esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Health Problems Using Deep Learning Images and Bio-Signals
## 2 Related Work
### Prediction of Health Status Using Bio-Signals  
Research on health condition prediction using biometric signals has been studied for a long time as precedent works for wearable devices and telemedicine services. Body temperature and pulse waves are bio-signals that can be used to quickly and easily check health status in a non-invasive measurement method. Body temperature is the easiest way to check your health because the immune system inside the body works to generate heat when you catch a cold or respiratory disease. Pulse wave is a waveform that records the beats of the chest wall and large blood vessels according to the beat of the heart, and heart rate (HR) and HRV can be obtained by analyzing the components of the waveform. HR and HRV are closely related to stress and affect health status [35; 36]. However, since it is difficult to determine the state of health only by body temperature, it is determined by measuring other bio-signals together. In the past, research on systems that measure and integrate pulse waves, electrocardiograms, and blood pressure was the main focus. However, recently, as body temperature measurement has become the main focus, methodsto measure body temperature, heart rate, and respiration using RGB-thermal image sensors are also being studied [37; 38].  
Our research combines individually separated methods into one to measure multiple bio-signals and predict health abnormalities through comparison.
# A Comprehensive Review of Milk Components: Recent Developments on Extraction and Analysis Methods
## 9 Conclusions  
The diversity of macro- and micro-constituents in milk demands a broad spectrum of analytical techniques, each with unique strengths tailored to specific compounds. From traditional spectroscopic and chromatographic methods to cutting-edge mass spectrometry and omics technologies, these tools collectively enable comprehensive milk analysis. Continued innovations in this field will further enhance the understanding of milk's nutritional and functional properties, supporting advancements in food science, nutrition and dairy product development. As emerged from the overview discussed in this review, the choice of the analytical platform for milk constituents' analysis heavily depends on the physico-chemical nature and concentration of the target analyte. For instance, macro-constituents such as proteins, fats and carbohydrates are typically present in higher concentrations and are often extracted using liquid-liquid extraction techniques. These methods are straightforward, cost-effective, and do not usually require concentration steps. In contrast, micro-constituents, including vitamins, polyphenols and other bioactive compounds are usually present in trace amounts and require solid-liquid extraction, sometimes combined with pre-concentration techniques (e.g., lyophilization or solid phase extraction) to improve detection limits and reduce sample complexity.  
After extraction, spectrophotometric assays are commonly used for general quantification, especially for compounds such as total phenolics or antioxidant capacity. However, because these methods lack specificity, they are often complemented or replaced by selective techniques such as LC-MS/MS or GC-MS, depending on the volatility and polarity of the analyte. For example, GC-MS is preferred for some flavor compounds, while LC-MS is more suited for non-volatile, thermally labile compounds like peptides, vitamins, or phytochemicals.  
Furthermore, method development must consider factors such as matrix effects, instrument sensitivity and sample stability. Internal standards and matrix-matched calibration curves are often necessary to achieve reliable quantification. Overall, the use of LC-MS provides enhanced sensitivity and selectivity, enabling the identification of a wide range of milk constituents, even at very low concentrations, making it a powerful tool for both routine quality control and advanced research.
## 5 Experimental Analysis
### Number of Trainable Parameters and FLOPs  
A brief analysis of the number of trainable parameters and the computational complexity (measure in number of FLOPs) of each model for the different case studies is reported in this section. For what concerns the number of trainable parameters, as shown in Table 5, STREED-Net has a generally low number compared to other models as only STAR features lesser parameters to train. Such a reduced number of parameters is due to the fact that the dimensionality of the input is reduced by the encoder downsampling mechanism. The model with the highest number of parameters is 3D-CLoST, which uses both 3D convolutions and LSTM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAPE** & **APE** \\ \hline STREED-Net\_N3 & \(4.75\pm 0.04\) & \(21.18\pm 0.18\) & \(3.28\cdot 10^{5}\pm 2.73\cdot 10^{3}\) \\ STREED-Net\_N5 & \(4.74\pm 0.03\) & \(21.03\pm 0.22\) & \(3.26\cdot 10^{5}\pm 3.43\cdot 10^{3}\) \\ STREED-Net\_NoLSC & \(4.84\pm 0.04\) & \(21.53\pm 0.24\) & \(3.33\cdot 10^{5}\pm 3.71\cdot 10^{3}\) \\ STREED-Net\_NoAtt & \(4.78\pm 0.04\) & \(20.95\pm 0.27\) & \(3.25\cdot 10^{5}\pm 4.20\cdot 10^{3}\) \\ STREED-Net\_NoExt & \(4.76\pm 0.04\) & \(20.99\pm 0.29\) & \(3.26\cdot 10^{5}\pm 4.55\cdot 10^{3}\) \\ \hline STREED-Net & \(\mathbf{4.67\pm 0.03}\) & \(\mathbf{20.85\pm 0.15}\) & \(\mathbf{3.23\cdot 10^{5}\pm 2.31\cdot 10^{3}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results obtained from ablation studies.  
