esa-sceva/satcom-chunk-collection huggingface dataset:
# OBU for Accurate Navigation through Sensor Fusion in the Framework of the EMERGE Project
## 1 Introduction
The navigation system emerges as a pivotal enabling factor within the framework of the EMERGE project [18], co-funded by the European Union and spearheaded by RadioLabs in collaboration with esteemed partners such as the University of L'Aquila, Telespazio, Leonardo, and Elital. The project aims to design and develop a novel OBU to provide dual use capabilities to commercial vehicles (i.e., last mile delivery and emergency operations) enabled by dedicated cloud-edge services. The OBU is equipped with vehicle-to-everything (V2X) connectivity [19], procedures for cyber-secure operations [20] and an accurate geo-localization platform. The role of this latter is to guarantee real-time, precise positioning of all nodes and their relative positions, accomplished through innovative fusion techniques that leverage data from multi-constellation GNSS sensors and inertial sensors. When elements with different characteristics regarding data rate, coding, data type, and time references are integrated, an infrastructure capable of handling all the information flows must be designed. For this reason, this work continues in Section 2, describing an architectural proposal applied in the OBU realisation process. Also, a multi-sensor approach is employed, consequently using data fusion techniques to achieve accurate and robust navigation in different scenarios. Section 3 details the integration algorithm based on an Extended Kalman Filter. The experimental setup is divided into two parts: one dedicated to evaluating the individual sensors' performance and the other to evaluating the whole system's performance based on the integration algorithm. Section 4 describes the experimentation procedure, and the main results obtained are presented in Section 5. Finally, Section 6 concludes this article.
# Applications of Smart Glasses in Applied Sciences: A Systematic Review
## 3 Results
### Smart Glasses Research Trend
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sub-Field** & **References** & **Year** & **Aim of Study** \\ \hline \multirow{5}{*}{Clinical and Surgical assistance} & [PERSON] et al. [28] & 2020 & Enhancing the learning experience for trainees practicing microsurgery techniques for science and surgery \\ \cline{2-3}  & [PERSON] et al. [29] & 2019 & To determine feasibility and accuracy of holographic neuronavigation (HN) using AR smart glasses. \\ \cline{2-3}  & [PERSON] et al. [30] & 2018 & Objective measurement of concussion-related disorders through smart glasses \\ \cline{2-3}  & [PERSON] et al. [31] & 2018 & Assessing the accuracy of visualizing 3D graphics in neurosurgery surgery \\ \cline{2-3}  & [PERSON] et al. [32] & 2019 & Multiple patient monitoring \\ \cline{2-3}  & [PERSON] et al. [33] & 2018 & Potential evaluation of smart eyeglasses in urology \\ \cline{2-3}  & [PERSON] et al. [34] & 2016 & Data exchange between medical information systems \\ \cline{2-3}  & [PERSON] et al. [7] & 2020 & Verification of the AR system accuracy of smart glasses in percutaneous needle interventions surgery \\ \cline{2-3}  & [PERSON] et al. [35] & 2020 & Analysis of emotional awareness of autistic children \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of smart glass applications in the field of healthcare. Figure 7: Location of user study: renovated office area with available building information modeling data regarding the building itself as well as the furniture. **Top**, real furniture after the renovation, **bottom**, augmented reality visualization during construction [27]. The industry field includes technical research on maintenance [47; 48], safety [11; 49], and work support [50; 51] (Table 3). Among them, in the safety field, the following studies have been conducted: [PERSON] et al. [11] developed a smart glass-based wearable personal proximity warning system (PWS) for the safety of pedestrians at construction and mining sites. Smart glasses receive a signal transmitted by a Bluetooth beacon that is attached to a heavy machine or vehicle and that provides a visual warning to the wearer based on the signal strength (Figure 9). Regardless of the direction the pedestrian is looking, all warnings are issued normally over a distance of 10 m or more. In addition, under the same conditions, a smartphone-based PWS or a smart glass-based PWS was used to evaluate the workload index of 10 subjects. Test results of mental, temporal, and physical stress were underestimated when using a smart glass-based PWS. The evaluation results showed that the use of PWS based on smart glasses can free both hands of pedestrians, improve work efficiency, and help enhance the safety of pedestrians at construction and mining sites. In addition to this study, in the field of safety, two studies were conducted on maintenance
# Concatenated Coding for GNSS Signals in Urban Environments
## 1 Introduction
The organization of this paper is as follows: In Section 2, the GNSS message concatenated coding system model is briefly described; in Section 3, the proposed RS-LDPC concatenation scheme is introduced in detail and the computational complexity is listed; in Section 4, the Monte Carlo simulation results of the RS-LDPC concatenation in both the LMS and AWGN channels are provided, as well as two interleaved 64-ary LDPC codes in the B-CNAV1 messages and RS-CC concatenation code being evaluated for comparison. Finally, some concluding remarks are given in Section 5.
