esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### VOC Metabolite Analysis  
#### 2.4.1 Chemicals and VOC Sampling Materials  
Isoprene (\(\geq\)99%), \(\alpha\)-pinene (\(\geq\)98%), MeJA (\(\geq\)95%), MeSA (\(\geq\)99%), and (\(Z\))-3-hexen-1-ol (\(\geq\)98%) were supplied by Sigma-Aldrich (Castle Hill, NSW, Australia) and used as external standards, and d\({}_{6}\)-1,8-cineole (CDN Isotopes, Pointe-Claire, QC, Canada) was the internal standard. Stock solutions (1 g L\({}^{-1}\)) were prepared in redistilled ethanol and subsequently diluted with the same solvent to obtain working solutions for calibration of instrument response. A 2-cm DVB/CAR/PDMS SPME fibre assembly (50/30 \(\upmu\)m 24 ga, Supelco, Bellafonte, PA, USA) was used for sampling of volatiles. Prior to use, SPME fibres were pre-conditioned at 270 \({}^{\circ}\)C in the gas chromatograph (GC) inlet for 30 min and stored in sterilised glass tubes.  
To ensure minimal handling of the plants, volatiles from plant leaves were collected in 1 L fluorinated ethylene polymer (FEP) gas sampling bags (FEP 31C_1 L, HedeTech Co., Ltd., Dalian, Liaoning, China) fitted with a PTFE valve and septum port used for SPME sampling (Figure 1). The concentrations of pre-selected compounds (\(\alpha\)-pinene, isoprene, (\(Z\))-3-hexen-1-ol, MeSA, and MeJA) were determined in the different treatments at drought initiation, peak drought, and after re-watering (recovery). For each treatment, a leaf was placed in the sampling bag containing a 4.25 cm Whatman filter paper loaded with 20 \(\upmu\)L of a 120 \(\upmu\)L\({}^{-1}\) d\({}_{6}\)-1,8-cineole solution. The bag was sealed firmly without detaching from the plant and the SPME fibre inserted into the septum was exposed for 50 min prior to GC-MS analysis.  
Figure 1: In situ VOC sampling of _V. vinifera_ L. cv. Shiraz potted vine. Volatiles from an intact leaf were collected in a fluorinated ethylene polymer (FEP) gas sampling bag fitted with a PTFE valve and septum port for SPME sampling. Filter paper loaded with d\({}_{6}\)-1,8-cineole solution was included in the bag as an internal standard.
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 2 Background
### Deep Learning-Based Super-Resolution Method
#### 2.3.2 Hybrid-Based Super-Resolution Method  
Hybrid-based SR techniques are referred to as a class of methods that utilize multiple components or approaches to construct HR digital frames. Figure 3 demonstrates the taxonomy of hybrid-based SR methods.  
There are two components of hybrid-based SR techniques, including conventional methods and deep learning-based methods. Conventional methods contain the traditional signal processing methods, for instance, interpolation, edge enhancement, and regularization methods. Secondly, deep learning-based techniques encompass the utilization of the use of deep neural networks, such as convolutional neural networks (CNNs), to learn about the link between LR and HR images.  
Enhanced Deep Super-Resolution (EDSR) [52] is an example of a hybrid-based SR technique that utilizes deep learning-based approaches. EDSR uses a deep neural network architecture utilizing CNNs to study the LR and HR images. EDSR consists of multiple convolutional layers including several residual blocks. This process helps to smooth the flow of gradients and aids in the convergence of the network. Apart from EDSR, deep Laplacian pyramid networks for fast and accurate SR (LapSRNs) [53] represent a well-known hybrid-based SR method. Traditional methods use iterative optimization techniques or sophisticated models, which can be computationally and time costly. The authors try to overcome these constraints by utilizing deep learning and a multi-scale architecture. LapSRNs are trained to utilize pairings of LR and HR images in a supervised way. The network studies how to reduce the reconstruction error between the expected HR image and the ground truth during training. Typically, the loss function contains both pixel-wise and perceptual changes. This approach offers a balance of precision and speed, making it suited for a wide range of real-world applications.