Finally, Table 6 provide the computational complexity of each model in terms of floating point operations (FLOPs) as in [53] for each case study. As can be seen from the results obtained, the model with the higher computational complexity is PredCNN, which is based on CMUs. While, 3 DCLoST is the model with the shortest forward and backward times. STREED-Net, instead, has a middle-range computational complexity compared to the other models, despite its autoencoder structure, the use of attention blocks, and CMUs. This occurs because although the number of network parameters is small, the network employs high complexity operators. However, the training and execution times of STREED-Net are compatible with its applicability in full-scale real-world scenarios.
## 4 Potential of Bioactive Compounds Obtained from By-Products of the Wine Industry
### Feed and Food Industry
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Grape By-Product** & **Variety/Origin** & **Food Product** & **Concentration** & **Effects** & **References** \\ \hline \multirow{4}{*}{Pomace} & Grape pomace (Ives noir cultivar, Vitis labrusca species) by the company & & Addition of grape pomace flour in the process of parriboling the rice; GP.rice ratio 1:2. & -Improved the antioxidant activity. & \multirow{4}{*}{[64]} \\  & & & & & \\ \hline \multirow{4}{*}{Pomace} & Vitis labrusca cv. Isabel & Salmon burger & Addition of 1 and 2\% of grape pomace flour to the burger recipe. & -Increased dietary fiber content and storage stability. & \multirow{4}{*}{[182]} \\  & & & & & \\ \hline \multirow{4}{*}{Pomace} & _Vitis vinifera_ L. of the & & & & \\ \cline{2-5}  & 'Arinto’ and 'Touriga Nacional' & Crackers & 5\%, 10\%, and 15\% & -Through high fiber’, as per the Regulation (EC) No. 1924/2006, suggesting a functional food. & \multirow{4}{*}{[183]} \\ \cline{2-5}  & & & & & \\ \hline \multirow{4}{*}{Pomace} & 'Syrah’, Merlot’, and 'Cabernet Sauvignon’ & Beef hamburger patties & 0\%, 2\%, and 4\%. & -Provided hamburger with health promoting factors, such as antioxidant and other functional components. & \multirow{4}{*}{[26]} \\  & 'Pinot Noir’ (PN) and & & & & \\ \cline{2-5}  & 'Italian Riesling’ (IR) grape varieties from western Romania (Teremia Mare Winery, Timis County) & Pasta & Replacement of wheat flour with 3, 6, and 9\% of grape skin flour. & -Increased antioxidant activity total phenolic content. & \multirow{4}{*}{[184]} \\ \cline{2-5}  & & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: Studies regarding the application of grape by-products and derived extracts in food products.
## 3 Torque and Battery Distribution Strategy  
### Torque Distribution Strategy: Particle Swarm Optimization  
The total torque, \(T_{t}\), used to accelerate the vehicle was  
\[T_{t}=(F_{x}/r_{t})=T_{mf}n_{g}+T_{mr}+T_{ml} \tag{17}\]  
From (14), the corresponding yaw moment, \(M_{z}\), in the steady state of a small steer angle was provided by the differential torque of the left and right in-wheel motors. The maximum and minimum torque ranges of the three traction motors were first determined on the basis of the TNE maps of the three traction motors, wheel angular speeds, SOC of the batteries, and current limits. PSO was then used to determine the best torque distribution for the three traction motors under the constraints of their operation ranges and Equations (14) and (17).  
The PSO theory originated by observing the hunting behavior of a swarm of birds or fish. In the process of torque distribution, a particle is a point on a search space of TNE map. As shown in Figure 2a,b, three swarms of particles were initially distributed at random on the three TNE maps of the traction motors, and each swarm had N particles. Each particle had a position state, which was defined as the torque where the particle was located. The pedal command given by the EV driver was their common target at a specific vehicle speed.  
