esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Seasonal Drought Trends and Heterogeneous Vegetation Responses  
This study reveals significant seasonal differences in the impact of drought on vegetation in the West Liao River Basin. Drought during the growing season notably affects photosynthesis and biomass allocation. Spring, a critical period for vegetation regrowth, sees drought inhibiting plant germination and early growth [40]. For instance, the 2017 spring drought likely reduced vegetation cover, particularly affecting herbaceous plants and crops [28]. The 2002-2005 spring drought may have shortened the growing season and reduced photosynthetic efficiency, impacting ecosystem productivity [41].  
The western basin's persistent drought (2000-2018) may drive vegetation succession, with drought-tolerant species replacing moisture-dependent ones [3]. Summer, the peak growth period, experiences drought, which limits water uptake and transpiration and stunts growth. The 2000 summer drought likely affected crops (e.g., corn, soybeans) and grasslands, while pre-2005 droughts reduced biomass, especially in drought-sensitive areas [42], as discussed by [PERSON] et al. [43]. Autumn droughts, which affect late-season crops, hinder nutrient accumulation and overwintering preparation, potentially reducing yields [44]. The 2001 autumn drought may have forced early dormancy, impacting future growth. Winter droughts, though less directly impactful, deplete soil moisture, which affects spring regrowth. Spring droughts in the northwest likely reduce grassland and crop productivity. As indicated by the study of [PERSON] [45], spring drought was negatively related to vegetation GPP and may lead to a decrease in grassland production. Additionally, vegetation in arid conditions is more sensitive to drought than in wetter conditions [46].  
Furthermore, vegetation recovery exhibits a 'drought memory effect', with restoration capacity declining by 40% after repeated drought events [47].  
The West Liao River Basin exhibits significant spatial heterogeneity in vegetation responses to drought. grasslands are highly sensitive to short-term droughs, while forests respond more slowly, likely due to differences in root systems and groundwater reliance [48]. For example, shallow-rooted vegetation (grasslands/restoration forests) exhibits greater vulnerability compared to deep-rooted forests when adapting to seasonal drought [49]. Croplands (e.g., corn, wheat) are directly impacted, especially during critical growth periods, threatening regional food security [50]. These regional differences underscore the need for targeted ecological restoration and management measures.
## 2 Integration of Quantum Nodes and Systems in the Current Network
### SDN Orchestrator  
A typical use case for using an SDN orchestrator in building a QKD network involves efficiently managing two different SDN controllers responsible for managing an optical transport network (OTN) and the QKD network, respectively [11], when network operators choose to separate quantum channels in a QKD network and classical data channels in an OTN, with each network controlled by different SDN controllers. When QKD-derived keys are to be used in secure application entities in an OTN, network operators need to know which QKD nodes in the QKD network can supply those keys. Adopting an SDN orchestrator that can control both the QKD and OTN networks via each SDN controller achieves end-to-end service provisioning of QKD-derived key generation and use. The SDN orchestrator matches the addresses of the QKD nodes in the QKD network and secure application entities in the OTN to supply the keys. Additionally, an SDN orchestrator interfaced with the SDN controller of the QKD network can be used to manage and configure the QKD network, reducing the burden of integrating the QKD network into existing communication networks. The information model for the interface between the SDN orchestrator and the SDN controller of the QKD network is presented to simplify management and configuration through the northbound interfaces of the SDN controllers of the QKD networks. This functionality is defined in the ETSI GS QKD 018 standard [11].
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
### Survey Methodology
#### 1.4.2 Research Questions  
RQ0: What is OCR? What is deep learning? What are the steps and challenges in Arabic OCR? How is OCR accuracy evaluated?  
RQ1: What are the dee- learning techniques used in Arabic OCR during the period 2020-2023? Which are the notable works in the literature?  
RQ2: What are the deep-learning techniques used in Arabic text correction for the period 2020-2023? Which are the notable works in the literature?  
RQ3: What were the limitations and challenges involved in the use of deep-learning techniques in Arabic OCR and text correction for the period 2020-2023?  
RQ4: What are the findings, recommendations, and future trends for deep-learning techniques to be used in Arabic OCR and text correction?
