esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Temporal data aggregation is another important consideration, analogous to the challenge of footprint characterization and matching. Averaging flux and proxy NDVI measurements reduced fine-scale variability of both datasets suggesting that proper temporal aggregation can improve model accuracy when comparing optical and flux data (Table S1, Supplementary Materials). Our findings show that it is crucial to optimize the temporal aggregation when fluxes are related to optical measurements since aggregation can deeply influence the accuracy of the resulting model. For our study, a 5-h averaging period (Table S1) worked well, but we would expect the optimal aggregation period to vary slightly with conditions, e.g., depending upon the stability of the fluxes and optical sampling, which would vary with latitude and daylength across sites. Thus, the topic of optimal aggregation period deserves further study and should be considered as an important issue when predicting fluxes from optical data. Clearly, one of the challenges of integrating remote and proximal sensing with LUE model in the context of the flux footprint is the need for defining an appropriate temporal and spatial sampling scale. More efforts are needed in the future to further clarify this issue. A detailed investigation of the flux footprint and its dynamics was beyond the scope of this study but was a topic of a separate investigation at this site [41]. While sampling footprint error was low for our site, a possible improvement might derive from integrating a high number of wireless proxy NDVI sensors in order to expand the footprint of optical sampling while maintaining high frequency to better match the footprint and time scale of eddy covariance tower. At our site, we are examining this method of improving the spatial representation of proxy NDVI sampling while preserving the rich time series of automated measurements. More attention should also be given to the potentially confounding effects of spatial or temporal autocorrelation, which were not explicitly considered in our study. In the future, UAVs offer a way to improve the footprint of field optical sampling, but obtaining sufficiently rich time series data will remain a challenge. The integration of optical and flux measurements can add insights and capabilities beyond those of either measurement alone, in part by using one dataset to inform the other, and by addressing the common problem of missing data. Ongoing integration of optical and flux measurements in the framework of the LUE model offers rich potential for improved evaluation of carbon uptake and sequestration for rangeland sites. Comparisons of optical and flux measurements can provide a basis for extrapolating fluxes beyond the scale of the flux tower footprint to larger areas (\"upscaling\"), a topic of separate research at our site [41]. Further attention should be given to standardizing these methods across sites and research groups if we are to reach the full potential of integrating optical with flux measurements to deepen our understanding of ecosystem carbon fluxes.
# 3D FEA of Infilled RC Framed Structures Protected by Seismic Joints and FRP Jackets
## 2 Brief Presentation of Existing Experimental Results
### Seismic Protection of Infilled RC Structure with Highly Deformable Joints
#### 2.2.3 Loading Phases  
Only PHASE 1 and PHASE 2 were considered for the numerical analysis. The original position of the model was such that both walls of type B were loaded in plane, and both walls of type C were loaded out of plane. In this position (PHASE 1), the structure suffered at least 10 dynamic earthquake runs of increasing maximum acceleration. The structure sustained a top slab relative displacement of 88.9 mm, corresponding to a 3.7% drift during the last run of PHASE 1. The damage to the infills of type B after the last run is depicted in Figure 2a,b. However, it should be noted that during that run, both brick infills of type B (tested in plane), protected by the PUFJ seismic joint, did not exhibit any significant damage up to a 2.5% drift of the structure. Both brick infills of type C (tested out of plane) had no significant damage after the last run of PHASE 1. After the first PHASE, the damaged brick infills of type B received FRPU external strengthening bonded on both sides of the damaged wall using a flexible adhesive of type Sika PS without any special treatment of the infill face and no crack repair (see Figure 2c). Then, three additional runs were performed during PHASE 2 up to a 1.62% drift of the structure. Further excitation during PHASE 2 was avoided in order to secure the integrity of the structure for PHASE 3 of the experiments (not presented herein) as the RC columns had already suffered significant damage. No additional significant damage to the infill walls of type B or to the infill walls of type C or any damage to the FRPU retrofit was observed after PHASE 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Columns** & **Beam** \\ \hline Longitudinal & 8\(\Phi\)10 & 8\(\Phi\)10 \\ Transverse peripheral & \(\Phi\)8/50 & \(\Phi\)8/50 \\ Transverse rhombic & \(\Phi\)8/50 & - \\ \multicolumn{3}{c}{Additional Information} \\ Clear concrete cover & 42 mm & \\ Slab thickness/Reinforcement & 200 mm/Q503 & \\ \multicolumn{3}{c}{Total 7200 kg mass anchored on the top of the slab} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reinforcement detailing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Height (mm)** & **Length (mm)** & **Width (mm)** \\ \hline Infill & 2300 & 2300 & 100 \\ Brick Unit & 240 & 250 & 100 \\ Bed Joint & - & - & 3 (thickness) \\ Head Joint & - & - & 3 (thickness) \\ Wall B FRPU & - & - & 20 (thickness) 3 sides \\ Wall C FRPU & - & - & 20 (thickness) 4 sides \\ Beam & 200 & 2300 & 200 \\ Columns & 2500 & 200 & 200 \\ Beam–Column Joint & 200 & 200 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dimensions of structure and of different parts.
