esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Machine Learning Algorithms  
#### 2.5.1 Regression Models  
For the regression tasks aimed at predicting plantar pressure metrics from temperature data, the following algorithms were utilized:  
* **Extra Trees Regressor**: An ensemble learning method that aggregates results from multiple randomized Decision Trees to improve prediction accuracy.
* **K Neighbors Regressor**: A non-parametric method that predicts the output based on the average value of the k-nearest neighbors in the feature space.
* **Dummy Regressor**: A simple baseline model that makes predictions using basic strategies such as the mean or median of the target values.
* **Light Gradient Boosting Machine (LightGBM)**: A gradient boosting framework that uses tree-based learning algorithms, optimized for efficiency and performance.
* **Bayesian Ridge**: A linear regression model that uses Bayesian inference to estimate the regression coefficients.
* **Random Forest Regressor**: A tree-based ensemble model that builds multiple Decision Trees and averages their outputs to enhance predictive accuracy.
* **Gradient Boosting Regressor**: An ensemble technique that builds models sequentially, optimizing the prediction by minimizing the error of previous models.
* **AdaBoost Regressor**: A boosting method that combines weak regressors to produce a strong predictive model by focusing on the most difficult-to-predict instances.
* **Extreme Gradient Boosting (XGBoost)**: A highly efficient and flexible boosting algorithm that improves performance by reducing overfitting and increasing accuracy.
* **Orthogonal Matching Pursuit**: A greedy algorithm for linear regression that selects the most correlated features in each iteration.
* **Elastic Net**: A regularized regression model that linearly combines L1 and L2 penalties of the lasso and ridge methods to improve prediction and feature selection.
* **Lasso Least Angle Regression (LassoLARS)**: A variant of linear regression that automatically selects the most relevant features by shrinking the less important ones to zero.
* **Lasso Regression**: A regression method that performs both variable selection and regularization to enhance prediction accuracy.
* **Ridge Regression**: A technique used when multicollinearity exists, adding a degree of bias to the regression estimates.
* **Decision Tree Regressor**: A non-linear regression model that splits the dataset into subsets based on the feature values to make predictions.
* **Huber Regressor**: A robust regression technique that is less sensitive to outliers in the data than least squares regression.
* **Linear Regression**: A basic regression model that assumes a linear relationship between the input features and the target values.
* **Passive Aggressive Regressor**: An online learning algorithm that updates the model in response to each individual sample.
* **Least Angle Regression (LARS)**: A regression algorithm particularly suited for high-dimensional data, similar to forward stepwise regression.
## 3 Experiments and Results
### Comparison of Average Behavior  
This section compares the load-displacement behavior and resulting energy absorption characteristics of the chiral structure, the hollow tube and the chiral-filled tube using their average behavior. Uniaxial and lateral crush plots are compared in Figure 11a,b, for the uniaxial and lateral loading respectively. In addition, the average crush properties for chiral structure, hollow tube and filled tube are reported in Tables 8 and 9.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**FILLED**} & \multicolumn{4}{c}{**Lateral**} & \multicolumn{4}{c}{**Uniaxial**} \\ \cline{2-9}  & **1** & **2** & **3** & **Average** & **1** & **2** & **3** & **Average** \\ \hline PCF (kN) & 12.37 & 14.55 & 12.57 & \(13.16\pm 1.39\) & 76.30 & 77.49 & 75.32 & \(76.37\pm 1.12\) \\ MCF (kN) & 6.28 & 8.34 & 7.31 & \(7.31\pm 1.03\) & 49.28 & 51.95 & 45.31 & \(48.85\pm 3.54\) \\ CFE & 0.51 & 0.57 & 0.58 & \(0.55\pm 0.04\) & 0.65 & 0.67 & 0.60 & \(0.64\pm 0.04\) \\ AE (kJ) & 0.12 & 0.16 & 0.15 & \(0.14\pm 0.02\) & 3.45 & 3.58 & 3.41 & \(3.48\pm 0.01\) \\ SEA (J/g) & 1.29 & 1.74 & 1.55 & \(1.53\pm 0.24\) & 36.70 & 38.05 & 36.24 & \(37.00\pm 1.05\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Properties of the chiral-filled tubes under lateral and uniaxial crushing.  
Figure 10: Load–displacement curves related to the lateral and uniaxial crush of chiral-filled tubes.  
Figure 9: Load–displacement curves related to the lateral and uniaxial crush of hallow tubes.