# Recognition Algorithm Based on Improved FCM and Rough Sets for Meibomian Gland Morphology
## 3. MGD Identification Based on Improved FCM and Rough Sets
### Image Preprocessing
#### 3.1.2 Image Segmentation  
Image segmentation segments the region of interest in the image to provide a reliable basis for subsequent analysis and processing [25]. Segmentation quality directly affects subsequent image recognition. The eyelid part is of great significance in the diagnosis of meibomian gland morphology. Using the differences of textures between the eyelid region and other regions, and combining morphological and local entropy filtering, we designed a segmentation method based on texture filtering. The local entropy is defined as an entropy operation on an area of \(n\times n\) centered on the selected pixel, and the meibomian gland image was filtered using local entropy to obtain the texture image. Local entropy calculation expressions are shown as follows:  
\[H=-\sum_{i=1}^{n}\sum_{j=1}^{n}p_{ij}\log p_{ij}\]  
\[p_{ij}=f(i,j)/\sum_{i}^{n}\sum_{j}^{n}f(i,j)\]  
where \(f(i,j)\) denotes the local pixels of \(n\times n\), and \(p_{ij}\) is the probability that the current pixel gray level occupies the local total gray level. The larger the local entropy is, the smaller the texture difference is in the window. Therefore, threshold segmentation can be performed according to the local entropy of the image to extract the target region. Due to the limitation of the artificially defined threshold, the segmentation result may appear over-segmentation, holes or the boundary of segmented image may not be smooth or other phenomena may result. In this paper, we used a morphological method to smooth edges, and filled the empty holes to obtain high-quality segmentation images. As shown in Figure 7, our method accurately and effectively segmented meibomian gland images while ensuring image integrity. The details of the target area were all retained, which laid the foundation for follow-up treatment.
## 3 Model and Algorithm Framework
### The Model  
**Food-Ordering Delivery Applications.** The model is mainly motivated by food-ordering applications, such as Meituan, Eleme, and Freshhema. There are a set of \(n\) couriers \(A=\{a_{1},a_{2},\cdots,a_{n}\}\) available for food delivery services. Each courier \(a_{i}\) has a capacity \(c\in\mathbb{N}_{+}\), indicating the maximum number of customer orders that can serve at a time (Here, we assume that each courier has the homogeneous delivery capacity, which can be easily extended to heterogeneous capacity scenario by making a match between the courier and delivery plans). Each day is discretized into periods; each period includes \(\delta\) minutes (e.g., 5 min). The daily horizon \(\mathcal{T}=\{1,2,\cdots,T=\frac{24\times 60}{\delta}\}\), and the parameter \(\delta\) can be tuned such that \(\frac{24\times 60}{\delta}\) is an integer.  
**City Network.** Let \(G=\langle V,E,W,D\rangle\) denote the weighted city network, where \(V=\{v_{1},v_{2},\ldots,v_{m}\}\) indicates \(m\) regions in the city and each \(e_{ij}\in E\) indicates the edge between regions \(v_{i}\) and \(v_{j}\). The time distance on edges \(W=\{w_{ij}\}_{e_{ij}\in E}\) indicates that it will take \(w_{ij}\) periods to travel from \(v_{i}\) to \(v_{j}\), and \(w_{ii}=0,\forall v_{i}\). Let \(D=\{d_{ij}\}_{v_{i},v_{j}\in V}\) denote the time distance of the shortest path from \(v_{i}\) to \(v_{j}\). The time distance matrix \(D\) satisfies the triangle inequality, i.e., \(d_{ik}\leq d_{ij}+d_{jk},\forall v_{i},v_{j}\) and \(v_{k}\).  
**Customer Orders.** Let \(R=\{r_{1},r_{2},\cdots,r_{l}\}\) be a collection of customer orders. Each \(r_{j}\in R\) is defined as a tuple \((v_{j}^{p},v_{j}^{d},[t_{j}^{e},t_{j}^{l}])\), where \(v_{j}^{p}\in V\) denotes the pick-up region of \(r_{j}\), and \(v_{j}^{d}\in V\) denotes the drop-off region. Each \(r_{j}\) has a flexible drop-off time window \([t_{j}^{e},t_{j}^{l}]\subseteq[1,T]\), during which a customer wishes to finish her order. For example, \([t_{j}^{e},t_{j}^{l}]\) = [16:30, 17:30] indicates that the food delivery order \(r_{j}\) must be delivered to customer home between the time 4:30 p.m. and 5:30 p.m.  