## 2 Materials and Methods
### Data Collection  
The proposed framework was trained and tested using OCT data, acquired utilizing a Zeiss Cirrus HD-OCT 5000 machine. The machine is located at the Department of Ophthalmology and Visual Science, University of Louisville. The machine has an axial resolution of 5 \(\upmu\)m. University of Louisville's Institutional Review Board (IRB) certifies the data collection and acquisition. The study adhered to the Declaration of Helsinki. The collected dataset includes 200 OCT images from 100 subjects (i.e., two eyes per subject, one scan for each eye). The database is balanced and contains 100 OCT normal images and 100 DR images. The image resolution is 1024 \(\times\) 1024.
Sensors_**2011**, _11_, 6799-6815; doi:10.3390/s110706799  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Comprehensive Ubiquitous Healthcare Solution on an AndroidTM Mobile Device**  
**[PERSON] 1 and [PERSON] 2,***  
* Department of Electronic Engineering, Graduate School, Pukyong National University, Busan 608-737, Korea; E-Mail: [EMAIL_ADDRESS]
* Department of Electronic Engineering, College of Engineering, Pukyong National University, Busan 608-737, Korea
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-51-629-6223; Fax: +82-51-629-6210.  
_Received: 20 April 2011; in revised form: 10 June 2011 / Accepted: 12 June 2011 / Published: 29 June 2011_  
**Abstract:** Provision of ubiquitous healthcare solutions which provide healthcare services at anytime anywhere has become more favorable nowadays due to the emphasis on healthcare awareness and also the growth of mobile wireless technologies. Following this approach, an AndroidTM smart phone device is proposed as a mobile monitoring terminal to observe and analyze ECG (electrocardiography) waveforms from wearable ECG devices in real time under the coverage of a wireless sensor network (WSN). The exploitation of WSN in healthcare is able to substitute the complicated wired technology, moving healthcare away from a fixed location setting. As an extension to the monitoring scheme, medicine care is taken into consideration by utilizing the mobile phone as a barcode decoder, to verify and assist out-patients in the medication administration process, providing a better and more comprehensive healthcare service.  
**Keywords:** personal healthcare; ECG monitoring; wireless sensor network; android smart phone; mobile barcode decoder
## 5 Results and Discussion
### Velocity Deficit  
The results for the wake simulations obtained with the AD are now shown. The first comparison is made from the results of the streamwise velocity deficit along the vertical direction at different longitudinal positions, normalized by the freestream velocity at \(y=1.5D\). For these and other quantities extracted across the wake field shown in Sections 5.2-5.5, the values are sampled at every cell centre along a line in the \(y\)-direction (at the mid-plane \(z=0\)) at different \(x/D\) positions. These quantities correspond to time averages made during 20 LFT (13.33 s). No further spatial averaging is made. Note that LES results at \(x=3D\) have been added to the available \(x\)-positions of the LDA data as this is a position where values computed from HWA are later shown.  
Figure 4: Longitudinal evolution of \(L_{1}\) and \(L_{2}\) for the Ti3 (**left**) and Ti12 (**right**) cases without disk.  
