esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Impact of Phenology on Simulated Productivities  
Our analysis indicates that errors in simulated GPP and NEP result from the bias in simulated phenology (Figures 5 and 6). Although the sign of NEP did not change due to the bias in phenology, as the study site is a relatively large carbon sink, this might not be the case at other locations that are closer to carbon neutral. The relationship between the errors in simulated GPP and NEP and the bias in phenology also has implications on the impact of phenological shifts on carbon assimilation. The linear relationship between errors in GPP and NEP and the bias in leaf onset means that a one-day advance in leaf onset would result in an increase of 9.48 g C m\({}^{-2}\) yr\({}^{-1}\) in GPP and 5.02 g C m\({}^{-2}\) yr\({}^{-1}\) in NEP (Figure 7a,b). The quadratic relationship between errors in GPP and NEP and the bias in leaf offset (Figure 7a,b) suggests that delayed leaf offset leads to higher GPP and NEP. However, the marginal increase in GPP and NEP declines with the days of delay. This might be because the environmental condition becomes less and less favorable for carbon uptake late in a year.  
Because growing season length (GSL) is determined by leaf onset and offset, productivity is usually correlated with GSL. Previous studies have shown significant control of GSL on the productivities. For example, a modeling study found that an extension of one day in GSL would result in an increase of \(9.8\pm 2.6\) g C m\({}^{-2}\) yr\({}^{-1}\) in GPP for temperate deciduous broadleaf forest [55]; carbon flux measurements showed an increase of \(5.57\) m\({}^{-2}\) yr\({}^{-1}\) in NEP with a one-day extension of carbon uptake period (_i.e._, number of days when it is a net carbon sink), which is an alternative definition of GSL [56]. These relationships are similar to the impact of leaf onset on the productivities estimated from our simulations. This might be explained by the fact that the variation in GSL in those studies was dominated by the variation in leaf onset [55]. On the other hand, although our analysis indicates that the productivities are less sensitive to leaf offset than to leaf onset in Agro-IBIS, further analysis with measurements is still needed to evaluate to what extent these sensitivities represent reality. For example, a study using different phenological indicators suggested that the productivities are more sensitive to autumn senescence [57]. Despite the discrepancy, our analysis suggests that it might be better to examine the individual impact of leaf onset and offset instead of the overall impact of GSL.
## 4 Evaluation Methodology
### Experiment Result of Feature Selection with Permutation Decoding
#### 4.4.1 Result of Detection Rate  
First, the detection rate performance was compared and the results of this comparison are shown in Table 3.  
Figure 6 shows the results in graphical form. Reviewing the results of the experiment, it can be seen that the performances are good overall, since this is used as the evaluation index of feature selection on detection rate in wrapper-DR. When 20% of features are selected, the proposed algorithm and wrapper-DR showed very similar detection rates, with 0.988781 and 0.98828, respectively. Since the simple detection rate also converges in the direction of finding high optimal solutions as the error rate approaches 0 in the proposed algorithm, the performance results are similar to those of wrapper-DR. However, as mentioned above, the single detection rate evaluation has not been calibrated for the problem of class imbalance; the proposed method is expected to show more objective and better performances in overall anomaly-detection performance. A more precise comparison of performances was performed through F1-measure.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Forecast** & **Label** & **Normal** & **Abnormal** \\ \hline Normal & Hit (like TP) & False alarms (like FP) \\ Abnormal & Misses (like FN) & Correct negatives (like TN) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix in forecast.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Subset** & **Algorithm** & **Proposed** & **Wrapper-DR** & **PCA** \\ \hline
10\% & 0.983925 & 0.985000 & 0.960384 \\
15\% & 0.987082 & 0.987214 & 0.934998 \\
20\% & 0.988781 & 0.988280 & 0.967096 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Result of detection rate: table.
