esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Classical Inferences
### Approximate Confidence Intervals  
Because there are no closed-form solutions available for the MLEs, the precise bounds of the confidence intervals (CIs) for these parameters are impossible to be derived. Hence, we resort to establishing approximate confidence intervals (ACIs) for the three parameters using the Fisher information matrix.  
Denote \(\mathbf{\theta}=(\alpha,\lambda,\beta)^{\top}\), then the asymptotic covariance matrix denoted as \(I(\mathbf{\theta})^{-1}\) is utilized to obtain the approximate CIs. \(I(\mathbf{\theta})^{-1}\) is given by:  
\[\begin{split} I(\mathbf{\theta})^{-1}&=-\left(\begin{array} []{ccc}\frac{\partial^{2}l}{\partial a^{2}}&\frac{\partial^{2}l}{\partial a \partial\lambda}&\frac{\partial^{2}l}{\partial a\partial\beta}\\ \frac{\partial^{2}l}{\partial a\partial a}&\frac{\partial^{2}l}{\partial a \partial^{2}}&\frac{\partial^{2}l}{\partial a\partial\beta}\\ \frac{\partial^{2}l}{\partial\beta a\partial a}&\frac{\partial^{2}l}{ \partial\beta\partial a}&\frac{\partial^{2}l}{\partial\beta\beta^{2}}\\ \end{array}\right)^{-1}\\ &\approx\left(\begin{array}{ccc}\text{Var}(\hat{\alpha})&\text{Cov}( \hat{\alpha},\hat{\lambda})&\text{Cov}(\hat{\alpha},\hat{\beta})\\ \text{Cov}(\hat{\lambda},\hat{\alpha})&\text{Var}(\hat{\lambda})&\text{Cov}( \hat{\lambda},\hat{\beta})\\ \text{Cov}(\hat{\beta},\hat{\alpha})&\text{Cov}(\hat{\beta},\hat{\lambda})& \text{Var}(\hat{\beta})\end{array}\right)^{-1}\end{split} \tag{8}\]  
The detailed expressions of the second derivatives of the log-likelihood function are given in Appendix A. Given that \(\sqrt{n}(\hat{\mathbf{\theta}}-\mathbf{\theta})\) conforms to a multivariate normal distribution (the asymptotic distribution of MLEs) featuring the covariance matrix described by (8) and an expectation vector of zero. Subsequently, the \(100(1-\tau)\%\) ACIs are eventually expressed as shown below:  
\[\begin{split}\left[\hat{\alpha}\pm z_{\tau/2}\sqrt{\text{Var}( \hat{\alpha})}\right],&\left[\hat{\lambda}\pm z_{\tau/2}\sqrt{\text{ Var}(\hat{\lambda})}\right],&\left[\hat{\beta}\pm z_{\tau/2}\sqrt{\text{ Var}(\hat{\beta})}\right].\end{split} \tag{9}\]
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 2 Technology Advancements
### Spaceborne Receivers
Despite not being specifically designed for GNSS-R, the high directive and dual linearly polarized SMAP antenna comprises a unique platform from which to study polarimetric applications of GNSS-R over land [50; 51; 52; 53; 54; 55]. Other space mission proposals have been also developed in the recent years including iGNSS-R receivers [56; 57], but neither mission has ever been implemented or launched.
### Research Aims and Contributions  
Considering this question, this research aimed to develop a technology to test an integrated analysis and decision support environment based on a machine learning model with computational tools to aggregate real and simulation data, aiming at the maintenance and integrity of surface installations. To accomplish this overarching goal, the following specific objectives (SO) were set.  
SO1: Propose a decision support system based on 3D CAD/CAE and machine learning models to optimise maintenance and painting planning on offshore platforms.  
SO2: Assess the impact of optimisation strategies (person-hour/PH limits, corrosion integrity, regulatory demand, and criticality) on generating painting plans for offshore platforms.  
By integrating the 3D CAD/CAE design model with simulation and inspection data, computational mechanisms will be applied to improve maintenance and integrity planning activities, generating a more predictive and less reactive maintenance plan.  
