esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The increasing interest in additive manufacturing (AM) technologies today is driven by various factors, including their speed and accessibility for average or novice consumers compared to traditional acquisition methods for drones [1, 2]. The inherent flexibility of AM technologies in geometry creation and the ability to produce lightweight structures have opened up new frontiers in customized, rapid, and cost-effective prototyping [3; 4]. A key enabler of this innovation is design for additive manufacturing (DfAM) [5], which integrates computational design with manufacturing constraints to optimize both shape and performance. Nevertheless, the physical properties of the manufactured components hinge on the production conditions, including the polymer material, printing temperature, and speed [6; 7; 8]. Several studies have focused on utilizing DfAM principles to reduce weight and enhance aerodynamic efficiency by employing varying levels of complexity in internal geometries and applying topological optimization techniques to drone frames and housings, e.g., [2; 6; 9; 20; 21; 22]. Table 1 summarizes the current research trends and identifies gaps in the literature. A growing area of research in this field centers on the mechanical performance of AM drone components, specifically their ability to withstand mechanical loads and absorb energy during impacts [2; 16]. This capability is crucial for ensuring flight safety and enhancing crashworthiness. Research into various polymer materials, particularly polylactic acid (PLA), has shown encouraging results due to their advantageous mechanical properties and suitability for fused filament fabrication (FFF). However, the natural brittleness of traditional PLA has led to investigations into modified or lightweight PLA composites (LW-PLA), which provide improved ductility and higher strain energy absorption [12; 13; 14; 15]. Studies by [PERSON] [13], [PERSON] et al. [14], and [PERSON] et al. [15] have demonstrated that modifications to internal structures, including lattice infills and shell thickness, can significantly influence energy absorption properties. Despite these advancements, a gap remains in experimentally validating how internal geometric modifications within FFF parts affect deformation energy absorption under quasi-static and dynamic loads, particularly in drone-specific applications [2]. While simulation studies and theoretical models exist in the literature, empirical studies focusing on DfAM strategies tailored to real-world use cases, such as drone impact resistance, are limited [17; 18]. Moreover, most of the literature isolates material behavior from structural optimization (e.g., [9; 10; 12]), whereas a holistic view--integrating material selection, internal geometry, and weight constraints--is crucial for effectively designing drone components [19; 20]. Recent research has also focused on lightweight structures that utilize porous materials or foamed filaments [6; 11; 12; 13; 21]. These materials can reduce weight while maintaining structural integrity, particularly under repeated loading scenarios. Research conducted by
## 4 Parameter Effects on VFA-SF and VFA-SF-OPT
### Effect of External Central Force
Figure 6 presents the variation of PCD curves from the VFA-SF-OPT optimization for indicated external central force \(F_{extern}=\)0, 1, 2, 3.5, 5.5, 8 and 10, which correspond to the cases in Table 3. When external central force is larger, the PCD value dramatically increases at the early stage of the VFA-SF-OPT. The reason is that the nonhexagonal structure in the central area significantly affects PCD calculation. Larger external central force compresses the central area and causes a broader gap between the nodes involved in the VFA-SF-OPT and those absent from the VFA-SF-OPT. The PCD value reaches its maxima around the 1400 th time step. It should be noted that, once the external force is smaller than the theoretical value (\(F_{extern}=\) 2.873 in [13]), like \(F_{extern}=\) 0, 1, 2, the PCD curve cannot be reduced to be 0, i.e., a perfect hexagonal structure cannot be achieved. The reason is that a small \(F_{extern}\) makes less-significant contribution to the total \begin{table}
\begin{tabular}{c c c} \hline \hline
**External Central Force** & **Average Node Distance** & **Average Moving Distance** \\ \hline
0 (only spring force) & 1.7317 & 1.2182 \\
1 & 1.6964 & 1.6711 \\
2 & 1.6805 & 2.1062 \\
3.5 & 1.6682 & 2.6069 \\
5.5 & 1.6642 & 2.7995 \\
8 & 1.6602 & 3.0432 \\
10 & 1.6563 & 3.0828 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average equilibrium distance and average moving distance within radius = 10 for different values of external central force from the VFA-SF-OPT. Figure 6: Variation of the PCD curves from VFA-SF-OPT optimization for indicated external central force \(F_{extern}=\) 0, 1, 2, 3.5, 5.5, 8 and 10, which correspond to the cases in Table 2.  