Each particle at its initial position in the search space determined its best direction, and all the particles approached their common target with the minimal global effort.  
\[\eta=\frac{T_{mf}\omega_{mf}}{\eta_{mf}\Big{(}T_{mf},\omega_{mf}\Big{)}}+ \frac{T_{ml}\omega_{ml}}{\eta_{ml}(T_{ml},\omega_{ml})}+\frac{T_{mr}\omega_{mr} }{\eta_{mr}(T_{mr},\omega_{mr})} \tag{18}\]  
During the PSO process, the N particles were renewed through J generations and reached the target of minimal energy consumption in the end. After the least energy consumption converged in each generation, the particles were updated according to their own best and swarm best solutions as  
\[\Delta T_{mf,i}^{j+1}=wT_{mf,i}^{j+1}+rand_{1}\times c_{L1}\Big{(}p_{i}^{j}-T_{ mf,i}^{j}\Big{)}+rand_{2}\times c_{L2}\Big{(}G^{j}-T_{mf,i}^{j}\Big{)} \tag{19}\]  
\[T_{mf,i}^{j+1}=T_{mf,i}^{j}+\Delta T_{mf,i}^{j+1} \tag{20}\]  
where the sub-index i stands for the ith particle; j stands for the generation number; \(c_{L1}\) and \(c_{L2}\) are learning factors; \(G\) represents the swarm's best known solution of all the particles; \(P_{i}\) represents its own best known solution of the ith particle; \(rand_{1}\) and \(rand_{2}\) are random values between 0 and 1; and \(w\) is the inertia weight. At the final generation, the output torques of the rear left and right motors were calculated:  
\[T_{ml}=\frac{T_{t}-T_{mf}\ n_{g}}{2}-\frac{r_{t}M_{z}}{L_{w}} \tag{21}\]  
\[T_{mr}=\frac{T_{t}-T_{mf}\ n_{g}}{2}+\frac{r_{t}M_{z}}{L_{w}} \tag{22}\]  
Details of the original work of the real-time torque-distribution strategy by PSO for a pure EV with three traction motors were described in [16].
# Research Advances and Application Prospect of Low-Temperature Plasma in Tumor Immunotherapy
## 4 Activation of Anti-Tumor Immunity by LTP
### The Impact of LTP on Non-Specific Immune Responses
#### 4.1.2 LTP Enhances the Anti-Tumor Effects of Macrophages  
Macrophages are one of the important components of the innate immune system, essential for the local balance of inflammation and anti-inflammation. They have anti-tumor effects [29]. Macrophages have the characteristics of high plasticity, local tissue function specificity, and abnormal differentiation induced by inflammatory factors. Tumor-associated macrophages can develop into either cytotoxic M1 or M2 macrophages depending on the tumor microenvironment. M1 macrophages are able to kill tumor cells, while M2 macrophages produce factors that suppress T cell proliferation and activity [30]. By reducing M2 and enhancing the function of M1, tumors caused by an impaired immune response can be controlled. [PERSON] et al. [31] found that LTP acted as an immunomodulator for immune cell activation, stimulating the polarization of M1/M2 macrophages. The co-cultivation of tumor cells and LTP-activated macrophages reduced the invasiveness of tumor cells, and polarized macrophages weakened the maintenance ability of tumor stem cells. LTP-treated medium increased the number of macrophages and decreased that of M2. [PERSON] et al. [32] injected plasma-activation solution into pancreatic mice abdominal cavities, and found that the expression of CD206 (M2 marker) was significantly reduced. [PERSON] et al. [33] found that low-plasma-treated macrophages showed an increased migratory activity and mediated tumor cell killing in a TNF-\(\alpha\)-mediated manner. [PERSON] et al. [34] studied plasma exposure of pancreatic ductal adenocarcinoma (PDA), and subsequent co-culture with macrophages significantly reduced the number of macrophage clusters compared to untreated PDA cells. Therefore, tumor therapy by targeting pro-inflammatory macrophages may become an attractive anti-tumor treatment strategy.
