esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Design and Implementation of the Chatbot  
This phase comprises the CA development and its integration into the ONParkinson mobile application. During this phase, a suitable technology was selected for implementing the agent, and an overall architecture was designed, including information flow, knowledge base, and motivation system. These components determine message generation based on the user's motivation and exercise execution states. As the agent is intended to support exercise performance, additional web services were required to access user data and exercise programs. For the implementation, Google's services were utilized, including Dialogflow [40], text-to-speech (TTS), and speech-to-text (STT) services. Google's TTS and STT services were chosen for their high accuracy in the Portuguese language, supported by advanced machine learning (ML) models, and their ability to process speech in real time, which is crucial for applications requiring immediate feedback [41].  
User feedback from People with Parkinson (PwPD) and Physical Therapists (PTs), collected during the MoveONParkinson platform's development, played a critical role in shaping the mobile application. PwPD recommended including images alongside exercise descriptions to improve clarity and comprehension, especially for users with cognitive impairments. Similarly, PTs emphasized the importance of providing detailed and easily understood exercise descriptions to address potential cognitive challenges faced by PwPD. They also suggested using a relaxed tone of voice for audio descriptions, prioritizing a calm, clear delivery with unhurried pacing to avoid confusion or distress. Additionally, PTs highlighted the need for measurable data to demonstrate progress towards exercise goals, proposing features that track metrics such as daily steps or distance covered to enhance motivation through visual representations of achievement.  
Special attention was also given to designing the user interface (UI) to interact with the CA. According to [PERSON] and [PERSON] [42], designing simple, intuitive, and user-friendly interfaces is critical, particularly for older adults, who often experience lower self-efficacy and higher levels of technology anxiety. These principles, combined with the insights from PwPD and PTs, guided the interface design to ensure accessibility and usability for this demographic. For example, integrating visual aids and progress-tracking features into the UI not only supports clarity and engagement but also addresses the specific needs and challenges highlighted by users.
## 5 Discussion
Consequently, the project focuses on extracting the multimodal features of the signals, enabling the magnetic field contribution rate indicators to objectively reflect the contribution rates. However, the study employs an equivalent simulation model based on real experimental tests as the data source, which can reflect some signal characteristics to a certain extent but lacks completeness. In the next phase of research, a broader range of actual measurement signals will be incorporated into the analysis, and the equivalent simulation will be further refined to better represent the complete signal characteristics. In this study, the judgment matrix between the target layer and the feature layer is defined as the Threat Matrix. Assuming there are r features in the feature layer, there exists an \(r\)\(\times\)\(r\) judgment matrix between the target layer and the feature layer, referred to as the Threat Matrix \(\mathbf{T}\). The construction of the Threat Matrix must be based on the enemy's detection means, analyzing the sensitivity of different detection methods to the magnetic signal characteristics of the submarine. The Threat Matrix is constructed according to the analysis results. Regarding the detection equipment and parameters, the data available in this study is not yet sufficient. In the next phase of research, it is hoped that more parameters of detection equipment under different detection modes can be obtained. By comparison and analysis, a more effective Threat Matrix can be constructed, which can fully reflect the threat level of different signal characteristics under different detection modes.
## 1 Introduction
### Cooperating Autonomous Maritime Vehicles as a Cyber-Physical System
In this way, they will be able to perform monitoring activities on the pipe and share the information obtained from the vehicles in a way that services can be composed from the data acquired. 3. Another practical example would imply using a set of underwater vehicles to supervise the building of a subsea berm: having several vehicles deployed at once will reduce the time required to scan all the subsea structure, and will allow a faster assessment of the construction works performed in that location.
###### Abstract
Although these rice paddies identification methods have been implemented using SAR data with the medium and high spatial resolution of less than 50 m, and have been proven effective at small regional scales [40], the exorbitant requirements of SAR data storage and computing should be considered when developing rice maps, especially for higher spatial resolutions of around 10 m and more frequent mapping over larger areas (regional to continental scales). Fortunately, with free access to Sentinel-1 SAR imagery and free sharing of Google Earth Engine remote sensing cloud computing platform, provides a convenient means for us to develop a rice paddy mapping method with a high spatial resolution of 10 m.  
