esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Climate Data  
ClimateAP [23] was used to generate climate data across the region. ClimateAP is a climate data downscaling tool developed for the Asia Pacific region, which extracts and downscales PRISM [24] and WorldClim [25] 1961-1990 monthly normal data (2.5 \(\times\) 2.5 arc minute, approximately 4 \(\times\) 4 km) to produce seasonal and annual climate variables for specific locations (scale-free) based on latitude, longitude, and elevation. The output of the program includes both directly calculated and derived climate variables. We used climate normals (30 year averages) of monthly data for the period between 1961 and 1990 to represent the reference or current climate conditions (_i.e._, baseline), which are commonly used in climate niche modeling and is common practice in the literature. Often, selection of the baseline period has been limited by availability of desired climate data [26]. For this study, we generated climate data at 1 km spatial resolution for the reference normal period 1961-1990 (current) and climate data for 2020s (2011-2040) using the Coupled Global Climate Model 3 (CGCM3) from the Canadian Centre for Climate Modelling and Analysis with two future projections A1B and A2. The A1B represents a moderate projection assuming a fast growth in global population and economy and a rapid adoption of new technologies in mitigating climate change. A2 is a severe projection assuming fast population and economic growth, while the adoption of new technologies in climate change mitigation is limited.  
A step change approach was used where we modeled the distribution and productivity using the climate normals for the current period and the predicted 2020 normals for the future projections.  
Figure 1: Study area—Provinces with present natural distribution of Chinese fir.
# Pathophysiologyiology and Treatment of Soriasis: From Clinical Practice to Basic Research
## 5 Challenges and Innovations in Transdermal Drug Delivery System for Poriasis Treatment
### Microneedle-Based Transdermal Delivery for Psoriasis Treatment  
Microneedle (MN) technology is typically used to enhance transdermal drug delivery into systemic circulation. Made from biocompatible materials, these needles range in length from several hundred micrometers to a few millimeters, allowing them to penetrate the stratum corneum without reaching the nerve and blood vessels in the dermis, thereby minimizing pain and bleeding (147). Various types of microneedles have been developed, including solid microneedles, dissolvable microneedles, coated microneedles, and hollow microneedles (148). A microneedle system primarily consists of two components: invasive needles and a support base. The invasive part is composed of numerous needles, each measuring approximately 25 to 2000 \(\upmu\)m in length, while the support base ensures that the needle tips can effectively penetrate the stratum corneum (149). A brief introduction to microneedling is given in Figure 5.  
In psoriasis treatment, microneedle technology offers precise dosage control and stable drug release, significantly enhancing transdermal drug absorption, minimizing systemic side effects, and simplifying the procedure, making it more suitable for patient self-administration (150). [PERSON] (151) et al. developed a calcipotriol monohydrate (CPM)-loaded microneedle using nanotechnology to enhance skin penetration and prolong skin retention. The invasive component of this microneedle consists of CPM in a nanosuspension form combined with polyvinylpyrrolidone (PVP) and polyvinyl alcohol (PVA) with a 3D-printed polylactic acid (PLA) backing. Compared to traditional creams, this microneedle exhibits superior mechanical strength, allowing effective skin insertion, and its rapidly dissolving needle tips enhance drug delivery efficiency. [PERSON] (152) et al. formulated a novel microneedle system comprising MTX/EGCG-HP gel as the invasive material and PVA/PVP as the support matrix. Once inserted into the skin, this microneedle extends drug retention time, enables rapid diffusion, and offers ROS-responsive release, optimizing therapeutic outcomes. [PERSON] (153) et al. developed a hyaluronic acid-based microneedle to enhance the delivery of \(\beta\)-elemene, investigating, for the first time, its direct pro-apoptotic effects on psoriatic keratinocytes. This approach also suppresses inflammation in psoriatic keratinocytes by downregulating IL-1, IL-6, and IL-8 expression.
