esa-sceva/satcom-chunk-collection huggingface dataset:
# Smartphones as a Light Measurement Tool: Case of Study
## 2 Prior Research
### Light Measurement Analysis  
The most commonly used device for measuring light conditions is the luxmeter, a device that allows for simple measurements quickly and not the subjective illuminance of an environment. This device is composed by an integrating light meter with a circuit that develops a signal representing the logarithm of the light being measured and utilizes a display having a linear f-number scale [22]. These devices are too specialized to be available in many situations where light conditions should be measured. For this reason, some researchers have used CCD (Charge-Coupled Device) cameras to obtain luminance [23; 24], glare and illuminance measurements [25]. The utilization of CCD as a luminance meter in order to calculate visibility level has big advantages because it reduces the time required for the measurements. However, there are still limitations concerning the reliability on low levels [26]. In addition, to obtain the illuminance level using this kind of camera, a computer, connections between the computer and the light meter and finally software to collect, store and analyze data are necessary. Due to its high-precision resolution, these cameras are very expensive and are not commercially viable for many image-resolving measuring tasks. To solve this problem, the research conducted by [PERSON] [27] analyzed the possibility to measure luminance with digital still cameras (DSC). The main advantage of this kind of camera is that it can capture the luminance of an entire scene. Despite this research showing that it is possible to obtain luminance value with a digital camera, the quality of the device used is very important to the accuracy of the values. In this respect, the results show how professional cameras are more qualified for use as a luminance measure camera owing to its high color rendering.  
Similar principles used by [PERSON] in the use of digital still cameras are applied to the research carried out by [PERSON] et al. [12]. This study shows how it is possible to design a webcam based luxmeter that is applicable and suitable for lighting monitoring as well as the ability to be used in an embedded application. The research carried out by [PERSON] and [PERSON] [28], which shows how it is possible to evaluate the illuminance directly to the image obtained by the digital camera of a mobile phone, follows similar criteria. However, another research study that analyzes different mobile applications for different platforms such as Android, iOS and Windows phones, that ensure that they can measure the light directly with the mobile, shows how a serious measurement of illuminance is only possible with professional hardware. This is due to the error found in the measurement compared with a professional luxmeter being very high, and the fact that each mobile phone has different measurement values because of its hardware characteristics.
## 4 Experimental Results: Short Beams  
Tables 2-4 constitute the reference data to which compare the findings from the corrupted beams. The results are presented in what follows.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Elastic Modulus (GPa)** & **Mean (Per _w/c_ Ratio) \(\pm\) RSD** \\ \hline C42D & 40.1 & \\ C42E & 37.0 & 38.6\% \(\pm\) 3.29\% \\ C42F & 38.8 & \\ \hline C45D & 36.2 & \\ C45E & 32.4 & 35.7\% \(\pm\) 4.51\% \\ C45F & 35.2 & \\ \hline C50D & 32.6 & \\ C50E & 31.8 & 32.4\% \(\pm\) 1.33\% \\ C50F & 32.8 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Modulus of elasticity estimated using ASTM C469.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cylinder**} & \multicolumn{5}{c}{**Elastic Modulus (GPa)**} \\ \cline{2-5}  & **P1** & **P2** & **P3** & **Mean \(E\) (GPa) (Per Sample)** & **Mean (Per _w/c_ Ratio) \(\pm\) RSD** \\ \hline
42A & 41.9 & 41.9 & 44.7 & 42.8 & \\
42B & 41.9 & 37.0 & 44.7 & 41.2 & \(41.1\%\pm 3.38\%\) \\
42C & 41.9 & 37.0 & 39.3 & 39.4 & \\
45A & 39.3 & 39.3 & 41.9 & 40.2 & \\
45B & 39.3 & 34.9 & 34.9 & 36.4 & \(37.6\%\pm 4.82\%\) \\
45C & 37.0 & 37.0 & 34.9 & 36.3 & \\
50A & 28.0 & 33.0 & 33.0 & 31.3 & \\
50B & 28.0 & 33.0 & 28.0 & 29.7 & \(31.8\%\pm 6.00\%\) \\
50C & 37.0 & 33.0 & 33.0 & 34.3 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Elastic modulus estimated for the cylinders and based on the time of flight of the reflected wave.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cylinder** & **Wave Speed (m/s)** & **Elastic Modulus (GPa)** & **Mean Elastic Modulus (GPa) (Per _w/c_ Ratio) \(\pm\) RSD** \\ \hline
42A & 4692 & 34.4 & \\
42B & 4730 & 34.8 & 34.9\% \(\pm\) 1.43\% \\
42C & 4740 & 35.6 & \\ \hline
45A & 4340 & 31.8 & \\
45B & 4456 & 32.1 & 31.8\% \(\pm\) 0.90\% \\
45C & 4407 & 31.4 & \\ \hline
50A & 4367 & 29.6 & \\
50B & 4344 & 29.6 & 29.5\% \(\pm\) 0.64\% \\
50C & 4330 & 29.2 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Modulus of elasticity estimated for each cylinder and based on the value of the ultrasonic velocity of the longitudinal wave propagating along the cylinder axial direction.
