esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Combination of PRI and SIF for GPP Estimation  
Photosynthesis, fluorescence, and NPQ are three pathways to dissipate the absorbed light energy, which interrelate and compete with each other. The relationship between photochemical yield (\(\Phi\)P) and fluorescence yield (\(\Phi\)P) is divided into two distinct phases: under high light, NPQ dominates the variations of \(\Phi\)P, with constant photochemical quenching (PQ); under low light, the variations of \(\Phi\)P are mainly driven by PQ, with constantly low NPQ [67]. \(\Phi\)P is in proportion to \(\Phi\)P under the NPQ stage and becomes inversely proportional under the PQ stage because of the opposite effects on \(\Phi\)with decreasing PQ and increasing NPQ. PRI has been proven to be a representative of the temporal dynamics of NPQ [69]. The combination of PRI and SIF observations could be a solution for a better estimation of photosynthesis. [PERSON] et al. [63]) and [PERSON] et al. [70] combined the PRI and SIF data in simple linear regression models and found that their combinations improved the estimation of GPP. However, their models were more mathematical than mechanistic. [PERSON] et al. [71] combined PRI and SIF via NPQ model and \(\Phi\)F to predict the dynamics of photosynthetic efficiency at leaf scale and short-time scale from a theoretical perspective. Different from their direct combination of PRI and SIF, we treated SIF and PRI as alternatives for the estimation of GPP and pointed out the better performance of the PRI-based LUE model under most environmental conditions for the maize field in our study. Additional studies on the theoretical combination of PRI and SIF at various temporal and spatial scales would be helpful for a more accurate estimation of GPP.
# Depth Estimation from a Hierarchical Baseline Stereo with a Developed Light Field Camera
## 4 Experimental Results
### Capturing System  
As shown in Figure 2c, a capturing system using the lab-designed light field camera is constructed. The camera's position and imaging parameters are fixed, while the object moves horizontally on a slide rail. Most objects in the experiments are approximately small in size, around 10 cm \(\times\) 20 cm, and captured at a near distance. However, measuring highly detailed depth values poses a challenge for some consumer 3D imaging devices at the same distance. A TOF camera with a valid depth range of 1.0 m-5.0 m, only can capture the object's silhouette but without any detailed geometric depth information.  
The lab-developed light field camera presents a compelling solution for capturing fine details of small-sized objects by offering precise control over the FoV (field of view) and depth range. Meanwhile, the lighting condition is continuously controlled based on the output depth values.  
The target object is segmented from the background in light field raw images. In the feature vector construction step, the _k-ring_ neighboring macro-pixels are chosen to be \(1,1,2,3\) from the top to the bottom layer of the pyramid. And in the smoothness constraint enhancement, we set the neighboring macro-pixels to \(3\times 3,3\times 3,5\times 5\), and \(7\times 7\). Both the matching cost computation and constraint enhancement are linear operations, contributing to reduced processing time.
## 5 Grasp
### Latency GRASP  
Algorithm 1 shows the generic code for the slightly tuned GRASP meta-heuristic used for the latency model. Regarding its input parameters, \(Perms_{i}\) contains the identification of the current nodes under a criticality test condition. \(T\) makes reference to the table of relationships between each transmission range and the energy needed to transmit at that level, as shown in Figure 1. \(MaxIter\) defines the number of iterations to perform before finishing. \(\alpha\) is a mandatory GRASP parameter that controls the level of randomness to be applied when creating a solution. It has been finally decided to use an \(\alpha=0.5\) for all the tests, since this value introduces a fair amount of randomness. Lower values might unable the GRASP to explore different neighborhoods due to starting most of the times from the same initial solution, and higher values might create bad initial solutions that the local search is then unable to properly improve.  
Finally, \(G\) specifies a valid structure of a solution. As an output, the best solution found in terms of the current cost function being used is returned.  
As for the actual code, line 2 contains the creation of an initial _random_ solution to the problem. Then, this solution is improved by applying a local search algorithm as specified in line 3. Finally, if the cost of the new solution is better than the momentary best, the solution is updated.  