## 3 Results and Discussion
### Adsorption of Hg(II) at Ultralow Concentrations  
To further demonstrate the efficiency of 500 Cys-C@Fe\({}_{3}\)O\({}_{4}\) for Hg(II) removal, adsorption studies were conducted using ultralow Hg(II) concentrations. Adsorption experiments using Hg(II) concentrations ranging from 100 to 5 ppb (pH 6) demonstrated the performance of 500 Cys-C@Fe\({}_{3}\)O\({}_{4}\), with \(>\)99% Hg(II) removal being possible in 1 h over the entire concentration range, from 100, 50, 20, 10, and 5 ppb to 0.86, 0.45, 0.36, 0.34, and 0.31 ppb, respectively. These final Hg(II) concentrations were less than 1 ppb, meeting acceptable limits for drinking water set down by the WHO and EU [8].
# Design Parameters Affecting Rill Swell Events for Block Caving Applications
## 4 Results
### Drawpoint Geometry
#### 4.1.1 Drawpoint Height  
Drawpoint height was analyzed in terms of the brow-beam height while the drawpoint width was fixed at 5 m and the amount of fine material over the hang-up was defined at a height of 7 m. Brow beams of 1 m, 2 m, and 3 m high were simulated. A brow beam of 2 m was the base case equivalent to the setup in the calibration phase. Table 5 presents the magnitude of the RS events simulated.  
Results showed a clear relation between the BB height and the RS magnitude. A brow beam of 1 m in height implied an RS event of 374 t, 46% larger than the event associated with the brow beam of 2 m in height. On the other hand, the 3 m high brow beam implied an RS event of 125 t, 51% smaller than the base case. In addition, in the event associated with the 1 m high brow beam, large particles were dragged to the production drift axis. On the contrary, the event associated with the brow beam of 3 m in height did not drag large particles through the extraction drift. Plan views of the material deposited are presented in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Base Event** & **Numerical Model** \\ \hline Tonnage (t) & 260 & 257 \\ Run-out distance + (m) & - & 6.4 \\ Run-out distance \(-\) (m) & 9.4 & 9.3 \\ Large rocks in drift & yes & yes \\ Deposition angle + (\({}^{\circ}\)) & 7 & 8 \\ Deposition angle \(-\) (\({}^{\circ}\)) & - & 7 \\ Height of flow (m) & - & 1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of main output variables calibrated with base event.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **BB 1 m** & **BB * 2 m** & **BB 3 m** \\ \hline Tonnage & 374 & 257 & 125 \\ Run-out distance towards the north (+; m) & 11 & 6.4 & 6.5 \\ Run-out distance towards the south (\(-\); m) & 7.8 & 9.3 & 2.8 \\ Large rocks in production drift? & Yes & Yes & No \\ Deposition angle in direction +, 1 (m) over the floor & 14\({}^{\circ}\) & 7\({}^{\circ}\) & - \\ Deposition angle in direction \(-\), 1 (m) over the floor & 17\({}^{\circ}\) & 8\({}^{\circ}\) & - \\ Height of flow (m) & 1.6 & 1.1 & 0.4 \\ \hline \hline \end{tabular}  
* Height of the base case.  
\end{table}
Table 5: RS magnitude for different brow beam (BB) heights.
## 2 Data  
CyL-GHI--a global horizontal irradiance dataset from the region Castile and Leon in Spain--was used for the development of this study. CyL-GHI [25] is public, distributed under a CC-BY-SA-NC (\"Creative Commons Attribution-ShareAlike-NonCommercial\") license, and can be downloaded from the Zenodo repository [26]. The data are provided in three csv (comma-separated value) files (\"CyL_GHI_ast.csv\", \"CyL_meteo.csv\", and \"CyL_geo.csv\"). The CyL-GHI dataset contains records from 1 January 2002 to 31 December 2019, with a time granularity of 30 min, from 37 stations located in Castile and Leon (Figure 1).  
The CyL_GHI_ast.csv file contains data on global horizontal irradiance, solar elevation angle, upper atmospheric radiation, and the azimuthal angle of the sun. The CyL_meteo.csv file contains meteorological data on the variables of air temperature, humidity, wind speed, wind direction, and precipitation. The CyL_geo.csv file contains the geographical latitude, longitude, and altitude data of the 37 stations, as well as the station codes and names. The data are for all stations for the period considered.  