# Designing an Adaptive Underwater Visible Light Communication System +
## 6 Receiver Design  
The receiver works in a similar fashion as the transmitter, incorporating the receiving USRP, the optical Li-Fi receiver, and the workstation. Key challenges like overflow and offset frequency are already discussed in [4]. This paper studies the adaptive reception of the signal. Hence, the receiver is modified to deal with this kind of reception by mounting the LFRx to the assembly, consisting of a positional servo motor SG90M (manufactured in China) connected to an Arduino Uno R3 microcontroller (manufactured by Arduino, designed and assembled in Italy), which is controlled by MATLAB.  
The angular position adopted by the positional servo motor is calibrated as shown in Figure 9 below. The receiver dynamically adjusts itself between the angles on both sides of the line-of-sight alignment to optimize signal reception. This calibration is different to the manual UVLC system in [4].
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Congestion Control Mechanisms in CoAP
#### 4.1.15 BDP-CoAP  
BDP-CoAP proposes a rate-based congestion control method for CoAP [31] which is derived from the BBR (Bottleneck Bandwidth and Round-trip propagation time) protocol. BBR's estimator of bottleneck bandwidth (BW) is redesigned to cope with the issues of lossy links and the short-term unfairness of channel in IoT networks. In case of short-term unfairness, a particular node in IoT network may acquire channel for short period of time in the time window and obtain high instantaneous delivery rates. This causes the BBR to overestimate the available bandwidth. BDP-CoAP uses an estimator that combines both max and min delivery rate measurements to derive bottleneck bandwidth estimates to avoid the overestimation of BW in case of short-term unfairness of channel access.  
BDP-CoAP also tracks the number of missed bandwidth samples over the observation period, and it leverages this information to make the bottleneck BW estimator either more or less aggressive to vary the pace of transmission accordingly.
# Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV
## 4 Flight Control System
### Fundamentals of Control Law Design and Optimization  
Previously, we established the dynamic equations of the aircraft. In light of this, this study employs a robust advanced control law to enable the autonomous flight, stability, and maneuvering of UAVs. The objective of this control law is to minimize the quadratic performance index while ensuring system stability and compliance with constraints. Additionally, it can address system uncertainties and disturbances, thereby enhancing the control law's robustness and adaptability.  
Assuming that the UAV's dynamic model can be described by a linear time-invariant state-space model, the control objective is to enable the UAV to engage in autonomous flights along a predetermined route while meeting specific performance indices and constraints. Utilizing a robust control law, the feedback gain provided by the linear-quadratic regulator (LQR) serves as the initial value for the model predictive control (MPC). Furthermore, by leveraging the MPC's predictive capabilities and constraints, the performance and robustness of the control law are enhanced.  
Extensive simulation experiments and field tests have been conducted to fine-tune the parameters of the control law to adapt to various complex environments. The optimization outcomes indicate that the control law enables the UAV to fly along the preset course, satisfying certain performance indices and constraints. Furthermore, the control law can manage system uncertainties and disturbances, improving its robustness and adaptability. The comparative results of this control law against other control laws are depicted in Table 10.
## 3 Integration of Multi-Domain MEC Environments
### Business Implications
#### 4.1.1 Coordination Models  
The relationship among different administrative domains affect the business coordination, i.e., the way in which multiple stakeholders interact to enable an operational MEC infrastructure. Such business coordination will allow the trading of elementary resources and capabilities combined and orchestrated for realizing MEC services end-to-end.  
Possible alternatives of coordination among stakeholders can be found. Some possible situations are:  
* Push vs. Pull, where resources or capabilities may be requested on-demand by the requesting domain, or advertised by the different providers, and purchased or traded off-the-shelf; and,
* Distributed vs. Centralized, where the exchange and trading of resources or capabilities may either be performed in a fully distributed fashion through bilateral (and possibly cascading) communication among stakeholders, or by means of a centralized entity that serves as the focal point for the aggregation/dissemination of information and orchestration.  