```
0:\(Perms_{i}\): subset of critical nodes to remove T: transmission levels \(\alpha\): randomness level specific for GRASP G: node graph
0:\(G_{best}\): new node graph
1:while (MaxIter \(>0\)) do
2:\(G_{current}\leftarrow\) greedyRandomizedSolution(\(\alpha,Perms_{i}\))
3:\(G_{current}\leftarrow\) localSearch(\(G_{current},maxNoImproIter\))
4:if (cost(\(G_{current}\)) \(<\) cost(\(G_{best}\))) then
5:\(G_{best}\gets G_{current}\)
6:endif
7: MaxIter = MaxIter \(-1\)
8:endwhile
9:return\(G_{best}\)
```  
**Algorithm 1** GRASP(\(G\), \(Perms_{i}\), \(T\), \(MaxIter,\alpha\))  
As it can be seen, in order to define the GRASP meta-heuristic, the greedy solution construction and the local search methods need to be defined. The following sections show both of these methods in more detail.
# Required Moment Sets: Enhanced Controllability Analysis for Nonlinear Aircraft Models
## 2 Materials and Methods
### Calculation of Required Maneuver Set and Assembly of Total Required Set
#### 2.5.1 Evaluation of Attainable Moment Set  
In order to finally perform a control set-based controllability analysis, the attainable control set needs to be calculated and transformed to the query direction-based control set interface. There are methods for evaluating the attainable moments for nonlinear control effector models [5]. For the exemplary multirotor electric vertical take off vehicle (eVTOL) configurations that are the focus of this paper, simplified propellers with thrust and propeller torque that is linear in the squares of the angular propeller rates are used. For this reason the attainable set can be obtained by evaluating the angular and translational accelerations of all combinations of minimum and maximum propeller angular rates. The corresponding convex hull can again be found by using the quickhull algorithm [8].  
In general, the proposed approach allows more complex nonlinear propeller models for calculating the AMS as well. It is important to mention here that the comparison with the RMS does not allow an inflow dependent AMS, as the RMS is chosen for the worst case inflow in the envelope. A possible solution to this problem is to also choose the worst case AMS via intersecting multiple AMSs that were calculated at different points in the aerodynamic velocity envelope.  
After the convex hull is calculated, the same approach as for the required moments is used to assemble the intersections in the query directions in the vector \(\mathbf{a}_{AMS}\in\mathbb{R}^{1\times n_{\beta}}\).
# Chitosan-Coated Nanoparticles: Effect of Chitosan Molecular Weight on Nasal Transmucosal Delivery
###### Abstract  
Drug delivery to the brain represents a challenge, especially in the therapy of central nervous system malignancies. Simvastatin (SVT), as with other statins, has shown potential anticancer properties that are difficult to exploit in the central nervous system (CNS). In the present work the physico-chemical, mucoadhesive, and permeability-enhancing properties of simvastatin-loaded poly-\(\epsilon\)-caprolactone nanocapsules coated with chitosan for nose-to-brain administration were investigated. Lipid-core nanocapsules coated with chitosan (LNC\({}_{\text{chit}}\)) of different molecular weight (MW) were prepared by a novel one-pot technique, and characterized for particle size, surface charge, particle number density, morphology, drug encapsulation efficiency, interaction between surface nanocapsules with mucin, drug release, and permeability across two nasal mucosa models. Results show that all formulations presented adequate particle sizes (below 220 nm), positive surface charge, narrow droplet size distribution (PDI < 0.2), and high encapsulation efficiency. Nanocapsules presented controlled drug release and mucoadhesive properties that are dependent on the MW of the coating chitosan. The results of permeation across the RPMI 2650 human nasal cell line evidenced that LNC\({}_{\text{chit}}\) increased the permeation of SVT. In particular, the amount of SVT that permeated after 4 hr for nanocapsules coated with low-MW chitosan, high-MW chitosan, and control SVT was 13.9 \(\pm\) 0.8 \(\upmu\)g, 9.2 \(\pm\) 1.2 \(\upmu\)g, and 1.4 \(\pm\) 0.2 \(\upmu\)g, respectively. These results were confirmed by SVT ex vivo permeation across rabbit nasal mucosa. This study highlighted the suitability of LNC\({}_{\text{chit}}\) as a promising strategy for the administration of simvastatin for a nose-to-brain approach for the therapy of brain tumors.  