###### Abstract  
Volatile compounds are the main chemical species determining the characteristic aroma of food. A procedure based on headspace solid-phase microextraction (HP-SPME) coupled to gas chromatography-mass spectrometry (GC-MS) was developed to investigate the volatile compounds of sweet potato. The experimental conditions (fiber coating, incubation temperature and time, extraction time) were optimized for the extraction of volatile compounds from sweet potato. The samples incubated at 80 \({}^{\circ}\)C for 30 min and extracted at 80 \({}^{\circ}\)C by the fiber with a divvinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) coating for 30 min gave the most effective extraction of the analytes. The optimized method was applied to study the volatile profile of four sweet potato cultivars (Anna, Jieshu95-16, Ayamursaki, and Shuangzai) with different aroma. In total, 68 compounds were identified and the dominants were aldehydes, followed by alcohols, ketones, and terpen. Significant differences were observed among the volatile profile of four cultivars. Furthermore, each cultivar was characterized by different compounds with typical flavor. The results substantiated that the optimized HS-SPME GC-MS method could provide an efficient and convenient approach to study the flavor characteristics of sweet potato. This is the basis for studying the key aroma-active compounds and selecting odor-rich accessions, which will help in the targeted improvement of sweet potato flavor in breeding.  
sweet potato; volatile compounds; HS-SPME; GC-MS Article
# Laser-Based Mobile Visible Light Communication System
## 1 Introduction
For clarity, we summarize previous research on mobile VLC in Table 1.  
In this study, we designed an experimental platform for mobile VLC and conducted a series of experiments to assess its performance. Throughout these experiments, we transmitted OOK signals at a rate of 500 Mbps. The lens-free system demonstrated a stable link performance at distances of one meter and two meters, showing adaptability to mobility-induced perturbations. However, beginning at a distance of three meters, increases in both distance and speed contributed to a larger fluctuation range in the BER. Despite these challenges, at a distance of four meters, the system was still capable of supporting a transmission rate of 500 Mbps at a speed of 0.48 m/s. This choice of speed takes into account the mobile communication needs of people indoors such as offices and classrooms. Though distance and mobility impact the system's performance, it maintained a considerable degree of reliability and efficiency.
## 4 The Proposed Detection System
### Recognition and Tracking Module  
The Recognition and Tracking module consists of four stages, arranged in pipes and filters. The first filter receives raw images and applies background subtraction, resulting in a binary image. The second filter takes binary images, detects blobs (set of adjacent pixels that belongs to the front of the image) and transfers the set of blobs to the blobs filter, which discards those blobs that do not contain objects of interest and adds spatial information to those relevant blobs. The last stage takes the information of the objects of interest in the image space and associates each one of them to the position of previously detected objects; thus, calculating the movement of each object. The different stages that compose the tracking and recognizing module are presented in Figure 2.
# Anthocyanin Delivery Systems: A Critical Review of Recent Research Findings
## 1 Introduction
### ACN Interactions with Wall Materials  
Encapsulation technologies for ACNs are aimed at maintaining their stability under different pH conditions, as well as heat and light exposure. ACN interactions with encapsulated wall materials usually result from electrostatic interactions or hydrogen bonding. Liposomes (1% (\(w/w\)) soy lecithin) loaded with grape seed extract were formed by the electrostatic interaction of polyphenols with the phospholipid bilayer by using high-pressure homogenization (22,500 psi) [41]. Hydrogen bonding was observed in encapsulated flours prepared from sweet potatoes [purple-fleshed] spray-dried with different concentrations of maltodextrin and ascorbic acid, resulting not only in smoother flour granules but also higher total ACNs content, and improved antioxidant capacity as compared to non-encapsulated flours [42]. A similar phenomenon was observed between strawberry ACNs [negatively charged] and protonated amino groups of chitosan (CH) nanoparticles with an encapsulation efficiency of 58.09% with the 36.47% of ACNs (300-600 nm particle size) [43]. Other interactions were observed, such as hydrophobic interactions between \(\beta\)-lactoglobulin (\(\beta\)-Lg) and malvidin-3-O-glucose, forming a complex that enhances the thermal stability, oxidative stability, and photostability of ACNs [44]. These interactions act as the main driving forces for the encapsulation of grape seed ACNs in chitin microspheres [45]. [PERSON], [PERSON], and [PERSON] [46] observed that microencapsulation of ternatin ACNs isolated from the floral extract of _Clitoria ternatea_ (CT) with alginate and calcium chloride (CaCl\({}_{2}\)) improved the stability, as well as the biological activity of ACNs in this case, although, the Fourier-transform infrared spectroscopy (FTIR) assay revealed no interaction between CT ACNs and alginate.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 6 Importance of Hybrid Control Techniques  
Hybrid control theory is a branch of modern control theory that deals with systems that exhibit both continuous and discrete behavior. These systems are called hybrid systems, and they are characterized by the interaction between continuous dynamics and discrete events, such as switching between different modes of operation or the occurrence of events that trigger changes in system behavior [116]. Some developments in hybrid control are also provided in Table 6.  
