esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Dataset Collection  
Considering the bedridden elderly were weak in health and not suitable for collecting the diverse postures, a total of 20 volunteers (including 15 males and five females) participated in the first-stage experiment for dataset collection and initial training. The volunteers were requested to perform the demanded lying postures, which were supervised by the nursing officer. On-site RGB-D video recording of a limited number of elderly patients was then conducted for cross-testing and classifier refinement.  
Since the elderly patients were mobility-compromised and suffered from muscle atrophy, the repositioning for them was realized by placing pillows on specified locations which are very different from those defined in fetus/log/yearner positions in [21; 23]. To meet practical situations, we made some arrangements in collecting the dataset which were considered as the changing factors and are listed in Table 2. As a result, 162 samples were recorded (54 samples for each posture with blanket covering) for each volunteer subject. Figure 4 illustrates some captured samples for left-side posture in the dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Kind of Body Posture**} & \multicolumn{2}{c}{**Right-Side, Left-Side, or Supine (3 Cases): Achieved with/without the Pillow Under the Back**} \\ \hline \multirow{4}{*}{Variations of each body posture} & 1. & For left- or right-side posture: low, medium, or high \\  & & level of body sideward orientation (controlled by \\  & & pillow number and location) (3 cases) \\  & 2. & For supine posture: slightly-left, right, or -centered \\  & displacement of the body (3 cases) \\ Inclined angle of the bedhead & & 0-degree, 20-degree, and 40-degree (3 cases) \\ Head orientations & & Left-side, right-side, or supine (3 cases) \\ Bed-side support & & Pillow support: upper or lower position (2 cases) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Changing factors in collecting the posture dataset.  
We also collected clinic data of two bedridden elderly patients, including one man and one woman. For the man, his postures include left, right, and supine with 431, 532, and 562 samples, respectively. However, for the woman, only left and right lying with 504 and 552 samples were collected, since the supine posture was not clinically allowed for her.  
Both of the experiments for volunteers and clinic patients were conducted by the Institutional Review Board (IRB) of Dittmanson Medical Foundation Chiayi Christian Hospital, Taiwan.
## 2 Analysis Methods
### Shock Reflection in the Dual Solution Domain
#### 2.3.2 Shock Reflection in the Dual Solution Domain  
When the freestream velocity exceeds a critical Mach number (about 2.202), it is theoretically possible for both regular reflection and Mach reflections to occur at the same Mach number [25; 26; 27]. This region is known as the dual solution domain, which lies between the von Neumann and detachment criteria. Figure 4 shows the shock reflection region as a function of the wedge angle in the range of the freestream Mach number. At Mach 3.45, the dual solution was obtained when the wedge had an angle of 20 to 24\({}^{\circ}\).  
Figure 1: Computational grid for energy deposition in front of the sphere.  
Figure 2: Comparison between experiment [8] and the simulation results.  
Figure 5 shows the oblique shock and the following reflected shock for wedge angles of 23\({}^{\circ}\) and 24\({}^{\circ}\). When the wedge angles are between 21\({}^{\circ}\) and 23\({}^{\circ}\), the flow structure and the characteristics of the reflected shock wave are the same, but as the wedge angle increases, the angle of the oblique shock wave also increases and reflection occurs upstream. The results suggest that the two symmetric oblique shocks emanating from the opposite wedge generate regular reflection when the wedge angle  
\begin{table}
\begin{tabular}{c c} \hline Mach Number & 3.45 \\ Static temperature [K] & 85.8 \\ Static pressure [Pa] & \(1.97\times 10^{6}\) \\ Wedge angle [Deg] & 22, 23, 24 \\ Wedge length [mm] & 25.4 \\ Distance between the wedges [mm] & 25.4/30.2 \\ \hline \end{tabular}
\end{table}
Table 2: Flow conditions for the shock reflection study.  
Figure 4: Domains of possible shock reflection configurations.  
Figure 3: Computational grid for the shock reflection study.  
is less than 23\({}^{\circ}\). However, Mach reflection is observed when the wedge angle is larger than 23\({}^{\circ}\). This is consistent with the observation in previous theoretical studies that Mach reflections are favorable in the vicinity of the detachment criteria.