Keywords: nasal permeability; nose-to-brain; simvastatin; nanocapsules; mucoadhesion; CNS disorders; chitosan +
Footnote †: journal:  
_Article_  
Chitosan-Coated Nanoparticles: Effect of Chitosan Molecular Weight on Nasal Transmucosal Delivery
# Construction and Application of an Ecological Quality Evaluation System Based on a PIE-Engine +
## 2 Materials
### Construction of the Remote Sensing Index of Ecological Quality
#### 2.2.3 Ecological Threat Index  
Different land use types have different disturbances to the ecosystem, so land cover data obtained by remote sensing are used to calculate the ETI, and the formula was detailed in Equation (3).  
\[ETI=\frac{\sum_{i=1}^{n}A_{i}P_{i}}{TA} \tag{3}\]  
\(ETI\) is the Ecological Threat Index. \(n\) is the number of land use type. \(A_{i}\) is the area of land use type \(i\), \(P_{i}\) is the coefficient of land use type \(i\), and \(TA\) represents the total area. The ecological threat coefficient for land use was determined by referencing the Lohani list method, the Leopold matrix method, and the Delphi method [7]. The coefficients obtained by these methods were averaged to establish the ecological threat coefficients corresponding to different land use types.
# Patient Safety Culture in Hospital Settings Across Continents: A Systematic Review
## 3 Result
### General Characteristics of the Reviewed Studies
#### 3.3.1 The Hospital Survey on Patient Safety Culture Version 1.0 (HSOPSC 1.0)  
A comprehensive overview of studies using HSOPSC 1.0 as the primary instrument for assessing PSC across various continents and countries is provided in Supplementary Materials. It highlights global efforts to understand the status of PSC and its enhancement within hospital settings.  
This study reviewed several research studies conducted between 2010 and 2023, noting that more than 25 studies were conducted in the recent years of 2021, 2022, and 2023. The studies cover a wide range of geographical regions, including Asia, Europe, South America, and Africa, with countries such as Indonesia, Portugal, Iran, China, Brazil, Qatar, and others. Also, the study participants range from hundreds to over five thousand participants, with some highly exceeding this due to reliance on national data or agencies [26; 30; 42].  
Supplementary Materials shows the scores of twelve PSC dimensions assessed through HSOPSC 1.0. These dimensions include Teamwork within units, Supervisor expectations and actions to promote patient safety, Organizational learning: continuous improvement, Management support for patient safety, Nonpunitive response to error, Feedback and communication about errors, Communication openness, Handoffs and transitions, Teamwork across units, Staffing, Frequency of events reported, and Overall perceptions of patient safety. The studies used two measures to reflect the level of PSC dimensions. The first measure was Mean \(\pm\) Standard Deviation (M \(\pm\) S), which relied on the Likert scale. The second measure was Positive Response Rate Percentage (PRR%), which ranged from 0 to 100. High values indicate stronger positive perceptions of patient safety within hospital settings and vice versa.  
Our review of the studies revealed variations in methodology. Some studies employed surveys across multiple countries for comparison [29; 48; 50], or a comprehensive view [40], while others focused on data within hospital settings, with analysis periods ranging from two years [26; 39; 66; 74] to four years [76]. Not all studies examined all 12 dimensions [39; 59; 61; 63; 69; 89]; some used a modified HSOPSC instrument measuring most dimensions, but not all [60; 89]. The PRR% measure was the most common, but some studies used mean and standard deviation [36; 52; 71; 74; 81; 86; 89; 92], and only one used safety culture index percentage [47]. Additionally, certain studies published results for both pre- and post-intervention [56; 92], while others investigated outcomes across different hospital settings [52; 94].