The control system of a UAV is a crucial and complex component that enables the UAV to achieve stable flight, precise maneuvering, and controlled behavior. The control system involves a combination of hardware and software elements that work together to ensure safe and effective operation. Hybrid control is important for this system because it provides a more robust and flexible control approach. In a hybrid control system, multiple control techniques are combined to provide better performance and stability. This is particularly useful in UAVs, where the system must operate in a wide range of environments and conditions. By combining different control techniques, the system can adapt to changing conditions and maintain stability [123]. In the case of the UAV, the hybrid control approach combines a linear controller with a nonlinear controller. The linear controller is used to stabilize the system in a small range of motion, while the nonlinear controller is used for larger motions. This approach allows for better control of the system in both stable and unstable flight conditions [124]. Another advantage of hybrid control for the UAV is that it can handle disturbances and uncertainties in the system. UAVs are subject to various disturbances, such as wind gusts and turbulence, which can affect the stability of the system. Hybrid control can help the system recover from these disturbances more quickly and effectively. The paper introduces a new control scheme for a quadrotor UAV to perform standoff tracking of a moving ground target. The control system is divided into outer and inner loops, handling position and attitude control, respectively. The outer loop uses a cylindrical coordinate system to describe target motion and employs a Lyapunov-based guidance law for stability. Acceleration signals are converted to Euler angles for the inner control system. An integral backstepping controller stabilizes UAV attitude, and a disturbance observer addresses non-uniform target motion and constant wind effects. Numerical simulations confirm the proposed approach's feasibility and performance [125]. The hybrid control strategy described in the study combines a linear control approach for attitude stabilization and a nonlinear control approach for position control. Attitude
# Modeling and Test Results of an Innovative Gyroscope Wave Energy Converter
## 4 Physical Model and Test Environment  
### Characteristics of GWEC  
As shown in Figure 3, the gyroscope wave energy converter (GWEC) system is mainly composed of a flywheel, rotor motor, support frame, speed increase gearbox, rotating PTO, and floating platform. The flywheel rotates in one direction at high speed in the vacuum flywheel housing, and the vacuum environment bestially reduces the air resistance to the flywheel. The outer ring of the flywheel is subjected to greater centrifugal force under high-speed rotation, while the internal influence is less. Therefore, the split flywheel structure is composed of a flywheel disc and a flywheel outer ring. Among them, the flywheel disc made of titanium alloy contributes little to the moment of inertia of the flywheel. The choice of this material not only ensures strength but also reduces unnecessary weight. The high-strength and high-density X30 Cr13 is adopted in the rotating mass that contributes a large moment of inertia. The rotor motor makes the load obtain a fast acceleration rate so that the system has high sensitivity and a quick response. Another advantage is that it is easy to achieve the dynamic balance of the flywheel. The small number of parts of the motor means it is simple in design and has high inherent reliability while reducing manufacturing costs. It can be seen from Figure 3 that there are circular holes arranged in a matrix on the side of the support frame. Their function is to disperse the internal stress in the frame and avoid potential safety hazards caused by stress concentration. Besides, reducing the weight of non-power output parts also can improve energy conversion efficiency. However, the precession angular velocity of the flywheel is much smaller than the rated speed of the generator. Therefore, a 4-3-3-3 gear ratio is applied to increase the precession angular velocity of the flywheel to the speed required for rotating the PTO to generate electricity. Besides, it is fixed at both ends and integrally sealed, which is not only convenient for maintenance but also highly reliable.  
