esa-sceva/satcom-chunk-collection huggingface dataset:
# Method of Optical Diagnostics of Grain Seeds Infected with Fusariumum
The early non-destructive detection of damage during cooling in eggplants was investigated using spectroscopy. The results indicate the promising potential of NIR spectroscopy to provide non-invasive, fast and reliable detection of damage in eggplants [20]. The possibility of using spectroscopy in the mid-infrared or near-infrared range to predict the vitamin C content in plum powder samples has been shown [21]. Hyperspectral imaging (380-1020 nm) and machine learning were used to develop a technique for detecting various stages of the disease (asymptomatic, early, intermediate and late stages) of powdery mildew in zucchini [22]. Computer vision technology was developed in combination with an artificial neural network to identify frogeye soybeans, moldy soybeans, wormy soybeans and damaged soybeans [23]. At the same time, reflective infrared spectroscopy requires expensive, high-precision equipment, so luminescent spectroscopy of the ultraviolet and visible range can be used as an alternative to it. A deeper and longer interaction of radiation and biological tissue occurs during the excitation and emission of luminescence than during reflection. Unfortunately, very little attention is paid to optical photoluminescence diagnostics of plant diseases in the modern scientific literature. For example, the article [24] describes the spectral luminescent diagnostics of citrus fruits. The purpose of this study is to take up the spectral luminescent properties of healthy and infected seeds and to develop a technique of optical diagnostics for fusariosis of grain plants.
# Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2
## 4 Discussion
This shows that the improved GhostNetV2 has strong performance and generalization ability, and has excellent performance in crop disease recognition. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{6}{*}{Plant-Village [38]} & Improved GhostNetV2 & 98.12 & 98.05 & 98.09 & 98.07 \\  & MobileNetV3-Large & 98.57 & 98.70 & 98.63 & 98.67 \\  & EfficientNet-b0 & 98.76 & 99.71 & 98.71 & 98.71 \\  & ShuffleNetV2 & 98.52 & 98.49 & 98.43 & 98.46 \\  & MobileNetV2 & 97.83 & 97.43 & 97.94 & 97.72 \\ \hline \multirow{6}{*}{LWDCD2020 [39]} & Improved GhostNetV2 & 97.87 & 97.93 & 97.91 & 97.92 \\  & MobileNetV3-Large & 96.58 & 96.66 & 96.67 & 96.67 \\ \cline{1-1}  & EfficientNet-b0 & 97.17 & 97.26 & 9720 & 97.23 \\ \cline{1-1}  & ShuffleNetV2 & 93.22 & 93.53 & 93.19 & 93.36 \\ \cline{1-1}  & MobileNetV2 & 92.81 & 92.96 & 92.82 & 92.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Test results for the three datasets with the improved model.
## 2 Analytical Method
### Springback Prediction
#### 2.4.2 Nonlinear Unloading
For simplicity, the neutral surface shift during the unloading, which is caused by the variation of elastic modulus, is neglected. By integrating the equation and substituting the equivalent plastic strain, the final equation for a nonlinear unloading moment with variable elastic modulus is derived as:  