**Delivery Service Sharing with Flexible Time Windows (DSS-Fle).** A delivery sharing plan \(\mathcal{P}\) is a temporally-ordered route of pickup and drop-off regions of orders. Let \(R_{\mathcal{P}}\subseteq R\) denote the subset of orders served in \(\mathcal{P}\), and for each \(r_{j}\in R_{\mathcal{P}}\), \(v_{j}^{p}\) should precede \(v_{j}^{d}\), and the drop-off time should be within its flexible windows \([t_{j}^{e},t_{j}^{l}]\). A courier is responsible for a plan, and for each plan, there are at most \(c\) orders in service at a time. DSS-Fle is formally defined as follows: given the set of \(n\) couriers \(A\) and the set of \(l\) customer orders \(R\), find the \(n\) order-disjoint delivery sharing plans with the objective of optimizing order service rate, i.e., maximizing the number of orders served.
## 1 Introduction
Migration testing employing aqueous food simulants lead to under-estimation of the migration processes occurring in real food products [26; 27; 28; 29; 30]. On the other hand, it has been recognized that certain food simulating liquids like ethanol/water mixtures, n-heptane or isooctane strongly interact with styrene based materials leading to an overestimation of the migration which results into too high diffusion coefficients due to swelling and stress cracks [27; 31; 32]. The aim of this study was to systematically investigate the migration behavior from GPPS and HIPS polymers and simultaneously the swelling effect using both, food simulants stipulated in EU regulations and real foods. Migration kinetic data from extruded sheets were obtained for a set of spiked model substances as well as for the monomer styrene and styrene oligomers at different time/temperature conditions in 50% ethanol, isooctane and 95% ethanol. Migration of the volatile compounds were quantified in UHT milk (3.5% fat), UHT cream (30% fat) and olive oil. The extent of polymer swelling was characterized gravimetrically and visual changes of the test specimens were recorded after migration contact at temperatures of 20 \({}^{\circ}\)C, 40 \({}^{\circ}\)C and 60 \({}^{\circ}\)C with following food simulants: 3% acetic acid, 10% ethanol, 50% ethanol, 95% ethanol and isooctane. Additionally, several food types (lard, butter, fish oil, orange juice, coffee beans, coffee, noodles, oat flakes, wheat loops and water) as well as the alternative fatty food simulant Miglyol(r) 812 were gravimetrically investigated for their interactions with GPPS and HIPS under different storage conditions. They were selected in order to cover the majority of foreseeable food contact applications for these materials. Obtained data show that the use of isooctane as well of high concentrations of ethanol-water, especially at high temperatures, causes swelling and physical changes of polystyrene and results in significantly higher migration values compared to the investigated foods, which tested under typical contact condition do not show polymer interactions. It is expected that the results of this study will assist in acquiring information about the selection of suitable migration testing conditions for styrene polymers, especially if a migration limit for styrene and styrene oligomers will be set out in the future, considering the swelling effect caused by some food simulants prescribed in EU regulations.
## 4 Results and Discussion
### Deflection of the Upper Cascades  
The impact of the thrust control configuration on the aircraft's thrust, lift, and pitch moment was measured under different angles of deflection of the upper cascades. As  
Figure 16: \(T\) variation with deflection angles in symmetrical cascade deflection.  
Figure 14: \(C_{L}\) variation with deflection angles in symmetrical cascade deflection.  
Figure 15: \(C_{M}\) variation with deflection angles in symmetrical cascade deflection.  
shown in Figure 17, the additional lift coefficient remained positive across the range of deflection angles, implying that, as the upper plate deflected, it contributed to an increase in lift due to the change in the airflow pattern over the control surface. The lift coefficient initially increased with the angle of deflection, showcasing an augmented aerodynamic efficiency. However, this trend reversed beyond a certain deflection point, as evidenced by the descent in the additional lift beyond a 50\({}^{\circ}\) angle. This decline corresponds to the aerodynamic limit, whereupon further deflection no longer contributes positively to lift generation, potentially due to flow separation or stall.  
In Figure 18, the curve representing the pitch moment illustrates an initial decrease and subsequent increase with an increase in the deflection angle of the upper cascades. In the 0 to 50\({}^{\circ}\) range, the aircraft experiences a nose-down pitch moment, while past this deflection threshold, the pitch moment transitions to a nose-up behavior. This phenomenon can be attributed to the changing center of pressure on the upper surface as the deflection angle increased, which initially shifted aft and then forward as the angle progressed beyond 50\({}^{\circ}\).  