## 2 Related Works
### Application of Reinforcement Learning in VSL and LCC
Simulation experiments demonstrate that the combined controller outperforms single sub-controllers in reducing traffic accidents and increasing road capacity, showcasing the complementary benefits of VSL and LCC. Although existing research has made progress in the field of traffic control, several issues and challenges remain. First, the synergy between different control methods is difficult to achieve, and determining their interactions and control cycles is a pressing issue. Second, while decentralized reinforcement learning is theoretically feasible, its effectiveness in practical applications requires further empirical validation. Moreover, single neural networks may have limitations in handling the complexity and variability of traffic environments, restricting their potential in practical traffic control scenarios. Finally, despite the theoretical appeal of joint control strategies, their practical application is highly complex, requiring significant advancements in computational capability and execution efficiency.
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 2 Overview of Feature Detectors and Descriptors
### Star Detector (Censiure)  
[PERSON] et al. [49] introduced the Center Surround Extremas (CENSURE) detector. It is characterized by two considerations: stability (features invariant to viewpoint changes) and accuracy. First, it uses Hessian-Laplacian method to calculate the maxima. Center surround filters known as bi-level filters are used to approximate the Laplacian. Then, a basic center-surround Haar wavelet is constructed by using this Laplacian; this is the CENSURE response. Then, necessary features are provided by a non-maximal suppression.
## 2 Materials and Methods  
### Materials for the Risers  
In this study, X80 steel was utilized for steel riser and for composite risers, titanium alloy (Ti-6 Al-4V) was used for the inner liner and AS4-epoxy was employed for the composite layers. The properties of these materials can be found in Tables 2 and 3 as well as in Figures 1 and 2. For the X80 steel (for riser 1) and titanium (for liner in risers 2 and 3), bilinear kinematic hardening model was selected to consider the elastic-plastic stage. For steel riser 1, 67% of the yield stress of X80 was identified as the allowable stress according to American Bureau of Shipping (ABS) standard [45]. For the titanium liner in risers 2 and 3, 59.5% of the yield stress of titanium was regarded as the allowable stress [46]. In terms of the properties of unidirectional lamina of AS4-Epoxy FRP composites, an elastic model was considered, i.e., the plastic stage was disregarded. The maximum stress failure criterion [47] considered failures in the fiber direction, transverse direction and in-plane shear separately for all composite layers. In Table 3, subscripts 1, 2 and 3 represent the fiber direction, in-plane transverse direction and through-thickness direction of a composite lamina, respectively (Figure 2).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Year** & **Project/Funder** & **Riser Type \({}^{*}\)** & **Materials** \\ \hline
1973 & Ahlstone [35] & D & glass fiber with epoxy \\
1980s & Institut Francais du Petrole and & P & glass and carbon fibers \\  & Aerospatiale of France [36] & & with epoxy \\  & National Institute of Standards and & & carbon and E-glass fibers \\
1990s & Technology, Advanced Technology & P and D & with epoxy \\  & Programs [37] & & with epoxy \\
1995–2001 & Norske Conoco AS and Kvaerner Oilfield & D & glass and carbon fiber \\  & Products [38] & & with epoxy \\
2003 & ConocoPhillips, Kvaerner Oilfield Products & P and D & carbon fiber with epoxy \\  & and ChevronTexaco [39; 40] & & carbon fiber with epoxy \\
2007 & Doris Engineering, Freyssinet, Total and & P & carbon fiber with \\  & Soficar [41] & & thermoplastic PA11 \\
2008–2011 & Research Partnership to Secure Energy for & D & carbon fiber with epoxy \\
2011 & Magma [43; 44] & P and D & carbon fiber with PEEK \\ \hline \end{tabular} \({}^{*}\) riser type: P–production riser, D—drilling riser.  
