esa-sceva/satcom-chunk-collection huggingface dataset:
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 6 Sleep Pattern Extraction and Sleep Quality Analysis
### Daily Sleep Profile and Sleep Quality Score
#### 6.4.2 Sleep Quality Score  
Additionally, we aim to define a sleep quality score based on the daily sleep profiles, capable of providing a quick overview of a patient's sleep status to the caregiver. According to the recommended sleep quality guideline for elderly adults in [16], we propose a scheme that combines night sleep and nap quality features. We adjusted the original quality score scale of the sleep efficiency measurement, because our sensor only detects physical off-bed moments rather than awakenings in a neurological sense or micro movement way, thus it is not rare to see a sleep efficiency score above 80%, and sleep durations longer than 7 h.  
Referring to the National Sleep Foundation's recommendations [15; 16], Table 3 illustrates the metrics, scales, and scoring schemes we applied. The overall score range is [0, 16]. A higher sleep score indicates a better sleep quality. Any measurement beyond the listed 'value ranges' does not contribute to the sleep score. In practice, we found that night sleep quality often has a greater impact to the overall sleep quality than daily maps. Therefore, the overall range of the night sleep score is [0, 12], while the range of the daily nap score is [0, 4].  
Figure 9 shows daily sleep quality scores derived using this scheme, from the same user as shown in Figure 3. We can see a subtle, but consistent quality drop starting in the middle of May 2019, followed by poor sleep quality scores from 27 May 2019. From the daily profile plot (Figure 6) an increase in maps can be observed from the middle of May, followed by a sleep-onset absence starting on the 27 th of May (which was due to a hospitalization of the patient). Additionally, a quality drop can be observed from March 2020, which is due to increased maps during the day, and reduced sleep efficiency at night. Interestingly, this behavior could be observed among multiple patients across the cohort and was caused by the COVID-19 lockdown, which started on March 8 th and ended in May 2020 in Northern Italy. An increased night sleep duration was observed for 6 users, among which 5 were observed to have reduced sleep efficiency. Additionally, increased maps were observed for 3 users. Overall, we observed a sleep score drop for 8 patients.  
Figure 8: Projection of the daily sleep profile into the t-SNE space for a single patient.  
Figure 7: Extracted daily sleep patterns from daily sleep profiles, indicated by the feature values of their cluster centers.
## 2 Materials and Methods  
### Background on Material Used in Experimentation  
A commercial-grade sheet of polylactic acid was graciously donated by Ex-Tech Plastics. The PLA sheets were approximately 25.4 cm \(\times\) 20.3 cm with a thickness of 0.0304 cm. The material was identified and characterized as received using optical microscopy (Olympus BX40 microscope, Tokyo, Japan), X-ray diffraction (XRD) (Rigaku Miniflex 600 with D/teX Ultra silicon strip detector, Tokyo, Japan), Fourier transform infrared spectroscopy (FTIR) (Nicolet iS50 FTIR spectrometer, Thermo Scientific, Waltham, MA, USA), differential scanning calorimetry (DSC) (DSC Q100, TA Instruments, New Castle, DE, USA), and gel permeation chromatography (GPC)(VE 2001 GPCMax, Viscotek, Houston, TX, USA). The L and D composition of the amorphous PLA was determined to be racemic after analysis of optical microscope images. We used XRD to confirm that we had highly amorphous PLA, as shown in Figure 1 by the two wide peaks and no sharp peak corresponding to a crystalline phase. We explain these results in detail in Section 3.5. We subsequently performed GPC and obtained a number average molar mass (M\({}_{\text{n}}\)) of 119,500 Da, weight average molar mass (M\({}_{\text{w}}\)) of 196,400 Da, and polydispersity index of 1.646 for the unirradiated PLA samples. As shown in Figure 1, DSC results demonstrated that the material is not a stereocomplex, but rather a copolymer of P(DL)LA with equal proportions of L and D subunits. The double melting peak due to stereocomplexation would occur at a higher temperature than we observed, indicating that the double peak in our DSC results can be attributed to perfected spherulites. We expand on this analysis in Section 3.3. PLA without plasticizers and additives is used, as confirmed by FTIR spectral analysis on unirradiated PLA, shown in Figure 1 and analyzed in Section 3.2.