virtual force and finally yields the survival of the coverage holes. An extremely large external force, like \(F_{extern}=10\), excessively compresses the central region and breaks the balance of the hexagonal structure. In addition, sensor nodes need to move for a longer distance in such a case. In addition, two more values of the external central force have been tested to further check the influence of this effect on the VFA-SF-OPT. The corresponding average equilibrium distances and average moving distances are shown in Table 4. It is still obvious that, when \(v_{max}=0.3\) and \(1.0\), the variations of the average node-equilibrium distance and the average moving distance are the same as those of \(v_{max}=0.6\). However, in the VFA-SF-OPT (\(F_{extern}\
eq 0\)), all moving distances from \(v_{max}=0.6\) are smaller than those from the other two cases. It means that an appropriate value of \(F_{extern}\) has less energy consumption in the optimization process.
## 3 Experimental Results and Discussion
### Performance of Leaf Temperature Measurement
The average leaf temperature measurement error of Plant-1 and Plant-2 during five days (7 June 2023 to 11 June 2023) is given in Table 13. Table 13 shows that the measurement errors on both Plant-1 and Plant-2 are almost the same (i.e., 1.41% and 1.53%, respectively). The small values of the standard deviations denote the slight variations in the measurement errors during five days. These results prove that our proposed method to segment the objects and average the temperature values works effectively for measuring the leaf temperature.
# Improvement of Global Ionospheric TEC Derivation with Multi-Source Data in Modip Latitude
## 2 Data and Analysis Method
### Analysis Method
#### 2.2.3 Modip Coordinate System  
The performance of GIM construction strongly depends on using a coordinate system where ionospheric variability is able to be as smooth as possible in space and time [20]. The geomagnetic (GM) latitude of the usual solution is defined similar to geographic latitude but rotated to coincide the geographic pole to the dipole axis. However, the actual magnetic field is not a simple dipole and is distorted. The'modified dip' or the'modip' latitude (\(\mu\)) is a more suitable magnetic coordinate system for describing the variability of the ionosphere at mid- and low-latitude areas, which is calculated directly from the three components of Earth's magnetic field fitted by the International Geomagnetic Reference Field (IGRF) model, and is defined as [19]  
\[\tan\mu=\frac{I}{\sqrt{\cos\varphi}}, \tag{9}\]  
where \(I\) is the true magnetic dip and \(\varphi\) is the geographic latitude. The modip equator is the locus of points where the magnetic dip (or inclination) is 0.  
The comparison of geomagnetic latitude (blue) and modip latitude (red) in September 2014 is shown in Figure 1, where the difference between geomagnetic latitude and modip latitude in the equatorial region is obvious. In principle, the modip coordinate system is more suitable for global ionosphere mapping, since it is parallel to the magnetic equator at low latitudes and the spacing is dense in the equatorial area.