The innovation presented in this paper has two components. (1) An operational approach was developed for identifying rice paddy extent using high-resolution, dual-polarization SAR images of Sentinel-1, based on the outstanding temporal behavior of water and rice backscattering. (2) Time-series Sentinel-1 SAR datasets were used to map the extent of rice paddy in the Mun River basin in Thailand on the Indochina Peninsula. These methods and the rice map could be used to aid in local sustainable rice production, food security, water resource utilization, and policy making.
# Recent Advances in Geographic Information System for Earth Sciences
## 1 Introduction  
Geographic Information System (GIS) is a computer-based technology and methodology for collecting, managing, analyzing, modeling, and presenting geospatial data for a wide range of applications. GIS plays a vital role in Earth sciences by providing a powerful means of observing the world and various tools for solving complex problems. The scientific community has used GIS to reveal fascinating details about the Earth and other planets.  
This special issue on recent advances in GIS for Earth sciences includes 12 publications from esteemed research groups worldwide. The research and review papers in this special issue belong to the following broad categories: Earth science informatics (geoinformatics), mining, hydrology, natural hazards, and society.
## 4 Changing the Cultivation Conditions for Optimizing the Production of Bioactive Compounds
### Lipid and Fatty Acid Plastid Synthesis in Microalgae
Analysis of subcellular membrane fractions demonstrated an accumulation of these lipids together with a betaine lipid, diacylglycerolhydroxymethyl-_N_,\(N\), _N_-trimethyl-\(\beta\)-alanine, in non-plastid membranes [278]. Using radiolabelled acetate as precursor of lipid and fatty acid synthesis, the authors suggested that the betaine lipid DGCC acted as a first acceptor of _de novo_-formed or exogenous fatty acids which undergo processing and redistribution. It was suggested that C18 and C20 fatty acids were individually transferred from cytoplasm to chloroplast, allowing the synthesis of eukaryotic MGDG without import of DAG. In this microalgae, it has been shown that it was able to synthesize long chain-polyunsaturated fatty acids (LC-PUFA) from radiolabelled carbon sources such as hydrogenocarbonate or acetate, by successive elongation and desaturation steps. Synthesis of lipids, including galactolipids and phospholipids/betaine lipids has also been demonstrated. According to light-dependent studies, two distinct enzyme pools involved in the LC-PUFA synthesis have been proposed, one light-dependent inside the chloroplast, and another one, light-independent and outside the chloroplast [279]. Using both acetate and hydrogenocarbonate, this microalga was able to synthesize LC-PUFA by successive desaturation and elongation steps. The use of both substrates induces different synthesized fatty acids. Indeed, from hydrogenocarbonate as substrate, the main synthesized fatty acids are C16 fatty acids and EPA, while with acetate, the main fatty acid was a C18. One hypothesis proposed by the authors was that the synthesis of acetyl-CoA from hydrogenocarbonate is mainly intra-chloroplastic by opposition with the synthesis of acetate outside the chloroplast. Thus, fatty acids synthesized from chloroplastic acetyl-CoA can be metabolized by enzymes of thylakoid such as desaturases. Using radiolabelled acetate as substrate, radioactivity incorporated into fatty acids is supposed to derived from cytosolic acetyl-CoA formed, hypothesizing a _de novo_ fatty acids synthesis outside the chloroplast [279]. A proposed lipid synthesis pathway in the marine microalga _Pavlova lutheri_ is reported in Figure 2. The role of chloroplast and endoplasmic reticulum compartments is proposed for the lipid synthesis and the location of omega-3 fatty acid incorporation.