\[ \begin{split} M_{U}^{\rm VE}&=\frac{1}{24\Delta r}(E ^{\prime}\Big{(}8\big{(}{y_{t}}^{3}-{y_{c}}^{3}\Big{)}\Big{)}+E_{a}^{T^{\prime }}\Big{(}\big{(}{t_{0}}^{3}-8{y_{t}}^{3}\Big{)}\Big{)}\\ &\qquad\qquad-\frac{\phi\big{(}E^{\prime}-\overline{\varepsilon} _{T}^{p}\big{)}}{\xi_{T}{y_{t}}^{3}}\Big{(}\big{(}{R_{t}}{{}^{2}}{{}_{\xi}}{{} _{T}}{{}^{2}}{{}_{\gamma}}{{}_{t}}^{2}+4R^{2}{t_{0}}\xi_{T}\gamma_{t}+8R^{3} \Big{)}exp\Big{(}-\xi_{T}\overline{\varepsilon}_{T}^{p}\big{|}_{\frac{y_{c}}{2 }}\Big{)}\\ &\qquad\qquad-4\Big{(}2R^{3}+2R^{2}{y_{t}}\xi_{T}\gamma_{t}+{Ry_{ t}^{2}}\xi_{T}{{}^{2}}{{}_{\gamma}}{{}_{t}}^{2}\Big{)}exp\left(-\xi_{T} \overline{\varepsilon}_{T}^{p}\big{|}_{y_{t}}\right)\Big{)}\\ &\qquad\qquad+E_{a}^{C^{\prime}}\left(8{y_{t}}^{3}+{t_{0}}^{3} \right)\\ &\qquad\qquad-\frac{\phi\big{(}E^{\prime}-\overline{\varepsilon}_{ c}^{C^{\prime}}\big{)}}{\xi_{T}{y_{c}}^{3}}\Big{(}4(2R^{3}+2R^{2}{y_{c}}\xi_{C} \gamma_{c}+{Ry_{c}}^{2}{{}_{\xi}}{{}_{C}}^{2}{{}_{\gamma}}{{}_{c}}^{2})exp(- \xi_{C}\overline{\varepsilon}_{C}^{p}\big{|}_{y_{c}})\\ &\qquad\qquad-(R{t_{0}}^{2}\xi_{C}{{}^{2}}\gamma_{c}^{2}-4R^{2}{t_ {0}}\xi_{C}\gamma_{c}+8R^{3})exp\Big{(}-\xi_{C}\overline{\varepsilon}_{C}^{p} \big{|}_{-\frac{y_{c}}{2}}\Big{)}\Big{)}\end{split}  \tag{32}\]  
For the equivalent plastic strains we have:  
\[ \begin{split}\overline{\varepsilon}_{T}^{p}\big{|}_{\frac{y_{c}}{2 }}&=\gamma_{t}\Big{(}\frac{y_{t}-2D}{2R}-\gamma_{t}\frac{y_{t}}{E} \Big{)}\\ \overline{\varepsilon}_{T}^{p}\big{|}_{y_{t}}&=\gamma_{t }\Big{(}\frac{y_{t}-D}{R}-\gamma_{t}\frac{y_{t}}{E}\Big{)}\\ \overline{\varepsilon}_{C}^{p}\big{|}_{y_{c}}&=\gamma_{c }\Big{(}\frac{y_{t}-D}{R}-\gamma_{c}\frac{y_{t}}{E}\Big{)}\\ \overline{\varepsilon}_{C}^{p}\big{|}_{-\frac{y_{c}}{2}}& =\gamma_{c}\Big{(}\frac{y_{t}-2D}{2R}-\gamma_{c}\frac{y_{t}}{E}\Big{)}\end{split}  \tag{33}\]  
where \(D\), \(R\), \(y_{t}\) and \(y_{c}\) are the neutral surface shift, bending radius and the coordinates from the origin to the interface of elastic and plastic zones in tension and compression regions, respectively. It is worth mentioning that the above parameters are constant during the unloading.
###### Abstract  
This study investigated the volatile composition and aromatic features of passion fruit wines using a combination of gas chromatography-quadrupole mass spectrometry (GC-qMS), gas chromatography-Orbitrap-mass spectrometry (GC-Orbitrap-MS), electronic nose (E-nose) and sensory evaluation. The results showed that these passion fruit wines possessed different aromatic features confirmed by E-nose. Seventeen sulfur compounds and seventy-eight volatiles were detected in these passion fruit wines using GC-Orbitrap-MS and GC-qMS, respectively. Forty-four volatiles significantly contributed to the overall wine aroma. These wines possessed passion fruit, mango, green apple, lemon and floral aromas confirmed by sensory evaluation. The partial least squares regression analysis indicated that sulfides, esters and terpenes, and terpenes mainly correlated to the passion fruit, mango and green apple aroma, respectively. Sulfur compounds significantly affected the aroma of passion fruit wine. The findings in this study could provide useful insight toward the quality control of passion fruit wine.  
