esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Interactions between Vaccine Administration and Gut Microbiome Status
### Gut Dysbiosis and Its Influence on Vaccine Efficacy in Poultry
[105] evaluated the cecal pouch microbiota of Ross 308 broiler chicks administered with two regimens (two- versus four-intramuscular administrations) of a _Campylobacter jejuni_ recombinant protein vaccine candidate and challenged with live _C. jejuni_. At 42 days of age, compositional differences in the genera of cecal pouch microbiota were evident between the two vaccine administration regimens: _Faecalibacterium_ and _Oscillibacter_ genera increased among broilers vaccinated with the two-administration regimen of a candidate protein antigen, while _Blautia_ and _Subdogralinum_ genera increased among broilers vaccinated with the four-administration regimen of a candidate protein antigen [105]. However, these genera did not differ from each regimen's respective control group, indicating that the vaccination procedure rather than the protein administered altered the cecal pouch microbiota. Furthermore, broilers vaccinated and challenged with _C. jejuni_ exhibited lower cecal microbiota diversity when compared to those that were non-vaccinated and challenged, but there was no difference in richness among vaccinated and unvaccinated broiler groups [105]. Although not tested in a poultry model, [PERSON] et al. [106] correlated antigen-specific adaptive immune responses to changes in mucosal microbiota during the vaccination period. Intradermal vaccination with an HIV-1 DNA vaccine in female rhesus macaques increased the T cell responses in the blood at 7- and 14-days post-tertiary immunization along with elevated serum IL-6, CXCL10, IL-21, and IL-17 protein concentrations at 7 days post-immunization. At the same time, the presence of lactic acid-producing microbes (_Lactobacillus_ and _Streptococcus_ genera) decreased in the rhesus macaque vaginal mucosa from one to four weeks post-tertiary immunization, while the abundance of microbesassociated with bacterial vaginosis (_Atopobium_ genus) were negatively correlated with HIV-1-specific antibodies in the serum at 8 weeks post-tertiary immunization [106]. Ultimately, the influence of vaccine administration on gut microbiota appears to be as variable as the beneficial effects of probiotic supplementation during vaccine administration (Table 2). The type of vaccine (bacterial versus viral; attenuated versus killed) and the test subject (human versus animal; regional variations) seem to play critical roles in how the gut microbiota is altered during vaccine administration. For example, results such as those from [PERSON] et al. [86] exhibit diverse microbiome states that are usually individual- or flock-specific. Even so, regardless of the experimental animals, it appears that microbes essential for fiber digestion and lactic acid secretion play critical roles in regulating inflammatory responses to vaccine administration [55; 86; 106].
## 5 Evaluation and Results
### Simulation Setup
#### 5.1.1. Evaluation Environment  
The simulation was conducted on a locally hosted Hyperledger Fabric v2.2 network (Figure 7) with the following configuration:  
1. Network Topology: Two peer organizations (Org1, Org2), one ordering service, and one Certificate Authority (CA) per organization.
2. Channel: A single communication channel named mychannel for all smart contract transactions.
3. Smart Contract: Written in Go, encapsulating three core functions--RegisterUser, CreatePolicy, and EvaluateAccess.
4. Security: All communications were secured using Transport Layer Security (TLS). Peer CLI invocations used MSP identities and certificate chains.  
Figure 6: Script flow summarizing the automated access evaluation process.
# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 3 Methodology
### Explanatory Variables
#### 3.2.1 Flight Attributes  
1. Airline properties Airports have different priorities and resource allocations for different airlines. Airline bases are the airports where an airline has its hubs. Airlines often select certain airports as bases that serve as hubs, where their flights can stay overnight. Airline properties consist of two factors, i.e., the airline and whether the airport is used as a base.
2. Aircraft capacity Larger aircraft typically take up more airport resources and are given priority in takeoff and landing. Therefore, aircraft capacity is considered a potential factor affecting flight delays. The number of aircraft seats can be utilized to measure aircraft capacity and graded accordingly, as shown in Table 2.
3. Delay of previous flight The delay of previous flights will affect the operation of subsequent flights. If the previous flight has been delayed for some reason and fails to arrive at the airport outline, and the delay has not been fully absorbed, the subsequent flight cannot take off on time. \[FA_{i}^{3}=\frac{\tau}{t_{i}^{s}-t_{ip}^{a}}\cdot d_{i}^{p}\] (1)where \(FA_{i}^{3}\) indicates the delay of the previous flight, \(d_{i}^{p}\) denotes the delay of the previous flight; and \(t_{i}^{s}\), \(t_{ip}^{it}\) indicate the scheduled time of flight \(i\) and the real time of the previous flight \(i\), respectively. To predict the delay of a flight \(\tau\) hours in advance, if \(t_{i}^{s}-\tau<t_{ip}^{it}\), that is, the previous flight has not yet taken off at this time, then \(FA_{i}^{3}=0\). This setup considers the impact of flight spacing on delays. The larger the interval between two flights, the more delay is absorbed over time.
## 4 Results
### Telecommunication Tests
#### 4.2.2 Comparison between a Regular User and Enterprise Solution with SLA  
Figure 8 shows the latency considering an MNO contract with SLA and as a regular user.  
The results show similar average latency along the network. However, there is substantially more stability for the SLA case. This is because the SLA grants priority over the network. Therefore, as the figure shows, its availability increases in latency. In contrast, the jitter results are very similar both cases.
