esa-sceva/satcom-chunk-collection huggingface dataset:
# Nutritional Care Process in Hospitalized Patients with Obesity-Related Multimorbidity
## 6 Estimation of Nutritional Requirements  
The determination of Total Energy Expenditure (TEE) should be obtained through indirect calorimetry (IC), which is considered the \"gold standard\" [15; 18; 19]. However, if IC is not available, TEE may be determined using validated predictive equations selected based on the patient's specific condition and/or type of disease associated with ORM (Table 2). To determine protein requirements, the following factors must be considered: quality and quantity of preexisting muscle mass, acid-base status, protein losses due to proteinuria, drainage, dialysis, diarrhea, and/or vomiting, and the degree of metabolic stress. Calculating protein requirements using NB may overestimate protein intake in ORM patients who experience nitrogen retention in conditions such as the acute phase of the metabolic response to injury in critically ill patients, renal injury, or liver disease [15; 19]. Protein requirements must be individualized and established for each stage of illness or clinical condition and therapeutic goals, particularly in obesity (Table 2). Moreover, recent studies highlight the need to individualize protein dosing in critically ill patients with obesity. An analysis from the effect of higher protein dosing in critically ill patients with high nutritional risk (EFFORT Protein) study showed that the group with a BMI of 30 to 39.9 kg/m\({}^{2}\) that received high protein doses (>2.2 g per kg of ABW per day) tended to have lower mortality compared to the group that received low doses (0.8 to 1.2 g/kg/day) [47; 48]. However, high protein doses tended to increase the number of days on ventilatory support and mortality in the group with BMI >40 kg/m\({}^{2}\) compared to the group that received low doses. Therefore, protein doses >2.5 g/kg of IBW/day currently recommended in clinical practice guidelines may be excessive in patients with class III obesity and highlight the need for further research stratified by obesity class to strengthen current guidelines [48]. Fluid intake depends on the chosen feeding route. Oral fluid intake is recommended at 1 mL per kcal; for bariatric surgery, a minimum of 1500 mL is recommended (Grade GPP) [49]. For enteral or parenteral administration, the recommended dose is body surface area (BSA) = (ideal weight \(\times\) 4) + 7/(weight + 90) per day. Without edema: 1.2 to 1.5 L \(\times\) BSA or 30 to 40 mL/kg/day. With edema or ascites: 0.8 to 1.2 \(\times\) BSA or 20 mL/kg/day [16; 31].
## 3 Methodology
### Regression Techniques  
Regression analysis is suitable in instances such as that presented above, when there is a dependence between one or more independent variables and the dependent variable, as [PERSON] et al. highlighted in [22]. Multiple types of regression model are available--as many as 35 according to [23]-and they are classified based on factors such as the relationship between the predictors and the outcome, the selection of the predictors, the correlation between the predictors, and the type of expected outcome. Some of the main regression techniques are depicted in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Weight** \\ \hline  & 0.05 \\ \cline{2-3}  & 0.1 \\ \cline{2-3} Value & 0.15 \\ \cline{2-3}  & 0.20 \\ \cline{2-3}  & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Weight \(w\) values.  
Figure 2: Visual comfort index scale.  
Furthermore, regression models can be classified as non-parametric and parametric. Parametric models, such as linear regression, logistic regression, and simple neural networks, are suitable when one has prior knowledge or has made an educated guess about the relationship between the predictors and the outcome. Non-parametric models, such as decision trees, K-nearest neighbours, and support vector machines, are better for complex input data that cannot be modeled by parametric models. These models are described further below:  
1. Linear regression: This regression model is widely used because it employs a linear equation to predict the dependent variable based on the independent variables. It fits a straight line or surface that minimizes discrepancies between the predicted and actual output values [24].
2. Logistic regression: Logistic regression models the likelihood of a discrete result given an input variable [25]. It is commonly used for binary results but can also handle multinomial outcomes. It is a useful tool for classifying new samples into categories.
3. Neural networks: This predictive deep learning method was inspired by the workings of the human brain, using similar interconnected nodes or neurons in a layered structure. These networks can be utilized to solve a wide variety of problems, including regression tasks.
4. Decision trees: A decision tree partitions an initial dataset into smaller subsets with similar values while building a decision tree structure containing both leaf and decision nodes. The algorithm comprises a top-down, greedy search through the possible branches with no backtracking [26].