# The Biosynthesis and Medicinal Properties of Taraxerol
### Biosynthesis Pathway of Taraxerol  
The biosynthesis pathways of taraxerol in plants have yet to be definitively elucidated. [PERSON] et al. (2012) hypothesized that the biosynthesis of taraxerol in plants begins from the mevalonic acid pathway in the plant's cell cytoplasm ([PERSON] et al., 2012). The mevalonate pathway begins with acetyl-CoA and ends with the production of IPP and DMAPP, which are the basic building blocks of various terpenoid compounds including taraxerol ([PERSON] et al., 2012; [PERSON] et al., 2013). The DMAPP produced will then undergo condensation with IPP which is catalyzed by geranyl pyrophosphate synthase, producing geranyl pyrophosphate (GPP) that will be further subjected to condensation with IPP to produce famesyl pyrophosphate (FPP) catalyzed by farnesyl diphosphate synthase (FPS) ([PERSON] et al., 2012; [PERSON] et al., 2012). Squalene synthase catalyzes the condensation of the FPP molecules through reduction by NADPH to produce one molecule of squalene ([PERSON] et al., 2012; [PERSON] et al., 2012). Squalene is then oxidised by NADPH and O\({}_{2}\) to produce 2,3-oxidosqualene, which results in the reduction of NADPH into NADP\({}^{+}\) and O\({}_{2}\) to H\({}_{2}\)O ([PERSON] et al., 2013). 2,3-oxidosqualene is then utilised as a precursor for the biosynthesis of various triterpenoids, starting with a proton-initiated cyclization to produce damarenyl cation, following which subsequent rearrangement leads to the pentacylic oleanyl cation via bacchanenyl and lupenyl cation intermediates ([PERSON] et al., 2013). A series of 1,2-hyride shifts and/or methyl groups leads to compound rearrangements. Finally, the rearrangements of compounds via taraxerol synthase eventually lead to the formation of taraxerol in plants, more specifically in the cuticular waxes ([PERSON] et al., 2013; [PERSON] et al., 2013; [PERSON] et al., 2013). A summary of the biosynthesis pathway is illustrated in Figure 2.  
Figure 2: A summary of the biosynthesis pathway of taraxerol. With the aid of taraxerol synthase, damranenyl cation undergoes rearrangements to produce taraxerol.
# Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System
## 2 Problem Formulation
### Optimal Allocation of ESSs
#### 2.2.1 Objective Function  
The aims of this work are to minimize the total costs, greenhouse gas emissions, and voltage fluctuations by optimally determining the location and sizes of ESSs, while considering the uncertainties of the wind power generation. More specifically, the system costs contain the fuel cost of diesel generators, the operation cost of the WT, and the ESSs. The multi-objective functions are shown as follows:  
\[\left\{\begin{array}{l}\min f1=\sum\limits_{i=1}^{3}Prob_{i}\cdot Cost_{i}\\ \min f2=\sum\limits_{i=1}^{3}Prob_{i}\cdot Emission_{i}\\ \min f3=\sum\limits_{i=1}^{3}Prob_{i}\cdot F_{i}\end{array}\right. \tag{7}\]Notice that  
\[Cost_{i}=\sum_{j=1}^{N}C(P_{d})+C_{w}+C_{e}=\sum_{j=1}^{N}\left(a+b\cdot P_{d}+c \cdot P_{d}^{2}\right)+c^{w}\cdot P_{w}+c^{e}\cdot P_{e} \tag{8}\]  
\[Emission_{i}=\sum_{j=1}^{N}E(P_{d})=\sum_{j=1}^{N}\left(d+e\cdot P_{d}+f\cdot P_ {d}^{2}\right) \tag{9}\]  
\[F_{i}=\sum_{k=1}^{n}\left(\frac{V_{k}-V_{k}^{spec}}{\Delta V_{k}^{\max}}\right)^ {2} \tag{10}\]  
where \(i\) is the scenario caused by the three-point estimate of wind power; \(V_{k}^{spec}\) is the expected voltage; \(\Delta V_{k}^{\max}\) is the maximum of voltage deviation. The expected value of Equation (7) is to calculate the desired system cost and the emissions by optimally allocating the ESSs and by determining the outputs of all the different types of generators factoring in the wind distribution. However, the voltage will fluctuate sharply with the change in the wind power generation, so the voltage profile is improved by the third objective function of (7).
# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 3 Experiments
### Datasets
#### 3.1.2 LoveDA Dataset  
LoveDA is a high-resolution remote sensing surface cover dataset containing 5987 images and 166,768 labeled objects from three cities in China: Nanjing, Changzhou, and Wuhan. It covers two different geographical environments: urban and rural environments. The pixel size is 1024 \(\times\) 1024, and the spatial resolution is 0.3 m; additionally, it includes three bands: red, green, and blue. The data come from the Google Earth platform. The images were cropped to 512 \(\times\) 512 image blocks, and images without water body information were removed. Finally, 8538 images were generated. The processing method used to perform image enhancement operations and divide the dataset into training and test sets was the same as that used for the GID dataset, and a random seed was set to 42. Several images and their labels obtained from this dataset are presented in Figure 5.
# Interference Monitoring from Low Earth Orbit: The OPS-SAT Experiment +
## 2 Experiment Setup
### OPS-SAT Satellite Coordination Group  
The OPS-SAT nanosatellite LEO mission, led by ESA, aims to demonstrate advanced satellite and ground control software under real flight conditions. This satellite features a small radio front-end and an onboard experimental Software Defined Radio (SDR), enabling signal spectrum monitoring across different frequency ranges. Complex raw I/Q samples can be recorded through the satellite's receiving antenna (SRA) and then transferred to Earth via a communication link between OPS-SAT and a dedicated ground station. The JRC team in Ispra coordinated with ESA colleagues on the identification of compatible satellite passages over the Andeya site and for SDR hardware configuration. The on-board sensor was set to record digital samples in the L1/E1 band, centered at 1.5754 GHz, at the maximum allowed 1.5 MHz sampling rate. The sample format is interleaved signed 12-bit samples, padded to 16 bits for each I and Q-branch sample. The platform has some limitations; first of all, the GNSS receiver, which is a fundamental component for precisely determining the satellite position as well as for time-tagging I/Q samples, was out of order. The expected receiver sensitivity was roughly predictable, considering the coarse attitude dynamic information and high level of uncertainty on antenna pointing. Moreover, OPS-SAT can store around 18 MB of data, with some processing time delay to pack the data for download. This delay depends on the amount of data to be stored, and 3 s of acquisition must be followed by 22 s of no acquisition. The JRC-ESA team has worked hard through different passages and dry runs to maximize the signal-to-noise ratio (SNR) and optimize the acquisition scheme against these constraints. The best tradeoff was obtained on 26 September 2023 (maximum SNR), by scheduling four acquisition windows (Figure 1) with maximum durations of 3 s each and distributed approximately 25 s apart.
# A Novel Framework for Identifying Customers' Unmet Needs on Online Social Media Using Context Tree
## 2 Literature Review  
### Methods for Identifying Customer Needs  
This section describes the four most widely used methods of identifying customer needs, such as prototype, market research, FGI, and user observation [8; 9; 10]. Prototyping helps develop new products for selected subjects and obtain feedback. This method can be used when the product specification is clearly defined. Market research methods: market research refers to methods of delivering the questionnaire to many people and analyzing their responses. Structured questions about newly developed products or services are constructed, and individual opinions are recorded. It is important to configure the questions appropriately at an early stage to understand what potential users think about the product. FGI is an in-depth interview with a small group of potential users of products and services. The target size is small in comparison to market research; however, it is considered to obtain a more specific picture of what users think. Users' observation methods help thoroughly understand users' behaviors and analyze their problems and needs. The data analyzed by the experts include images or written texts of users' daily records.  
