esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Relapse-Free Survival Analysis
Figure 4: Kaplan-Meier curves of RFS for 2-year follow-up. Relevant clinical features are shown. **(a)**: HPV; **(b)**: tumor stage; **(c)**: tumor location; **(d)**: SUVmax. The Kaplan-Meier curve for smokers is not included due to the lack of representativeness of the non-smoker group. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Optimal Cut-off Value** & **Survival Rate (\%)** & \(p\)**-Value (Log-Rank Test)** \\ \hline HPV & Negative (\(-\)) & 45.5 & 0.023 \\  & Positive (\(+\)) & 84.6 & 0.023 \\  & III & 83.3 & \\ Tumor stage & IV & 54.2 & 0.220 \\  & Non-Tonsils & 56.8 & \\ Location & Tonsils & 60.0 & 0.869 \\  & Smoker & 55.1 & \\ Smoker & Non-Smoker & - & 0.165 \\  & \textless{25.75} & 69.5 & \\ SUVmax & \(\geq\)25.75 & 50.0 & 0.177 \\  & \textless{176.36} & 80.9 & \\ TLG & \(\geq\)176.36 & 40.9 & 0.010 \\  & \textless{11.22} & 78.9 & \\ MTV & \(\geq\)11.22 & 36.8 & 0.006 \\  & \textless{4286.06} & 82.1 & \\ Surface & \textgreater{}4286.06 & 38.1 & 0.004 \\ GLCM & \textless{2.11} & 73.0 & 0.023 \\ Entropy & \(\geq\)2.11 & 37.5 & \\ NGLDM & \textless{0.26} & 80.0 & 0.040 \\ Business & \textgreater{}0.26 & 45.0 & \\ GLZM & \textless{14.11} & 71.8 & 0.047 \\ GLNUM & \textgreater{}14.11 & 41.2 & 0.047 \\ GLZM & \textless{75.13} & 78.4 & \\ ZLNU & \textgreater{}75.13 & 26.7 & 0.001 * \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Univariate relapse-free survival rates and log-rank tests for 2-year follow-up of relevant clinical characteristics and significant image features. * Statistically significant \(p\)-value after multiple-testing correction by means of the Benjamini–Hochberg procedure. In the case of RFS Cox regression analysis (See Table 6), three shape features and one texture feature were statistically significant predictors of RFS, with the most significant being TLG (HR = 1.003 (1.000-1.006), \(p\) = 0.026) and Surface (HR = 1.000 (1.000-1.000), \(p\) = 0.028). The multivariate analysis was not performed, considering that no feature remained statistically significant after multiple-testing correction in univariate Cox regression analysis.
## Appendix B Two-Leaf Canopy Radiative Transfer Model in BESS  
A two-leaf canopy radiative transfer model is used to calculate PAR absorbed by sunlit leaves and shade leaves [56]. This method modified the PAR penetration model [88] by incorporating foliar clumping and reflected PAR from the soil.  
The total absorbed incoming PAR by the canopy is  
\[\mathrm{Q_{P\downarrow}}=(1-\rho_{\mathrm{cBP}})\mathrm{I_{Pb}}(0)\big{[}1- \exp\bigl{(}-\mathrm{k^{\prime}_{\mathrm{rp}}}\mathrm{L_{c}}\mathrm{\Omega} \bigr{)}\big{]}+(1-\rho_{\mathrm{cdP}})\mathrm{I_{Pd}}(0)\big{[}1-\exp\bigl{(} -\mathrm{k^{\prime}_{Pd}}\mathrm{L_{c}}\mathrm{\Omega}\bigr{)}\big{]} \tag{1}\]  
where \(\mathrm{I_{Pb}}(0)\) and \(\mathrm{I_{Pd}}(0)\) are direct beam and diffuse PAR at the top of the canopy, respectively. \(\mathrm{L_{c}}\) indicates the leaf area index. \(\rho_{\mathrm{cBP}}\) and \(\rho_{\mathrm{cdP}}\) are canopy reflectance for beam and diffuse PAR, respectively. \(\mathrm{k^{\prime}_{\mathrm{pb}}}\) and \(\mathrm{k^{\prime}_{Pd}}\) are the extinction coefficient for beam and scattered beam PAR, and for diffuse and scattered diffuse PAR, respectively.  