\end{table}
Table 1: A summary of previous joint industry projects on composite risers.
## 4 Performance Evaluation  
### Dataset Description and Hyperparameters  
All data for this research were provided by a pharmaceutical manufacturing company which participates in the SPuMoNI Project. Part of these data were obtained from two production lines and were properly configured to register values from sensors throughout the production process. These values were mainly some physico-chemical parameters such as temperature, humidity, speed, pressure values, etc.  
The first production line data (I1000) contains 176 batches of independent drug batches and the second production line data (I600) contains 296 batches of independent drug batches. Each batch is treated as a new cycle of the production process. When a batch ends it starts a new cycle of drug production so consecutive batch productions are ordered chronologically. The data from the two production lines comprise the values from sensors and related data and metadata and JSON files describing the ALCOA values for each batch.  
For the regression part of our experimentation, the dataset was created and organized as follows:  
1. Gather each input data sample and associate a set of the corresponding ALCOA+ scores calculated as in [5].
2. Normalize and encode each input sample as described in the previous section.
3. Feed each data sample to the network and gather its output.
4. Compare the output of the network to the corresponding effective ALCOA+ values and calculate the mean squared error loss (Formula (1)).
5. Back-propagate the error.  
The ALCOA+ principle values were calculated according to the rules defined in [5].  
The hyperparameters which have been used for the training of all models are presented in Table 1.  
All of the the experiments described herein were written in the Python programming language and using the Tensorflow framework ([[https://www.tensorflow.org/](https://www.tensorflow.org/)]([https://www.tensorflow.org/](https://www.tensorflow.org/)), accessed  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hyperparameter Name** & **Value** \\ \hline Learning rate & 0.0001 \\ Batch size & 32 \\ Optimizer & Adam [29] \\ \(\beta\)1 & 0.9 \\ \(\beta\)2 & 0.999 \\ Max. epochs & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameters used during training of the models.  
Figure 4: By generating successive predictions based on the process shown in Figure 3, trends in the evolution of ALCOA+ values can be derived.  
on 27 June 2023). The experiments were executed on a PC with an AMD Ryzen 7 5800X CPU, with 64 GB of RAM and an nVidia Titan X GPU with 12 GB of VRAM.
## 4 Implementation Details
### Performance Evaluation  
The performance was evaluated using four indicators that are widely used in the literature to evaluate the performance of forecasting models. These are: the root mean square error (RMSE), the normalized root mean square error (NRMSE), the mean absolute error (MAE), and the normalized mean absolute error (NMAE), as defined by Equations (3)-(6) [65; 66; 67]:  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(x_{i}^{a}-x_{i}^{p}\right)^{ 2}}, \tag{3}\]  
\[\text{NRMSE}=\frac{RMSE}{max(x^{a})-min(x^{a})}*100\%, \tag{4}\]  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}\left|x_{i}^{a}-x_{i}^{p}\right|, \tag{5}\]  
\[\text{NMAE}=\frac{MAE}{max(x^{a})-min(x^{a})}*100\%, \tag{6}\]  
where \(x_{i}^{a}\) and \(x_{i}^{p}\) are the actual and predicted points, and \(N\) is the total number of samples. Thus, normalization of the RMSE and MAE is performed using the min-max method.