## 5 Result Discussion
### Comparison of Critical Loads in Failure Stage  
This section analyzes the relationship between the actual failure load and the theoretical values using the example of the P3 beam. As discussed in Section 3.3 and shown in Figure 8a,b, the load range determined from the acoustic emission (AE) test during the compaction stage precisely corresponds to the theoretical calculation of the load values for offsetting prestress and cracking of the bottom concrete of the beam, which is 19.92 kN. During the experiment, the load at which the top concrete of the mid-span was crushed was 85 kN, while the theoretical value was 89.92 kN, resulting in a difference of less than 5%. The strong correspondence between the failure stages identified based on AE signals and the actual calculation process indicates. This provides a robust validation of the rationale for dividing the failure stages based on AE signals.
## 4 Evaluation
### Performance Evaluation Metrics
#### 4.3.1 Average Network Delay  
_Delay_ refers to the average transmission time of all packets reaching the destination nodes.  
\[Delay=\frac{\sum_{j=0}^{J-1}\left(Tr_{j}-Ts_{j}\right)}{\sum_{i=0}^{N-1}Pr_{n_ {i}}} \tag{15}\]
# Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing
## 3 System Model  
Cloud computing, as a common way of resource allocation, has many optimization problems. This paper proposes a task scheduling framework called QMTSF based on reinforcement learning to solve the task allocation and scheduling problems in cloud computing environments. We define the cloud environment scenario as follows. There are n Servers in a cloud center, represented as \(S_{1},S_{2},\ldots,S_{i},\ldots,S_{n}\), where \(1\leq i\leq n\). For any server, the number of VMs is m, represented as \(VM_{1},VM_{2},\ldots,VM_{j},\ldots,VM_{m}\), where \(1\leq j\leq m\). QMTSF consists of two steps used to complete the scheduling of tasks to the VMs.
## 3 Hardware and Software Setup  
This section describes the hardware and software setup leveraged for the deployment of the proposed design and the validation of the evaluation methodology described in Section 4. The proposed parallelization of FastSLAM 2.0 is based on the CUDA programming paradigm and uses PyCUDA [63] for Python interfacing. The performance was tested on a high-end laptop equipped with an Intel Core i7-10750H CPU [63] and an NVIDIA GeForce GTX 1650 Ti [63].
## 2 Proposed Solution
### Group Identification  
The group identification model discussed in this section utilizes the people detection, tracking and distance estimation models introduced in Sections 2.1 and 2.2. This was achieved by two algorithms \(F_{g}\) and \(\overline{F}_{g}\). \(F_{g}\) was run on the information from individual frames, while \(\overline{F}_{g}\) analyzed the results from \(F_{g}\) across time to properly deduce which people fall into groups based on sustained physical proximity.  
Given a frame \(V_{in}(t)\), a matrix \(M_{d}(t)\) called the distance matrix is created based on the calculated distances between people. The affinity matrix \(M_{a}(t)\) was then calculated as follows,  
\[M_{a}=\exp{(-\alpha M_{d})} \tag{7}\]  
where \(\alpha\) is an input parameter that is used to introduce the camera and scene pair to a scale. This parameter acts as a threshold for the closeness of people in the 2D projected plane prior to clustering. Then, clustering was performed on \(M_{a}\) to split the people into clusters.  
\[clusters=spectral\_clustering(M_{a}) \tag{8}\]  
According to the group identification model, a person is considered to be a member of a group if they are close to at least one member of the group. While conventional clustering algorithms attempt to minimize the distance between individual elements and the cluster center, this is not how humans behave. As a result, this result was obtained using spectral  
Figure 3: Perspective transformation. The (**right**) frame is a visualization of how a camera-captured scene (**left**) is projected to the ‘floor plane’ after perspective transformation. The trapezoidal floor is being transformed into a square.  
clustering of affinity matrices [53]. Human behavior, on the other hand, cannot be analyzed in terms of discrete frames. As a result, a temporal analysis of the clusters was performed to determine the actual groups of people using a time threshold \(\tau\). The primary idea is that a group is detected only if it persists for a specified time period \(\tau\).  
People \(P_{i}\in P\) were being clustered from a video frame at time \(t\) as follows,  
\[cluster\_id(P_{i},t)\longleftarrow spectral\_clustering(M_{a}(t)) \tag{9}\]  
\[cluster\_id(P_{i},t)=cluster\_id(P_{j},t)\qquad\text{if}\quad\exists\quad t _{0}\text{ s.t}\quad t_{0}\leq t\leq t_{0}+\tau \tag{10}\]  
where \(P_{i}\) and \(P_{j}\) were considered to be in the same social group as per Equation (10). Social distancing violations between the people in the same social group was ignored in the proposed system as justified in Section 1. For cases involving a few people, a simplified algorithm based on naive thresholding of interpersonal distance violation occurrences was used instead of spectral clustering. When spectral clustering was used, \(\tau\) was picked so that a group should be in proximity for 10 s. For the thresholding case, people spending upwards of 20% of the time in proximity were considered groups.