passion fruit wine; volatiles; sensory attributes; aroma; _Saccharomyces cerevisiae_1  
1
# Towards the Quantum Internet: Satellite Control Plane Architectures and Protocol Design
## 1 Introduction
One tier of the CP consists of an SDN Controller integrated into a Master Control Station (MCS) on the ground, while the remaining component of the CP is deployed into the satellites of the constellation itself. Each satellite of the constellation, which are the elements that compose the Data Plane (DP), is a QR that is able to perform the swapping operations in order to create a path between two stations on the ground [22]. SDN technology is proven to be well suited to handle this type of architecture considering that quantum networks need to be very accurately controlled. In fact, reaching high rates requires a good entanglement generation scheme and efficient swapping procedures [29], which can be easily managed using SDN technology. Furthermore, considering that in specific cases, such as distributed applications, the number of remote operations has to be minimized in order to limit the decoherence effects that disentangle quantum states [30; 31] and to reduce the overhead due to the swapping operations [10], we have also developed a Network Layer protocol with the goal of creating E2E entanglement between two ground stations (GSs) in an efficient manner. Figure 1: Entanglement swapping performed by a quantum satellite repeater. The developed protocol is composed of two main phases, and it is adopted by the devices of the overall envisioned architecture that is depicted in Figure 2, which is made up of an MCS on the ground with an SDN Controller embedded that calculates the best path and performs the setup of the satellites that compose the selected path. Besides, the architecture makes use of additional controllers located in the constellation itself that are responsible for managing the operations of entanglement generation and swapping. Considering that the studies conducted to date on quantum satellite backbone networks include the use of a single SDN controller [22], in this paper, we propose a system for the management of a quantum satellite backbone network consisting of quantum satellite repeaters (QSRs) focusing on the CP, with the following contributions:  
* The design of a modular two-tier CP which includes an MCS and multiple controllers belonging to the constellation itself with entanglement generation and management functionalities;
* A Network Layer protocol for E2E entanglement generation specifically designed for the presented architecture;
* A first protocol test with the aim of interconnecting two QCs on a practical LEO constellation. This paper is organized as follows: in Section 2, the state of the art is described, with a particular focus on architectures and protocols. In Section 3, the overall system model and protocol are described. In Section 4, the protocol validation is presented. Finally, Section 5 concludes the paper and outlines future perspectives.
# Design and Implementation of Attitude Stabilization System for Marine Satellite Tracking Antenna
## 4 \(\mathcal{H_{\infty}}\) Loop-Shaping Design
\right](1-G_{s}K_{sp})^{-1}M_{l}^{-1}\right\|_{\infty}\leq\frac{1}{\epsilon} \tag{24}\]  
Getting the maximum \(\epsilon\) also means getting the minimum \(\gamma\), denoted as \(\gamma_{\min}\), which is obtained by  
\[\gamma_{\min}=\epsilon_{\max}^{-1}=(1+\rho(XZ))^{\frac{1}{2}} \tag{25}\]  
Figure 10: \(\mathcal{H}_{\infty}\) robust stabilization of velocity loop. Figure 9: Bode plot of the frequency response of \(G_{s}\). where \(\rho\) is the maximum eigenvalue, and for a minimal state-space realization \((A,B,C)\) of \(G_{sp}\), and \(X\) and \(Z\) are the unique positive definite solutions of the algebraic Riccati equations, Equations (26) and (27), respectively. Note that \(\gamma_{\rm min}\) is the inverse of the magnitude of coprime uncertainty we can tolerate before we get instability. \[A^{T}X+XA-XBS^{-1}B^{T}X+C^{T}R^{-1}C=0, \tag{26}\]  
\(Z\) is the unique positive definite solution of the following algebraic Riccati equation,  
\[AZ+ZA^{T}-ZC^{T}CZ+BB^{T}=0. \tag{27}\]  
Then, the \(K_{sv}\) that guarantees that  
\[\left\|\left[ \begin{array}{c}K_{sv}\\ I\end{array} \right](1-G_{s}K_{sv})^{-1}M_{l}{}^{-1}\right\|_{\infty}\leq\gamma, \tag{28}\]  