## 4 Results
### Population Spatialization Result Validation and Error Analysis
In summary, in the areas with larger prediction discrepancies, there is a high level of vegetation cover, sparse identifiable housing from Google imagery, and a lack of information on transportation networks and Points of Interest. Additionally, the nightlight data may have limited indicative value for rural areas. Therefore, predicting population in rural areas is more challenging compared to regions with more distinct and comprehensive features. In future research, supplementary data from various sources should be explored to obtain finer-scale population indicators for rural areas, allowing for a multi-dimensional and multi-scale characterization of rural populations.
# Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment
## 6 Validation of Environmental Measurements
### GPS Data  
The data acquired from GPS in this test involved location (latitude and longitude), altitude, and the number of satellites detected. The data received from the reference device included horizontal dilution of precision (HDOP), which depicts the error caused by the relative position of GPS satellites. Fundamentally, the dilution of precision (DOP) assures the quality of the signal and minimizes the positional error. In the GPS observation, the widespread satellites mean good DOP and vice versa. DOP values vary from 1 to 20 (1 > DOP > 20). Values below 1 represent ideal precision, and values greater than 20 denote the most inaccurate GPS readings up to 300 m from the actual position. The main causes of these errors are listed as follows:  
* Signal refraction by troposphere and ionosphere;
* The multipath effect i.e., deflection of the signal from building and mountains, etc;
* Satellite time and location (ephemeris). Error in synchronization to the satellite's atomic clock;
* Equivalent to 30 m positional inaccuracies due to selective availability code (nonexistent after May 2000 A.D.).  
Nowadays, GPS differential correction and satellite-based augmentation system (SBAS) are used to precisely read GPS signals.  
The accuracy of the reference device is rated within 2.5 m, while that of our prototype is rated below 5 m range. The readings from both devices seem fairly precise, but the prototype performed marginally better. The actual performances of both devices have been illustrated in Figure 19 for additional explanation.  
Figure 18: Validation graph for solar radiation.  
The altitude of the test site on the map is 517 m. The elevation was altered once during the test. The graph from the prototype approves slight inclination from 508.5 to 516.5 m at the third segment of the test. The readings from the reference device appear continuously wa  
Figure 19: Georeferencing accuracy visualized over campus map; GPS location mapped with Grasshopper3D.  
Figure 20: Validation graph altitude.
## 2 Results and Discussions
### Validation of Optimum Conditions  
The fitness of the model equation (Equation (1)) for predicting optimum response value was investigated under the conditions given in Table 3. In all five runs, desirability values were 1.000. Experiments were carried out twice and average values were given. In the comparison of predicted vs. experimental values, calculated error percentages must be as low as possible [60]. Optimum conditions that maximize TPC were determined as initial moisture content of 69.8%, incubation temperature of 24.6 \({}^{\circ}\)C, and incubation time as 4.6 days with the least error percentage. Table 4 also shows that the limit of errors for the response is within a tolerable range, supporting the correctness of the established model (Equation (2)).  
The optimum conditions obtained in this study resulted in the maximum TPC as 404.4 mg tryptone eqv./g dry matter (DM) of solid substrate. Unfermented (raw) HOC was determined to have 40.4 mg tryptone eqv./g DM of solid substrate, which means that SSF by _A. oryzae_ under the optimized conditions achieved to increase TPC of HOC by 10-times.  
Figure 1: Response surface plots for the effects of (**a**) initial moisture content-incubation temperature (incubation time hold at 3.5 days), (**b**) initial moisture content-incubation time (incubation temperature hold at 30 \({}^{\circ}\)C), and (**c**) incubation temperature–incubation time (initial moisture content hold at 50%) interactions on TPC. (minimum TPC region: blue, maximum TPC region: red).  
[PERSON] et al. (2020) reported a comparable maximum bioactive peptide content of solid-state fermented (with _Bacillus subtilis_ MTCC5480) corn gluten meal as 369.4 mg/gdp ([PERSON] et al., 2020). There is a relatively lower total peptide content recently reported after the SSF of some by-products. The maximum peptide content during the fermentation of soybean meal by _B. subtilis_ was 135.6 mg/g ([PERSON] et al., 2020). The optimum conditions of SSF of okara/chicken feather powder by _B. licheniformis_ revealed a total peptide content of 185.99 mg/g ([PERSON] et al., 2020). SSF of another _B. licheniformis_ strain on brewer's spent grain and soybean meal increased the peptide content of these co-substrates from 25.71 mg/g to 112.72 mg/g under optimal SSF conditions ([PERSON] et al., 2020).