Seven stations (BU04, LE05, P03, P07, SG01, SO01, and ZA02) were evaluated to compare two conditions regarding the position of neighbors. Some stations were surrounded by neighbors, following the traditional approach, while others had no neighbors in ei  
Figure 1: CyL-GHI dataset: global horizontal irradiance dataset from the region Castile and Leon in Spain. Source: “Figure 4. Distribution of the 37 stations selected for the CyL-GHI dataset after the exploratory data analysis process.” by [PERSON] et al. [25], licensed under Creative Commons Attribution 4.0 License.  
ther direction. All stations had the same number of neighbors and were located within 50 to 100 km of the target station.  
Other studies that have used data from this region have focused on stations that are surrounded by neighboring stations. [PERSON] et al. [27] used station VA01 to compare the impact of the spatial dispersion of neighboring stations on forecast skill by comparing stations from two pyranometer networks with different spatial coverage. [PERSON] et al. [28] used the data from station VA08 and and its eight nearest neighbors using as a discriminant the database capacity to limit the number of neighbors. In both studies, it was ensured that the stations studied had neighboring stations in all directions. This is an optimal way of choosing stations because it is guaranteed that no matter where the clouds originate from, they will always have information from a neighbor in that direction. The selection of stations in this study aims to ensure that the models are prepared for a non-ideal case where they may not have information from neighbors in all directions.
## 6 Conclusions  
1. The theoretical and experimental results show that the tensile stress distribution in the upstream and downstream regions of the deformation zone is different from that in the entry and exit regions of the deformation zone. Specially, between the exit of the deformation zone and the downstream region, the velocity distribution gradually becomes more uniform, while the tensile stress distribution gradually stabilizes.
2. The tension stress fluctuation of the strip outside the deformation zone can be regarded as a plane stress problem. A basic differential equation can be established based on the stress function method, and the finite difference method can be used to solve the differential equation. The calculated results indicate that the stress amplitude at different distances from the exit has a linear relationship with the amplitude of the initial stress, which falls roughly 98.8% after a distance of one strip width. The initial stress amplitude and strip width have little effect on the change trend of the stress evolution caused by the uneven velocity distribution, and the initial stress form is influential. The initial stress form can be broken down into two basic modes, that is, a quadratic or quartic coefficient. Any type of initial stress form can be considered as a linear combination of the two basic modes.
3. To save time, a \"Gaussian\" curve is proposed to fit the calculated results of the finite difference method. The simulation results reveal that the stress evolution calculated by the fitting equation is in good agreement with that calculated by the finite difference method.
4. To verify the practicability of the proposed model, a tensile stress rheology experiment is conducted, as shown above, and it can be seen that the experimental results fit well with the model results. Thus, it can be concluded that the model is correct and practical. The comparison of the fitting results with the model results and experimental results reveals that the proposed \"Gaussian\" curve is accurate and practical.  
Figure 25: Comparison between fitting results and model and experimental results for sample 2. (**a**) Comparison with model, (**b**) Comparison with experiment.  
**Author Contributions:** Conceptualization, K.Z. and D.W.; methodology, K.Z. and D.W.; writing--original draft preparation, K.Z. and Y.X.; writing--review and editing, Y.X. and D.W.; supervision, D.W. and H.L.; funding acquisition, D.W. and H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science foundation of China (No. U21A20118) and the National Key Laboratory of Metal Forming Technology and Heavy Equipment, China National Heavy Machinery Research Institute Co., Ltd., Xi'an (No. S2208100.W04).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion
### Potential Applications  
The high-resolution continuous surface ANPP maps generated here have many possible applications; in particular, as an indicator of habitat condition and for mapping spatial patterns of biodiversity, since biodiversity and productivity are strongly (often unimodally) related [2; 32]. Previous studies have demonstrated the close agreement between biodiversity and NDVI (e.g., tree species richness [33]). Other potential applications of the ANPP maps include: mapping ecosystem services and natural capital; improving estimation of above ground carbon and the terrestrial carbon cycle [34]; estimating forage yield; modelling land surface processes; and species distribution modelling. Many of the applications listed above may already incorporate NDVI imagery, but the advantage of using ANPP maps over NDVI imagery is that the influence of seasonal effects is reduced. This is because the NDVI values vary depending on the time of year the images are captured. By calibrating NDVI imagery to ANPP this provides a more consistent indicator for monitoring change, allowing images from different dates to be more readily compared.