where \(\gamma=1.1\), \(\gamma_{\rm min}\) is typically used, is given by  
\[A_{K_{sv}} = A-BB^{T}X+\gamma^{2}(L^{T})^{-1}ZC^{T}C \tag{29}\] \[B_{K_{sv}} = B^{T}X\] (30) \[C_{K_{sv}} = \gamma^{2}(L^{T})^{-1}ZC^{T} \tag{31}\]  
where \(L=(1-\gamma^{2})I+XZ\), and \((A_{K_{sv}},B_{K_{sv}},C_{K_{sv}})\) is the minimum state-space realization of \(K_{sv}\). The velocity controller, \(K_{v}\), for the plant, \(G_{\rm ts}\), is \(K_{v}=W_{1}K_{sv}W_{2}\). In our case,  
\[A = \left[ \begin{array}{cccccc}-46.20&-44.13&-17.56&-23.03&0&0\\ 64&0&0&0&0&0\\ 0&32&0&0&0&0\\ 0&0&32&0&0&0\\ 0&0&0&0.06&0&0\\ 0&0&0&0&0.03&0\end{array} \right], \tag{32}\] \[B = \left[ \begin{array}{cccccc}4096&0&0&0&0&0\end{array} \right]^{T},\] (33) \[C = \left[ \begin{array}{cccccc}0&0.02&0.01&0.02&3.11&2590.73\end{array}  \right]. \tag{34}\]  
The specific expression of \(K_{sv}\) and \(K_{v}\), respectively, are  
\[K_{sv} = \frac{511.37(s+22.09)(s^{2}+32.74s+982.6)(s^{2}+1.201s+745.3)}{(s^ {2}+13.29s+371.6)(s^{2}+1.201s+745.3)(s^{2}+429.6s+53780)}, \tag{35}\] \[K_{v} = \frac{3463.9(s+22.09)(s^{2}+13.3s+361)(s^{2}+32.74s+982.6)(s^{2}+1.201s+745.3)}{s^{2}(s^{2}+13.29s+371.6)(s^{2}+45s+2025)(s^{2}+429.6s+53780)}. \tag{36}\]  
and \(\gamma_{\rm min}\) is 1.89, corresponding to 52.91% allowed coprime uncertainty. The Bode plot of the velocity open-loop frequency response after robust stabilization is shown in Figure 11. Compared with Figure 9, it can be seen that the phase margin is enlarged in Figure 11 for more robust stabilization, and the system bandwidth is decreased from 74 rad/s to 49.1 rad/s, which shows the conservative characteristic of robust control. That is, the control performance will be decreased for the increase of system robustness.
# Improvement of Damage Segmentation Based on Pixel-Level Data Balance Using VGG-Unet
## 6 Pixel-Level Data Balance
### Results
Figure 12: Examples of prediction results: (**a**) ground truth, (**b**) no data drop results, (**c**) BDDR of 0.2, (**d**) BDDR of 0.5, (**e**) BDDR of 0.8, (**f**) BDDR of 0.9. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Data Set** & **BDDR** & \begin{tabular}{c} **PA** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **MPA** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **MIoU** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **FWIoU** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **RWIoU0.1** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{All images (50)} & 0 & 99.439 & 73.755 & 73.382 & 98.907 & 79.905 \\  & 0.2 & 99.493 & 75.715 & 75.318 & 99.016 & 81.412 \\  & 0.5 & 99.451 & 78.761 & 77.104 & 99.014 & 82.753 \\  & 0.8 & 99.048 & 85.457 & 78.515 & 98.655 & 83.745 \\  & 0.9 & 99.374 & 82.013 & 78.433 & 98.974 & 83.747 \\ \hline \multirow{4}{*}{Damage images only (27)} & 0 & 98.969 & 51.405 & 50.715 & 97.983 & 62.795 \\  & 0.2 & 99.079 & 55.046 & 54.311 & 98.196 & 65.595 \\ \cline{1-1}  & 0.5 & 99.023 & 60.707 & 57.639 & 98.214 & 68.101 \\ \cline{1-1}  & 0.8 & 98.596 & 73.428 & 60.571 & 97.868 & 70.256 \\ \cline{1-1}  & 0.9 & 98.986 & 66.835 & 60.206 & 98.244 & 70.047 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: All results of evaluation method.
## 5 Performance Evaluation of Auto-Configuration Protocols
### Performance Evaluation of Studied Protocols  
The submitted protocols share some common characteristics. However, they also differ in a wide range of issues. Table 2 presents a comparison of the characteristics of IPv4 addressing protocols.  
\begin{table}
\begin{tabular}{|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|} \hline \multicolumn{2}{|c|}{**Metrics**} & \multicolumn{1}{p{42.7 pt}|}{**Description**} \\ \hline Uniqueness & Each MANET node must have a unique IP address for each network interface because duplicate addresses can cause serious routing problems. & \\ \hline Overhead & Exchanged packet number to obtain an IP address. & \\ \hline Latency & Node timeout to obtain the IP address. & \\ \hline Routing Protocol Independence & Auto-configuration protocols can work in two ways: leaning on a routing protocol to allow the routing of the new nodes joining the network, or regardless of routing algorithm. & \\ \hline Uniformity & All nodes perform the same function in the auto-configuration process. & \\ \hline \end{tabular}
\end{table}
Table 1: Evaluation metrics.  