5. K-nearest neighbors (KNN): This model utilizes the feature of \"similarity\" and predicts the output value based on how similar a data point is to its neighbors, in other words, the cluster of points (neighborhoods) to which it is closer [27].
6. Support vector machines (SVMs): SVMs use kernel functions to support their operation. SVMs classify a dataset into two groups utilizing a line or hyperplane in a multidimensional space [28].
# Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
## 3 Results
### Upscaling GPP to the Peatland Scale  
In order to demonstrate the applicability of the simple regression model with high resolution satellite data for upscaling, we applied the GPP model (Section 3.2, Equation (4)) with the average parameters from the LOOCV (Table S5) to Sentinel-2 data for the Lompolojankka site (Figure 8). We selected Sentinel-2 pixels for upscaling based on the wetland land cover classification in Finland (SYKE, 2018), so that only pixels in the class \"open peatbog\" were used. Figure 8 shows the estimated average GPP during the growing season in 2017 and 2018. GPP varies from 1 up to 5 umol m\({}^{-2}\) s\({}^{-1}\) within the flux footprint area, and similar spatial variation can be seen also beyond the flux footprints in both years.  
Figure 8: Mean growing season GPP modelled using Equation (4) with LOOCV-parameterization and Sentinel-2 data as input during the growing season in 2017 (top left) and 2018 (top right) at the Lompolojankka site. The black lines show 80% of the annual EC flux footprint climatologies. The background image is an aerial photograph recorded in 2018 by the National Land Survey of Finland.  
Figure 7: Time series of cumulative NEE for Abisko-Stordalen in 2017, 2018, and 2019 for the original EC measured NEE (EC orig), the TIMESAT smoothed NEE (EC smooth), the non-linear regression model of NEE with joint parameters (RS joint), and the non-linear regression model of NEE with site-specific parameters (RS site). There is nearly complete correspondence between the original and the spline-smoothed EC data.
## 1 Introduction
Specifically, this study aims to address the following questions:  
* How can we effectively integrate the high accuracy of behavior recognition models in specific tasks with the general knowledge and reasoning capabilities of MLLM? * How can we leverage existing data more effectively (e.g., through data augmentation, transfer learning) and potentially utilize smaller, more specialized datasets? * How can generative AI models be leveraged to provide reliable and explainable decisions for autism behavior recognition? To this end, the proposed MS-RRBR framework comprises four innovative stages: category candidate generation, visual question answering, fact verification, and inferential decision-making. It effectively integrates UniformV2 (for behavior recognition), InternVL (for multi-modal understanding), and Grounding Dino (for object detection), all coordinated by GPT-4o. This design achieves a high accuracy rate of 94.94% and demonstrates exceptional interpretability. This approach not only enhances the zero-shot visual recognition capabilities of MLLM through guided questioning but also ensures the reliability of recognition results via fact verification and interpretable reasoning. This study offers the following contributions:  
* We propose a novel Multi-Model Synergistic Restricted and Repetitive Behavior Recognition method (MS-RRBR), effectively combining the advantages of behavior recognition models and MLLM. Our method achieves excellent recognition accuracy without requiring fine-tuning of MLLM, demonstrating its practical significance in autism diagnosis and rehabilitation. * We construct the Autism Restricted and Repetitive Behavior Dataset (ARRBD), which encompasses 10 visually observable ASD-related behaviors. The dataset includes 780 video clips with a total duration of 4886.09 s, providing a solid data foundation for the training and evaluation of RRBs recognition systems. * We design an interpretable reasoning mechanism based on GPT-4o capable of generating detailed explanations for recognition decisions. This mechanism not only improves recognition accuracy but also provides clear insights into the system's decision-making process, enhancing its reliability and acceptance in human-computer interaction applications, both for professionals and non-experts. The remainder of this paper is organized as follows: Section 2 reviews the definitions of repetitive and stereotyped behaviors and related research on behavior recognition. Section 3 provides a detailed introduction to the proposed MS-RRBR framework and its components. Section 4 describes the construction of the ARRBD dataset and the model experiments. Section 5 presents a comprehensive analysis and discussion of the experimental results. Finally, Section 6 concludes the research.
## 3 Methodological Procedure
### Performance Metrics  
To assess the effectiveness and reliability of the proposed regression models, there are different quantities that serves to evaluate the performance of such algorithms.  