# Imputation Methods for scRNA Sequencing Data
## 2 Imputation Methods
### Deep Learning Methods  
This type of approach identifies the potential spatial representation of cells based on the deep learning method (capturing nonlinear relationships) and then reconstructs the observed expression matrix from the estimated potential space. The availability links, programming language, whether a package is available, year of presentation, operative system, distribution compatibility and reference of the representative methods based on deep learning see Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Availability**} & \multirow{2}{*}{**Code**} & \multirow{2}{*}{**PKg**} & \multicolumn{2}{c}{**Operative System**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Ref**} \\  & & & & & & \\  & & & & & & \\ \hline \multirow{4}{*}{2 DImpute} & https: & & & & & \\  & \multicolumn{2}{c}{} & & & & \\  & \multicol1. DCA is an imputation method based on automatic encoder, it uses a negative binomial noise model with or without zero expansion. By considering the count distribution, super-dispersion, and sparsity of data, it can capture nonlinear gene-gene dependence. DCA learns gene-specific distribution parameters by minimizing the reconstruction error in an unsupervised manner, rather than reconstructing the input data itself, for more detail, please refer to Ref. [32]. We give a simple framework as shown in Figure 11a. 2. LATE uses the initial values of a randomly generated parameter to train an autoencoder on highly sparse scRNA-seq data, and the TRANSLATE method builds on LATE, further using the reference gene expression data set to provide LATE with an initial set of parameter estimates. Hence, the user can train the autoencoder on a reference gene expression dataset and then use the weights and biases as initial values for imputing the dataset of interest. These algorithms are highly scalable in graphics processing units and can handle more than 1 million cells in a few hours [60]. 3. scVI is a fully probabilistic method for the standardization and analysis of scRNA-seq data. The method is based on a hierarchical Bayesian model with conditional distribution specified by a deep neural network. The transcriptome of each cell is encoded into low-dimensional potential vectors of normal random variables by nonlinear transformation. This potential representation is decoded by another nonlinear transformation to generate an a posteriori estimate of the distributional parameters of each gene in each cell, for more detail, please refer to Ref. [84]. We give a simple framework as shown in Figure 11b. Both the deep learning method and the low-rank matrix representation method use the idea of data reconstruction.
# Three-Dimensional Path-Following with Articulated 6 DoF Robot and ToF Sensors
## 4 Discussion
Secondly, during calculations of tool rotation, rotation reduction is applied so the tool cannot be rotated too much. Without reduction, the tool may be extensively rotated in every move when the path is detected in different detection points in two subsequent steps. Reduction decreases this effect. Vector reduction values (\(a\) and \(b\) in (17)) are adjustable, and their values should therefore be the result of a compromise. Stronger reduction will limit the ability to follow path bends with small arcs but, on the other hand, will reduce the \"waving\" movement of the tool around the path. This will result in an overall smoother path-following. Additionally, path width (i.e., diameter), detection tool dimensions, sensor spacing and characteristics (ex. cone-shaped detection field), etc., also influence the ability to follow sharp path bends. During the tests, the smallest arc of bend was around 10 cm. During the experiments, it appeared that sometimes, especially when the tool was moving away from the path, the path was lost and the algorithm was not able to locate it. When the tool was moving towards the path, such a problem almost never occurred. To prevent losing the path, more sensors should be used, and they should be placed more densely. Another solution may be to return to the last successful path detection position if the path is not detected for a given number of steps, and then to restart the moves in a slightly modified direction. The ToF sensors that were used in the detection tool, as well as Bluetooth communication, proved to be very reliable. In practice, the only malfunctions were caused by the low energy level of the battery powering the tool. In such situations, communication was lost and the path-following procedure was aborted as the battery had to be replaced.
## 3 Methodology
### Rainfall Erosivity Factor-R  
R-factor plays a main role in RUSLE. This factor is a major cause of soil erosion and represents the erosivity of rainfall over time. It measures the amount of soil erosion in response to precipitation at a specific place. For soil erosion evaluation, the R-factor is crucial when land use and climate scenarios are changing [59; 60; 61]. It determines the effect of rainfall and runoff rate [62; 63].  
In order, to compute the R-factor, the Annual and monthly rainfall data for the Jhelum Basin watershed were used. After the data were imported into a GIS system, the yearly rainfall data from the netCDF raster were converted to point data, and then the Kriging interpolation method was applied to generate the Precipitation map. The monthly R-factor was computed by Equation (2), developed by [PERSON] and [PERSON] [39] and revised by [PERSON] [64].  