Compared with other power generation devices, the GWEC converts wave energy into mechanical energy to produce electric energy directly. The device omits the intermediate transformation in the structure, thereby improving transmission efficiency. Additionally, it is sensitive to external excitation and can absorb a wider range of wave energy due to its working principle. Moreover, it is lightweight and pollution-free.  
The entire device is installed in a sealed container floating on the sea surface and moored on the seabed. The movement direction of the device is strictly restricted to ensure the wave movement direction is perpendicular to the flywheel rotating direction, so that GWEC can stably convert wave energy into electric energy. Besides, a motion limit device is also added to the mechanical structure to prevent damage to the entire equipment under extreme weather conditions to improve survivability.
## 2 Materials and Methods
### Data Processing: Identification Procedure
#### 2.3.1 Fish Detection and Feature Extraction  
The detection of the fish body was based on subtracting the background (green) followed by detecting the object (the largest) inside the area of the background. The area of the background was detected as an object with a known color based on the hue, saturation, value (HSV) representation of the image [31]. The hue and saturation channels were used for segmentation. The rest of the pixels belonged to the fish. Then, size-based noise filtration was used to remove the pixels that did not represent the fish. The morphological close operation (the MATLAB R2020b function imclose) was applied to the filtered image to connect the fish body parts that could be divided during the segmentation procedure. The output image was represented as a binary mask of the fish object. Fish body rotation was estimated via the MATLAB R2020b function regionprops ('Orientation') to align the fish horizontally. The final fish mask contained the full fish body (Figure 4, left images). The mask was used for localization of the fish in the original color image.  
Figure 3: Identification scheme.  
Figure 2: (**Left image**) seabass data collection design. Digital camera photographing the lateral view of the fish. Image of the seabass (**middle**, **bottom**) and carp (**middle**, **top**) on the uniform green background. This image was used for automatic fish localization. (**Right image**) experimental design for carp imaging using a tent with controlled lighting conditions.  
The height and width of the fish object bounding box was then used for specific ROI localization. Each ROI was defined as a rectangle with a relative location to the fish bounding box to eliminate scaling and fish growth. Examples of ROI localization are shown in Figure 4.
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 3 System Model
### Comparative Study of Methodologies Based on Metasurface  
The IRS-assisted method (Equations (13)-(15)) employs a single-layer reconfigurable surface alongside hybrid analog-digital precoding, whereas stacked intelligent metasurfaces (SIM) [35] utilize multi-layer phase-shift configurations to enhance beamforming gain. This design decision results in reduced calibration overhead; SIM requires the simultaneous optimization of stacked layers, whereas RCTS-AMP-RSSI-AS optimizes the IRS coefficients (Section 4.2). Energy consumption: Power usage diminishes with a reduction in active components. Implementation cost: In densely populated metropolitan settings, SIM hardware is overly complex [36], but our HB-IRS integration is compatible with the existing 5G infrastructure.