This research is innovative in proposing an integrated approach to improving maintenance planning in offshore installations, focusing on integrity management through anti-corrosive maintenance optimisation. Its originality lies in combining design, simulation, instrumentation, and inspection data with computational mechanisms, applying the DSR methodology to develop the prototype. The main contributions include filling gaps in the literature on corrosion assessment models, developing prototypes as practical tools for optimising painting planning, creating a machine learning model for predicting corrosion progression, conducting case studies on real platforms, and generating knowledge about factors influencing the criticality and progression of corrosion. These contributions offer valuable insights for future studies and decision-making in the offshore sector, promoting the development of more effective solutions to maintenance and integrity challenges.  
This work is structured into six sections. Section 1 describes the context and the current challenges in maintenance management and corrosion control in offshore installations, establishing the theoretical foundation of the research. Section 2 presents a structured literature review on offshore maintenance and asset management. Section 3 details the DSR methodology adopted for developing the prototype and the specific tools and approaches employed, such as CAD/CAE modelling and machine learning. Section 4 presents the findings from case studies conducted on oil and gas platforms, highlighting the proposed system's benefits and limitations. Section 5 discusses the current literature's findings, exploring the model's practical and scientific implications. Finally, Section 6 concludes the article, summarising the contributions of the research and suggesting directions for future investigations in the field of maintenance optimisation in offshore infrastructure.
# Evaluation of In Vitro Antihypertensive and Anti-Inflammatory Properties of Dairy By-Products
## 2 Materials and Methods
### Anti-Inflammatory Properties of Digestates  
2.5.1. Cell Culture, Cell Viability, and Activation of RAW 264.7  
Cells of the murine macrophage cell line RAW 264.7 were cultured in Dulbecco's modified Eagle's medium supplemented with 100 U/mL penicillin, 100 \(\upmu\)g/mL streptomycin, 10 U/mL L-glutamine, 100 \(\upmu\)M NEMA, 1 mM sodium pyruvate, and 10% FBS and incubated at 37 \({}^{\circ}\)C in a humidified atmosphere containing 5% CO\({}_{2}\). Cells (up to passage 25) were passaged after reaching 80% confluency and detached with cell scraper; a fresh medium was added following the aspiration of old media after centrifugation. RAW 264.7 cells were allowed to attach to 96-well plates at a seeding density of 5 \(\times\) 10\({}^{4}\) cells/well overnight before the experiments. After 24 h incubation, the medium was removed, and the cells were washed with PBS. Subsequently, the cells were pretreated with D-P3 (10% of initial protein of dairy by-products) for 1 h and then incubated in the presence of 1 \(\upmu\)g/mL LPS (containing DMEM without phenol red) for an additional 24 h.  
2.5.2. Determination of Nitric Oxide (NO) Production by LPS-Stimulated Macrophage RAW 264.7 Cells  
To quantify NO release by LPS-stimulated macrophage cells, Griess assay, a colorimetric method, was used. The assay is considered an indirect approach and relies on a diazotization reaction that was firstly described by [PERSON] [29]. The Griess assay was performed according to [PERSON] et al. [30], with some modifications. In brief, 100 \(\upmu\)L of each supernatant was transferred to a new 96-well plate, 50 \(\upmu\)L of sulfanilamide [1% (\(w/v\)) in 5% H\({}_{3}\)PO\({}_{4}\) (\(v/v\))] was added, and reactions were incubated for 10 min at 25 \({}^{\circ}\)C in the dark. Subsequently, 50 \(\upmu\)L of 10% (\(w/v\)) NED [N-(1-naphthyll) ethylenediamine dihydrochloride] in H\({}_{2}\)O, then 5% (\(v/v\)) in H\({}_{3}\)PO\({}_{4}\), was added and was further incubated for 10-30 min at 25 \({}^{\circ}\)C in the dark. The absorbance was measured at 540 nm using an Epoch 2 microplate reader (Biotek, Winooski, VT, USA). The total nitrite concentration was determined using a curve prepared with NaNO\({}_{2}\) (concentration range, 1.5-100 \(\upmu\)M) and Griess reagent solutions. Each sample was tested in quadruplicate, and the experiment was performedfour independent times. The results are expressed as a percentage (%) of NO production to positive control cells (cells exposed only to LPS).
## 4 Model Comparison and Validation  
In order to reflect the advantages of the three-dimensional cloud model, this paper selects the same case data and compares and analyzes the two-dimensional cloud model with the three-dimensional cloud model. The main feature of the two-dimensional cloud model is to evaluate the risk through the dimensions of \"probability of risk occurrence\" and \"risk consequences\", and the result can reflect the significance of the risk more clearly. However, the model does not consider the important factor of risk concealment, which limits its performance in complex risk scenarios.  