Figure 3: Comparison of the TI decay for the Ti3 (**left**) and Ti12 (**right**) cases without disk. C-B & C corresponds to Equation (5) as found in [41]. The inserted frames contain the same data but with a logarithmic scale also in both axes and denoting the distance from the inlet in \(x/D\) units.  
Figure 5 shows the results for the high and low solidity disks under the inflow Ti3. The agreement to the experimental results is very good, with the larger difference observed around the shear layer (i.e., the wake envelope at \(y\simeq\pm 0.5D\)) from the disk edges, especially for the disk with higher thrust.  
In the case of the Ti12 inflow, Figure 6 shows a minor reduction in the agreement of the LES with the measurements. The predictions commence to differ when moving further into the far wake, around the centreline and shear layer regions. Remarkably, the results of RANS are almost identical to those from LES in both TI cases.
## 4 Mathematical Modeling
### Sensitivity Analysis  
Considering the level of variation in capacity and production rates for the SC, a sensitivity analysis is presented, which incorporates the in-state space analysis for the LTI with proper dynamics along the SC. For the sensitivity analysis of production rates: Considering Equations (8)-(11), and deriving with respect to \(\varphi_{1}\), \(\varphi_{2}\), \(\varphi_{3}\), and \(\varphi_{4}\), it produces, respectively:  
\[\frac{d\dot{I}_{k}}{d\varphi_{k}}=-\frac{I_{k}}{C_{k}} \tag{28}\]  
With \(k=1\), \(2\), \(3\), \(4\).  
Analyzing the capacity control sensitivity analysis.  
\[\frac{\dot{d}I_{l}}{dC_{l}}=\varphi_{l}\frac{I_{l}}{C_{l}^{2}} \tag{29}\]With \(l=1\), 2, 3, 4.  
In order to relate capacity level with production rate, our aim is to work with Equations (28) and (29); from this we have:  
\[\frac{\partial I_{1}}{\partial C_{1}}=-\frac{\varphi_{1}}{C_{1}}\frac{\partial I _{1}}{\partial\varphi_{1}} \tag{30}\]  
After some mathematical analysis and integrating once, Equation (30) can be expressed as:  
\[C_{1}\frac{\partial I_{1}}{\partial C_{1}}=-\varphi_{1}\frac{\partial I_{1}}{ \partial\varphi_{1}} \tag{31}\]  
Equation (31) relates capacity level with production rate, based on the following:  
\[\frac{dC_{1}}{C_{1}}=-\frac{d\varphi_{1}}{\varphi_{1}} \tag{32}\]  
Integrating and solving Equation (32), we have: \(ln(C_{1}\varphi_{a})=K\), where \(K\) corresponds to a constant of integration from Equation (32). Generalizing for each node and considering the LTI system:  
\[C_{1}=\frac{e^{K}}{\varphi_{1}} \tag{33}\]  
Assuming \(K=0\), Equation (33) reduces to: \(C_{1}=\frac{1}{\varphi_{1}}\).
## 4 Results  
### Experimental Environment and Dataset  
The experiments in this study were conducted using a hardware environment consisting of an NVIDIA RTX 3090 GPU (24 GB), an Intel Core i9-10900K CPU, and 64 GB of RAM. The software environment included Ubuntu 20.04 LTS, Python 3.8, PyTorch 1.10.0, CUDA 11.3, and OpenCV 4.5.4, with Docker used as the development environment. All models were implemented based on the PyTorch framework, and the OpenCV library was used for data preprocessing and post-processing. For real-time processing performance evaluation, the models were optimized using NVIDIA TensorRT.  
The dataset used in this study consisted of self-built datasets and public datasets. For corner detection, the self-constructed dataset was labeled for corners of license plate images collected from Roboflow and Alhub. It consisted of a total of 3619 images, split into 3319 for training and 301 for validation. The Roboflow dataset for license plate detection contained a total of 16,188 images, split into 16,060 for training and 128 for validation. The Roboflow  
Figure 5: License plate recognition system pipeline.  
dataset for vehicle detection contained a total of 24,914 images, split into 23,575 for training and 1339 for validation.  