The variation in thrust with variation in the deflection angle of the upper cascades is shown in Figure 19. As the deflection angle widened, a consistent decrease in thrust was observed, with the thrust coefficient being reduced to approximately 37.5% of its original value at maximum deflection. This diminishing thrust can be rationalized by the increased obstruction to the outflow of the jet stream caused by the deflected plate, which effectively altered the exit geometry and flow dynamics of the propulsion system, resulting in a decrease in thrust.  
Figure 17: \(C_{L}\) variation with deflection angles in upper cascade deflection.  
Figure 18: \(C_{M}\) variation with deflection angles in upper cascade deflection.
## 3 Results and Discussion
### Effect of the Controller  
An analysis was performed on the SSQ scores obtained by different controllers, and the results revealed that there was no significant difference between the controllers (F(1,46) = 0.059, \(p=0.809\)). Without the FCACIS, the average SSQ score of the 3 DOF controller (34.28) was higher than that of the 6 DOF controller (31.48), but this difference was not significant (t(22) = 0.393, \(p=0.349\)). If the FCACIS was activated, the average SSQ score of the 3 DOF controller (18.077) was lower than that of the 6 DOF controller (23.063), but this difference was not significant (t(22) = \(-\)1.164, \(p=0.129\)). Further analysis revealed that the FCACIS significantly reduced the symptoms of cybersickness for the 3 DOF controller (t(22) = \(-\)3.034, \(p=0.003\)), whereas it did not significantly decrease symptoms for the 6 DOF  
Figure 12: The average SSQ scores in the three major categories obtained by different displays with and without the FCACIS.  
controller (t(22) = \(-\)1.318, \(p\) = 0.101); that is, when the FCACIS was enabled, the occurrence of cybersickness symptoms could be effectively reduced when a 3 DOF controller was used. The reason might be that the FCACIS decreases the transparency of the blur filter in narrowing the field of view. For 3 DOF controllers with poor flexibility, this could reduce cybersickness symptoms caused by unsatisfactory operation; 6 DOF controllers can move along the X, Y, and Z axes and can rotate on these three axes. Although this increases overall mobility, it also increases the risk of cybersickness. The FCACIS seems to be ineffective in addressing this.
## 3 Results
### Spectrophotometric Study
#### 3.2.2 Trolox Equivalent Antioxidant Capacity Assay  
The results obtained from the Trolox equivalent antioxidant capacity assay show that in comparison to non-irradiated samples of peeled seeds in the irradiated ones, upon increase of the dose, the TEAC decreased. A different effect was observed in shell seeds. There, after  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dose** **(kGy)** & **Sample** & \begin{tabular}{c} **TEAC 1** \\ **(\#g/mL)** \\ \end{tabular} & \begin{tabular}{c} **TPC 2** \\ **(mg GAE 4/mL)** \\ \end{tabular} & \
\begin{tabular}{c} **TFC 3** \\ **(mg QE 5/mL)** \\ \end{tabular} &
\begin{tabular}{c} **TFC 3** \\ **(mg CE 6/mL)** \\ \end{tabular} \\ \hline
0 & PS & 5.176 \({}^{\text{a}}\)\(\pm\) 0.002 & 0.183 \({}^{\text{a}}\)\(\pm\) 0.002 & 1.458 \({}^{\text{a}}\)\(\pm\) 0.02 & 0.699 \({}^{\text{a}}\)\(\pm\) 0.004 \\
1 & PS & 5.160 \({}^{\text{b}}\)\(\pm\) 0.006 & 0.169 \({}^{\text{a,b}}\)\(\pm\) 0.005 & 1.513 \({}^{\text{b}}\)\(\pm\) 0.02 & 0.684 \({}^{\text{b}}\)\(\pm\) 0.004 \\
5 & PS & 5.034 \({}^{\text{c}}\)\(\pm\) 0.002 & 0.174 \({}^{\text{a}}\)\(\pm\) 0.005 & 1.505 \({}^{\text{b}}\)\(\pm\) 0.006 & 0.725 \({}^{\text{c}}\)\(\pm\) 0.001 \\