The absorbed incoming beam PAR by sunlit leaves is  
\[\mathrm{Q_{PbSun\downarrow}}=\mathrm{I_{Pb}}(0)(1-\sigma_{\mathrm{PAR}})[1- \exp(-\mathrm{k_{b}}\mathrm{L_{c}}\mathrm{\Omega})] \tag{2}\]  
where \(\sigma_{\mathrm{PAR}}\) is the leaf scattering coefficient for PAR. \(\mathrm{k_{b}}\) is the extinction coefficient for black leaves.  
The absorbed incoming diffuse
## 3 Error Characterization and Simulation Method
### Errors Description
#### 3.1.3 A Priori Errors  
The external errors mainly come from the forward model and the a priori profiles of temperature, species and pressure. The model error could not be estimated, since there is no absolute value that can be measured. So, no forward model errors are applied in this study.  
The trace gas species can be retrieved by using the optimal estimation (OEM) retrieval method, which requires good knowledge of the atmospheric profiles as well as the measurement noise [34]. Since the method involves the nonlinear weighted least squares optimization and includes the use of a priori constraints for regularization, the a priori data mainly affects the region where measurement information is lacking, and the noise dominates the region where the measurement signal is large [35].  
To estimate the effect of the a priori profile, the species profiles are multiplied by 120%, which is to serve as the a priori profiles, and the temperature profile is added as a 5 K offset. They are the standard deviation commonly used in building the a priori covariance matrix [36]. Pressure can be retrieved together with temperature, and a 5% error is added to the pressure profile to be the a priori pressure profile.  
The assumed uncertainties are summarized in Table 5.
## 3 Results and Discussion
### Statistical Analysis of the VOCs Profile in the Sabrina and Elide Strawberry Samples  
The separate contributions of strawberry cultivar and mulching films to the profile of VOCs was inferred on a statistical basis. The data set consisted of 69 and 65 VOCs for Elide and Sabrina, respectively, and 18 observations (3 samples for each of the 3 mulding films and 2 technical replicates for each sample). The two biplots obtained by modelling by PCA the HS-SPME/GC-MS semi-quantitative data (% RPA), separately collected for the two cvs, are reported in Figure 1. The biplot was obtained by plotting the correlation loadings and the scaled scores of the PCA model on the same plot. The two components accounted for 96.1% and 98.4% of the variation in the dataset of Elide and Sabrina, respectively, since PC1 and PC2 explained the 66.7% and 29.4% of the total variance of the plot in panel A and 80.0% and 18.4% in panel B, respectively. For the two cvs, the observations for each of the three mulching films lie in corresponding regions of the PCA plot. In fact, PE fruit was characterized by a negative PC1 and a positive PC2, while samples obtained using N5 film presented both negative PC1 and PC2. On the other hand, strawberries obtained using N18 much presented a positive PC1 and a negative PC2 (Figure 1). These findings revealed that the mulching films (PE, N5, N18) represent the main source of data variation. Moreover, it can be observed that different VOCs characterise different mulching films and that the effect of each mulching film on the profile of VOCs is different for each cv. Indeed, in the case of Elide, L3, T3, E30, and E21 were higher in N5 than N18, and PE, E20, E19, E34, and E17 were higher in PE than N5 and N18, while Ald1, T7, O1, and E25 were higher in N18 than in N5 and PE, for example. On the other hand, in the case of Sabrina, Al5 was higher in N5 than N18, and PE, E15, Ac1, L3, and Ald3 were higher in PE than N5 and N18, while E3, E1, E22, and E28 were higher in N18 than in N5 and PE, for example. Consequently, it can be affirmed that the VOCs that characterise each mulching film depend on the cultivar.  
The semiquantitative variability of individual VOCs induced by the mulching films was visualized through the heatmaps obtained for the two data sets. In Figure 2A, the heatmap for Elide is reported, while Figure 2B shows the heatmap for Sabrina. In both varieties, the VOCs showed very different trends: some VOCs increased both in N5 and N18 with respect to PE, while others decreased in both N5 and N18 with respect to PE, and some VOCs were higher in N18 and lower in N5 or vice versa, while others showed a level similar to PE. Since the two heatmaps are different, it is possible to conclude that the effect of the mulching film on the VOCs composition depends on the cultivar, in line with the outcomes of PCA analysis.
## 5 Modes of Operation  
### Data Acquisition System (Subpayload 1) RSO Imaging  
The image acquisition application was developed in C++ using the header files and functions provided with the PCO camera. In the event of a power outage, the system was designed to reboot, in order to prevent any open drivers from causing interference with image acquisition. The application was specifically designed for continuous imaging at resolutions of \(512\times 512\) pixels, \(1024\times 1024\) pixels, and \(2048\times 2048\) pixels upon startup. After boot-up, the system initialized into the low-resolution mode (low-res) with a resolution of \(512\times 512\) pixels in order to capture four images for downlink, each with an exposure time of 500 ms. The next step involved imaging in medium resolution mode  
Figure 4: Example of a sequence of PCO camera images captured from a field campaign. The red circle shows the location of the RSO as it transits. These images have been enhanced with the use of the Zscale algorithm.  