## 3 Method Implementation and Application
### Assessment of the Developed Pylons  
The method, applied to the territory of the Abruzzo region, leads to the definition of two new pylon designs that need to be assessed by citizen's preferences.  
Similarly to [PERSON] (2018) and in accordance with ([PERSON], 2018), we defined and carried out a survey to investigate some options including the new pylon designs as well as the current design in the region. To this aim, renders of pylons placed in the internal (mountains) and coastal settings were prepared and shown to survey respondents, depending on their area of residence. Due to the Capestrano Warrior elimination, only people living in internal and coastal areas were asked to join the survey. Respondents were asked to express their preferences about the pylon designs, comparing the traditional pylon design with the new one (Figure 17). Specifically, the photographs of already installed HVPL pylons (shown at the top of Figure 17) were compared to the renders (shown at the bottom of Figure 17) of the new pylons. To this aim, we deleted the real pylon from the photographs and used these modified pictures as scenes for the renders.  
The survey respondents, whose sample characteristics are listed in Table 10, were 132 citizens between the ages of 18 and 65, divided into 63 men and 69 women, from different social backgrounds, mainly residents or tourists in the coastal area and in the internal area (which corresponds to the province of L'Aquila and the regional park of Velino--Silente).  
The citizens were asked to compare the design and the visual appearance of the traditional pylon with the new one. In particular, survey respondents were asked: \"Compare the visual appearance of these pylons within the internal or coastal landscape. Then, select one of them if preferable to the other, as well as none of them\". The results are shown in Table 11. The analysis of the data suggests that both the new pylon designs best fit the landscape of the installation areas.  
Figure 17: Traditional pylons (top) vs. renders of the developed eagle-shaped and sail-shaped pylons (bottom) in the internal (**a**) and coastal (**b**) landscape.
# Crop Land Change Detection with MC&N-PSPNet
## 2 Materials and Methods
### Training and Validation  
In this paper, PyTorch is used as the development framework, and the hardware environment is Intel(R) Core(TM) i9-12900 HCPU and NVIDIA GeForce RTX 3060 GPU, with 16 GB of RAM and 8 GB of video memory, and the software environment is Window11, python3.9, CUDA12.1.68, and pytorch1.13.1. Based on the experimental results, the optimal parameters are selected, and the data are randomly divided according to the ratio of 9:1; there are a total of 2736 images in the training dataset and 306 images in the validation dataset. The accuracy and efficiency of the method in this paper are affected by factors such as the down-sampling multiplier, the structure of the backbone network, the type of optimizer, and the training ratio of the dataset. The basis for the determination of the four parameters is mainly evaluated by the effect of the network performance on the dataset (overall accuracy, Kappa, training time, Loss, Val_Loss). In this paper, the backbone networks are selected as MobileNetV2 and ResNet50 for experimental comparison. The size of the down-sampling multiplier is chosen between 8 and 16. The optimizer type is chosen between SGD (Stochastic Gradient Descent), and Adam (Adaptive Gradient Algorithm); the proportion of the training part of the dataset decreases step by step, and the experimental results are shown in Figure 6.  
From Figure 6a, it can be seen that the best training effect is achieved when the backbone network is MobileNetV2, the down-sampling multiplier is 8, the optimizer is Adam, and the ratio of the training set to the validation set is 1:9. From Figure 6b,c when epoch = 120, both Loss and Val_Loss are stabilized. Batch size is set to the maximum within the acceptance range of the graphics card. Therefore, in this paper, the parameters are set as follows: the backbone network structure is MobileNetV2, the down-sampling multiplier is 8, the optimizer is Adam, the batch size is 8, the number of training rounds epoch is 120, the gradient descent function is cos, and the loss function is chosen as Cross Entropy Loss (CEL). The computational formulas are as follows:  
\[Loss=-\frac{1}{N}\sum_{i}\sum_{c=1}^{M}y_{ic}\text{log}(p_{ic}), \tag{14}\]  
In Equation (14), the M represents the number of total categories of classification and takes 1 when the \(y_{ic}\) predicted feature type and the labeled feature type are the same and 0 otherwise; \(p_{ic}\) represents the probability that a feature is i predicted to be c a feature; N represents the number of samples.