# Exploring Vitamin D Synthesis and Function in Cardiovascular Health: A Narrative Review
## 6 The Impact of Processing on Vitamin D Levels in Foods and Mitigation Strategies
### Mitigation Strategies to Improve Vitamin D Content in Processed Foods
#### 6.2.1 Fortification  
The most widely used approach to restore or enhance the Vitamin D content of processed products is fortification. Vitamin D fortification, particularly in dairy products, cereals, juices, and margarine, has been instrumental in enhancing population-wide intake, especially in regions with limited sunlight exposure. In many countries, including the UK, the exposure of the skin to UVB radiation or supplementation is the primary means of obtaining Vitamin D. According to the latest UK National Diet and Nutrition Surveydata, collected in 2012/13 and 2013/14, mean dietary intakes of Vitamin D were reported as 3.1 \(\upmu\)g/d for adult men and 2.5 \(\upmu\)g/d for adult women. However, lower dietary intakes ranging from 1.25-1.6 \(\upmu\)g/d have been observed among South Asian women in the UK [111].  
To achieve sustainable fortification, direct addition, emulsification, or encapsulation are widely employed. Direct addition is predominantly used for enriching milk and dairy products [112]. Typically, Vitamin D is dissolved in a food-safe organic solvent, such as ethanol and butter oil, before being evenly distributed through homogenization within the food matrix. However, challenges arise with Vitamin D's stability within aqueous food matrices and its tendency to adhere to packaging materials, like polypacks or tetrapacks, which can lead to degradation. In the emulsification technique, an oil phase containing Vitamin D is dispersed into fine droplets within water, and these droplets are then incorporated into target foods like cheese, milk, and bread. Achieving a stable emulsion faces hurdles such as the homogenization of Vitamin D in the food matrix and the limited availability of food-grade emulsifiers. Manufacturers may utilize fortification techniques to add Vitamin D to dairy products post-pasteurization, offsetting the losses incurred during processing. Adding Vitamin D at stages of processing that expose it to the least amount of heat, light, and oxygen can help in retaining its levels. However, fortification levels may vary by region and regulations but are typically designed to provide a significant portion of the recommended daily intake of Vitamin D per serving. To reduce the loss of Vitamin D upon food processing, complementary approaches include the optimization of processing as the pasteurization parameters, such as temperature and duration, to minimize Vitamin D degradation while ensuring food safety. Modifying processing conditions, such as lowering temperatures and reducing exposure to oxygen and light during manufacturing, can help preserve Vitamin D content.
# Ground Deformation Associated with Deep Excavations in Beijing, China
## 2 Typical Details concerning Deep Excavation
### Database of Cases  
The open excavation method is widely used in Beijing for its advantages of being fast, having high efficiency, being economical, offering a good working environment, and having strong controllability. Approximately 60% stations have been constructed using the open-cut method in Beijing (subway lines 4, 5, 6, 7, 8, 9, 10, and 14). As shown in Figure 3, details of 88 cases have been collected, including design data and site monitoring information. Design data include length, width, excavation depth, embedded depth ratio (EDR) (the ratio between the embedment depth of the maintenance structure below the bottom which is being excavated and the excavation depth [1]) and support system.  
The main maintenance structure of all the stations is bored pile, and the supporting system includes a steel brace, a few soil nails, and a preloaded anchor cable. The shape of the pits is long and narrow, with a concentrated length of about 150-250 m, and a concentrated width of about 20-25 m. The excavation depth is usually 15-25 m, and the maximum excavation depth reaches 32 m.  
Figure 3: Distributions of excavation length, width, and depth.
## 4 Discussion  
### Analysis of the NPP Impact Factors  
In summary, NPP variations in the Songnen Plain were influenced by changes in PRE and SM, with PRE exerting a more significant effect. Previous research results have indicated that TEM is the primary limiting factor for NPP in humid to semi-humid regions, while PRE is the primary limiting factor for NPP in arid to semi-arid regions [68]. Since the study area is characterized by a temperate monsoon climate and a semi-arid environment, the vegetation is susceptible to moisture shortage, making moisture stress a main constraint of vegetation NPP increase in this area. These findings were consistent with the research results in other areas of the Northeast Plain of China, such as the Sanjiang Plain and the Liaohe River Basin [69; 70]. In contrast to previous studies that solely considered the relationship between precipitation and NPP in climate analysis [29], we have discussed the combined effects of precipitation and soil moisture from the perspective of water stress.  