10 & PS & 4.721 \({}^{\text{d}}\)\(\pm\) 0.002 & 0.161 \({}^{\text{b}}\)\(\pm\) 0.002 & 1.362 \({}^{\text{c}}\)\(\pm\) 0.01 & 0.628 \({}^{\text{d}}\)\(\pm\) 0.006 \\
0 & SS & 56.770 \({}^{\text{a}}\)\(\pm\) 0.01 & 1.272 \({}^{\text{a}}\)\(\pm\) 0.007 & 0.228 \({}^{\text{a}}\)\(\pm\) 0.003 & 9.895 \({}^{\text{a}}\)\(\pm\) 0.05 \\
1 & SS & 57.870 \({}^{\text{b}}\)\(\pm\) 0.04 & 1.363 \({}^{\text{b}}\)\(\pm\) 0.01 & 0.295 \({}^{\text{b}}\)\(\pm\) 0.003 & 10.228 \({}^{\text{a}}\)\(\pm\) 0.01 \\
5 & SS & 61.000 \({}^{\text{c}}\)\(\pm\) 0.02 & 1.521 \({}^{\text{c}}\)\(\pm\) 0.02 & 0.323 \({}^{\text{c}}\)\(\pm\) 0.001 & 11.567 \({}^{\text{b}}\)\(\pm\) 0.02 \\
10 & SS & 55.990 \({}^{\text{d}}\)\(\pm\) 0.03 & 1.397 \({}^{\text{d}}\)\(\pm\) 0.01 & 0.252 \({}^{\text{d}}\)\(\pm\) 0.004 & 10.762 \({}^{\text{c}}\)\(\pm\) 0.05 \\ \hline \end{tabular}  
* \({}^{1}\) Trolox equivalent antioxidant capacity; \({}^{2}\) Total phenolic content; \({}^{3}\) Total flavonoid content; \({}^{4}\) gallic acid equivalents; \({}^{5}\) quercetin equivalents; \({}^{6}\) catechin equivalents. Each value of PC and FC is represented as arithmetic mean \(\pm\) SD, \(n\)\(\geq\) 3. Different superscript letters (a-d) within each column indicate significant differences between values (\(p\) < 0.05).  
\end{table}
Table 2: Summarized data from spectrophotometric analysis: TEAC, TPC, and TFC of non-irradiated and irradiated by 1, 5, and 10 kGy peeled (PS) and shell seed (SS) horse chestnut samples.  
Figure 4: Dependence between percentage of scavenged DPPH radicals and volume (mL) of non-irradiated and irradiated horse chestnut extracts: (**a**) peeled seeds; (**b**) shell seeds.  
gamma treatment by doses of 1 and 5 kGy, their antioxidant capacity increased, whereas in the samples irradiated by 10 kGy, the values of TEAC slightly decreased (Table 2).
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 3 Materials and Methods
### Experiment Design
#### 3.3.6 NO\({}_{2}\) Validation Metrics  
So far, we have been measuring models' performance based on manually created labels. To evaluate the uncertainty hidden in human labeling, the reference value is required. Due to the fact that there are no on-site emission measurements available at the scale of this analysis, it is, therefore, necessary to use a ship emission proxy to represent the reference value. Here, we propose to use a theoretically derived NO\({}_{\text{x}}\) emission proxy \(E_{s}\) defined as follows:  
\[E_{s}=L_{s}^{2}\cdot U_{s}^{3}, \tag{6}\]  
where \(L_{s}\) is the length of the ship in \(m\) and \(U_{s}\) is its speed in m/s. The details of the derivation of the given measure can be found in [11], where the proxy was introduced. As is noted in [11], the advantage of \(E_{s}\) in comparison to other ship emission proxies (e.g., [46]) is that it can be calculated based on AIS data only, while other existing emission proxies require ship information that is not in the AIS data and is not available publicly.  
The ship emission proxy is calculated for each ship of the test sets (see Figure 10). We compared the obtained values of the emission proxy with the estimated on the basis of segmentation results amount of produced NO\({}_{2}\). We estimated the amount of produced NO\({}_{2}\) by summing up NO\({}_{2}\) concentration within the pixels classified as a \"plume\" by each of the studied models. For the comparison between the emission proxy and the estimated amount of NO\({}_{2}\), Pearson linear correlation was used.