## 4 Conclusions  
The surface character of Al2O3 and TiO2 ceramic membranes (tubular and planar) was changed from a hydrophilic to hydrophobic one by chemical modification with silane grafting agents possessing various structures (length of alkyl chain and type of reactive groups). After modification, highly hydrophobic surfaces were fabricated.  
The length of hydrophobic PFAS molecule has a significant impact on the hydrophobicity level. Much higher values of CA and LEPw were observed for planar and tubular membranes grafted by C18 molecules comparing with C8 ones.  
Hydrophobization process changed the physicochemical properties of ceramic membranes expressed by their roughness, surface free energy (polar and dispersive component), and hydrophobicity level.  
The obtained hydrophobic tubular ceramic membranes were used in membrane separation process--air gap membrane distillation. In AGMD, process impact of concentration of NaCl in the feed, grafting time, as well as type of membrane materials on the transport properties was observed and discussed in detail. Higher values of permeate flux were observed for alumina membranes than for titiania ones, which resulted from higher LEPw values for grafted alumina. The retention coefficient of NaCl in AGMD process using hydrophobized ceramic membranes was over 98% for all investigated membranes.  
This research was supported by NN 209 255138 grant from the Polish Ministry of Science and Higher Education and by 2012/07/N/ST4/00378 (Preludium 4) grant from the National Science Centre. This research was also partially supported by statutory funds of Nicolaus Copernicus University in Torun (Faculty of Chemistry, T-109 \"Membranes and membrane separation processes--fundamental and applied research\").  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed experiments and collaborated with the data analysis; [PERSON] collaborated with interpretation of the experimental data (contact angle, membrane distillation), assisted with critical corrections in the manuscript drafting, and prepared the drawings; [PERSON] and [PERSON] participated in the analysis and interpretation of the experimental data, cooperated with the drafting and correction of the final version of the manuscript.  
The authors declare no conflict of interest.
## 2 Methods
### G-LiHT Headwall VNIR  
The post processing of G-LiHT's imaging spectrometer data consists of applying radiometric calibration coefficients to data from both the imaging spectrometer and the downwelling radiometer, effectively converting the raw digital number (DN) values to at-sensor upwelling surface radiance and downwelling irradiance, respectively. Normal G-LiHT operations occur at or below 600 m AGL and cross-calibration of the two instruments allows computation of the surface reflectance data product with minimal atmospheric effects. Imaging spectrometer spectral bands are aggregated from the 1.5 nm native sampling interval using a 3\(\times\) pixel aggregation approach such that distributed hyperspectral data have 114 spectral bands between 418 nm and 918 nm, with 4.5 nm band spacing and a 10 nm FWHM spectral resolution. Swath based hyperspectral data cubes are provided for at sensor radiance (W m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\)) and reflectance products.  
Co-registration for G-LiHT's data products is achieved by a \"backward projection approach\" from the LiDAR surface models. As G-LiHT sensors are rigidly mounted in a fixed orientation, the geometric relationship remains constant between the LiDAR scan angle and pixel centers of the push broom optical sensors. Each grid cell is populated with observations with the same angular view from each of G-LiHT's optical instruments. If no coincident optical views are available to populate a grid cell then the nearest neighbor is used to ensure contiguous coverage. A record is retained as to whether a matching view (1) or a nearest neighbor (0) was used to populate the raster grid and this information is stored in an associated ancillary file. Geo-located optical data products (L1G) are available for a portion of these and other G-LiHT acquisitions at other sites, and can be accessed at: [[ftp://fusionftp.gsfc.nasa.gov/G-LiHT/](ftp://fusionftp.gsfc.nasa.gov/G-LiHT/)]([ftp://fusionftp.gsfc.nasa.gov/G-LiHT/](ftp://fusionftp.gsfc.nasa.gov/G-LiHT/)).  
In addition to G-LiHT's swath-based products, mosaicked data products are automatically generated for mapped areas that have overlapping flight lines of the same heading. The IDL code that generates these products populates predefined grid cells from the LiDAR-generated surface model with the closest to nadir views available from the coincidently collected imaging spectrometer swath data. A record is retained in the ancillary image file indicating swath number and corresponding view angle for each pixel in the final data product. Further, a number of commonly used vegetation indices are generated including; NDVI and the Photochemical Reflectance Index (PRI). Further details regarding G-LiHT configuration and data products can be found in references [4; 6].