## 3 Results  
Separate simulations were conducted for each condition and configuration, as detailed in Table 7. The layouts were modelled based on the volumes depicted in Figures 10 and 11. To ensure the convergence of the simulations, a scaled residuals threshold of \(10^{-5}\) was applied for continuity, turbulence kinetic energy (k), and momentum in the x, y, and z directions. The simulations for the open transformer enclosure considered both the temperature and relative humidity at the sensor locations (66, 72, and 84), utilizing longitudinal and transverse sections positioned at the building center, with a height of 1.50 m. For the closed transformer enclosure layout, this was not feasible due to the coverage of the transformer enclosure at those locations. Thus, only the orthogonal sections taken at the same positions as the open transformer enclosure layout are presented. All temperature and relative humidity data correspond to the worst-case scenario when temperatures peak at the three sensor locations. The discussion will begin with the open transformer enclosure layout, followed by the closed layout, highlighting the main advantages and disadvantages of both configurations. For brevity, the results from sensors 72 and 84 are included in the ESM document (Figures S4, S5, S9 and S10). Additionally, the velocity results for both layouts in the longitudinal section (Figures S6, S11, S14 and S17) and the simulations for the transverse sections (Figures S7, S8, S12, S13, S15-S18) are compiled in the ESM.
## 3 Segmentation of Nanostructured Materials for Gas Sensing
### Indium Oxide (In\({}_{2}\)O\({}_{3}\))
This reversible charge transfer underlies the real-time sensing capability of In\({}_{2}\)O\({}_{3}\), making it suitable for applications where continuous monitoring is required [94]. Light activation has been studied as a method to enhance In\({}_{2}\)O\({}_{3}\) sensor performance at even lower temperatures, as UV or visible light irradiation generates electron-hole pairs that facilitate the gas interaction process without the need for thermal activation. This approach has proven effective in enhancing sensitivity and enabling low-temperature operation for gases like NO\({}_{2}\) and CO\({}_{2}\). Photo-induced charge carriers not only improve the reaction kinetics, but also increase selectivity for certain gases by promoting specificsurface reactions under light exposure, providing a pathway for developing low-power, light-activated gas sensors [94]. Composite materials that combine In\({}_{2}\)O\({}_{3}\) with other metal oxides, such as SnO\({}_{2}\), TiO\({}_{2}\), or WO\({}_{3}\), further enhance the selectivity, stability, and sensitivity of In\({}_{2}\)O\({}_{3}\)-based sensors. In\({}_{2}\)O\({}_{3}\)-SnO\({}_{2}\) composites, for example, leverage the high sensitivity of SnO\({}_{2}\) to reducing gases with In\({}_{2}\)O\({}_{3}\)'s selective response to oxidizing gases, creating a sensor with balanced performance characteristics across multiple gas types. These composites form heterojunctions that enhance charge transfer and carrier separation, allowing for improved sensing accuracy in multi-gas environments. Similarly, In\({}_{2}\)O\({}_{3}\)-TiO\({}_{2}\) composites exhibit increased stability under humid conditions, extending the sensor's applicability to varied environments [91]. In\({}_{2}\)O\({}_{3}\)'s high sensitivity, low-temperature operation, and adaptability to nanostructuring and doping make it a valuable material for gas sensors in applications ranging from environmental monitoring to portable air quality measurement devices. Continued research into doping, light activation, and composite engineering is likely to further expand the capabilities and applications of In\({}_{2}\)O\({}_{3}\)-based gas sensors (Figure 10).