\begin{table}
\begin{tabular}{|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|} \cline{2-7} \multicolumn{2}{|c|}{} & \multicolumn{1}{p{42.7 pt}|}{**Protocol**} & \multicolumn{1}{p{42.7 pt}|}{**Guarantee of Uniqueness**} & \multicolumn{1}{p{42.7 pt}|}{**Overhead**} & \multicolumn{1}{p{42.7 pt}|}{**Latency**} & \multicolumn{1}{p{42.7 pt}|}{**Dependent Routing**} & \multicolumn{1}{p{42.7 pt}|}{**Uniform**} \\ \hline \multirow{4}{*}{**Parameter**} & ManetConf & No & High & High & No & Yes \\ \cline{2-7}  & DAAP & Yes & Medium & Medium & No & No \\ \cline{2-7}  & Buddy System & Yes & Medium & Medium & No & Yes \\ \cline{2-7}  & EMAP & No & Low & High & No & Yes \\ \cline{2-7}  & D2 HCP & Yes & Low & Low & Yes & Yes \\ \hline \multirow{4}{*}{**Parameter**} & SDAD & No & High & High & No & Yes \\ \cline{2-7}  & WDAD & No & Medium & Low & No & No \\ \cline{2-7}  & PDAD & No & Medium & Low & No & Yes \\ \cline{2-7}  & APAC & No & High & High & Yes & No \\ \cline{2-7}  & AROD & Yes & High & High & No & No \\ \cline{2-7}  & AIPAC & No & High & High & No & No \\ \hline \multirow{4}{*}{**Parameter**} & HCQA & Yes & High & High & Yes & No \\ \cline{2-7}  & PACMAN & No & High & High & Yes & Yes \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of the characteristics of IPv4 addressing protocols.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 6 Discussion and Analysis of Results
### Signal Synthesis and Modulation Performance  
The MATLAB simulations confirm that both PWM and PPM signals can be synthesized effectively using mathematical operations on delayed and non-delayed signal components. The PWM generation demonstrates direct correlation between message signal amplitude and pulse width, maintaining analog information integrity within the digital pulse train format. The PPM synthesis through differential processing of PWM signals successfully converts width-modulated information to position-modulated pulses, with signal quality improving as higher harmonic components are incorporated.  
The derivative-based PPM approach demonstrates favorable performance in the tested conditions, as evidenced by the VCO-based implementation. The frequency modulation of the VCO output based on the differentiated input signal produces clear position-modulated pulses, validating the theoretical foundation presented in Section 3.2.1.
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 5 Results  
In order to evaluate the robustness and efficiency of the ZEROES method, experiments are conducted in both LOS and NLOS scenarios to measure its performance in terms of communication and execution times. These results are also compared to other demodulation methods--also based on the first-order derivative like BPBPLFT [12] and HS [13] or on an adaptive threshold with 3 rd-OPF [11]--using the exact same ROI method (here, PCA) [19] to only see the effect of demodulation on performance. To achieve this, 300 images are captured for each camera position with a distance to the LED source ranging from 40 to 100 cm with a 10 cm step in LOS, while the distance from the camera to the wall (without considering LED/wall distance) increases from 50 to 450 cm with a 25 cm step in NLOS. All taken images are transferred to the computer in order to be post-processed by a Python program (available at [27]). As images taken in LOS are subject to chromatic aberration, images are converted in grayscale, while value representation is used in NLOS to enhance the strips' contrast. While the efficiency is evaluated through the percentage of received bits, the robustness is measured with the Bit Error Rate, defined as the number of errors divided by the number of received bits. To ensure the statistical reliability of the BER measurement, only the BER values computed with a minimum of 10 errors are considered in the results. Furthermore, in order to provide a clearer visualization of the BER and percentage of received bits as a function of the distance, an extrapolation curve is plotted for each demodulation method (Figures 16-19).