# Isoprene Emissions, Oxidation Chemistry and Environmental Impacts
## 6 Measurements of Isoprene and Model Validation
[PERSON] and co-workers [156] developed the first direct retrieval of global isoprene using Cross-track Infrared Sounder (CrIS) satellite data, which was built upon by [PERSON] et al. [157; 158] using machine learning approaches to improve sensitivity and spatial resolution. Such an approach has been used to determine isoprene emissions over large ecosystems such as tropical rainforests for daily, seasonal, and interannual variability, which can highlight the processes controlling isoprene abundance and the drivers of variability over time. However, CrIS retrievals contain no information about the diurnal profile of isoprene and only provide a snapshot at 13:30. The emission of isoprene is significant in the afternoon, and the shape/peak of the diurnal profile depends on season and location [124], which cannot be captured by current spaceborne approaches. Comparison of the model and measurements of isoprene concentration of the sites near the strong source region (e.g., vegetation) is problematic because of the coarse model grid and small-scale horizontal variations in its concentration. Given the short lifetime of isoprene and the large model emission grids, the model produces a realistic concentration of global isoprene levels (Figure 5). The ensemble of model-measurement data presented in Table 2 gave a general evaluation of the regions featured. A disagreement between model and measured isoprene concentration with the median bias of \(-0.36\) ppb for all campaigns is found. The observed bias is most probably due to the use of the MEGAN emission inventory, where errors in land cover and meteorological driving variables lead to a factor of 2 uncertainty [2]. The BVOC emissions algorithm in MEGAN parameterizes isoprene emission rates as a vegetation-specific standard rate, which is adjusted by scaling factors that depend on meteorological and environmental driving variables, e.g., temperature, sunlight, leaf area index, leaf age, and soil moisture. The MEGAN isoprene emissions are strongly dependent on the accuracy and resolution of the meteorology and land cover inputs [159; 160]. Model performance is found to be good when accurate driving variables are used [161; 162; 163; 164], but significant sources of uncertainty still remain: for example, given the variety of plant species, the parameterizations will not match every species present but be a representation of the class as a whole. This highlights that whilst the chemistry of isoprene oxidation is now more thoroughly understood, the uncertainty of the emission inventory is still high. Deforestation through either direct human impact or climate change can decrease the emissions of isoprene as well as consequent environmental impact but can contribute to global climate change through the release of stored carbon, contributing to the rise in atmospheric CO\({}_{2}\). Understanding the potential effects of deforestation and future reforestation scenarios on the climate is important in shaping CO\({}_{2}\) mitigation approaches, strategy, and policy.
## 5 Discussion
### Limitations of Polygon Feature\(-\)Based Normalization Algorithms  
For the most widely used relative radiometric normalization algorithms, automatically selecting pseudo\(-\)invariant point features is the most important step [37]. How to extract pseudo\(-\)invariant polygon features effectively and precisely is, therefore, fundamental to preprocessing. In Section 4.1, we discussed the influence of polygon size. Integrating the pseudo\(-\)invariant points with a suitable polygon size, as demonstrated by [PERSON], [PERSON] [38], [PERSON], [PERSON] [24], and [PERSON], [PERSON] [25], is a practical way to automatically select polygon features and will be included in future research and monitoring.  
In this study, we only considered contemporary remote sensing images. Due to changing atmospheric conditions over time, regression relationships are not universal, especially in temporal sequences. When normalizing temporal image radiation, it is necessary to extract multiple invariant polygon characteristics and establish separate regression equations for each. Over the history of remote image data collection, most differences in radiation have been caused by atmospheric conditions. This kind of radiation normalization usually relies on roads, developments, and other types of artificial surfaces for georeferencing. However, different image resolutions and spatial mismatches may result in the same pixel from two images containing different landcover types and different endmember abundance. Unlike artificial surfaces, land cover types usually change with the seasons, thus, image data across periods may not maintain radiation consistency at the pure pixel scale. However, mixed pixel decomposition can extract pure artificial surface radiation of invariant feature points and improve radiation normalization accuracy. Our study highlights avenues to be explored in future research relying on polygon feature\(-\)based normalization. We considered a variety of ground object types in a ground survey. PIFs were determined by manual processing, which will affect the application in an area with or without limited samples. Classification methods to automatically extract PIFs are active  
Figure 10: Reflectance variation of \(5\times 5\) artificial pixels from Landsat\(-\)8 OLI.  
methods that will be discussed in future studies, as will the comparison to some classical algorithms, such as IRMAD [28] and key\(-\)points\(-\)based RRN [18; 39], to identify the accuracy and efficiency of these algorithms.  