According to the relevant formula of the two-dimensional cloud model, the numerical characteristics of the risk of each indicator are calculated [20], and the comprehensive indicator evaluation cloud and the evaluation cloud of the first-level indicator are plotted against the risk level standard cloud, respectively, which can be seen in Figures 9 and 10. The evaluation results of the two-dimensional cloud models obtained through the proximity calculation are then organized and shown in Table 7. In order to show the comparison of the results of the two cloud modeling methods more clearly, the evaluation results of the two methods are summarized in Table 8.  
As can be seen from the results in Table 8, the results of the two-dimensional cloud model risk evaluation method and the three-dimensional cloud model risk evaluation method are basically the same, and that the risk level of the whole project and the five level 1 indicators are all level II, and that there are only differences in the risk level of individual indicators. The risk level of the secondary indicators U\({}_{37}\) (compliance rate of stacking protection and loading/unloading measures) and U\({}_{51}\) (frequency of monitoring of facilities and maintenance of equipment) in the two-dimensional cloud model is III and I, respectively, while the corresponding three-dimensional cloud model assesses the risk level of both as II, which is more in line with the actual situation of the project. This shows that the two-dimensional cloud model fails to fully reflect the potential impact of stacking protection measures and the hidden risk of later equipment maintenance, while the three-dimensional cloud model can more comprehensively capture the dynamic changes of these two types of risks by adding the \"hidden\" dimension, which can more effectively reveal the hidden risks, especially in the complex quality management process of assembled buildings, which has a more applicable and practical value and can be applied and promoted in other fields.  
Figure 10: Comparison of composite indicator evaluation cloud and risk rating criteria cloud.  
Figure 9: Comparison of tier 1 indicator evaluation cloud and risk rating criteria cloud.
# Occlusion-Aware Worker Detection in Masonry Work: Performance Evaluation of YOLOv8 and SAMURAI
## 2 Methodology  
### Research Framework  
This section presents the proposed framework that integrates experimental setup and data collection and compares YOLOv8 and SAMURAI. The primary objective is to evaluate the applicability of these models for detecting construction workers during masonry tasks, focusing on their performance under different levels of occlusion.  
Figure 1 illustrates the overall process. YOLOv8 and SAMURAI are applied to both brick transportation and bricklaying tasks, using datasets with labeled worker images to ensure accurate detection. Video data are collected using a single camera positioned at a fixed location, capturing worker movements and occlusion scenarios. The framework categorizes occlusion into three levels: (1) non-occlusion (no obstruction), (2) partial occlusion (partial obstruction by materials or tools), and (3) severe occlusion (significant obstruction of the worker's body).  
While YOLOv8 processes each frame independently, SAMURAI maintains temporal continuity across frames, enabling more consistent detection even when workers are partially or fully obscured. The detailed structure and methodology of each algorithm will be explained in subsequent sections.
# Ad Hoc Data Foraging in a Life Sciences Community Ecosystem Using SoDa +
## 3 SoDa Architecture  
As shown in the conceptual model of SoDa in Figure 1, it has four major components: (i) ResCom resource recommender, (ii) CroW wrapper generation system, (iii) Crowd curation tool QCurator, and (iv) Application designer SoDaPro. ResCom helps identify databases of interest when prompted using a natural language description of the required data. It also helps generate a query plan. CroW is a semi-automatic wrapper generator that helps SoDa extract data from deep web databases. If users make mistakes or the CroW's database access instructions fail to function as expected, users are able to seek community help using QCurator to correct the errors. Finally, using SoDaPro subsystem's QBuild tool, users can construct queries or analytics and execute them.  
In the sections to follow, we discuss these components on intuitive grounds. As a preamble to the discussion of the architecture of SoDa, we introduce a potential scientific expedition a biologist might want to carry out using our cloud-based analysis platform for open science over linked open data.