# Non-Compliance Distalization Appliances Supported by Mini-Implants: A Systematic Review
###### Abstract  
Background: A common strategy for the correction of Class II malocclusion is to initially distalize the maxillary molars to create a Class I relationship. **Material and Methods:** PubMed, Embase, Cochrane Library, Google Scholar, and Clinicaltrials.gov databases were searched to identify and retrieve orthodontic articles that evaluated non-compliance distalization appliances supported by mini-implants up to 11 November 2022. **Results:** A total of 505 articles were initially identified, and after applying the inclusion criteria, 28 studies were enlisted for evaluation. For the prospective studies, the Risk of bias in non-randomized studies of interventions assessment tool was used, and for the retrospective studies, the Newcastle-Ottawa quality assessment scale. Regarding the palatal devices with mini-implants, the maxillary molars were distalized with a mean value ranging from 2.4 to 5.9 mm, along with a distal tipping ranging between 0.01\({}^{\circ}\) and 11\({}^{\circ}\), while when Pendulums were used with mini-implants, the maxillary molars were distalized with a mean value from 1.8 mm to 7.9 mm, and the distal tipping ranged from 7.34\({}^{\circ}\) to 22.8\({}^{\circ}\). Further, in the second subgroup, including the appliances placed bucucally, the maxillary molars were distalized with a mean value ranging from 1.83 mm to 4.2 mm and a distal tipping ranging between 0.6\({}^{\circ}\) and 4.8\({}^{\circ}\). **Conclusions:** Non-compliance appliances supported by mini-implants are effective in maxillary molar distalization, presenting no anchorage loss of the anterior dental unit in most of the appliances, while distal tipping was found to be more pronounced when the mini-implants were used with Pendulums.  
Keywords:Class II malocclusion; non-compliance; maxillary molar distalization; miniscrew implants; systematic review +
Footnote †: journal: Applied Sciences
## 4 Discussion
Precipitation during June and July 2020 (36.7 mm) was more than three times larger than that of June and July 2019 (11 mm), suggesting that short-term effects of photosynthetic stomatal limitation might not be as important as other process such as the drought memory phenomenon [11]. In our case this would mean that in 2019, trees were adapted to dry conditions from the previous dry months, therefore creating the drought memory [109], reflecting a higher photosynthetic rate in July 2019 in contrast to July 2020, when trees did not have the pre-adaption conditions. Experiments have shown that pre-stressed plants maintain higher photosynthetic rates than non-pre-stressed plants at a drought event, even if both show similar stomatal conductance and respiration [110; 111], supporting the use of seasonal shifts in photosynthetic capacity rather than regulations of stomatal conductance based on short-term weather variation. The 13 year-old plot, which is the youngest in our plot network, illustrated the lower stand basal area and LAI. Due to small tree size the stand has not yet achieved canopy closure and is characterised by the most heterogenous microclimatic conditions. The low LAI in the 13 year-old stand results in a relatively higher percentage of tree foliage being sunlit compared to other plots (13 year-old, 56.6%, 40 year-old, 40.0%, 72 year-old, 35.9% and 92 year-old, 38.2%) [112], leading to higher \(A_{\text{sat}}\) due to photo-acclimation. [PERSON] and [PERSON] [113] supported the view that light-demanding tree species (such as _P. brutia_) can acclimate by changing their leaf chlorophyll content and subsequently their photosynthetic capacity. Our data revealed that photosynthetic capacity illustrated a higher seasonal variation in plots with lower LAI and canopy closure. In such stands, parts of the canopy are sunlit for longer, or if shaded, receive more radiation than denser stands. This could lead to morphological and physiological adaptation of leaves to optimise the rate of photosynthesis [114; 115], as has also been found across our study sites where needles at younger stands are optimized along the LMA-photosynthetic capacity spectrum [116]. In addition, the heterogenous within-stand microclimatic conditions also probably lead to the increased variability that has been observed in gas exchange (13 year-old \(A_{sat}\) and \(K_{m}\) CV vs. other plots, Table A3), because of limited buffering of abiotic variability from neighbouring trees. A negative trend in \(A_{sat}\) variability with LAI was also observed across plots, further supporting the potential effects of canopy structure-dependent photo-acclimation. Thus, the effects of canopy structure on GPP variation can be both direct (LAI amount of available photosynthetic tissue) and indirect (buffering effects on photosynthetic variability).