## 3 Results and Discussion
### Overall Discussion  
The consistency of the pulse plateau magnitudes (Figure 4) and pulse duration periods (Figure 4c) with the one-dimensional theory (Equations (4)-(7)) discussed in Section 3.4 verifies the flange length-dependent tensile stress mechanism in Section 3.4 on a quantitative basis. The consistency of the numerical analysis results (Figure 4) with the one-dimensional theory (Equations (4)-(7)) also verifies the reliability of the numerical analysis (Section 2) carried out in this study.  
The flange length-dependent stress transfer mechanism revealed above may serve as a solid background for the design of SHTBs having a striker and flange. Investigation into the flange length-dependent interactions of the **F** and **B** waves revealed that the superposed magnitude of the **F** and **B** waves is simply the sum the respective wave magnitudes, despite the area mismatch of the respective medium, and that no reverberation occurs as the **F** wave enters the bar from the flange structure with a different cross-sectional area. This interesting phenomenon occurs if the **F** wave arrives at the impact plane before the generation of **F** and **B** waves (which occurs under the influence of area mismatch) is completed. This phenomenon is observed when the impact and reflection surfaces are close, which may also serve as a background for interpreting wave interaction and impact-induced deformation at regions near the free surface of a three-dimensional protective structure.  
Figure 7: **B** and **F** waves when the flange length is short (\(L_{\mathrm{f}}<\mathrm{\sim}\mathrm{D_{b}}\)), such that the critical plane for the **F** wave (\(p_{\mathrm{c,F}}\)) is positioned in the incident bar and not in the flange structure.
## 2 Materials and Methods  
### Sample Collection, DNA Extraction, and Library Construction  
Hexi (HX) WWTP (44.030\({}^{\circ}\) N, 87.356\({}^{\circ}\) E) and Changji (CJ) WWTP (43.963\({}^{\circ}\) N, 87.522\({}^{\circ}\) E) in Xinjiang (China) were selected for sample analysis. The design scales of HX and CJ WWTPs were 200,000 m\({}^{3}\)/d and 100,000 m\({}^{3}\)/d, respectively. Both WWTPs use the oxidation ditch process and ultraviolet disinfection, and the main influent is municipal sewage from Urumqi city and Changji city separately. Specific information on sample collection is provided in Table 1. On site, each sample was randomly mixed by collecting three subsamples at the sampling point to enhance the representativeness. The solids from influent samples were collected by centrifugation using a TDL-5-A centrifuge (Anting Scientific Instrument, Shanghai, China) at 4380\(\times\)_g_ for 10 min, while effluent samples were collected using vacuum filtration with a 0.22 um cellulose nitrate membrane. All pre-treated samples were transported to the laboratory and immediately stored at \(-\)80 \({}^{\circ}\)C.  
Total genomic DNA was extracted using the E.Z.N.A. Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA) according to the manufacturer's instructions. The concentration and purity of extracted DNA were determined using a TBS-380 mini fluorometer (Invitrogen, Carlsbad, CA, USA) and NanoDrop2000 (Thermo Fisher Scientific, Wilmington, NC, USA) spectrophotometer, respectively. The DNA integrity was checked by 1% agarose gel electrophoresis at 5 V/cm for 20 min.  
Extracted DNA was segmented into approximately 400 bp by a Covaris M220 Focused-ultrasonicator (Gene Company Limited, Hong Kong, China), and DNA fragments were used to construct a paired-end library via NEXTFLEX Rapid DNA-Seq (Bioo Scientific, Austin, TX, USA). Adapters containing the full complement of sequencing primer hybridization sites were ligated to the blunt ends of fragments.