(med-res) at a resolution of \(1024\times 1024\) pixels with an exposure time of 100 ms capturing 5100 images. Then the application was programmed to capture at a high resolution (high-res) of \(2048\times 2048\) pixels with an exposure time of 100 ms acquiring 600 images. Afterwards, the application transitioned to low-resolution mode, then medium-resolution and back to high-resolution, operating as a closed-loop system until it was powered off.  
A system delay of 200 ms was added between each image when imaging at med-res and high-res modes to account for the microSD write speed of 10 MB/s. The system health status comprises the exposure rate, resolution, and system delay at the current mode of acquisition, along with three temperature recordings of the PCO camera at various locations (sCMOS sensor, camera, and power supply). Low-resolution mode images were then downlinked to the ground station along with the sensor's health status. These modes are designed to enable continuous data acquisition for the planned flight duration of 12 h, with an added 2-h margin requiring the payload to be powered on prior to launch. Refer to Figure 5 for an overview of the image acquisition process from boot-up.
## 4 Discussion
[PERSON] investigated the role of AKT in the production of COL-I in HSF and its role in systemic sclerosis fibrosis, and found that AKT is a positive regulator of collagen gene expression and a negative regulator of synthetic MMP-1 in HSF [12]. Blocking AKT inhibited the production of COL-I and led to the upregulation of MMP-1. AKT is a core element of the PI3K/AKT signalling network. This study also confirmed that CLRF and CW can effectively inhibit the upregulation of MMP-1 expression triggered by oxidative stress through activating the PI3K/AKT signalling pathway, contributing to the synthesis of cellular COL-I and the significant upregulation of hyaluron synthetase 1 (HAS 1) expression. It has also been shown that PI3K/AKT often works with transcription factor NF-E2-related factor 2 (Nrf2) to regulate cellular damage caused by oxidative stress, and the activation of this pathway can slow down the aging of the body. The antioxidant genes SOD, CAT and GSH-Px are downstream genes of the Nrf2-Keap-1 pathway, whose main role is to scavenge ROS and protect cells from oxidative damage [31]. [PERSON] [32] found that purple river flavonoids could activate the PI3K/AKT signaling pathway, affecting Nrf2-mediated Mn SOD gene expression and upregulated SOD protein expression, and thereby reducing mitochondrial oxidative stress [33]. This study shows that the expression of ROS protein was significantly reduced in HSF cells treated with the sample group, and the expression of SOD and CAT antioxidant enzyme mRNA was significantly enhanced. This suggests that CLRF and CW may regulate cell proliferation by activating the PI3K/AKT pathway, and inhibit intracellular ROS expression due to oxidative stress. However, whether Nrf2 mediates this and whether CLRF and CW can have protective effects against mitochondrial oxidative stress needs further validation. In summary, CLRF and CW have strong protective effects against H2O2-induced HSF damage and great application potential in improving skin aging damage due to oxidative stress. As shown in Figure 6, our study demonstrated that CLRF significantly reduced intracellular ROS levels, increased total cellular antioxidant capacity and slowed MMP-1-mediated COL-I degradation, contributing to HA synthesis and mitigating skin aging. Furthermore, in the exploration of the antioxidant mechanism, it was found that both CLRF can enhance the cellular antioxidant system and relieveskin aging by activating the PI3K/AKT signaling pathway and increasing the expression of antioxidant enzymes and hydrolyticoproteins. H2O2-induced oxidative HSF cellular damage and macroscopic skin aging are multifaceted, and there are some limitations in this study. This experiment did not verify the main indicators of cellular loss of function and senescence at the cellular level. Another study showed that H2O2-induced oxidative stress causes DNA damage in HSF cells, causing telomere shortening and reduced telomerase activity in HSF cells [34]. This is an issue worthy of in-depth consideration in subsequent experimental investigations, which can verify the preventive effect of Coix Seeds on HSF cells aging in many ways.
## 4 Test Description and Equipment
The longest GPS period during which a standalone GPS solution is unavailable (herein termed an \"outage\") is about 60 s and is highlighted by a dotted circle in the top part of Figure 7. In the GPS/GLONASS case this period is still largely present, but is shortened by a combined GPS/GLONASS fix as shown in bottom part of Figure 7.  