\[\mathrm{R}=\sum\
olimits_{i=1}^{12}1.735\times 10\times\left\{1.5\times\log_{10} \left\{\frac{P_{i}^{2}}{P}\right\}-0.08188\right\} \tag{2}\]  
where, R (MJ\(\cdot\)mm\(\cdot\)ha \({}^{-1}\cdot\)h\({}^{-1}\cdot\)y \({}^{-1}\)) shows the rainfall erosivity, \(P_{i}\) for the rainfall (mm) occurring each month, and P stands for the rainfall (mm) occurring annually. Mean annual rainfall and distribution of rainfall erosivity (R-factor) are shown in Figure 3a,b, respectively. For the study area, the value of R ranges between 544.8 and 766.8, with a minimum value of 267.26 and a maximum of 2043.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sr. No.** & **Type of Data** & **Description** & **Source** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{DEM} & SRTM DEM 1 Arc-Second Global & USGS Earth Resources Observation and \\  & & with spatial resolution of 30 m & Science [56] \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{Soil Data} & Digital soil Map of the World by FAO & Food and Agriculture Organization of the \\  & & spatial resolution of \(5\times 5^{\prime}\) & United Nations [57] \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{Rainfall Data} & CRU TS Monthly High-Resolution Gridded & \multirow{2}{*}{Climatic Research Unit (University of East \\  & & Multivariate Climate Dataset & \multirow{2}{*}{Anglia) and Met Office [58]} \\  & & Spatial resolution 0.5\({}^{\circ}\) & \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{Satellite Data} & Landsat 8-9 OLI/TIRS Collection 2 & USGS Earth Resources Observation and \\  & & with spatial resolution of 30 m & Science [56] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sources of remotely sensed data.  
Figure 3: (**a**) Map of mean annual rainfall; (**b**) distribution of rainfall erosivity (R-factor).
## 2 Experimental Investigation of Tape Spring Hinges  
### Moment-Rotation Profile of TS Hinges  
As mentioned previously, the TS hinge is a flexible hinge having nonlinear hysteresis behavior according to folding and unfolding, which stores strain energy during folding to release it to unfold the appendages of satellites. A conventional moment-rotation profile of TS hinges is plotted in Figure 2. Typically, single TS showed a different moment-rotation behavior according to equal-sense bending and opposite-sense bending (see Figure 2a). However, single or multi-layer TS hinges showed the same moment-rotation behavior according to the positive and negative torque. In this work, we limit our attention to the MLTS.  
The moment-rotation of TS is linearly elastic for small rotations, but the moment is almost constant for large rotations for positive torque. At the end of the linear elastic range, this TS suddenly buckles due to the snap-through buckling, thus making the stiffness dramatically lowered (A\(\rightarrow\)B). Accordingly, the moment path is changed from O \(\rightarrow\) A \(\rightarrow\) B \(\rightarrow\) C while it is folded to store the strain energy. Then, it is released to go back to O state through a different path of C \(\rightarrow\) B \(\rightarrow\) D \(\rightarrow\) O by dissipating some of the stored energy surrounded by the area connecting A, B, and D. The moment-rotation profile of TS hinges can be tailored according to the number of tape layers (n), height (H), length (L), width (W) of tape springs, and radius (R), thickness (t) and subtended angle (\(\alpha\)) of the tape springs as shown in Figure 1c.
## 3 Software and Image Processing
### Multivariate Statistical Analyses  
After image preprocessing, multivariate statistical analysis is needed to extract useful information and easily show the relationship between hyperspectral data and the feature of the sample under evaluation. Multivariate analyses can be divided into two main groups: unsupervised and supervised methods.
## 3 Materials and Methods
### Methods
#### 3.2.2 Generation of Personalized Medical Advice
After training and fine-tuning with mental and psychological disorder-related data, the model outputs a relevance score ranging from 0 (unrelated) to 1 (closely related), effectively measuring the connection between Text_Question and Text_Answer. This architecture is designed to deeply understand text content and capture complex relationships. 4. Generate medical advice  
We propose an innovative framework for generating personalized medical advice, integrating natural language processing technology with expert medical knowledge. Initially, patient information collected via the M.I.N.I tool is used to form a preliminary subset of candidate advice using a keyword-weight-matching algorithm. This subset is then processed through the semantic-matching model, which scores the advice based on relevance, selecting the top M pieces. The refinement process includes data cleaning and extraction, filtering out various treatment methods, medications, and suggestions, and information filtering and analysis, identifying commonly and uniquely occurring treatment plans, removing duplicates, and retaining unique, relevant advice. Finally, new templates are designed based on the filtered information to generate personalized medical advice containing core treatment plans. The detailed process of generating medical advice is illustrated in Figure 4:0 \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Function** & **Description** \\ \hline Input & Text Preprocessing & Merges text and uses a tokenizer to convert it into the three standard inputs for RoBERTa. \\ Model & Semantic Extraction & Uses the pre-trained RoBERTa model to extract semantic information from text, \\  & & outputting token embedding vectors. \\  & & Extracts the CLS token vector from \\ Fully Connected & Feature Transformation & RoBERTa’s output and performs a nonlinear transformation using Equation (1). \\  & & Uses a fully connected layer with the \\ Output & Relevance Scoring & activation function in Equation (2) to map to \\  & & a relevance score between 0 and 1. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model structure. Figure 8: Medical advice generation process flowchart. Letters a-e represent different keywords. Compared to advice generated solely by text generation technology, this method ensures data reliability and effectiveness, as all medical advice originates from human medical experts and undergoes professional review. It also guarantees the quality of the final medical advice by extracting key information from multiple sources, providing patients with more trustworthy and personalized advice.