The statistical characteristics of the dataset are shown in Table 1. In particular, the image distribution according to angle distortion was maintained evenly to ensure fair performance evaluation under various distortion conditions. The dataset labeling includes information such as vehicle bounding box coordinates, license plate bounding box coordinates, four corner coordinates of license plates, license plate text, and shooting conditions (angle, lighting, weather, etc.). Through this detailed labeling, performance could be analyzed in detail under various conditions.
## 7 Results and Discussions
Figure 10: Theoretical vs. simulated results for spectral efficiency of conventional and DS-PIM-SIM OFDM. Figure 9: Spectral fricency vs. modulation order, for conventional and DS-PIM-based SIM OFDM (index word lengths of 2-bits, 3-bits and 4-bits) where K = 3. In Figures 8-11, the conv-2 and DS-PIM-2 stands for conventional and proposed approach, respectively, for a 2-bit index word. Conv-3 and DS-PIM-3 represent the conventional and proposed variants of SIM OFDM for 3-bit index word, while conv-4 and DS-PIM-4 stands for conventional and proposed approach for a 4-bit index word, respectively.
## 2 Fundamental Models and Literature Search
### Planning Model
#### 2.2.1 Objectives
The first component is the travel time of the AMV, while the second component involves the time slot allocation for time-division multiple access (TDMA), which constitutes the total residence time of the AMV at the cluster head nodes. 5. **Energy consumption of AMV**[35; 36; 37]: The energy consumed by the AMV is made up of three main components: hotel energy, propulsion energy, and maneuvering energy. The first one refers to the energy consumed by the AMV for all its non-movement-related activities or systems. These activities could include maintaining the necessary onboard systems, sensors, communication devices, and other equipment that are essential for the AMV's operation but are not directly related to its movement along the path. The propulsion energy is required for the AMV to move along the designatedpath segment. It involves motors, propellers, or any other mechanisms that generate thrust and propel the AMV forward through the water. Furthermore, the work in [35] has demonstrated that the maneuvering of the AMV within the turning line segment is considerably influenced by the intricate ocean environment. The process of maneuvering encompasses alterations in direction, depth, or orientation, which are necessitated by obstacle avoidance, bearing currents, and other variables affecting the trajectory of the AMV. This dynamic can potentially result in a significant rise in both travel time and energy consumption. Therefore, the significance of maneuvering energy cannot be understated either. 6. **Value of information (VoI)**[38; 39; 40]: The VoI is used to reflect the importance of data and its sensitivity to time. Many studies drive an AMV to select the nearest sensor (or data collector) based on geographic proximity for its next visit. However, this geographical closeness does not inherently capture the VoI related to physical parameters registered by sensors. These parameters might significantly differ concerning event significance and timeliness [41]. Consequently, an optimal path determined solely by geographic closeness might yield limited value, making a negligible contribution to the overall accumulation of valuable data. Some applications exhibit time-sensitive characteristics, implying that VoI peaks when sensor data are captured, gradually diminishing as time elapses until the data reach the base station. Delayed data transmission leads to reduced VoI at the base station, hindering the effective analysis and response to monitoring necessities.