# Improving Safety in Food Allergen-Specific Oral Immunotherapy
## 3 Food Allergen Immunotherapy
### Adjuants in Oral Immunotherapy
#### 3.4.3 Probiotics  
Similarly to IFN-\(\gamma\), probiotics, beneficial bacteria, inhibit allergic diseases by suppressing the Th2 response with possible additional effects on dendritic and Th17 cells [80; 81]. The suppression of IL-4, IL-5, and IL-13 production, accompanied by T-regulatory responses, increased IL-10 and IFN-\(\gamma\) production, making probiotics promising candidates for adjuvants in OIT [82]. One of the largest studies was conducted in 2017, where the effects of combined OIT and Lactobacillus rhamnosus CGM MCC 1.3724 were compared to a placebo [83]. Even though desensitization was achieved in the majority of children who were allergic to peanuts, there was no control group subjected to OIT only for comparison. In the follow-up studies, SU and therapy benefits persisted at the 12-month and four-year checkpoints [84]. In a more recent multicenter RCT, children were assigned to receive peanut OIT and placebo, peanut OIT and probiotic Lactobacillus rhamnosus ATCC 53103, and placebo and placebo for 18 months [85]. In terms of SU, eight weeks after the end of treatment, the group receiving probiotics and OIT had no advantage over children treated with OIT only. During the 12-month post-treatment period, almost identical percentages were eating peanuts with few anaphylactic reactions.
## 6 Discussion  
Taking eight BRDF archetypes as prior knowledge, daily land surface albedo inversion research in the Hefei area was realized by using FY-2G geostationary satellite data. In this study, the BRDF archetype solved the problem of the small distribution range of observation angles from geostationary meteorological satellites, and the high-frequency  
Figure 19: Scatter diagram between the albedo obtained by least squares fitting and the albedo obtained by Huber loss fitting.  
Figure 20: Scatter plot between vegetation index and AFX in the FY-2G visible band.  
Earth observations from geostationary satellites solved the problem of the high demand for inversion data. The daily inversion of albedo provides support for the study of the daily variation in the surface albedo and the modeling of the daily radiation environment field in Hefei. Compared with the least squares fitting method, the Huber loss fitting method proposed in this study has stronger robustness performance and higher adaptability to abnormal observation data.  
However, there are still several problems in this study: 1. The constructed BRDF archetype has relatively large limitations, and it is only for the Hefei area. 2. There is no further analysis of the applicability of the kernel-driven BRDF model when the geostationary satellite has a large observation angle. 3. The amount of data was small, and only four days of data were inverted. In response to these problems, several schemes have been conceived. 1. The considered surface reflection characteristics are related to the surface type, so several typical surfaces (such as deserts, water bodies, sparse vegetation, forest vegetation, etc.) are selected to construct their typical BRDF archetype databases to realize the wider application. 2. Based on the kernel-driven BRDF model, a hotspot-corrected RossThick-LiSparseReciprocal model is introduced. 3. More geostationary satellite observation data need to be obtained for long-term land surface albedo retrieval. The next work plan will also be carried out around these three points.
# Mapping with Pleiades--End-to-End Workflow
## 3 Test Sites and Data Sets
### Test Site Ljubljana (Slovenia)  
This data set consists of three Pleiades stereo acquisitions from adjacent orbits over the region north to Ljubljana, Slovenia, first presented in Reference [5]. The stereo sets were acquired within three days in July 2013, the first and third set with the Pleiades-1A platform and the second with Pleiades-1B. The ellipsoidal terrain height of the region of interest ranges from 390 m to 1950 m and the scene covers about 400 km\({}^{2}\) consisting of agricultural land, managed forest, villages, the airport Brnik  
Figure 4: The test site Trento (Italy): (**a**) topographic map (opentopomap.org (CC-BY-SA)), (**b**) CIR ortho-image and (**c**) relief shaded DSM. (**b,c**) were produced employing the proposed and implemented mapping workflow.  