The coefficient of determination (\(r^{2}\)), defined as  
\[r^{2}=1-\frac{\sum_{i}(x-\hat{x}_{i})^{2}}{\sum_{i}(x-\hat{x}_{i})^{2}}, \tag{15}\]  
where \((x-\hat{x}_{i})^{2}\) denote the sum of the squares of the residuals, \(\bar{x}_{i}=\frac{1}{n}\sum_{i}x_{i}\) is the mean of the estimated values, and \((\sum_{i}x-\bar{x}_{i})^{2}\) corresponds to the total sum of squares. This metric measures the linear correlation between the actual values, \((x)\), and the estimated values, \((\hat{s})\).  
The RMSE is defined as  
\[\text{RMSE}=\sqrt{\frac{\sum_{i}(\hat{x}_{i}-x_{i})^{2}}{n}}. \tag{16}\]  
This metric is computed, like the coefficient of determination, using the residuals \((x-\hat{x})\). Its computation serves to discriminate how far from the mean the estimated values are. Finally, the MAPE is defined as  
\[\text{MAPE}=\frac{100\%}{n}\sum_{i=1}^{n}\frac{|x-\hat{x}|}{x}, \tag{17}\]  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **\# of Hidden Layers** & **\# of Hidden Neurons** & **\# of Hidden Weights** \\ \hline M1 & 1 & 16 & 12,577 \\ M2 & 2 & 32–16 & 25,665 \\ M3 & 3 & 64–32–16 & 52,865 \\ M4 & 4 & 128–64–32–16 & 111,361 \\ M5 & 5 & 256–128–64–32–16 & 244,737 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Architectural description of the Deep Neural Network (DNN) regression models used to assess the estimation of the HFF slope from time-frequency pixel maps.  
which is a performance metric that measures the accuracy of our estimations evaluating the residuals, \((x-\hat{x})\), divided by \((x)\) and summed over every estimated value on a certain number, \((N)\), of observations. These performance metrics provide support to evaluate the accuracy of each regression model from different points of view and clarify the results obtained.
# Combining Dilution of Precision and Kalman Filtering for UWB Positioning in a Narrow Space
## 4 Discussion
This was because the fusion algorithm combined the DOP numerical analysis model to improve the algorithm. Thus, the negative influence of the high DOP values in this scene was automatically suppressed by the fusion algorithm. However, due to the problem of ranging error, there were still some errors between the positioning trajectory obtained by this method and the real trajectory. In Scene B, the results obtained by WLS and the fusion positioning algorithm were basically the same and quite fitted the real trajectory. This was because the DOP in this scene was relatively small, and the whole space was transparent. There was less reflection and diffraction in the propagation of electromagnetic waves. Consequently, both algorithms had good positioning accuracy in this scene. These phenomena indicated that the algorithm proposed in this paper was not only suitable for the airtight narrow space, but also for general indoor environment. In general, although the spatial structure of Scene A and Scene B was different, the accuracy of the algorithm proposed in this paper had different degrees of improvement in both scenes, especially in the scene with the higher DOP values (scene A). It can be seen that the proposed positioning method improved the accuracy more in the narrower space.
## 2 Materials and Methods
### DPPH Radical-Scavenging Activity  
DPPH evaluation was performed following a previously reported method [22]. Briefly, a 0.25 mM solution of 2,2-diphenyl-1-picrylhydrazyl (DPPH) reagent was prepared using methanol as the solvent. Next, 0.5 mL of the DPPH solution were mixed with 1 mL of _Z. officinal_ extract at different concentrations, and resultant solutions were incubated for 30 min at 25 \({}^{\circ}\)C in the dark. Blanks were prepared for each concentration and DPPH absorbance was measured at 517 nm. The inhibition percentage was determined as shown in the following equation:  
\[\textit{Inhibition percentage}\ (\%)=\frac{\left(Abs_{blank}-Abs_{sample} \right)}{Abs_{blank}}*100\]  
The percentage of the radical-scavenging activity of the sample was plotted against its concentration to calculate IC\({}_{50}\), which corresponds to the amount of sample necessary to reach the 50% radical-scavenging activity. Each sample was analyzed in three independent assays.