Despite being widely used, they have several drawbacks from the point of view of identifying customers' unmet needs. The first weakness is that most of the methods require substantial monetary and time costs. It takes a lot of time and money to develop a prototype to obtain a user's feedback. Market research and user observation often incur large costs due to the recruitment of participants and compensation for responses. The second weakness occurs in terms of sample size. Prototypes, FGI,and user's observation require substantial money and time to increase sample sizes for investigation. In market research, the more people who participate in the survey, the higher is the cost. The bigger problem, however, is that the specificity of survey results may deteriorate due to limitations on the number of questionnaires [20]. The third weakness is that people who participate in surveys already know the intentions of marketers. For example, if the company conducts a survey on a new product right after users experience it, some of them become aware of not only the intent of the questionnaire but also the responses the company wants [11]. This is likely to lead to biased results.  
Table 1 summarizes the characteristics of the four methods discussed above.
## 5 Experimental Results
### Comparative Experiment  
To validate the overall performance of our model, we conducted experiments on both the Synapse Abdomen Multi-Organ and ACDC Heart Segmentation datasets. The experimental results are shown in Tables 1 and 2. To fully demonstrate the superiority of our model, we compared the experimental results with traditional CNN segmentation models and Transformer-based segmentation models, and the data showed that our experimental results have significantly surpassed some based on CNN or Transformer models. From the table, we can see that the CNN-based model generally has higher results in arterial segmentation than the Transformer method, mainly because the span of the arterial organ is small and the local correlation of the CNN can be fully utilized, while for a larger span of an organ, such as the pancreas, Transformer has better results due to its global correlation. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **DSC\(\uparrow\)** & **RV** & **Myo** & **LV** \\ \hline DAE-Former [27] & 89.00 & 87.78 & 86.99 & 92.22 \\ DAE-Former + CTrans & 90.08 & 88.51 & 88.58 & 93.16 \\ DAE-Former + EA & 89.68 & 87.12 & 90.23\({}^{1}\) & 91.69 \\ DATUnet(Ours) & 90.35\({}^{1}\) & 88.89 \({}^{1}\) & 88.79 & 93.42\({}^{1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of ablation experiments on the ACDC dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **Model** & **DSC\(\uparrow\)** & **RV** & **Myo** & **LV** \\ \hline \multirow{8}{*}{CNN} & R50 U-Net [13] & 87.55 & 87.10 & 80.63 & 94.92 \\  & R50 Att-UNet [38] & 86.75 & 87.58 & 79.20 & 93.47 \\  & CE-Net [45] & 87.21 & 85.68 & 83.97 & 91.98 \\  & UNet [13] & 88.28 & 86.08 & 86.04 & 92.72 \\  & UNet++ [33] & 89.06 & 87.66 & 86.47 & 93.06 \\  & UNet3+ [39] & 88.28 & 86.08 & 86.04 & 92.72 \\ \hline \multirow{8}{*}{Transformer} & R50 ViT [15] & 87.57 & 86.07 & 81.88 & 94.75 \\  & TransUNet [15] & 89.71 & 88.86 & 84.53 & 95.73 \\ \cline{1-1}  & Swin-UNet [17] & 90.00 & 88.55 & 85.62 & 95.83\({}^{1}\) \\ \cline{1-1}  & UNETR [19] & 88.61 & 85.29 & 86.52 & 94.02 \\ \cline{1-1}  & DAE-Former [27] & 89.00 & 87.78 & 86.99 & 92.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative experimental results on the ACDC dataset. For each data, we mark the best results with the superscript “1”. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **DSC\(\uparrow\)** & **RV** & **Myo** & **LV** \\ \hline DAE-Former [27] & 89.00 & 87.78 & 86.99 & 92.22 \\ DAE-Former + CTrans & 90.08 & 88.51 & 88.58 & 93.16 \\ DAE-Former + EA & 89.68 & 87.12 & 90.23\({}^{1}\) & 91.69 \\ DATUnet(Ours) & 90.35\({}^{1}\) & 88.89 \({}^{1}\) & 88.79 & 93.42\({}^{1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of ablation experiments on the ACDC dataset.
# PCB Network Analysis for Circuit Partitioning
## 3 Circuit Analysis of PCB Netlists
### Circuit Pattern Analysis  
**Repetitive pattern:** It is also found that there are often many identical or similar modules in the netlist. First, from the netlist visualization, the same or similar modules can be roughly inferred based on the size of the circle representing the IC. Secondly, we can also find the same module from the connecting circuit around each IC. The surrounding circuits of these same modules are not only partitioned the same, but the layout and routing can also be reused; see Figure 2. There are so many cases where the same submodule appears in the netlist. We give the statistics of the same modules in \(11\) netlists to show the importance of repetitive pattern recognition in Section 5.1. This pattern will be used later by the netlist partition method.  
