incentives of the proposed DO sensor faults identification for the WWTP efficient and environmentally friendly operation.  
fault identification; Fisher discriminant analysis; dissolved oxygen sensor; energy costs assessment; GHG emissions assessment
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 3 Key Challenges in AI for Constrained Devices  
### Computational and Memory Limitations  
One of the most significant challenges in edge AI stems from the inherently limited computational resources available on these devices. Many microcontrollers lack hardware floating-point units or are restricted to a handful of specialized accelerators [16]. Standard deep neural networks, which can contain millions or even billions of parameters, are highly impractical in this setting due to the sheer size of their model weights and the complexity of their mathematical operations [18]. Memory constraints further exacerbate the problem,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Board** & **MCU** & **Main Features** & **Dimensions** & **Applications** & **References** \\ \hline \multirow{4}{*}{Sony Spresense} & \multirow{4}{*}{CXD5602} & ARM Cortex-M4F (\(\times\)6 cores), & \multirow{4}{*}{50 \(\times\) 20.6 mm} & Sensor data & \multirow{4}{*}{[46]} \\  & & (\(\times\)6 cores), & & analysis and & \\  & & 156 MHz, 8 MB & & image processing & \\  & & flash, 1.5 MB SRAM & & for advanced AI & \\ \hline \multirow{4}{*}{SparkFun Edge} & \multirow{4}{*}{Apollo3} & ARM Cortex-M4F, & \multirow{4}{*}{40.6 \(\times\) 40.6 mm} & Ultra-low-power & \multirow{4}{*}{[47]} \\  & & up to 96 MHz, & & motion sensing for & \\  & & 1 MB flash, & & IoT edge & \\  & & 384 KB SRAM & & applications with & \\  & & & TinyML & & \\ \hline \multirow{4}{*}{Spritant TinyML} & \multirow{4}{*}{NDP101} & Cortex-M0+, 48 MHz, 256 KB & \multirow{4}{*}{24 \(\times\) 28 mm} & Speech recognition & \multirow{4}{*}{[48]} \\  & & flash, 32 KB SRAM & & & interfacing for \\ \cline{1-1}  & & & & TinyML edge tasks & \\ \hline \multirow{4}{*}{Wio Terminal} & \multirow{4}{*}{ATSAMD51P19} & ARM Cortex-M4F, & \multirow{4}{*}{72 \(\times\) 57 mm} & Remote control & \multirow{4}{*}{[49]} \\  & & 120 MHz, 4 MB & & and monitoring & \\ \cline{1-1}  & & flash, 192 KB & & systems with & \\ \cline{1-1}  & & SRAM & & TinyML for & \\ \cline{1-1}  & & & low-latency tasks & \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._as loading large models into the main memory can easily exceed the device's capacity. Even storing intermediate activations during inference can become a hurdle if the model is not carefully optimized [52].  
These limitations place a ceiling on the complexity of algorithms that can be feasibly deployed on constrained hardware. For example, a device with a few hundred kilobytes of RAM might only be able to accommodate simple feedforward networks or highly compressed convolutional architectures [15]. If the model demands recurrent computations or multi-branch topologies, the overhead for storing hidden states and intermediate layer outputs might overwhelm the device's resources [53]. Consequently, edge-focused AI has turned to novel compression and optimization techniques to ensure that the final compiled model fits within the device's memory envelope while still achieving acceptable accuracy levels [32].
## 6 Results and Discussion
### Groups Comparison  
Figure 18 shows the comparison between the mean values and the associated standard deviations for the RSSIs and SNRs divided by SF and test groups. Furthermore, the same Figure displays the percentage of received packets split by SF and test trials.  