## 6 Results and Discussion
### Group-Wise Classification Performance
These observations may be related to the fact that individuals' music perception and music preference were found to be closely related to an array of personality trait dimensions, especially openness to new experiences [3, 60], which is sensitive to art and beauty [60]. In a more general sense, this result verifies previous findings that people's responses to music result from multiple factors, some of which are relatively changeable and short-term such as physiological signals, while others are more stable and long-term such as personality traits [61], which further implies the importance of personalized modeling of music-induced emotion. With respect to the classification experiments using feature sets of single modalities (i.e., audio-only vs. physiology-only), the audio-only feature set achieved better performance with statistical significance (at \(p<0.001\)) for arousal prediction in most cases, while neither modality showed apparent advantage for valence prediction. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Personality Trait**} & \multicolumn{4}{c}{**Arousal**} & \multicolumn{4}{c}{**Valence**} \\ \cline{2-9}  & **Size** & **Acoustics** & **Physiology** & **Combined** & **Size** & **Acoustics** & **Physiology** & **Combined** \\ \hline O (Low) & 515 & 80.59\% – & 55.01\% – *** & **80.67\% –** & 512 & **62.92\% –** & 47.86\% – *** & 61.47\% – *** \\ O (High) & 421 & **80.76\% –** & 58.06\% – & 80.43\% – & 418 & 63.61\% – & -63.87\% – & **72.29\% –** \\ C (Low) & 558 & **83.43\% –** & 57.10\% – & 83.30\% – *** & 555 & 59.68\% – *** & 53.68\% – *** & **60.09\% – *** \\ C (High) & 378 & 76.76\% – *** & 52.53\% – *** & **77.37\% –** & 375 & 60.11\% – & 62.31\% – & **66.94\% –** \\ E (Low) & 394 & **82.22\%** & 59.60\% – & 81.64\% – *** & 386 & **55.48\% – *** & 52.02\% – *** & 55.03\% – *** \\ E (High) & 542 & 79.09\% – ** & 56.38\% – * & **79.35\% –** & 544 & 60.91\% – ** & 61.43\% + & **68.39\% –** \\ A (Low) & 760 & **80.57\% –** & 56.01\% – ** & 80.54\% – & 754 & 65.01\% – & 59.84\% – & 69.71\%+ \\ A (High) & 176 & 82.46\% – & 61.88\% – & **82.49\%** & 176 & 38.95\% – ** & **49.08\% – & 44.85\% – \\ E-S (Low) & 562 & 80.12\% – & 62.94\% – *** & **80.20\% –** & 556 & 60.27\% – & 57.45\% – & **67.12\% –** \\ E-S (High) & 374 & **81.84\%** + & 50.01\% – *** & 81.82\% + & 374 & 59.52\% – ** & 61.93\% + & **63.77\% –** \\ \hline \hline \end{tabular} Note: The best performances of arousal and valence prediction within each participant group are highlighted with bold font. Note that “size” denotes the sample size in terms of music pieces. The “\(\pm\)”/“\(\ldots\)” signs indicate the performance was better/worse than that on the whole dataset (resampled) presented in Table 6. The Benjamini–Hochberg procedure was applied to control Type I error when comparing classification performance (“\(p<0.05\), \"\"\(p<0.01\), \"\"\(p<0.001\)). O = Openness, C = Conscientiousness, E = Extraversion, A = Agreeableness, E-S = Emotional Stability.  
\end{table} 
Table 7: Classification performance (in AUC) within each participant group.
###### Abstract  
High-precision retrieval of rainfall over large areas is of great importance for the research of atmospheric detection and the social life. With the rapid development of communication satellite constellations and 5G communication networks, the use of widely distributed networks of earth-space links (ESLs) and horizontal microwave links (HMLs) to retrieve rainfall over large areas has great potential for obtaining high-precision rainfall fields and complementing traditional instruments of rainfall measurement. In this paper, we carry out the research of combining multiple ESLs with HMLs to retrieve rainfall fields. Firstly, a rainfall detection network for retrieving rainfall fields is built based on the atmospheric propagation model of ESL and HML. Then, the ordinary Kriging interpolation (OK) and radial basis function (RBF) neural network are applied to the reconstruction of rainfall fields. Finally, the performance of the joint network of ESLs and HMLs to retrieve rainfall fields in the area is validated. The results show that the joint network of ESLs and HMLs based on OK algorithm and RBF neural network is capable of retrieving the distribution of rain rates in different rain cells with high accuracy, and the root mean square error (RMSE) of retrieving the rain rates of real rainfall fields is lower than 0.56 mm/h, and the correlation coefficient (CC) is higher than 0.996. In addition, the CC for retrieving stratiform rainfall and convective rainfall by the joint network of ESLs and HMLs is higher than 0.949, indicating that the characteristics of the two different types of rainfall events can be accurately monitored.  