## Abbreviations  
The following abbreviations are used in this manuscript:  
QKD Quantum Key Distribution  
CV Continuous Variable  
MDI Measurement-Device-Independent  
GHZ Greenberger-Horne-Zeilinger  
CM Covariance Matrix
# Optimizing Nigella Oil Extraction Temperature for Sustainable Production
## 3 Results and Discussion
### Fatty Acid Composition  
The fatty acids composition of Nigella oil extracted via screw press at five different temperatures is illustrated in Table 4. The results indicate no significant differences in the fatty acid profiles among the oil samples. While no similar studies on Nigella seeds were found in the literature, a study by [PERSON] et al. (2020) investigated almond, walnut, and pistachio oils screw press extracted at different temperatures (50, 100, 150, and 200 \({}^{\circ}\)C) and similarly found no significant effect of temperature on the fatty acid composition of these nut oils. A total of 11 fatty acids were detected, collectively contributing between 180.4 and 185.6 mg/g oil in the studied samples. The saturated fatty acids (SFAs) included myristic, palmitic, margaric, stearic, and arachidonic acids, while monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) each compromised palmitoleic, oleic and 11-Eicosenoic acid, and linoleic, alpha-linolenic and 11,14-Eicosadienoic acid, respectively. The predominant fatty acids were linoleic, oleic, and palmitic acids, each exceeding 25 mg/g of oil in all samples, with linoleic acid being the most abundant at over 100 mg/g of oil. The total content of SFAs, MUFAs, PUFAs, and unsaturated fatty acids (UFAs) in the studied oil samples ranged between 38.1 and 40.7, 28.2 and 29.2, 113.5 and 115.7, and 142.3 and 144.8 mg/g of oil, respectively. Comparable results have been reported in previous studies on Nigella oils worldwide (2015, 2016, 2017). For example, [PERSON] et al. (2015) investigated six different cold-pressed Nigella oils and found SFAs, MUFAs, and PUFAs within the ranges of 15.64-16.23, 23-24.91, and 59.02-61.36 g/100 g of total fatty acids, respectively, with linoleic, oleic and palmitic acids being major constituents. In the present study, fatty acids accounted for approximately 18% of the total weight of the oil.
## 4 Results and Discussion
### Evaluation of the Proposed Approach
In order to validate the proposed approach, we repeat the first experiment by using the normal class (N), related to scans of healthy subjects, as reference. Results on both the datasets are shown in Table 5. This table shows that the results, obtained by using the normal class as the reference, provide metrics very similar to those shown in Table 4. Moreover, once again, by using the proposed cosine distance, all the considered performance metrics are unit-valued. \begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Model** & \(d_{m}\) & \(\sigma_{d}\) & \(TH\) & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline \multicolumn{8}{c}{**Computed Tomography (\(\eta=2\))**} \\ \hline
**Proposed (Cosine)** & 0.0544 & 0.0664 & 0.1872 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
**Proposed (KL)** & 0.1666 & 0.1218 & 0.4102 & 0.9870 & 0.9870 & 0.9870 & 0.9870 \\
**Proposed ([PERSON])** & 0.0027 & 0.1037 & 0.2101 & 0.9870 & 0.9870 & 0.9870 & 0.9870 \\
**Proposed (\(\chi^{2}\))** & 0.2272 & 0.1119 & 0.4652 & 0.9860 & 0.9860 & 0.9860 & 0.9860 \\ \hline \multicolumn{8}{c}{**X-Rays (\(\eta=1.4\))**} \\ \hline
**Proposed ([PERSON])** & 0.0339 & 0.0325 & 0.0794 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
**Proposed (KL)** & 0.0011 & 0.0564 & 0.0801 & 0.9949 & 0.9950 & 0.9949 & 0.9949 \\
**Proposed ([PERSON])** & 0.0027 & 0.0447 & 0.0653 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
**Proposed (\(\chi^{2}\))** & 0.0016 & 0.0450 & 0.0645 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\ \hline \end{tabular}
\end{table} 
Table 4: Results obtained by the proposed approach under the two considered datasets. The results have been obtained by using \(N_{T}=500\) reference images from the CP class and a number \(N_{bin}=50\) of histogram bins. Figure 3: The obtained distances on the considered CT and CXRs datasets, and related threshold \(TH\) (the red lines) set using (2). The distances are: (**a**) Cosine for CT, (**b**) Cosine for CXRs, (**c**) Kullback–Leibler divergence for CT, (**d**) Kullback–Leibler divergence for CXRs, (**e**) Bhattacharyya distance for CT, and (**f**) Bhattacharyya distance for CXRs. The \(\chi^{2}\) distance behaves as in the KL and Bhattacharyya distances; hence, it is not explicitly shown. The results have been obtained by selecting \(N_{\rm T}=500\) reference images and a number \(N_{bin}=50\) of histogram bins. The threshold in (2) has been set to \(\eta=2\) for the CT dataset and to \(\eta=1.4\) for CXRs.