# Tibetan Judicial Event Extraction Based on Deep Word Representation and Hybrid Neural Networks
## 3 Methodology
### Deep Word Representation  
The CINO model is employed for Tibetan deep word representation learning. For a given Tibetan text sequence \(X=\{x_{1},x_{2},\cdots,x_{n}\}\) consisting of \(n\) syllables, special tags \"\(
\)\" and \"\(
\)\" are added to the start and end of the sequence, respectively, and processed into the standard input format, i.e., \(X=\{
,x_{1},x_{2},\cdots,x_{n},
\}\). In the CINO encoding layer, the input text sequence passes through the model's built-in word vector matrix, position vector matrix, and multi-layer Transformer structure. By leveraging the multi-head self-attention mechanism, the semantic association among words in the sentence is fully learned, transforming the static embedding of each word into a low-dimensional dynamic embedding. The procedure is illustrated in Figure 3.  
In Figure 3, \(E_{1},\ E_{2},\cdots,E_{n}\) and \(P_{1},\ P_{2},\cdots,P_{n}\) are the word vector representation and position vector representation of CINO, respectively. The dynamic context semantic rep  
Figure 3: Deep word representation process.  
Figure 2: Model architecture of TJEE.  
resentation \(V\) of the Tibetan text finally obtained is shown in Equation (1), which serves as the initial input for the BiLSTM and CNN hybrid neural networks.  
\[\begin{array}{ll}V&=CINO(x_{1},x_{2},\cdots,x_{n})\\ &=(e_{1},e_{2},\cdots,e_{n})\end{array} \tag{1}\]  
where \(V\) represents the deep vectorized representation of the input sequence, \(x_{i}\) is the \(i\)-th Tibetan syllable in the input sequence, and \(e_{i}\) denotes the vector representation corresponding to the \(i\)-th syllable.
## 4 Experiments and Results
### Experimental Environment and Implementation Details  
This study employed Python 3.9 with PyTorch 1.11, running with Nvidia GeForce RTX 4090 GPU acceleration. Table 1 shows the hardware and implementation details.  
Figure 4: The CrowdHuman dataset.  
Figure 5: The WiderPerson dataset.  
The input images underwent random preprocessing through operations including color space distortion, image flipping, and scaling, and we did not use any pre-training techniques.
## 3 Experimental Procedures
### Experimental Setup  
A coaxial wire deposition head (CoaxPrinter, Precitec GmbH & Co. KG, Gaggenau, Germany) was the core of the experimental setup. This enabled coaxial feeding of the wire inside an annular laser beam profile, leading to a direction-independent LMD process. For the generation of the laser radiation with a wavelength of 1030 nm, a 4 kW disk laser (TruDisk 4001, TRUMPF GmbH & Co. KG, Ditzingen, Germany) operating in continuous wave mode was employed. The radiation was transmitted to the wire deposition head by a fiber optic cable with a core diameter of 600 \(\upmu\)m.  
Through an industrial wire feeding system from the manufacturer DINSE GmbH (Hamburg, Germany), a defined feed rate of the wire was ensured. The wire feeding was realized through a front drive (FD100 LS) and a slave drive (WD 300 FD), which operated in push-pull mode, and were actuated by a control unit (FDE-PN 100 L). As the wire was delivered in a 15 kg coil, its curvature was compensated for by a precisely adjusted two-plane wire straightening unit. A six-axis industrial robot (KR 60 HA, KUKA AG, Augsburg, Germany) with a maximum payload of 60 kg was used for an accurate positioning of the wire deposition head. The robot was actuated by a robotic control system (KR C4, KUKA AG, Augsburg, Germany). To coaxially measure the melt pool temperature during the process, a pyrometer (METIS M322, Sensorthem GmbH, Steinbach, Germany) in one-color mode (sensitive in the range of 1.45-1.65 \(\upmu\)m) with a temperature measurement range of 600-2300 \({}^{\circ}\)C was mounted to the wire deposition head. The pyrometer was calibrated to the surface temperature of the melt pool following the procedure described by [PERSON] et al. [57].  
In wire-based LMD, it is still common to use a lateral shielding gas supply through a thin tube for many materials and applications [58; 59]. This also applies to previous works on the employed wire deposition head [8; 27; 37]. Therefore, this configuration was additionally considered as a reference. In the following, the configurations will be referred to as lateral nozzle and optimized nozzle. The respective experimental setups are depicted in Figure 5. In the lateral configuration, the shielding gas was supplied via a copper tube with an inner diameter of 4 mm (see Figure 5a). The system equipped with the optimized nozzle is shown in Figure 5b. To attach the assembly to the wire deposition head, the existing mounting ring for accessory parts was used.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Element** & **Cr** & **Ni** & **Mo** & **Mn** & **Si** & **C** & **P** & **S** & **Fe** \\ \hline Wire & 18.2 & 12.1 & 2.5 & 1.8 & 0.88 & \textless{}0.01 & 0.024 & 0.010 & Bal. \\ Substrate & 17.0–19.0 & 8.0–11.0 & – & \textless{}2.0 & \textless{}1.0 & \textless{}0.07 & \textless{}0.035 & \textless{}0.03 & Bal. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of the wire and the substrate material (wt%).