Figure 3: The test site Innsbruck (Austria): (**a**) topographic map (opentopomap.org (CC-BY-SA)), (**b**) RGB ortho-image and (**c**) relief shaded DSM. (**b,c**) were produced employing the proposed and implemented mapping workflow.  
and mountainous areas. The test site is shown in Figure 5. Reference data exists in terms of 18 GCPs and 12 ICPs measured in high resolution orthophotos, LiDAR reference DSM, LiDAR reference DTM and image coordinate measurements of the GCPs and ICPs. LiDAR reference data was taken in 2015 with a mean density of 14 points/m\({}^{2}\) over a region of 345 km\({}^{2}\). The LiDAR DSM and DTM with a GSD of 1 m were derived using OPALS [88]. This data set holds a temporal gap between LiDAR and Pleiades acquisitions of only two years. Thus, analysis can be performed on large regions as the structural changes are smaller than for the other data sets. For any other detail on the dataset we refer to Reference [5].  
The acquisition scenario is depicted in Figure 6 and is of particular interest. In contrast to classical stereo or tri-stereo along track acquisitions, here also across track stereo pairs can be employed for 3D mapping, which is not the case for the other test sites and which was never done in literature so far. Regarding the multiple view geometry concept our data set holds more than the three along tracks stereo pairs from descending orbit, namely 15 stereo pairs. Table 2 lists the convergence angles of all possible pairs based on the equations in Equation (1). Four pairs have a small intersection angle of about 12.5\({}^{\circ}\) (1-3, 2-4, 3-5, 4-6), while all others have larger convergence angles from 23\({}^{\circ}\) to 38\({}^{\circ}\).
## 2 Study Area  
The study site (Figure 1) is situated north of Constanta (Romania), at the boundary of Navodari town, approximately 100 m from the shore of the Black Sea. The total area of the industrial platform is 2 km\({}^{2}\), and the experimental zone (the tank park for storing petroleum products) is approximately 0.5 km\({}^{2}\). The sampling area is located at latitude 44\({}^{\circ}\)20\({}^{\prime}\)12\({}^{\prime\prime}\) N-44\({}^{\circ}\)20V30\({}^{\prime\prime}\) N and longitude 28\({}^{\circ}\)38\({}^{\prime}\)56\({}^{\prime\prime}\) E-28\({}^{\circ}\)39\({}^{\prime}\)6\({}^{\prime\prime}\) E and was divided into three distinct zones (1, 2, and 3, in Figure 1). The characteristics of the experimental zones are presented in Table 1.  
A moderate thermal regime characterizes the climate of the experimental area. During summer days, the vertical thermal stratification of the air is more stable, resulting in descending vertical currents that prevent cloud formation.  
Thus, cloudiness is reduced, and the duration of exposure to high temperatures is longer than in the rest of Romania. The recorded air humidity annual average is approximately 80%; the relative minimum frequently recorded in July is 70%, and the relative maximum recorded in December is 90%. The annual number of days with 80% humidity is 129 days. Precipitation has a torrential character in the warm half of the year and alternates with long periods of drought. Barometric pressure values fall within the range: a minimum of 978.1 mbar, a multiannual average of 1013.3 mbar, and a maximum of 1056.4 mbar. The absolute monthly minimum and maximum temperature values recorded in January are \(-\)24.7 \({}^{\circ}\)C and +18 \({}^{\circ}\)C, respectively, and in February are \(-\)25.0 \({}^{\circ}\)C and +24.5 \({}^{\circ}\)C, respectively. Because clear weather predominates in the experimental location area and there is a relatively high frequency of tropical and continental air invasions, the daily maximum air temperature exceeds 25 \({}^{\circ}\)C more than 60 days per year. Days with a maximum temperature higher than 20 \({}^{\circ}\)C are frequent during summer, especially in July and August when their monthly average number exceeds 20 days. In spring and autumn, the number of days with a maximum temperature higher than 20 \({}^{\circ}\)C is lower. During winter, temperatures lower than \(-\)25 \({}^{\circ}\)C have not been observed. Solar radiation depends on cloudiness, with heliographic data establishing that the annual duration of sunshine is 2330 h. The yearly average wind speed is approximately 2-5 m/s with a frequency of 40% and 6-10 m/s with a frequency of 30%.