Centralized models may be further classified as Fully Centralized, if there is a single facilitator used by all the MEC providers, or Per-MEC-Cluster (PMC) Centralized, in case that multiple of such facilitators co-exist, each serving a cluster of multiple MEC providers. For instance, the GSMA [30] introduces two interesting roles. On one hand, the role of Aggregator which aggregates different edge computing environments from different operators to be offered as a single platform to the application providers. On the other hand, the role of Hub serves to abstract the complexity of interacting simultaneously with multiple aggregators and operators. Clearly, the definition of robust and trusted coordination models is essential for the interplay of services and infrastructures.
## Results
### Bone Volume to Total Volume Ratio Varies across Samples  
Bone volume to total volume ratio measurements were used to examine regenerated bone volume across samples. Scaffold Group 2 experienced the largest average BV/TV measurement of 39.68%, followed by Group 3 with 36.32%, Group 1 with 36.27%, and Group 4 with 32.8%. Within Sub-set 1, measurements ranged from 23.3% to 49.1%, with Group 4 showing the highest average BV/TV at 40.15%. Measurements in Sub-set 2 ranged from 15.7% to 50.2%, and Group 2 showed the highest average measurements at 39.2%. Sub-set 3 BV/TV measurements ranged from 14.3% to 55.7%; average measurements were again highest in Group 2 at 44.9%, followed by Group 1 at 42.9% (Table 3, Figure 6). The average bone volume across all scaffold groups was 36.3%.  
Scaffold Group 1 and 2 experienced their largest average BV/TV measurements in Sub-set 3. This is consistent with the TbTh measurements for this scaffold group, suggesting that the least bone volume occurred closer to the joint surfaces. On the other hand, in Group 3 and 4, the highest average BV/TV was observed in Sub-set 1, closer to the surface of the joint, with the measurements decreasing in regions further from the surface. This is interesting as the TbTh in Group 3 followed an opposite trend, thus suggesting that the bone volume of the regenerated tissue is greater at the joint surface, despite the thinner trabeculae. However, in Group 4, TbTh did decrease in sub-set regions further from the joint, indicating that the bone volume and trabecular thickness were both greatest closer to the joint surface. Group 4 also experienced the greatest variation between the three sub-sets, with a difference of 15% between Sub-set 1 and 3, suggesting that this scaffold group experienced the least cohesive bone volume regeneration throughout the defect (Figure 7).  
Statistical analysis showed that the collagen/hydroxyapatite (Group 1 and 2) and Ti-PLA-collagen/PLGA scaffolds (Group 3 and 4), irrespective of BMC addition, generated significantly (\(p\) < 0.05) different volumes of bone between the three sub-sets (Table 4). No other variables had a significant effect on bone volume regeneration, but other trends within the data were noted. When comparing Group 1 and 3, Group 1 experienced a higher  
Figure 7: Three-dimensional models of samples from each scaffold group and sub-set in the X-Y plane showing bone tissue regeneration.  
overall average, but Group 3 contained less variation in the data, suggesting that control scaffolds generated a greater quantity of bone volume, yet the novel scaffold yielded more cohesive volume throughout the defects. Control scaffolds with BMC exhibited a slightly higher average BV/TV than control scaffolds without BMC; however, novel scaffolds without BMC exhibited a slightly higher average value than those with BMC. This again suggests that the addition of BMC did not consistently generate greater osseous regeneration by volume within the tested samples.