## 4 Experiments and Results
### Experimental Setup  
Our deep convolutional neural network model was implemented using the PyTorch library in Google Collaboratory (collab), which is well-suited to Deep Learning frameworks, and running it on a high-performance computing cluster with NVIDIA GPUs. The dataset was randomly split into training, validation, and testing sets with a ratio of 80:10:10, respectively. Table 1 shows the basic characteristics of the datasets used for comparison purposes. The Dual Attention Unit (DU), Residual Residing Modules (RRMs), and Selective Kernel Feature Fusion (SKF) are the same as in MIRNet [72].
## 3 Results
### NBP Simulation Results and Validation
#### 3.4.2 Results of Indirect Validation of NBP  
The comparison of the NBP and NPP results for all vegetation nodes in 2018 is shown in Figure 12. The black triangles and the left \(y\)-axis in the figure indicate the NPP results of vegetation nodes (extracted from MODIS), and the red squares and the right \(y\)-axis indicate the NBP results of vegetation nodes (simulated from the Biome-BGC model). The results of NBP and NPP for all vegetation nodes showed a significant correlation (\(r=0.75\), \(p<0.001\); Figure 12). In addition, it can be seen from Figure 12 that the trend of NBP at different vegetation nodes simulated by the Biome-BGC model was closer to that of NPP products from MODIS at different vegetation nodes. This indicates that the Biome-BGC model can better simulate the spatial variation pattern of productivity of vegetation nodes in the YRB.  
Figure 11: The spatial distribution map of the NBP results of the ecological nodes in the Yellow River Basin in 2018.
## 4 Discussion
### Exercise Prescription Variables  
The developed App can process exercise data from IMU and provides exercise prescription variables: exercise items, repetitions, number of sets, training capacity, work, average power, explosive power, and rest time between sets. Although a sequential minimization algorithm and a stochastic forest method to classify activities in the USC-HAD was proposed in [6], neither IMU-based device nor exercise prescription variables were proposed. Several different supervised machine learning models were tested to classify four fitness sports in [7]; however, no exercise prescription variables were considered. Other papers such as [10; 12] provided either no exercise prescription variables or only repetitions. As stated in [22], good exercise prescription variables of exercise training can provide correct feedback to the exercise operator and motivate him/her to become better. The developed App not only can provide real-time exercise prescription variables but also can offer the exercise history output information (exercise prescription variables) from the exercise state analysis and exercise event handling program. These variables can be used as a reference for individual's long-term training progress and transformed into a momentum of keeping exercise habit. The outcomes of exercise information from the designed App can be further shared to a social media group to receive social support from friends and relatives. The recorded avatar movement can be replayed for correcting any wrong-doing exercise movement. The heart rate module in the presented wearable device can also be used for fat burning heart rate calculation. Working together with InBody data [25] (including weight, muscle mass, body fat percentage, protein, bone mass, basal metabolic rate, visceral fat, and total body water content), the designed system can be used as a basis for exercise performance evaluation and provides recommendations for exercise and fitness goals. This system can be even applied in the future to medical rehabilitation or sports training where correctness of movement is of importance.
# Nanotechnology, Nanomedicine, and the Kidney
## 5 Nanotechnology for Renal Therapy
### Drug Delivery
#### 5.1.1 Liposomes  
Liposomes consist of single or multiple phospholipid bilayers. They can be generated artificially using different methods and range in size from 20 to 10,000 nm [54]. The particles are actively taken up by macrophages or can passively be incorporated into a cell's membrane. Liposome particles can be loaded with bioactive molecules and are generally utilized to deliver drug molecules.  
The modification of the particles allows control over vesicle distribution and the in vivo half-life. Using larger particles can help exclude the liposomes from small capillary vessels and restrict the distribution to larger blood vessels. The addition of polyethylene-glycol molecules to the lipid surface can 'hide' liposomes from macrophages and thereby increase the half-life of the particle and allow drug release over a greater period of time. The modification of liposomes with peptides holding binding domains or antibody fragments can be used to promote interaction with specific cell receptors or cell types. In addition to drugs or small molecules, liposomes can be used to deliver nucleotides and are, thus, a tool in gene therapy or gene editing strategies.  
The application of liposomes in kidney-related diseases has been well described.  