Figure 17: Interannual variations of NPP and its primary drivers in the Songnen Plain.  
Figure 18: NPP growth transfer matrix of different land covers in the Songnen Plain from 2001 to 2020.  
Overall, the moisture conditions in the Songnen Plain show an increasing trend, indicating improved growth conditions for vegetation within this region. Additionally, the Songnen Plain exhibits a weaker correlation with temperature, suggesting that the impact of global warming on this region may not be severe to a significant extent. However, it is worth noting that this consideration is primarily at an annual scale. At a monthly scale, due to the relatively cold climate in the northeastern region, global warming might advance the vegetation's growing season.
## 5 Experiment Results and Discussion
### Scenario 1: Loading Rate at 0.1 MPa/s
#### 5.1.2 Results on Shear Strain and Y-Direction Strain Dynamics  
According to the Hill-Tsai criterion [27], the tensile failure strain of the sandstone is about 0.0037 and the shear failure strain is about 0.011, which is three times as large as the tensile strain. The compressive strain is about 0.015-0.1, which is 5 to 50 times as large as the tensile strain.  
Based on Figure 6, the time of failure for point A is t = 131 s, for point B is t = 139 s, and for point C is t = 140 s. From Figures 10 and 11, at points A, B, and C, the y-direction strain and shear strain do not reach the failure value at the corresponding time points under study. That is to say, the failure effects caused by the y-direction strain and shear strain are not the main factors affecting the failures at points A, B, and C.  
Figure 8: X-direction strain–time relationship at point B (loading rate = 0.1 MPa/s).  
Figure 9: X-direction strain–time relationship at point C (loading rate = 0.1 MPa/s).
## 2 Materials and Methods
### Data Collection and Analysis  
Once the participants completed the driving simulations, various output parameters were collected using the STISIM Drive(r) 3. These parameters included longitudinal speed (km/h), lateral position (m), and acceleration/deceleration (m/s2), which were analyzed to evaluate the participants' driving behavior before and during horizontal curves across all road marking treatments. The lateral position is the distance between the vehicle's center and the road's centerline. This factor represents one of the drivers' behavior risks, whether a frontal crash or running off the road causes the crash. The higher the lateral position, the closer the vehicle will be to the road's edge. When the car is closer to the edge of the road, the risk of run-off-road crashes will increase.  
Figure 4: Preview of road marking in different scenarios in the right (R) and left (L) curve, with a radius of 350 m in the driving simulator; (**a**) control scenario—R; (**b**) RWE—R; (**c**) RE—R; (**d**) RWM—L; and (**e**) RDT—L.  
For this study, seven measurement points were taken from each curve to assess the effects of edge road marking treatments on driving behaviors. Two measurement points were positioned at 250 m (point 1) and 150 m (point 2) before the curve, while five were situated within the curve. These internal measurement points were located at the beginning of the entry transition curve (point 3), the start of the circular curve (point 4), the midpoint of the circular curve (point 5), the end of the circular curve (point 6), and the end of the exit transition curve (point 7). To clarify, Figure 5 illustrates the positioning of the seven measurement points for each curve.  
A within-subjects repeated measures ANOVA was conducted to analyze and compare driving behaviors in response to different road marking treatments, utilizing the Greenhouse-Geisser correction for sphericity violations on various output parameters. This technique enabled the evaluation of the significance of mean differences for each independent variable, such as measuring points, across multiple scenarios involving dependent variables like vehicle speed or lateral positioning. This approach identified the significance of differences across all possible pairings of independent variables for every dependent variable. It is important to understand the order of significance among the dependent variables, meaning which is the most important and has the least effect. The dependent variables in this research included longitudinal speed, acceleration/deceleration, and lateral position. The independent variables comprised parameters used to create horizontal curves for testing, such as the radius of the curve, curve directions, road marking conditions, and measuring points. A General Linear Model (GLM) with repeated measures was used to perform pairwise comparisons, accounting for within-subject variability. Bonferroni correction was applied to control for Type I error. This analysis focused on differences related to marking conditions in terms of driving speed, acceleration/deceleration, and lateral position.