joint network; earth-space links (ESLs); horizontal microwave links (HMLs); rainfall field retrieval; radial basis function (RBF) neural network
# Shoulder Kinematics Assessment towards Exoskeleton Development
## 1 Introduction  
The shoulder is one of the most important and complex human joints due to its wide range of motion. It plays a vital role in the activities-of-daily living in placing and orienting the arm. A limitation in the range of motion (ROM) of the shoulder can reduce the quality of life [1; 2; 3]. The ROM of the shoulder can be affected or reduced by motor impairments, due to strokes, Spinal Cord Injury (SCI), or any physical activity that results in an injury. Therapeutic interventions are usually required for recovering most of the lost ROM. Traditionally during therapy, subjects undergo one-on-one manually assisted exercises with qualified professionals. Depending on how severe the mobility problem of the shoulder is, these sessions can last several months, which ends up being very expensive. Furthermore, manually assisted or conventional therapy lacks training intensity. [PERSON] [4] suggested that an increase in intensity improves rehabilitation with the use of novel therapy methods (e.g., smart robot-based rehabilitation). However, it is difficult to determine the level of muscle activity being triggered in order to maximize the affected limb recovery during therapy sessions. One approach to overcome these limitations is with the use of exoskeletons. These mechanisms can perform more intensive training sessions, measure forces being applied and trajectories being followed, and muscles activities beingtriggered in order to enhance conventional therapy [4]. Nevertheless, these improvements do not solve the issues with exoskeleton alignment and fitting due to complex motion of the shoulder joint.  
A lot of effort has been put into studying and modeling the human motion with the hope of building exoskeletons that are capable of mimicking the movement of human limbs (i.e., replicating the workspace). In order to build effective exoskeletons, there has been considerable research directed toward the study and mathematical modeling of human anatomy [5; 6]. These models coupled with motion capture systems are used in order to design and assess assisted rehabilitation procedures for shoulder impairment and pain due to strokes [7].  
This study is aimed at reviewing the shoulder geometry of motion, its modeling and implication towards the design and application of exoskeletons for upper-arm rehabilitation tasks, and to provide a case study of exoskeleton joint impairment. The paper is organized into five related topics: (1) biomechanical models of the shoulder and its complexity; (2) shoulder kinematics and functional assessments; (3) upper arm and shoulder muscle integration and responses during shoulder joint movements; (4) shoulder kinematics and its implication on exoskeleton designs; and (5) best practices and considerations for the design of bio-inspired exoskeletons.
###### Abstract  
With the increase in orchard areas and the transfer of rural labor, various air-assisted sprayers have been widely used in China. However, the problem of off-target drift still exists, which has caused pesticide waste and environmental pollution. In order to improve the droplet deposition in the canopy of fruit trees, a V-shaped anti-drift spray device with multi-airflow synergy was designed in this paper. A droplet spatial motion model was constructed, and the anti-drift mechanism of multi-airflow synergy was clarified based on particle dynamics analysis. The influences of spray pressure and V-shaped wind speed on droplet movement were investigated by Matlab, and the experimental results showed that the machine's anti-drift effect was better when the V-shaped wind speed ranged from 15 m/s to 25 m/s. According to modern orchards with low root stock in a high-density planting, a simulation model of the flow field between the spray device and the fruit trees canopy was established by the method of computational fluid mechanics (CFD). By considering crosswind speed, V-shaped wind speed, and spray pressure, three-level simulation experiments of droplet deposition were designed for each factor using a partial multivariate orthogonal regression method. The influence of V-shaped wind speed on the droplets' spatial distribution was analyzed, and the prediction model of the drift distance of the droplets' deposition center was established. The simulation results showed that the three factors had a significant influence on the droplets' deposition characteristics, and the degree from big to small was V-shaped wind speed, crosswind speed, and spray pressure. The fitting degree of the prediction model was high, and the correlation coefficient was 0.998. The anti-drift experiments of the machine were carried out, and the results showed that when the crosswind speed, the spray pressure, and V-shaped wind speed were 2.2 m/s, 0.52 MPa, and 20.8 m/s, respectively, the droplet drift rate was 29.2% lower than that of single-airflow. The drift distance of the droplets deposition center was 5.0 cm, which was consistent with the prediction model. The research can provide a basis for the design and parameters optimization of the similar sprayers used in modern orchards with low root stock in a high-density planting.  
orchard sprayer; multi-flow synergy; anti-drift spray; fluent DPM; droplet deposition +
Footnote †: journal: Applied Sciences