# Description of Thermochromic Offset Prints Morphologies Depending on Printing Substrate
## 3 Results
### Structural and Optical Characterization of the Papers
Impurities that lower the whiteness (for example lignin) absorb the light in the blue part of the spectrum. Table 2 shows the results of optical density determination and CIELAB values of papers. Optical density is the ability of a material to absorb the light. The darker the color appears to the eye, the higher is the density. Of all tested papers, R paper has the highest optical density (0.13), while S paper has the smallest (0.06). Optical density of F and B papers are equal (0.08). S paper has the highest lightness value (97.7), while R paper has the smallest (88.7). All papers except S paper, have positive \(a^{\star}\) values (shift to the red area). B paper has the highest \(a^{\star}\) value (1.3), while S paper has the highest \(b^{\star}\) value (0.9). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Property** & **F** & **B** & **R** & **S** \\ \hline Grammage/(g m\({}^{-2}\)) & 85.8 \(\pm\) 3.2 & 80.4 \(\pm\) 1.1 & 81.6 \(\pm\) 1.1 & 72.9 \(\pm\) 0.64 \\ Porosity/air permeability & & & & \\ Bendtsen/(mL min\({}^{-1}\)) & \(>\)3000 & 1209 \(\pm\) 80.4 & 619 \(\pm\) 22 & 0 \(\pm\) 0 \\ Surface roughness/(um) & 5.1 \(\pm\) 0.5 & 4.0 \(\pm\) 0.4 & 3.3 \(\pm\) 0.3 & 0.3 \(\pm\) 0.05 \\ Smoothness, Bekk/(s) & 1.9 \(\pm\) 0.1 & 11.6 \(\pm\) 0.5 & 28.8 \(\pm\) 3.4 & - \\ Ash content 525 \({}^{\circ}\)C/(weight \%) & 1.9 \(\pm\) 0.34 & 24.6 \(\pm\) 0.61 & 21.0 \(\pm\) 0.87 & 32.3 \(\pm\) 0.15 \\ Ash content 900 \({}^{\circ}\)C/(weight \%) & 0 & 9.7 \(\pm\) 1 & 12.6 \(\pm\) 0.7 & 12.7 \(\pm\) 1.7 \\ CaCO\({}_{3}\)/(weight \%) & 4.3 & 33.9 & 19.1 & 44.5 \\ Optical density & 0.08 & 0.08 & 0.13 & 0.06 \\ \hline Color CIE \(L^{\star}\)/\(a^{\star}\)/\(b^{\star}\) & 92.4/0.6/0.8 & 92.7/1.3/0.6 & 88.7/0.4/0.4 & 97.7/\(-\)0.3/0.9 \\ \hline \hline \end{tabular} \(L^{\star}\)-lightness; \(a^{\star}\)-red/green value of color; \(b^{\star}\)-yellow/blue value of color.  
\end{table} 
Table 2: Some physical and chemical properties of papers; F-filter paper, S-synthetic paper, B-bulky paper, R-recycled paper.
# Research Progress and Effects of Light on Poultry Circadian Rhythm Regulation Based on CiteSpace
## 4 Light Affects the Circadian Rhythm in Poultry by Regulating Melatonin Levels
### Regulation of Light on Circadian Rhythm in Poultry
The nuclear receptor (REV-ERB\(\alpha\) and REV-ERB\(\beta\)) and orphan receptor associated with retinoic acid (ROR\(\alpha\) and ROR\(\beta\)) can compete for the ROR binding site on the BMAL1 promoter affecting the expression of BMAL1 [99]. Specifically, ROR activates the expression of BMAL1, while REV-ERB inhibits BMAL1, thereby inhibiting the function of the clock protein. Therefore, these genes together form a secondary regulatory feedback loop. The exploration of the relationships between clock genes and circadian rhythm was important for light regulation in poultry, which can shed light on the poultry breeding industry, improving poultry health and animal welfare.
# Wavelet-Based Optimum Identification of Vehicle Axles Using Bridge Measurements
## 7 Field Test on a Simply Supported Stiff Concrete Bridge  
### Test Bridge and Instrumentation  
The second bridge selected for axle detection testing is located on Route 459 near Exit 10, about 25 km southwest of Birmingham, Alabama. The beam-and-slab bridge, identified as BIN No. 012296, was built in 1980 and is typical of highway bridges in Alabama and other American states. It consists  
Figure 14: Wavelet coefficients for multiple-presence loading event: (**a**) Truck B in Lane 3; (**b**) Truck A in Lane 4.  
Figure 13: Raw FAD signals for multiple presence loading event: (**a**) Truck B in Lane 3; (**b**) Truck A in Lane 4.  
of three simply supported spans of length, 14.04 m (46'4\"), 24.38 m (80') and 14.04 m (46'4\") with four same-direction lanes (Figure 15). The bridge has no skew angle with the abutments or piers.  