# Applications of Artificial Intelligence to Photovoltaic Systems: A Review
## 3 Artificial Intelligence Applied to PV Systems
### Maximum Power Point Tracking (MMPT)
#### 3.2.2 Metaheuristics
The attractiveness of the light will depend on its brightness and distance. For solving MPPT, the position of the firefly is related to the PV voltage [56]. This algorithm assures fast convergence, with almost zero steady-state oscillations, providing good tracking speed. Artificial Bee Colony (ABC) [57] is focused on simulating the behavior of honey bees and was used for MPPT in [58; 59]. ABC consists of three different kinds of agents: workers, onlookers and scouts. First of all, worker bees go to the food sources, estimate their utility value and dance back in the hive. Every onlooker observes the dances, chooses one of their sources and goes there. Abandoned food sources are located by scouts and are exchanged with the new food sources found by them. The fitness function is set as the generated power when this algorithm is used for MPPT. The algorithm will continue until the solutions do not change. The main advantage of ABC is that it does not need hyper-parameter tuning as in the case of other metaheuristics such as GA. In MPPT, this algorithm provides quick convergence and accuracy in tracking. Ant Colony Optimization (ACO) [60] is used in [61] for optimizing neural networks in order to solve the MPPT problem more efficiently. The ACO method was adopted in the learning algorithms for adjusting the weights and biases of the neurons in the process of training. The final network had a single hidden layer with 20 nodes. The results show an improvement over the networks which are not optimized and over other traditional methods. Other metaheuristics have been used for MPPT as can be seen in other reviews, such as [62; 63]. A summary of the commented methods can be found in Table 3.
## 5 Conclusions  
In the current study, a 3D FE fluid-solid fully coupled dynamic analysis is conducted on seismic response of the stone columns composite foundations in liquefiable soils. The orthogonal method is applied to design the stone columns and work out the numerical experiment program firstly. Then, range analysis is adopted to evaluate the influence of factors on the mitigation effect of the stone columns. The design of the stone columns is optimized subsequently. The major conclusions are drawn as follows:  
1. The stone columns can effectively reduce the peak value of the EXPPR in the surrounding soils and accelerate the pore pressure dissipation. In each simulation case, the time histories of EXPPR within the depth of stone columns present a similar development pattern and have approaching peak values.  
2. The presence of stone columns will however lead to a larger PGA compared with that in free field, which can be attributed to the suppressing of vibration isolation and therefore higher transmitted ground accelerations. As a result, it could not be always beneficial to install stone columns to improve liquefiable ground which may underlie upper structures. 3. The peak values of EXPPR and the PGA are most sensitive to the permeability ratio of the stone columns among the investigated factors, which is followed by the replacement ratio, while the column diameter and shear stiffness ranging in the prescribed boundary cause negligible influence. 4. The reduction in peak values of EXPPRs and the rise in PGAs caused by the permeability ratio are rather significant when the ratio of stone columns' permeability to surrounding soils' reaches about 100 times. The ongoing increase of the ratio after that brings about a relatively smaller positive effect. 5. The analysis framework based on the orthogonal method and FE simulations can provide insights for design improvement in engineering practice. The framework is also referable and can be extended for other sensitivity studies on multiple factors.  
Conceptualization, W.L. and Y.Y.; methodology, P.C. and Y.Y.; software, X.L. and J.D.; validation, J.D. and Y.Y.; writing--original draft preparation, J.D.; writing--review and editing, Y.Y. and C.L.; visualization, J.D.; supervision, Y.Y. and C.L.; project administration, P.C. and X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (52061135112 and 51908425) and the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (311020009).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Feature Extraction  
The signal was processed in a uniform manner before feature extraction. The original heart sound recordings were all resampled to 1 kHz and passed through a second-order Butterworth filter within the range from 20-400 Hz.  
According to previous studies [3,13,15], we employed the homomorphic envelope, Hilbert envelope, wavelet envelope, and power spectral density envelope as the envelope features. Four envelopes were extracted from the filtered signal and downsampled to 50 Hz, and thus one time point in the feature sequence contained more than 20 ms of information from the original heart sound signal. Finally, all of the envelopes were normalized to zero mean and unit variance.  
We applied STFT in order to extract the frequency information for the signal, as described by [PERSON] et al. [15]. We also used a Hamming window with a window size of 80 ms and 75% overlap, so the feature sampling rate was also 50 Hz. Compared with the durations of the states in heart sounds, which were generally more than 60 ms, the frequency of features is 50 Hz, which could reduce the calculations required and ensured that the boundaries of different heart sound states were relatively clear.  