Liposomes have been used as carriers in gene therapy approaches to rescue experimental congenital diseases, such as Alport's syndrome, autosomal polycystic kidney disease, or acute kidney injuries such as ureteral obstruction [55].  
[PERSON] et al., have demonstrated that liposomal encapsulation of prednisolone can be used for the targeted delivery of glucocorticoids to the kidney. In an experimental model for renal ischemia-reperfusion injury, they showed that liposome-encapsulated prednisolone accumulated in the inflamed kidney and increased the presence of anti-inflammatory macrophages, suggesting a reduction in proinflammatory macrophages [56]. A Mannose-PEG-DSPE (Polyethylene glycol-1, 2-Disearoyl-sn-glycero-3-phosphoethanolamine)-modified liposome was developed as a vehicle for the targeted delivery to renal glomerular cells via the Glucose Transporter (GLUT). In diabetic nephropathy, GLUT is highly expressed in glomerular mesangial cells and renal vascular smooth muscle cells. The study by [PERSON] and colleagues demonstrated that mannose-PEG-DSPE modified liposomes can be used as a long-term vehicle for the targeted delivery in a rat model for diabetic nephropathy [57].  
The low affinity of liposomes to the kidney is also used to abate the nephrotoxicity of certain drugs. For example, the liposome-encapsulated antifungal drug Amphotericin B (liposomal AmB) has a demonstrated reduced nephrotoxicity [58]. Similar approaches have been developed for the antibiotic vancomycin [59], the cytostatic drug cisplatin [60], and other plain-based anticancer drugs [61].
## 3 Materials and Methods
### Softmax Temperature Annealing
As the temperature approaches 0, only one operator with the highest alpha value converges to a beta value close to 1, while the rest approach values close to 0. When the temperature is 10, as shown in Figure 3, multiple operators have similar beta values, meaning that their rankings remain the same, but their evaluations and contributions become similar. As the temperature increases, all the beta values of the operators become similar. In this case, selecting a single operator that dominates the layer or edge becomes meaningless. Since all operators contribute almost equally if the architecture is constructed by selecting only one operator, the resulting architecture will be significantly different from the architecture used during the search. Suppose the temperature is high, causing a large difference in alpha values while the difference in beta values becomes small. In that case, operators with low alpha values receive higher evaluations than their actual contributions. In other words, operators with low alpha values receive an advantage, while operators with high alpha values receive a disadvantage. Such changes provide the possibility for a reversal in subsequent epochs for operators that initially showed low performance. Conversely, if the temperature is low, causing a small difference in alpha values while the difference in beta values becomes large, operators with high alpha values receive higher evaluations, granting them an advantage, while operators with low alpha values receive lower evaluations, resulting in a disadvantage. In this situation, compared to when the temperature is 1, the beta vector becomes closer to 0 and 1. This leads to results similar to the one-hot encoded operator. In summary, by scaling the alpha values, the following benefits can be obtained:  
* Controlling the difference in beta values to regulate the search process;
* Intensifying or relaxing the competition among operators;
* Increasing search diversity through high temperature;
* Creating an architectural environment similar to the one-hot encoded operator through low temperature.
## 2 Numerical Models
### Boundary Conditions and Grid System  
The computational domain and the boundary conditions of the present study are displayed in Figure 1. The simulation is 2.5D. The cylinder in the upstream is named Cylinder A and the cylinder in the downstream is named Cylinder B (+y-direction) and C (-y-direction), as shown in Figure 2a. Cylinder A is located in the middle of the domain in the \(y\)-direction, and the distance from inlet boundary to the center of the Cylinder A is \(10D\). The computation domain is \(35D\) (\(D\) is the diameter of the cylinder, which is 1 m in the present paper) in the \(x\)-direction and \(16D\) in the \(y\)-direction. The size of the domain in the \(z\)-direction is \(4D\), which has been proved long enough to capture the relevant flow structure [36; 37]. The inlet boundary condition is set as velocity inlet boundary. The outlet boundary is set as a pressure outlet boundary. Periodic boundary conditions are set at spanwise boundaries (\(z\)-direction). As for the inlet boundary, turbulence viscosity is 5 times the molecular viscosity. Because when in the super-critical Reynolds regime, the transition in the boundary layer is turbulent. No-slip wall boundary conditions are set at the cylinder surfaces.  