## 4 Testing of the Solution
### Testing Activities during the SWARMs Demonstrator
The capabilities of the piece of equipment used to run the semantic middleware solution were as the equipment used to check vendor compatibility in the first collection of tests. Once the scenario was deployed, measurements were taken to know whether information could be retrieved and the frequency and pace of that information retrieval. The objectives with these testing activities were (a) ensuringthat information could be obtained by the system and (b) that it was collected at an acceptable rate. There were some challenges to be faced with these operations: to begin with, the transmission medium for the acoustic waves made not possible guaranteeing a flawless transmission of data all the time. Furthermore, during the testing activities the reliability of that medium of transmission would change frequently and become better or worse depending on the water temperature, how strong the wind blew during a certain moment of the day, etc. A relevant amount of data was retrieved during the tests performed with the equipment that was deployed. Considering the objectives of the tests, 357 relevant pieces of information were retrieved where information about altitude, depth, speed or remaining battery data were obtained. The time difference among each of the pieces of information retrieved has been displayed in Figure 13.  
As it can be seen, the results range from 2 ms (which is the minimum value) to 86 ms (the maximum value). The tests measurements shown here were taken with good weather and water conditions. When they were worse, suboptimal periods of time where data would take much longer to be received (several seconds) happened, but just during rare periods of time (less than 1% in the measurements taken), so they were deemed as not representative of the overall performance of the system. Median value was measured at 7 ms, whereas the obtained average value was 7.882 ms. There are several outlier values that distort the average value obtained and add certain difference with the median value (which can be considered as a more realistic way to assess the performance of the system, as it is not significantly accepted by outlying values), as the median value represents the 88.81% of the average one (and thus is somewhat significantly lower than the average). This can be due to the unpredictability of the medium of transmission of the acoustic waves, since it is the only part of the system with changing parameters. Also, it can be expected that should any of the modems be placed nearby submarine water currents, there could be a significant impact on the placement of the acoustic modems (for example, they could be occasionally tilted by the water current, which would affect how the acoustic waves are transmitted). Nevertheless, the measurements done prove that the semantic middleware solution makes possible retrieving information from the undersea environment without having a negative impact on the time required to transmit and receive data at the communications network. Figure 12: Deployment of elements during testing activities.
## 4 Discussion
Future work could explore the integration of the real-time monitoring of ambient temperature fluctuations or the adaptive control of the working cycles of the cooling system. Additionally, incorporating more complex models that capture spatial temperature variations within the sample and the calorimeter could provide even greater precision in calorimetric measurements. However, this would simultaneously increase the number of model parameters, complicate its structure, and require additional sensors for simultaneous measurements. Such modifications would also increase the computational complexity and extend the time required to find a solution through global optimization. In this article, we present a model of a non-adiabatic calorimeter dedicated to measuring the SAR of superparamagnetic nanoparticles. However, it should be noted that the measurement uncertainty of SAR depends not only on effects related to the non-adiabatic nature of the measurement system in which the experiment is conducted but also on various other factors. According to some authors, the cumulative uncertainty can reach up to 14% [47]. This uncertainty is largely attributed to the heat loss generated by nanoparticles in the non-adiabatic calorimeter. Additionally, significant components of this error stem from the positioning of the fiber optic thermometer probe inside the sample, due to the inhomogeneity of the magnetic field inside the coil, particularly within the volume of the tested sample. This affects the non-uniform distribution of heating power within the sample. Furthermore, the direct calculation of SAR using Formula (4) is as follows:  
\[SAR=\frac{P}{m_{MNP}}=\frac{P}{\phi_{MNP}V_{MNP}} \tag{4}\]  
where \(P\) is the heat power dissipated by the magnetic nanoparticles, \(m_{MNP}\) is the mass of magnetic nanoparticles, and \(\phi_{MNP}\) and \(V_{MNP}\) are the concentration and volume of the magnetic nanoparticle solution, respectively. Additional errors arise from inaccuracies in the reported concentration values or the measured sample volume. It is also important to consider that during long measurement durations, solvent evaporation may occur due to the insufficient sealing of the openings for the fiber optic probe or condensation on the walls of the sample container, introducing an additional source of error, which also affects the long-term calibration process. Improving the measurement uncertainty of SAR is a multifactorial and complex task. The RC lumped-element model of a non-adiabatic calorimeter presented in this article maintains practical complexity while identifying the error sources associated with heat transfer from the transmitting coil. As a result, in the calibrated range of 20-200 mW, the measurement error related to the non-adiabatic nature of the calorimetric system was reduced to 2.5%.