# The Effect of Inflatable Pressure on the Strain Deformation of Flexible Wing Skin Film
## 1 Introduction
For complex origami-based designs like sun covers, variable-rate inflation reduces stress concentration areas by up to 35%, mitigating tear risks [13]. This offers a promising solution for enhancing dynamic deployment reliability, especially under high dynamic loads encountered during ejection or transonic reentry [11; 17]. The biaxial mechanical properties of the membrane material critically determine deformation behavior. Orthotropic high-strength fabrics (e.g., Dyneema(r) composites) exhibit differences between longitudinal (E\({}_{1}\) = 5.45 GPa) and latitudinal (E\({}_{2}\) = 5.19 GPa) moduli, leading to non-uniform, post-inflation strain distribution. Crucially, hot-welded joint strength (e.g., 4 N/mm) must satisfy diaphragm design safety margins (e.g., factor of 6.6) to prevent interface peeling [16]. Bio-inspired structural designs offer significant strain distribution improvements; rotating rib arrays inspired by fish dorsal fins can reduce strain energy by 18% via non-uniform pressure distribution, and diaphragm inflatable wings optimized for the section moment of inertia can maintain an aerodynamic shape while reducing operational pressure to 25 psi [16; 17; 18]. Under extreme conditions, aerodynamic-structural coupling becomes pronounced. During transonic reentry (Ma = 0.8), turbulent wakes induce low-frequency vibrations, while concave skin deformation intensifies aerodynamic heating, elevating surface temperature and pressure by 15-20% compared to static models [17]. For the orbital deployment of space membrane structures, the pressure-strain relationship in microgravity must be modified using iterative membrane property (IMP) algorithms [12]. Furthermore, the inherent \"concave-convex\" surface of inflatable wings enhances flow adhesion at low Reynolds numbers (Re < 500,000). However, at high Reynolds numbers, incorporating a smooth polymer skin (e.g., silicone elastomer or electroactive polymer) becomes necessary to balance flexible deformation with aerodynamic efficiency [19; 20; 21]. Therefore, based on the theory of flexible mechanics and the balance principle of flexible inflatable thin skin, this paper constructs a theoretical model of structural deformation and internal inflation pressure and carries out finite element simulation analysis under different internal inflation pressure conditions. It is verified that the biaxial deformation of the flexible wing skin is closely related to the internal inflation pressure, local size, configuration, and film material properties. The strain deformation along the spanwise direction is quite obvious, the flexible skin works under the condition of biaxial deformation, and the strain deformation in the spanwise direction is significantly higher than that in the chord-wise direction, which is proportional to the internal inflation pressure. This study can provide effective theoretical support for the structural optimization design and inflation scheme setting of flexible inflatable wings.
# EpiInfer: A Non-Markovian Method and System to Forecast Infection Rates in Epidemics
## 7 Is Infection Prediction Sensitive to the Precise Values of Transition Parameters  
To determine how sensitive the accuracy of infection prediction is to the particular values of transition parameters, we re-analyzed the CoMix/JHU COVID-19 and EpiPolicy data as follows. Recall that at each day \(d\), _ContinuousCalibrate_ estimates values of \(p_{1}\) and \(p_{2}\) based on the information known up to day \(d\). Using that approach, Section 5 achieved certain Root Mean Squared Error values.  
We now ask how the RMSE changes if the values of \(inc\), \(p_{1}\), and \(p_{2}\) are changed.  
Appendix B shows results from CoMix/JHU (Covid) data for 1-, 5-, 10-, 15-, and 20-day forecasts. That appendix also performs sensitivity analysis for EpiPolicy-generated data using parameters that are the same as in the single locale experiment of Section 4.2.  
The appendix shows the quality (in terms of Root Mean Squared Error) of the predictions when changes are made to the parameters \(p_{1}\), \(p_{2}\), and \(inc\). There are 27 different values, corresponding to three possibilities for each parameter. For \(p_{1}\) and \(p_{2}\), the considered values are 90%,100%, and 110% of the _ContinuousCalibrate_-inferred values. For \(inc\), the additive offset is either -3, 0, or 3. Table 13 shows that the parameter setting determined by _ContinuousCalibrate_ has the lowest overall RMSE when considering both real and simulated data at 1-, 5-, 10-, 15-, and 20-day forecasts.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Days Predicted** & **DiffEq** & **LSTM** & **GNN** & **Transformer** & **ARIMA** & **EpiInfer** & \(p\)**-Value** \\ \hline
7 & 34.52 & 0.154 & 0.195 & 0.162 & 0.062 & 0.059 & 0.3507 \\
14 & 269.84 & 0.302 & 0.318 & 0.312 & 0.198 & 0.165 & 0.0815 \\
21 & 627.01 & 0.455 & 0.441 & 0.470 & 0.423 & 0.338 & 0.0495 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Root Mean Squared Error (RMSE) results for flu data predictions from 7, 14, and 21 days into the future. The results show that EpiInfer is the best method throughout, followed by ARIMA. As the rightmost column shows, the \(p\)-value of the difference between EpiInfer and ARIMA is quite high at 7 days and below 0.05 only at 21 days.