The computing mesh of the case when \(L/D=2.5\) (\(L\) is the distance between the centers of either two cylinders) is shown in Figure 2. A trimmed mesh is adopted in all the cases. The grids are refined in the region near the cylinder system. The size of each refined mesh region is illustrated in Figure 2a. The finest grid size is 0.025D. Grid size increases by a factor of 2. The grid is the same size in x, y, and z-directions. In the spanwise direction, for the finest grid region, there are 160 cells. The boundary layers are set to be 40 layers to keep \(y+<1\), as shown in Figure 3. The total number of the cells for \(Re=3\times 10^{6}\) is 10 million when \(L/D=2.5\). As the \(L/D\) increases, the total number of cells increases, too. As for temporal discretization, the time-step \(\Delta t\) is \(5\times 10^{-4}\) to ensure the maximum Courant number (defined as \(u\times\Delta t/\Delta x\)) less than 1. Ten internal iterations exist in each time step. In this paper, the diameter of the cylinder (\(D\)) is set to 1 m, the flow velocity (\(U_{0}\)) is 47.02 m/s, fluid density (\(\rho\)) is 1.225 kg/m\({}^{3}\), and the dynamic viscosity (\(\mu\)) of fluid is 0.0000192 Pa \(\cdot\) s.  
Figure 1: The boundary conditions and the schematics of the computing field.
## 4 Effect of Continuous Low Thrust on Orbital Elements
### Effect of Continuous Tangential Thrust on Changes in Orbital Elements  
When \(a_{R}=a_{N}=0\), \(a_{\rm T}\
eq 0\), Equation (10) simplifies to  
\[\left\{\begin{array}{l}\frac{\mathrm{d}a}{\mathrm{d}t}=\frac{2}{n\sqrt{1-e^{ 2}}}(1+e\cos f)a_{\rm T}\\ \frac{\mathrm{d}e_{x}}{\mathrm{d}t}=\frac{\sqrt{1-e^{2}}}{na}\Big{[}\Big{(}1+ \frac{r}{p}\Big{)}\cos u+\frac{r}{p}e_{x}\Big{]}a_{\rm T}\\ \frac{\mathrm{d}e_{y}}{\mathrm{d}t}=\frac{\sqrt{1-e^{2}}}{na^{2}}\Big{[}\Big{(} 1+\frac{r}{p}\Big{)}\sin u+\frac{r}{p}e_{y}\Big{]}a_{\rm T}\\ \frac{\mathrm{d}\lambda}{\mathrm{d}t}=n+\frac{1}{na}[a_{\rm T}(1+\frac{r}{p}) \frac{\sqrt{1-e^{2}}}{1+\sqrt{1-e^{2}}}e\sin f]\end{array}\right. \tag{11}\]  
For the simulation analysis of changes in the semi-major axis and eccentricity under tangential acceleration, the satellite mass is 750 kg. The acceleration generated by the existing space electric thrusters is typically in the range of \(10^{-3}\)-\(10^{-5}\)m/s\({}^{2}\). The simulation conditions assuming a tangential acceleration of 0.0002 m/s\({}^{2}\) are given in Table 1. The semi-major axis and eccentricity variations are shown in Figures 1 and 2. Under tangential acceleration, the semi-major axis is increasing. The eccentricity varies over a small range of cycles and is negligible. As the orbital altitude increases, the satellite velocity decreases and the argument of latitude gradually lags, as shown in Figure 3. Vertical coordinate \(\Delta\lambda\) indicates the difference between the argument of latitude of the satellite under tangential acceleration and the argument of latitude of the satellite at the same moment under the same conditions but without thrust.  
\begin{table}
\begin{tabular}{c c} \hline Initial semi-major axis (km) & 6758.4 \\ \hline Initial eccentricity & 0 \\ \hline Initial satellite mass (kg) & 750 \\ \hline Tangential thrust (N) & 0.17 \\ \hline Specific impulse (s) & 1666 \\ \hline Three-body gravity & Sun, moon \\ \hline Tide & Solid tide: IERS Conventions 2003 \\ \hline Non-spherical gravitational field & Gravity model: EGM2008 \\  & Degree: 21; order: 21 \\ \hline \multirow{3}{*}{Solar pressure} & Shadow model: Dual Cone \\  & Light pressure coefficient: 1.00 \\  & Area–mass ratio: 0.03 m\({}^{2}\)/kg \\ \hline \multirow{3}{*}{Atmospheric drag} & Density model: Jacchia–Roberts \\  & Drag coefficient: 2.20 \\ \cline{1-1}  & Area–mass ratio: 0.03 m\({}^{2}\)/kg \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameter settings.  
Figure 1: Variation in semi-major axis under the effect of tangential acceleration.  
Figure 3: Variation in argument of latitude under the effect of tangential acceleration.  
Figure 2: Variation in eccentricity under the effect of tangential acceleration.