Segment 2 represents a very difficult scenario. The number of visible GPS satellites on the trajectory is presented in Figure 8 (on the top), showing two long partial/total GPS outages (about 60 s) on the two east/west legs. These two parts of the trajectory are connected by a turn at western-most part of downtown with a sufficient visibility to continuously perform a GPS fix. The inclusion of GLONASS (Figure 8 on the bottom) does not improve significantly the satellite coverage during this segment as also shown in Table 4.  
Segment 3 represents a more benign scenario where the number of visible GPS satellites during the trajectory is presented in Figure 9 (on the top). After some short partial GPS outages (few seconds duration) at the beginning of the segment, the satellite visibility allows almost continuous GPS positioning. Only two short outages were encountered on the trajectory. Inclusion of GLONASS (Figure 9 on the bottom) further increases the number of available satellites, reducing the durations of the outages and increasing solution availability (Table 4). Figure 8: GNSS visibility on the Segment 2 trajectory.
# Deep-Learning-Based Models for Pain Recognition: A Systematic Review
## 6 Challenges and Future Directions  
Deep-learning methods are widely used in many applications. In recent times, pain recognition has been associated with sensory data rather than only facial expressions. Deep learning has shown promising results in many e-health systems with sensory data [2]. This achievement has resulted in greater interest in this approach by the pain-recognition community based on sensing data.  
Based on the review of state-of-the-art with respect to the use of deep learning in pain recognition, we have found few recent studies. Most pain-recognition studies used CNNs and LSTMS owing to their ability to resolve many challenges related to deep-learning methods. However, many challenges and future directions need to be solved and fixed.  
There is a need to better understand the problem of pain recognition. This will help to define the tools and additional methods such as the use of similarity measures and meta information [1].  
Furthermore, to build a recognition model, it is important to extract the most suitable features. The pain is subjective measure and so the finding of the discriminate features either from speech, facial or Physiological signal is very difficult. Therefore, the deep learning will help in such intuitive and hard to describe problem by people to be used in feature extraction.  
Moreover, the use of a single model depending on facial expression is limited and does not provide an accurate pain level. Therefore, the multimodality is considered as a solution and another challenge. This requires a combination of different kinds of signals, such as physiological signals with behavior expression [5].  
Moreover, it is important to find a new objective measure of pain based on sensory data and experiments, compared with the use of only instruments in the existing measures [5].  
The available datasets ranged from 22-142 participants, which is not enough to generalize the model of deep learning. This is considered an open issue that needs to be addressed, and a dataset should be developed with hundreds of participants.  
In addition, the challenge that faces deep-learning methods in pain recognition is strongly related to data acquisition. Data acquisition includes many issues that are related to: (1) the devices and sensors used, (2) processing of signals, (3) dimensionality of data [21] and (4) reliability of data [21]. With respect to the sensors, it is more convenient to deal with wearable and mobile devices, but such tools also have many challenges in terms of short battery life and more. More information about this issue can be found on our previous study [33] for such kind of wearable devices.  
Finally, most of the works were performed in a laboratory setting. However, the main challenge involves investigating the models in real clinical settings.
## Materials and Methods  
### Materials and Sample Preparation  
For preparation of samples, we used the following materials: titanium dioxide P25 (Aeroxide(r) Degussa, Evonik, Germany); copper (II) acetyl-acetonate (C\({}_{10}\)H\({}_{14}\)CuO\({}_{4}\)), 99% (Merck, Germany); zirconium (IV) butoxide Zr (OC\({}_{4}\)H\({}_{9}\))\({}_{4}\), 80% (Sigma-Aldrich, Germany); absolute ethanol (C\({}_{2}\)H\({}_{5}\)OH), 96% (Sigma-Aldrich); acetylacetone (C\({}_{5}\)H\({}_{6}\)O\({}_{2}\)), 99% (Merck, Germany); 1-butanol (Sigma-Aldrich, Germany) and double-deionized water 2 xDI (NANOpure, Barnstead).  