# Analytical Modeling of the Maximum Power Point with Series Resistance
## 1 Introduction
Whereas the present work focuses on the derivation, range of validity, and theoretical limitations of the model from a formal perspective, in [10] the focus was on its experimental applicability. To summarize, this work presents a novel approach based on the use of Lambert's W function to obtain closed-form analytical expressions for the MPP that account for the effect of the series resistance.
# UAV-Derived Himalayan Topography: Hazard Assessments and Comparison with Global DEM Products
## 2 Study Sites and Methods
### Applications  
#### 2.4.1 Elevation above the River Channel  
OpenStreetMap buildings data [40] were downloaded for Tal village and Lower Pisang AOIs and were validated and updated using the respective UAV orthophotos (0.2 m resolution). River channels were manually digitised using the UAV orthophotos and automatically delineated for all other DEMs using GIS hydrological tools (DEM fill, flow direction, and thresholded flow accumulation). The elevation of the terrain relative to the adjacent UAV- and DEM-derived river channels were derived for each DEM and used to extract the mean elevation of each building above the channel.
## 2 Materials and Methods
### Evaluation Parameters  
By inspecting other similar studies in the recent past [16; 18; 19; 20; 22; 23], we concluded that the following parameters should be used to evaluate the standard of the proposed predictor model. The following four metrics were chosen: (i) sensitivity (SP), (ii) specificity (SN), (iii) accuracy (ACC), and (iv) the Mathews correlation coefficient (MCC), which are described below:  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{7}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{8}\]  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FN}+\text{TN}+ \text{FP}} \tag{9}\]  
\[\text{MCC}=\frac{\text{TP}\times\text{TN}-\text{FP}\times\text{FN}}{\sqrt{( \text{TP}+\text{FP})(\text{TP}+\text{FN})(\text{TN}+\text{FP})(\text{TN}+ \text{FN})}} \tag{10}\]  
TN, TP, FN, and FP represent true negative, true positive, false negative, and false positive values, respectively. The connotations of SN, SP, ACC, and MCC are more natural and simpler to apprehend, and they have been discussed and utilized in a number of studies in numerous biological fields in the recent past. Metric specificity can also be represented as the true negative rate (TNR). It calculates the ratio of negative samples to the model. Authentic predictions of the test data are called accuracy. The Matthews correlation coefficient (MCC) shows the model's output as a binary classifier [32]. MCC values exist within a range of \([-1,1]\), where 1 signifies a 100% true prediction, \(-1\) signifies that the prediction does not entirely correlate with the observation, and 0 implies any random predicted value.
## 3 Results
### Overall SOS/EOS Comparison among the Three Grassland Types  
For the entire study region, SOS occurred mainly in April and May at an average day of year (doy) of 111 and EOS appeared mainly in October and November (doy 307). Spatially, SOS occurred earlier in the northeast than in the southwest and southeast (Figure 4a). A strong spatial gradient of EOS was noted suggesting a delay from northeast towards southwest (Figure 4b). In terms of different grassland types, SOS and EOS in meadow steppe (doy 109 and 305) both occurred significantly earlier than in typical steppe (doy 111 and 307) and desert steppe (doy 114 and 312) for several days (Figure 4c,d). Meanwhile, SOS and EOS in typical steppe also occurred slightly, but significantly earlier than in desert steppe. This suggests a similar growing season length of vegetation among meadow steppe (196 days), typical steppe (196 days), and desert steppe (197 days). As to inter-annual fluctuations of SOS and EOS, our results revealed that the mean standard deviation (SD) of EOS (9.5 days) was smaller than that of SOS (15.5 days). The greatest SDs of SOS and EOS were both found in desert steppe (23.3 and 11.3 days) when compared with that in meadow steppe (11.5 and 9.2 days) and typical steppe (15.1 and 9.1 days).  
Figure 3: Linear regression analysis between GPP-based SOS and MODIS-based SOS (**a**) and between GPP-based EOS and MODIS-based EOS (**b**).