# Blade Crack Diagnosis Based on Blade Tip Timing and Convolution Neural Networks
###### Abstract  
The diagnosis of blade crack faults is critical to ensuring the safety of turbomachinery. Blade tip timing (BTT) is a non-contact vibration displacement measurement technique, which has been extensively studied for blade vibration condition monitoring recently. The fault diagnosis methods based on deep learning can be summarized as studying the internal logical relationship of data, automatically mining features, and intelligently identifying faults. This research proposes a crack fault diagnostic method based on BTT measurement data and convolutional neural networks (CNNs) for the crack fault detection of blades. There are two main aspects: the numerical analysis of the rotating blade crack fault diagnosis and the experimental research in rotating blade crack fault diagnosis. The results show that the method outperforms many other traditional machine learning models in both numerical models and tests for diagnosing the depth and location of blade cracks. The findings of this study contribute to the real-time online crack fault diagnosis of blades.  
blade tip timing; blade crack faults; deep learning; fault diagnosis; experimental measurement Article Blade Crack Diagnosis Based on Blade Tip Convolution Neural Network
## 2 Results and Discussion
### Field Emission Scanning Electron Microscope (FESEM)  
Figure 2 shows the FESEM images of S0, S05, S07, and S10. Based on the capability of FESEM, the images of the samples show the bulk of the sample rather than the surface. Sample S0 shows  
Figure 1: XRD patterns of S0, S05, S07, and S10. X, halloysite (7 Å); M, sodium aluminum silicon oxide; S, quartz; R, silicon oxide; G, graphite.  
several sizes of HNTs, shown in Figure 2a. By adding SDS, PVA and MA at 5 wt. % to the mixture of HNTs and distilled water, the effects of these treatments are shown in the subsequent FESEM images. Apparently, HNTs were significantly affected by the treatment, as many HNTs were broken, as shown in Figure 2b. The condition of S07, shown in Figure 2c, was improved as the treatment increased from 5 wt. % to 7 wt. % of SDS, PVA, and MA. S07 underwent some changes, which resulted in improving the condition of the bulk sample by showing that the number of intact HNTs was higher than S05 and the distribution was better. As the mixture of HNTs and distilled water was treated with 10 wt. % of SDS, PVA, and MA, as shown in Figure 2d, the crosslinking caused by MA showed better distribution and the number of intact HNTs was increased. The agglomeration of HNTs in the nanocomposite significantly decreased.
## 2 Optimization Model
### Objective Function  
The main aim of this study is to incorporate FACTS devices into electrical distribution networks while aiming to minimize the annual equivalent operating costs represented by \(f\). These costs consider the reduction of energy losses and the installation expenses associated with FACTS. To this effect, the following objective function is used:  
\[\begin{split}\min f&=f_{1}+f_{2},\\ f_{1}&=CT\sum_{h\in\mathcal{H}}\sum_{l\in\mathcal{B }}(p^{s}_{lh}+p^{r}_{lh})\Delta h,\\ f_{2}&=T\bigg{(}\frac{k_{1}}{k_{2}}\bigg{)}\sum_{ k\in\mathcal{N}}q^{\mathrm{FACT}}_{k}\bigg{(}\omega_{1}\Big{(}q^{\mathrm{FACT}}_{k} \Big{)}^{2}+\omega_{2}q^{\mathrm{FACT}}_{k}+\omega_{3}\bigg{)},\end{split} \tag{1}\]  
where \(f_{1}\) is related to the annual energy loss costs; \(f_{2}\) is associated with the FACTS investment costs; \(C\) and \(T\) are the average costs of energy losses and the number of days in a year, respectively; \(p^{s}_{lh}\) and \(p^{r}_{lh}\) represent the sending and receiving active power flows of a distribution line, respectively; \(\Delta h\) represents the time interval analyzed on a daily basis (0.5 h); \(q^{\mathrm{FACT}}_{k}\) is the nominal size of the FACTS; \(\omega_{1}\), \(\omega_{2}\), and \(\omega_{3}\) represent the polynomial coefficients of the objective function \(f_{2}\); and \(k_{1}>0\) and \(k_{2}>0\) are constants denoting the annual investment costs for a planning horizon of ten years [18].