After the transmission tests that have just been illustrated, the setup for the communication ashore of the needed information has to be identified. First of all, an optimal strategy for the receiving antennas installation is their placement as high as possible since the transmitting one cannot be mounted any higher than the upper extremity of the seamark buoy. Indeed, in spite of the fact that the limited employed elevations for the Gateways antennas throughout these good performances, it is advisable to install higher those devices so as to better exploit the first Fresnel zone thus increasing the rate of correctly received uplinks from end nodes. However, a trade off between the installation costs, its complexity and the altitude at which the Gateways antennas are placed has to be reached. As it was mentioned earlier on, another approach to augment the percentage of received packets is to make use of smaller CRs thus reducing the probability that the Gateways cannot demodulate signals due to corrupted data. But, as a drawback though, longer packets would be broadcast hence increasing the relative Time on Air (ToA) and the end node power consumption as well. Finally, despite it is not theoretically suitable for long distance links, according to the aforementioned trial groups \(SF=7\) is the most favourable SF to convey the sampled data from offshore towards adhere due to several reasons:  
* Such a SF provided acceptable rates of received packets even though it was not the best one;
* In both groups #1 and #2 it turned out in having some of the best RSSI mean values and standard deviations, as well as satisfactory SNR statistics. While the differences among the RSSI values achieved using the different SFs can be attributed more to statistic fluctuations rather than to actual better performances, mostly due to the small number of transmitted packets, it is however possible to state that in the proposed conditions the performances of the six SFs are almost comparable, and do not suggest any particular choice in this sense;
* Its most important feature for the application scenario is that it is the SF ensuring the shortest ToA for the same packet length thus reducing power requirements.  
Figure 18: Group comparison graphical analysis divided by SFs: (**a**) RSSIs mean values and standard deviations; (**b**) SNRs mean values and standard deviations; (**c**) percentage of received packets.
# Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes
## 3 Results  
### Dry Matter Yield Distribution  
The average sampled _DMY_ across the whole growing season in 2019 and 2020 were, respectively, 1.16 and 0.56 t ha\({}^{-1}\) at As, 0.78 and 0.95 t ha\({}^{-1}\) at Lannas, and 1.38 and 1.32 t ha\({}^{-1}\) at Robacksdalen. At Ojebyn, the samples were only taken in 2019 and the average _DMY_ was 2.76 t ha\({}^{-1}\). The variation in sampled _DMY_ between different sites and years had a wide range (Figure 3), which was beneficial for model calibration. At Ojebyn, the _DMY_ was significantly higher than other stations in 2019, and the main reason might be that there was a more intense solar radiation (2852 MJ m\({}^{-2}\) during the growing season in 2019, see Table 1).  
The measured BC varied among different locations. At As, the clover was the dominant species (more than 50%). At Lannas and Robacksdalen, timothy was the main species (on average more than 70%). At Ojebyn, the proportion of weeds was higher compared to other places due to the organic management approaches (no herbicides were used).
## 3 Results and Discussion  
### Dynamic Balancing Method of Driven Drum  
As shown in Figure 4, in the dynamic balance test of the driven drum, the initial unbalance of the driven drum was measured to have a magnitude of 1.234 mm/s and a phase of 159\({}^{\circ}\). Therefore, it was necessary to add a test weight of 82.03 to 164 g. A test weight of 107.7 g and whose angle 90\({}^{\circ}\) were added. After the test was aggravated, the amplitude of the unbalance was 0.562 mm/s and the phase was 204\({}^{\circ}\). Therefore, it was suggested to add a test weight of 143.4 g, whose angle was 115\({}^{\circ}\). Then, a test weight of 142.45 g and whose angle 120\({}^{\circ}\) were added. The residual unbalance was measured to have amplitude of 0.37 mm/s and a phase of 161\({}^{\circ}\). The amplitude was reduced by 70% and the highest standard G 0.4 of the balance level ISO1940 was reached.  
Based on the results above, a mass of 58.8 g was added in the direction of 240\({}^{\circ}\) of the drum, which artificially caused the drum to be unbalanced. It can be obtained by the vector parallelogram rule. The angle of the unbalance after the weighting was between the initial unbalanced phase 161\({}^{\circ}\) and the emphasis phase 240\({}^{\circ}\). However, at the same rotational speed, the phase of the unbalanced amount after the actual measurement was 108\({}^{\circ}\). The phase relationship was shown in Figure 8. It can be seen that the side chain drive affected the unbalanced state of the threshing drum from this phase change.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Instrument** & **Performance Index** & **Parameter** & **Performance Index** & **Parameter** \\ \hline \multirow{4}{*}{5E106 eddy current sensor} & Sensitivity (V/mm) & 1 & Minimum measured & \multirow{2}{*}{\(\Phi\)58} \\  & Nonlinear error (\%) & \(\pm\)1 & Frequency range (Hz) & 0–10,000 \\ \cline{1-1}  & Range (mm) & 10 & Excitation voltage (Vdc) & \(\pm\)15 \\ \cline{1-1}  & Probe diameter (mm) & \(\varphi\)25 & Working temperature (\({}^{\circ}\)C) & –20–120 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance index and parameter of 5E106 eddy current sensor.  
Figure 7: Axis center trajectory test method and sensors.