Figure 11: Reflectance variation of 5 \(\times\) 5 artificial pixels from Sentinel\(-\)2A.  
Figure 12: Variance analysis of mean reflectance among artificial polygon sizes.
## 1 Introduction
They concluded that SIF may not track changes in photosynthesis due to stomatal closure as long as the light reactions of photosynthesis remained largely unchanged. However, [PERSON] et al. [27] observed saturation of, or even a decrease in, canopy SIF under high irradiance. The saturation of canopy SIF was probably due to their bihemispheric instrument setup and the sensor-canopy geometry, but it also implied that SIF might respond to changes in the dark reactions because the SIF yields decreased at a higher light intensity at the top of the canopy [28]. This discrepancy may be partially due to the designs of SIF-observing systems and protocols for measurements [29]. Compared with SIF variability driven by incident light, the magnitude of changes in SIF in response to changes in the dark reactions is so subtle that it may be unobservable or uninterpretable for some measuring systems [30]. In order to quantify possible changes in ChIP due to changes in the dark reactions, all of the involved processes in both the light and dark reactions of photosynthesis should be carefully observed. Specifically, one should simultaneously obtain not only high-quality measurements of passive ChIP radiance and CO\({}_{2}\) assimilation but also fluorescence parameters deriving from active pulse amplitude-modulated (PAM) ChIP yields under controlled conditions. In this study, we developed a leaf-scale measurement system by integrating active (PAM fluorometer) and passive (spectrometer) instruments into a portable gas exchange system. We then used the system to carry out two controlled leaf-scale experiments under constant light conditions to answer the following questions: (1) How do measurementsof passive and active ChIF, the derived photosynthetic parameters, and gas exchange measurements respond to changing CO\({}_{2}\) concentrations and leaf temperatures? (2) How do these measurements/parameters vary after inducing stomatal closure? We show how leaf-scale energy partitioning changes due to changes in the dark reactions of photosynthesis and relate it to previous studies reporting seemingly inconsistent results. We also discuss the key requirements needed for an SIF observing system to accurately track changes in the dark reactions of photosynthesis under natural conditions.
## 1 Introduction
This approach takes advantage of the susceptibility to error of the NHE pathway, namely the introduction of small insertions or deletions (indels) at the cleavage site. These indels may lead to a frameshift and a premature termination codon (PTC) [23]. The mRNA may be subjected to nonsense-mediated decay (NMD), depending on where the premature stop codon emerges. A risk of mutant allele ablation is haplosinfficiency, i.e., the insufficiency of a single copy of the target gene to produce adequate protein levels for cell function. However, in the case of _NR2E3_, this is not likely an issue. Homozygous mutations in _NR2E3_ give rise to an autosomal recessive IRD called Enhanced S Cone Syndrome (ESCS) and one of the most common is a loss-of-function splice site mutation in intron 1 that only spares the first exon [24]. As is common for autosomal recessive IRDs, heterozygous carriers of _NR2E3_ mutations are healthy subjects, as are heterozygous littermates of the _Nr2e3_-\(/-\) mouse models [25; 26]. Taken together, these data suggest that one wild type (WT) _NR2E3_ allele is sufficient for the correct development and function of rod photoreceptors. Therefore, we aimed to specifically knockout the G56R allele as a potential treatment for _NR2E3_-associated adRP. To this end, we designed a CRISPR/Cas genome-editing strategy to specifically ablate the mutant allele in induced pluripotent stem cells (iPSCs) of an _NR2E3_-associated adRP patient. Here, we report on the success of this approach, and on the pluripotency and retinal differentiation potential of the genome-edited iPSCs.