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AOA & Angle of Arrival \\ CRLB & Cramer-Rao Lower Bound \\ DOP & Dilution of Precision \\ ECEF & Earth-Centered Earth-Fixed Coordinate System \\ ECI & Earth-Centered Inertial Coordinate System \\ GNSS & Global Navigation Satellite System \\ KKT & Karush-Kuhn-Tucker Conditions \\ LEO & Low Earth Orbit \\ LOB & Line of Bearing \\ LOS & Line of Sight \\ LLH & Latitude, Longitude, Height Coordinate System \\ PDOP & Position Dilution of Precision \\ PQW & Perifocal Coordinate System \\ \end{tabular}
## 2 System Definition
### Wildfire Detection Methods  
Wildfires are quasi-natural hazards likely to occur under specific climatic, weather and vegetation conditions [9]. A small fraction ignite from natural causes that include lightning, volcanic eruption, sparks from rockfalls and spontaneous combustion, but most ignite from human activities, such as arson, discarded cigarettes, sparks from equipment, power line arcs and controlled field burning [10; 11; 12].  
While most common wildfire causes may vary throughout the world [13; 14], early detection is always very important, because fires may spread fast and escape firefighters' control, potentially causing significant economic damage, threatening human lives, homes and resources and devastating wildlife, habitats and ecosystems [15].  
Wildfire frequency and devastation make the continuous monitoring of the areas at risk one of the most well-known environmental monitoring activities. Although many detection techniques were proposed, cost-effective and reliable early wildfire alerting is still difficult to achieve.
However, iterative reconstruction (IR) has been the mainstream since the first IR technique was clinically introduced in 2009 [5]. IR reconstruction technology has evolved into hybrid IR, followed by model-based IR (MBIR). IR has been reported to reduce the radiation dose by 23-76% without compromising image quality compared to FBP [5]. In recent years, a technique called sparse-sampling CT that resembles compressed sensing in MRI has attracted attention as a possible new technique to reduce exposure. This technique reconstructs CT images using a combination of sparse-sampling CT and Artificial intelligence (AI), especially deep learning, which may reduce CT radiation exposure more than two-fold over the current technology [5]. A few studies show that with the application of sparse-sampling CT and deep-learning, lower-dose CT could be used [6; 7]. Research for the noise reduction of CT images using deep learning started around 2017 [6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. In 2017, image-patch-based noise reduction was performed using deep learning model on low-dose CT images [7; 11]. On the other hand, [PERSON] et al. show that entire CT images could be directly denoised using U-net [9]. To improve perceptual image quality, generative adversarial network (GAN) was introduced for CT noise reduction [12; 13]. Following the advancement in noise reduction using deep learning, [PERSON] et al. evaluated noise reduction using deep learning on a real CT scanner [16]. However, most of them focused on quantitative measures such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). To the best of our knowledge, there are few studies that radiologists visually evaluate abnormal lesions such as metastasis on CT images processed with deep learning [16]. Furthermore, the quantitative measure, such as PSNR and SSIM, and human perceived quality were not always consistent in agreement [17]. Therefore, we suggest that PSNR and SSIM alone cannot assure clinical usefulness and accuracy of lesion detection. The present study aimed to evaluate the usefulness of sparse-sampling CT with deep learning-based reconstruction for radiologists to detect the metastasis of malignant ovarian tumors. This study used both quantitative and qualitative assessment of denoised sparse-sampling CT with deep learning, including PSNR and SSIM, along with radiologists' visual score, and the detectability of metastasis.