**Generic circuit pattern:** There are many generic circuit patterns in both digital and analog circuits. Here, we take the filtering circuit for example. The voltage output by the rectifier circuit is a one-way pulsating voltage that cannot be used directly for the electronic circuit. Therefore, the output voltage should be filtered to eliminate the alternating current (AC) component in the voltage, and then it will be used by the electronic circuit after becoming direct current (DC). In the filter circuit, components with special impedance characteristics to AC are mainly used, such as capacitors and inductors. Therefore, there are some fixed circuit structures to filter various electrical signals. Different filtering processes have different characteristics and adapt to different situations. As shown in Figure 3, there are eight kinds of filter circuits, in which R represents resistance, C represents capacitance, L represents inductance, Q represents triode, and D represents diode. These fixed circuit structures are summarized and become fixed patterns. These patterns are not explicit in the netlist, but require to be further discovered. If these fixed patterns can be discovered from the netlist, it would be very useful undoubtedly for the design of circuit partitioning algorithms.  
Figure 1: Schematic diagram for initial partitioning by cutting important attribute networks.  
Figure 2: Repetitive pattern. Panels (**a**,**b**) are two of the same IC modules from a single netlist, U25 and U27, in green blocks. The capacitors in red blocks between the power net and GND net are scattered, and would be allocated equally by IC module matching in Section 4.5.
## 4 Discussion  
### Uncertainty in EC-Based GPP  
Compromised due to instrument malfunctions, inadequate power supply, and bad weather, certain carbon fluxes and meteorological data can have poor quality and are even lost, which is inevitable. In the above cases, the data with an occasional segment missing or quality problems have been dealt with by the methods of gap-filling using the R package. However, for data gaps or quality problems of more than one month, we gap-filled with multi-year mean imputation, which was mainly used at the S_Sdq site in 2016. To some extent, it covered up the difference left by the specific environment, which showed some uncertainties. Besides, previous work has reported the overestimation of plant photosynthetic uptake when using the integrated open-path eddy covariance system [42]. Despite uncertainties, the six flux sites selected in this study represent the most typical ASA ecosystems in western China. The results obtained in this study also clearly reveal the quadratic functional characteristics of carbon fluxes (GPP, \(R_{e}\), and NEE) concerning the seasonal dynamics in these ecosystems. Further, the results suggest that the cropland ecosystems achieved the highest annual GPP values of \(1182.68\pm 38.62\) g C m\({}^{-2}\) y\({}^{-1}\) among all ASA ecosystems, which was consistent with the result of Gu [15]. On the annual scale, the values of GPP\({}_{\rm EC}\) at the S_Ar and S_Dsl sites were about \(741\) g C m\({}^{-2}\) y\({}^{-1}\) and \(528\) g C m\({}^{-2}\) y\({}^{-1}\). The heavy precipitation events (over \(10\) mm day\({}^{-1}\)) as well as precipitation intervals have been reported to impact GPP in the temperate steppe or the alpine meadow ecosystems [43]. Compared with the S_Dsl site, we found that the S_Ar had more frequent precipitation events (an average of \(13\) days during the growing season of \(2014\)-\(2015\)), and annual average precipitation was about \(460\pm 63\) mm, which was likely to favor the higher GPP value of the S-Ar site. For semi-arid grasslands in the western United States, [PERSON] [44] examined three grassland ecosystems under water-limited conditions and found that the EC-based GPPs were about \(500\) g C m\({}^{-2}\) y\({}^{-1}\). Especially at the Vaira Ranch-Ione site with annual cumulative precipitation of approximately \(630\) mm, the mean GPP from \(2004\) to \(2006\) was approximately \(822\) g C m\({}^{-2}\) y\({}^{-1}\). Therefore, the carbon fixation in the arid and semi-arid ecosystems was strongly constrained by water availability and fluctuated annually.  
Figure 6: Comparisons of annual mean GPP observations (GPP\({}_{\rm EC}\)) and the remotely-derived products (GPP\({}_{\rm BESS}\), GPP\({}_{\rm MOD}\), and GPP\({}_{\rm SIF}\)) at the six flux sites between \(2014\) and \(2015\). Note: The values of both GPP\({}_{\rm MOD}\) and GPP\({}_{\rm BESS}\) were null at the S_Zy.