The 14.04 m end span (3)-(4) was selected for testing. The bridge deck is 21.56 m (70'9\") wide and has ten pre-stressed concrete girders, as shown. Strain transducer weighing sensors were mounted longitudinally on the softit of each concrete girder at mid-span. Eight FAD sensors, also strain transducers, were mounted longitudinally, 4.379 m (14'4\") apart, on the underside of the concrete slab. An additional six sensors (labeled here as Dia sensors) were mounted transversely on the internal diaphragm between adjacent girders from No. 2 to No. 8. In addition, a camera was installed on the site to monitor traffic in the relevant lanes. A commercial BWIM system (SiWIM) including sensors, data acquisition system, GMS connector, processor, etc., was used for the field test, where the ST350 strain transducers were used to measure the strains. The ST350 uses Wheatstone bridge circuitry with 350 \(\Omega\), which converts small changes in resistance to an output voltage that the data loggers can measure. Therefore, the recorded data is represented as a voltage signal, directly proportional to the strain signal. The sampling frequency was 500 Hz.  
In the initial calibration testing, four calibration vehicles, one half-loaded and one full-loaded 3-axle rigid truck, and one half-loaded and one full-loaded 5-axle semi-trailer, ran repeatedly over the bridge, at different speeds and in different lanes. The details of the calibration vehicles are given in Table 3. The initial calibration procedure took two days without interrupting the traffic. During the calibration test, four calibration trucks passed over the bridge along different lanes 128 times (each calibration vehicle 32 runs).  
Figure 15: Geometry and sensor locations on Route 459 bridge (units: feet’ and inches”).
# Assessing the Capacity and Coverage of Satellite IoT for Developing Countries Using a CubeSat
## 3 Coverage and Capacity Evaluation
### Efficiency Factor (\(\eta\))  
The efficiency factor is introduced in the equation to include the impacts of temperature and the Doppler effect on the reception capability of the designed system. The efficiency factor is derived using Equation (6).  
\[\dot{\eta}=\eta_{\rm T}\times\eta_{\rm ID} \tag{6}\]  
The efficiency factor, \(\dot{\eta}\) depends on the efficiency factor due to temperature impact, \(\eta_{\rm T}\) and the efficiency factor due to the Doppler-shift-impact mitigation method, \(\eta_{\rm ID}\). The  
Figure 16: Short data packet format (16 bytes) (**top**) and long data packet format (32 bytes) (**bottom**).  
approach of determining the efficiency factors for the proposed system are described in the following subsections.
# Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination
## 3 Materials and Methods
### Feature Extraction  
Deep learning models are greedy algorithms that require huge data to learn effectively. Having such data was a big problem to the research community in the field, in addition to lack of powerful computational hardware. With the emergence of large databases such as ImageNet [23] and hardware-accelerated devices such as graphical processing unit (GPU) respectively, deep learning research was revolutionized and the world started witnessing huge deep learning algorithms such as AlexNet,  
Figure 1: Sample of dataset images.  
VGGNet, GoogleNet and Residual Networks (ResNet) to mention but few. AlexNet the pioneer deepest learning model to be trained on ImageNet database to categorize 1000 different objects in 2012 [24] made an impressive breakthrough. Thereafter, different deeper neural models than AlexNet were proposed by different research communities including Visual Geometry Group (VGG) from Oxford University and GoogleNet from Google both in 2014 [25]. The deeper the network, the more patterns it can extract from the given data. However, building such deep neural network models have faced a vanishing gradient problem and difficulty during backpropagation and optimization processes. Microsoft Research Asia (MRA) in 2015 [26] proposed a very deep neural network model called Residual Networks (ResNet) to ensure that the performance of top layers is as good as the lower layers without vanishing gradient and optimization problem. This was achieved by using identity connection in parallel to the main convolution layers as illustrated in Figure 2, making back-propagation easier, quick learning and most importantly allows building network deeper enough to extract complex patterns from the data while maintaining an optimum accuracy improvement. MRA succeeded in developing very deep neural networks of at least fifty convolution layers such as ResNet50, ResNet101 and ResNet152.  
Interestingly, those pre-trained models can be used to achieve desired outcome in areas with deficient data via a process called transfer learning [27]. Transfer learning process is used in two approaches--fine-tuning where some modifications are made and as an off-the-shelf feature extractor where features are extracted in order to train a machine learning classifier. Both transfer learning approaches were utilized in this paper using the three ImageNet pre-trained models (that is, ResNet50, ResNet101 and ResNet152) to automatically discriminate whether a given abnormal skin image is burn or PUB.