# FF-PCA-LDA: Intelligent Feature Fusion Based PCA-LDA Classification System for Plant Leaf Diseases
###### Abstract  
Crop leaf disease management and control pose significant impact on enhancement in yield and quality to fulfill consumer needs. For smart agriculture, an intelligent leaf disease identification system is inevitable for efficient crop health monitoring. In this view, a novel approach is proposed for crop disease identification using feature fusion and PCA-LDA classification (FF-PCA-LDA). Handcrafted hybrid and deep features are extracted from RGB images. TL-ResNet50 is used to extract the deep features. Fused feature vector is obtained by combining handcrafted hybrid and deep features. After fusing the image features, PCA is employed to select most discriminant features for LDA model development. Potato crop leaf disease identification is used as a case study for the validation of the approach. The developed system is experimentally validated on a potato crop leaf benchmark dataset. It offers high accuracy of 98.20% on an unseen dataset which was not used during the model training process. Performance comparison of the proposed technique with other approaches shows its superiority. Owing to the better discrimination and learning ability, the proposed approach overcomes the leaf segmentation step. The developed approach may be used as an automated tool for crop monitoring, management control, and can be extended for other crop types.  
deep learning; ResNet50; feature fusion; PCA; _Alternaria solani_; LDA
## 3 Energy Harvesting Model  
Recent advances in energy harvesting technologies allow small, low-cost devices such as wireless sensor nodes to operate based solely on wireless harvested energy that is stored in a finite-capacity battery. Hence, in designing network protocols, it is essential to obtain a reliable energy-harvesting model to guarantee energy autonomy in the network. In many studies, the arrival of harvested energy is assumed to be identical and independently distributed [29], to follow a deterministic Markov model [30], or to follow a normal Poisson point process [20], all of which are discrete-time models. In [31], the authors considered the problem of decentralized hypothesis testing in energy harvesting wireless sensor networks, where the arrival energy during a time interval is assumed to be drawn from a Bernoulli distribution. The extensive experimental results from [PERSON] et al. [25] showed that the transformed Poisson distribution model produces the nearest fit for most of the empirical datasets.  
In this paper, the number of energy packets that an SU can harvest during a particular time slot, \(e_{h}\), is given as  
\[e_{h}\in\{e_{h,1},e_{h,2},\ldots,e_{h,max}\}, \tag{3}\]  
where \(0<e_{h,1}<e_{h,2}<\cdots<e_{h,max}<E_{ca}\), and \(E_{ca}\) is the maximum battery capacity of the SU. We assume that \(e_{h}\) follows a Poisson point distribution with mean \(e_{h,avg}\). Furthermore, the fit with the Poisson distribution can be improved by using a transformation \(x=e_{h}-e_{h,min}\), where \(e_{h,min}\) is the minimum harvested energy. The probability mass function (PMF) of \(e_{h}\) is then given by  
\[\Pr(e_{h})=\Pr(x=e_{h}-e_{h,min})=\frac{e^{-x_{avg}}x_{avg}^{(e_{h}-e_{h,min})}} {(e_{h}-e_{h,min})!}, \tag{4}\]  
where \(x_{avg}=e_{h,avg}-e_{h,min}\) is the sample average of the new variable \(x\). This new distribution is called the transformed Poisson distribution (TPD). This transformation of the original variable can improve the fitting to the empirical datasets, as proven in [25]. In practice, although it is not easy to measure the exact amount of harvested energy in a time-slot interval, we can always estimate the average, the minimum and the maximum values of the harvested energy. Meanwhile, if the normal Poisson point process is used, the minimum harvested energy is assumed to be 0 (or zero) by default, which is rarely true in practical scenarios.  
Figure 3 shows the difference in the PMF between the normal Poisson distribution and the transformed Poisson distribution when the average harvested energy is \(e_{h,avg}=8\) packets, with different values of minimum harvested energy: \(e_{h,min}\in\{1,2,3,4\}\) packets. As can be seen from the figure, the SU can harvest with a higher probability those energy values located near the mean by using the transformed Poisson model. As a consequence, we can also improve the learning rate of the actor-critic algorithm because the SU can focus on learning the variations of the energy values that are adjacent to the mean.