The two feature extraction methods described above were based on traditional signal processing methods. Due to the development of deep neural networks, it was possible to design neural network architectures that allowed the network to autonomously learn the features from the original signals.  
Figure 3: Block diagram and visualization of the heart sound segmentation (HSS) algorithm.  
We also used 1D-CNN to directly process the original heart sound signals, thereby allowing the network to directly learn the feature extraction method.  
If the original signals in the heart sounds were \(x(t)\), where for \(t=0\),..., \(T\)\(-\)1, and \(t\) indicates the time instants of signal acquisition, and the fragment of the original signal to be convolved is \(\mathbf{X_{n}}(i)=[x(n\cdot\tau)\ \cdots\ x(n\cdot\tau+i)]\) for \(n=0\),..., \(N\)\(-\)1, \(i=0\),..., \(M\)\(-\)1, where \(\tau\) is the stride size, \(n\) indicates all the time points for features, and \(M\) is the kernel length of 1D-CNN. One filter of 1D-CNN is \(f=[W_{0}\ \cdots\ \ W_{i}]\) for \(i=0\),..., \(M\)\(-\)1, then the formula used for calculating 1D-CNN is as follows,  
\[\mathbf{Z_{f}}(n)=\mathbf{X_{n}}*\mathbf{f}=\sum_{i=0}^{M-1}x(n\cdot\tau+i)\cdot W_{i} \tag{1}\]  
This formula yielded a sequence \(\mathbf{Z_{f}}(n)\) for \(n=0\),..., \(N\)\(-\)1, which could be considered the primary extraction features produced by 1D-CNN. To ensure consistency with the envelopes and STFT, we set the parameters for 1D-CNN as a kernel size of 80 ms and stride size of 20 ms.
## 4 Materials and Methods
### Correction for the PSI Fluorescence
To apply the rMLR model, ChIF\({}_{\rm L\_PSII}(\lambda)\) must be further downscaled to the photosystem level (ChIF\({}_{\rm P}\)(\(\lambda\))) by accounting for the probability that a fluorescence photon escapes from the PSII light reactions inside the leaves to the surface of the leaf (\(f_{\rm esc\_P\_L}\)):  
\[{\rm ChIF}_{\rm P}(\lambda){=}\,\,({\rm ChIF}_{{}_{\rm L\_PSII}}(\lambda))/f_{ {}_{\rm esc\_P\_L}}(\lambda), \tag{7}\]  
\(f_{\rm esc\_P\_L}\) is approximately equal to the sum of leaf reflectance (\(R\)) and transmittance (\(T\)) [71]:  
\[f_{{}_{\rm esc\_P\_L}}\approx R(\lambda)+T(\lambda)\, \tag{8}\]  
Note that ChIF\({}_{\rm P}\)(\(\lambda\)) in Equation (7) has units of mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\). To obtain ChIF\({}_{\rm P\_F}\) as required by Equation (3), we need to integrate ChIF\({}_{\rm P}\)(\(\lambda\)) between 640 and 850 nm and perform a unit conversion:  
\[{\rm ChIF}_{\rm P\_F}=\sum_{\lambda=640}^{850}\left[{\rm ChIF}_{\rm P}(\lambda )\right]\times\frac{\lambda\times 10^{6}}{h\times c\times N_{\rm A}\times 10^{3} \times 10^{9}}\, \tag{9}\]  
where \(h\) is the Planck constant (6.62607015 \(\times\) 10\({}^{-34}\) J\(\cdot\)s), \(c\) is the light speed (3 \(\times\) 10\({}^{8}\) m s\({}^{-1}\)), \(N_{\rm A}\) is the Avogadro constant (6.02 \(\times\) 10\({}^{23}\) mol\({}^{-1}\)), 10\({}^{6}\) is used to convert moles (mol) to micromoles (\(\mu\)mol) in \(N_{\rm A}\), 10\({}^{3}\) is used to convert milliwatts (mW) to Watts (W), and 10\({}^{9}\) is used to convert nanometers (nm) to meters (m) in \(\lambda\). For the application of the rMLR model at the canopy scale or beyond, see the see the Section 3.