# Analysis and Implementation of a Hybrid DC Converter with Wide Voltage Variation
## 2 Circuit Diagram of the Proposed Hybrid DC Converter  
Figure 1a gives the circuit diagram of a hybrid DC converter. The primary and secondary sides of the isolated transformer have the same circuit structure. Full-power switches and one AC switch are used on each side. The AC switches \(S_{ac1}\) and \(S_{ac2}\) are implemented by two switches connected back-to-back. On each side, the resonant circuit can be operated at the full-bridge structure (\(S_{1}\)-\(S_{4}\)) or half-bridge structure (\(S_{1}\), \(S_{2}\), and \(S_{ac1}\)), related to low or high input voltage range conditions. For forward power flow operation, \(S_{5}\)-\(S_{8}\) are all inactive. Therefore, the right-hand side circuit operates as a half-wave (\(S_{ac2}\) is on) or full-wave (\(S_{ac2}\) is off) diode rectifier. The proposed hybrid converter has three operation modes to achieve wide input voltage variation. In each operation mode, the circuit can achieve two times the input voltage range. Therefore, the wide input voltage range variation \(V_{in,max}=8V_{in,min}\) (where \(V_{in,min}=60\) V and \(V_{in,max}=480\) V) can be implemented in the proposed converter. When \(V_{in,min}\leq V_{in}<2V_{in,min}\), the high voltage gain is needed in the proposed converter. Therefore, the full-bridge resonant circuit (\(S_{ac1}\) is off) is operated on the left-hand side and the half-wave diode rectifier (or voltage doubler rectifier) (\(S_{ac2}\) is on) is operated on the right-hand side, as shown in Figure 1b, to achieve the maximum voltage gain. When \(2V_{in,min}\leq V_{in}<4V_{in,min}\), the half-bridge resonant circuit (\(S_{ac1}\) is on and \(S_{3}\) and \(S_{4}\) are off) is used on the left-hand side and half-wave diode rectifier (\(S_{ac2}\) is on) is selected on the right-hand side (Figure 1c) to obtain medium voltage gain. If \(4V_{in,min}\leq V_{in}<8V_{in,min}\), the half-bridge resonant circuit (\(S_{ac1}\) is on and \(S_{3}\) and \(S_{4}\) are off) and full-wave diode rectifier (\(S_{ac2}\) is off) are selected on the left-hand side and right-hand side, respectively (Figure 1d), to obtain the minimum voltage gain. For backward power flow operation (Figure 1e), \(S_{1}\)-\(S_{4}\) are off and \(S_{ac1}\) is on. Thus, the left-hand side circuit operates as a half-wave diode rectifier. \(S_{5}\)-\(S_{8}\) are controlled with the PFM scheme to deliver power from the \(V_{o}\) terminal to the \(V_{in}\) terminal.  
Figure 1: Presented hybrid DC converter: (**a**) circuit structure; (**b**) low voltage range input under forward power flow; (**c**) medium voltage range input under forward power flow; (**d**) high voltage range input under forward power flow; (**e**) under backward power flow operation.
# Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks
## 4 Methodology  
This section discusses in detail the methodology adopted in the paper. Figure 3 shows the model architecture of our FakeDetectionGCN. We next describe all the components.  
Given a set of \(k\) reviews, we create an undirected weighted graph \(G=(V,E)\) for each review and label each review accordingly as true or fake. Each node \(v\in V\) is an aspect word of the review, and edges are dependencies between words.  
We formulate our problem as a graph classification problem. Given \(k\) graphs with labels true or fake, predict the label for an unknown graph (review).  
We accomplish three key tasks:  
* Identify aspect terms (nouns representing aspects of a product/service) in the reviews and determine the associated sentiments for each aspect.
* Construct a graph that represents the different components of each review and how they are semantically related to each other.
* Classify the entire review graph as either real or fake. This is a binary classification, where 1 means the review is real, and 0 means it is fake.  
So, in essence, we take the review text, identify the aspect terms and sentiments, build a graph capturing the review's semantics, and then, using the graph representation that we obtain with max pooling of the hidden nodes' representations, predict whether the review itself is real or fake.