The surface-modified titanium dioxide (TiO\({}_{2}\))-based samples were prepared using the sol-gel method in combination with the impregnation technique by modification of commercial titanium dioxide (P25) to further enhance its photocatalytic performance. Proper amounts of copper (II) acetyl-acetonate (0.008 g; 0.016 g; 0.080 g) and zirconium (IV) butoxide (286 \(\mu\)L) were dissolved in 20 mL of absolute ethanol to obtain a titanium dioxide slurry containing 0.05 mol%; 0.1 mol% of copper and/or 1 mol% of zirconium, and after that, a chelating agent (acetylacetone) was added. The molar ratio of metal precursors (MPs) and C\({}_{5}\)H\({}_{8}\)O\({}_{2}\) was ensured to be 1/3. To such a mixture, 2 xDI was added drop by drop. The stoichiometric amount of 2 xDI to MP was 10. The slurry was stirred for 1 h, after which 5 g of P25 was added. The mixture was stirred for another 1 h and then dried at 80 \({}^{\circ}\)C, calcined at 500 \({}^{\circ}\)C for 2 h and ground in an agate mortar in order to obtain powders. BDS measurements require samples in the form of a plate. Thus, the previously prepared powders (0.3 g) containing a proper amount of Cu or Zr were pressed into pellets of around 0.5 mm in radius and around 1.0 mm in thickness by the use of a Laboratory Manual Hydraulic Press (Capital Lab Tech, Non CNC), with a maximum load of 30 tons.  
For testing the photocatalytic performance of the analyzed samples, we used the same materials and followed the procedure already reported in our previous work [16].
# Optoelectronic Instrumentation and Measurement Strategies for Optical Chemical (Bio)Sensing
## 4 Design of Applications
### Fiber Optic pH Sensor Based on Fluorescent Radiometric Intensity Measurement  
As is well known, pH is an important parameter in various fields of science and technology, such as environmental control, as well as in many industrial processes and in medicine, where physiological, rapid, and in situ pH measurements are sometimes needed. Historically, electrochemical sensors have been the most-used devices for pH measurements; however, these devices suffer from certain limitations, such as a lack of accuracy in measuring extreme pH values and difficulties in measuring aqueous suspensions of organic matter or low ionic resistance solutions. These problems limit the use of glass electrodes for certain applications. In these cases, optical sensors can be a good alternative, as they are less likely to suffer from the described problems.
## 2 System Model
### Power Consumption  
The system power consumption includes the operational power, which is the minimum amount of power to keep the SBS active, and data transmission power. Operational powers for SBSs and MBS are expressed as \(P_{o,SBS}\) and \(P_{o,MBS}\), respectively. The total power consumption of all SBSs is  
\[P_{T}=|\mathcal{J}^{active}|\cdot P_{o,SBS}+\sum_{j\in\mathcal{J}}\sum_{k\in C _{j}}\sum_{f\in F_{k}}P_{j,k,f},\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Definition** \\ \hline \(\mathcal{J},\mathcal{K}\) & set of SBSs and set of UEs \\ \(\mathcal{F}_{k}\) & set of subchannels allocated to the \(k\)th UE \\ \(\mathcal{S}_{k}\) & the SBS serving the \(k\)th UE \\ \(B_{sub}\) & subchannel bandwidth \\ \(N_{T}\) & MBS antenna array size \\ \(P_{T}\) & total power consumption at all the SBSs. \\ \(g_{j,k,f}\) & channel gain between SBS \(j\) and UE \(k\) in the \(f\)th subchannel \\ \(h_{j,k,f}\) & channel coefficient between SBS \(j\) and UE \(k\) in the \(f\)th subchannel \\ \(P_{SBS_{j},max}\) & maximum power available at the \(j\)th SBS \\ \(P_{j,k,f}\) & * transmit power from SBS \(j\) to UE \(k\) in the \(f\)th subchannel \\ \(p_{k}^{UE}\) & ** transmit power from the associated SBS to UE \(k\) \\ \(|\mathcal{J}^{active}|\) & number of active SBSs \\ \(\sigma^{2}\) & noise power \\ \(l_{k,f}\) & interference experienced by UE \(k\) in subchannel \(f\) \\ \(C_{j}\) & the set of UEs in cluster \(j\) \\ \(c_{j,k}\) & link indicator between SBS \(j\) and UE \(k\) \\ \(c^{UE}\) & user association \\ \(SINR_{k,f}\) & SINR for UE \(k\) in the \(f\)th subchannel \\ \(\rho_{k}\) & capacity for UE \(k\) \\ \(v_{k}\) & capacity threshold for UE \(k\) \\ \(\rho_{j}^{SBS}\) & user sum-rate for SBS \(j\) \\ \(R_{j}^{SBS}\) & maximum downlink data rate for SBS \(j\) \\ \hline \hline \end{tabular}  
* For the optimization approach. ** For the DRL-based approach.  
\end{table}
Table 2: Notation summary.  
where \(|\;\mathcal{J}^{active}\;|\) is the number of active SBSs.