# RCS and Aero Surfaces Control Allocation Research on RLV's Re-Entry Phase
## 5 Conclusions  
During the unpowered re-entry phase, RLV undergoes a sharp change of altitude, overload, and dynamic pressure, and transforms from the initial low-energy re-entry state, through the dense atmosphere, to the low-speed glide, which led to the AS's control efficiency to rise, while in contrast, the RCS's control efficiency dropped. Therefore, in the hybrid control system of RCS and AS, this paper proposed a proportional allocation scheme, based on dynamic pressure, a daisy-chaining allocation scheme based on the control errors, and a multi-object based optimal allocation scheme to distribute the virtual control command to the multiple actuators. Then, after a stability analysis of the whole system, the simulation results showed that even though these three schemes all had good control performances, the multi-object based allocation scheme had an optimal overall performance of the control system, by taking the control precision, allocation accuracy, fuel consumption, and other objective optimization functions into account.  
Conceptualization, X.Z. and R.M.; methodology, X.Z.; software, X.Z.; validation, R.M., P.W. and J.C.; formal analysis, X.Z.; investigation, X.Z.; resources, R.M.; data curation, R.M.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z., R.M., P.W. and J.C.; visualization, X.Z. and R.M.; supervision, R.M.; project administration, R.M.; funding acquisition, R.M.  
This research was funded by The Fourth Batch of Manned Space Pre-research Projects (Grant No. 060201).  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results  
### Solar Simulator Startup and Lamp Spectrum  
The igniter uses a very high voltage to ignite the Xenon gas encased in the Xenon arc lamp of the solar simulator. To ensure the ignition of the Xenon gas in the lamp, the simulator runs at 20% of its rated current. The lamp's irradiance and the input power of the solar simulator is measured by the PLC, and stored in the SCADA. The measurement made for 180 s is shown in Figure 7.  
Figure 7: Xenon arc lamp power and irradiance distribution at cold start.  
A percentage difference in power of less than \(\pm\)1% was obtained while running at 20% of the simulator's rated current. The corresponding irradiance percentage difference is \(\pm\)1.5%. The light source (xenon arc lamp) used to simulate sunlight was compared with the sun's spectrum, and is presented in Figure 8.  
Figure 8 clearly shows how closely matched the xenon arc lamp is when compared to the sun's spectrum in the visible light region (400-700 \(\upmu\)m). This makes the xenon arc lamp suitable for use as a light source for solar simulators [21]. Studies by [22,23] demonstrate the application of the solar simulator in the indoor characterisation of solar cells.
## 6 Conclusions  
In this paper, an advanced TSMC speed controller applied to the DITC strategy for SRMs was proposed to enhance the dynamic performance, reduce the torque ripple, and improve the anti-disturbance. In the research, an improved reaching law combined with TSMC was designed to accelerate the convergence speed and suppress chattering  
Figure 10: Experimental results under load torque change. (**a**) PI, (**b**) SMC, and (**c**) proposed TSMC.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Condition** & **Parameters** & **PI** & **SMC** & **TSMC** \\ \hline \multirow{2}{*}{Constant load torque} & Startup time & 0.18 s & 0.13 s & 0.09 s \\  & Torque ripple range & 0.4 Nm & 0.36 Nm & 0.3 Nm \\ \hline \multirow{2}{*}{Speed change} & Startup time & 0.2 s & 0.16 s & 0.12 s \\  & Response time & 0.15 s & 0.12 s & 0.1 s \\ \hline \multirow{2}{*}{Load torque disturbance} & Response time & 0.17 s & 0.15 s & 0.11 s \\  & Speed overshoot & 363 r/min & 269 r/min & 140 r/min \\ \multicolumn{2}{c}{} & Torque ripple range & 0.6 Nm & 0.47 Nm & 0.37 Nm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of three controllers.  
simultaneously. The load disturbances caused by the external environment can be further suppressed. Furthermore, the HWOA was employed to adjust the tunable parameters for better speed tracking and torque ripple minimization. The superiority of the proposed TSMC in the DITC scheme has been verified under different conditions according to the experimental results. Take the working condition under 3000 r/min and 0.3 Nm as an example, the torque ripple range of the proposed scheme can be reduced by 25% and 17% compared with PI and SMC. Meanwhile, the transient response time can be decreased by 50% and 31%, respectively. In summary, the proposed scheme performs well in speed tracking, dynamic response, torque ripple reduction, and robustness.  
In future work, other kinds of control techniques employed in speed controllers, such as active disturbance rejection disturbance, will be investigated and compared. Moreover, real-time optimization of adjustable parameters under different application conditions will be considered for better comprehensive performance.  
This research received no external funding.  
No applicable.  
Not applicable.  
The authors declare no conflict of interest.