###### Abstract
As a result, clinicians look at the changes in the measured EOA value more than at the value itself [11]. Dysfunction of the BMHV is due to pannus formation, which limits the opening and closing of the leaflets. Fluoroscopy of the valve could provide the necessary assessment of the leaflet motions but is not able to assess the hemodynamics. Additionally, multi-detector-row computed tomography would also be a good alternative for the detection of prosthesis obstruction [12]. Moreover, a drastic reduction of EOA would provide an indication of obstruction. Therefore, this study aims to provide an EOA measurement able to detect any blocked leaflet, which is meaningful to compare with the orifice shape and geometrical area. This work uses 4D Phase-Contrast -flow and Computational Fluid Dynamics to define the location of the vena contracta compared with the center of the valve, to quantify the BMHV metallic blurring expected in 4D Phase-Contrast measurements and to assess the previously defined EOA\({}_{\text{AST}}\) method to enhance BMHV functional assessment by MRI. More specifically, it aims: (1) to compare the computation of BMHV EOA by MRI using a derived echocardiography continuity equation-based method and an AST method, (2) to quantify the MR signal alteration created by the valve, and (3) to evaluate its consequence on EOA measurements as related to the MRI capacity to detect the vena contracta.
# Computational Modeling of the Thermal Behavior of a Greenhouse
## 3 Results
### Case Studies
#### 3.2.1 Geometry and Orientation  
The first group of case studies analyzes the thermal behavior of a greenhouse with different overall dimensions while maintaining the same floor area and total height, when applicable, as well as the impact of the greenhouse orientation.  
Figure 9 shows the first case study, in which a greenhouse with a cross-sectional profile of a quarter circle was adopted.  
Case study 2 implemented a cross-sectional profile of a semicircle, resulting in a greenhouse with a considerably smaller internal volume, which can be seen in Figure 10.  
The third case study used a similar geometry to case study 2, where the geometry of case study 2 was raised so that it had the same height at the highest point as the reference greenhouse. Figure 11 shows the third case study configuration.  
Figure 10: Model of the greenhouse—case study 2.  
Figure 9: Model of the greenhouse—case study 1.  
In order to assess the effect of greenhouse orientation, in the last case study of this group the reference greenhouse, keeping the same dimensions, is simply rotated 90\({}^{\circ}\) counterclockwise, so that the initially south-facing wall is now oriented to the east, as shown in Figure 12.  
The annual results of this group of case studies can be found in Figures 13 and 14, which show, respectively, the indoor air temperature and relative humidity for the reference greenhouse as well as for the four case studies. The outdoor air temperature is also shown in both figures.  
Figure 11: Model of the greenhouse—case study 3.  
Figure 12: Model of the greenhouse—case study 4.  
Figure 13: Annual air temperature values for the reference greenhouse and case studies 1–4.  
The results show that the different configurations tested have a worse thermal performance than the reference case. In the worst case, case study 3, the reduction in indoor air temperature goes from around 5 \({}^{\circ}\)C in January to 8 \({}^{\circ}\)C in July. Changing the orientation of the greenhouse--case study 4--makes it possible to slightly increase the indoor air temperature by around 0.8 \({}^{\circ}\)C in January and reduce it in July by around 1 \({}^{\circ}\)C.  
The annual air relative humidity results show a similar but symmetrical behavior to that of the annual air temperature. The highest indoor air relative humidity of 64% is obtained in case study 3, which is 11% above the reference case, in January. The minimum air humidity value is obtained in August in the reference case with a value of 31%, which is 8% below case study 3. It can be concluded that the reference case with the orientation provided by case study 4 is the better choice in terms of greenhouse indoor air temperature behavior.
## 2 Materials and Methods
### Data Analysis  
The traditional statistics for SWC in both coal mining and non-coal mining areas were calculated based on the spatial distribution and temporal trend. Additionally, the statistics for topographic factors and edaphic indicators, which relate to soil properties, were calculated separately for coal mining and non-coal mining areas. The correlation coefficients between SWC and edaphic or topographic factors were also determined for both areas. Furthermore, the effects of rainfall on the relationships between SWC and environmental factors were assessed, with the distinctive results between coal mining and non-coal mining areas highlighted. The parameter of percent area of significant coherence (PASC) was used to evaluate the strength of these correlations. Lastly, the common factors showing significant differences between the two areas across different soil layers were identified and the relationships between the correlation coefficients for the two areas were calculated.
## 3 Characterization of Invariant Relative Orbits  
In this section, canonical transformations are introduced to develop a model providing a compact characterization of relative orbits which are invariant with respect to the zonal harmonic perturbation, modeled using the \(J_{n}\) terms up to the desired degree. The first set of canonical transformations allows absorbing the effects associated with the perturbation, resulting in a form of \(H\) equivalent to \(H_{0}\). The last transformation allows expressing the Hamiltonian function as a sum of three terms, representing the nature of the relative orbit (bounded or unbounded) and the amplitude of the in-plane and out-of-plane oscillations, respectively.
# Hilbert-Huang-Transform-Based Seismic Intensity Measures for Rocking Response Assessment
## 3 Results
### Discriminant Analysis  
In the present study, the discriminant analysis is performed by adopting four classes. The classes are defined to agree with the limit states, which are stated in Section 2.5. The only difference is that for rocking rotations \(\theta/\alpha>0.5\), two classes are determined; the former corresponds to large rocking rotations without overturning (\(\theta/\alpha<1\)) and the latter to rocking overturn. The description of the four classes is listed in Table 4. This way, the discriminant functions are appropriately calculated according to the data. In order to evaluate the contribution of the proposed HHT-based IMs in describing the rocking response, discriminant analyses are performed, taking into account two sets of features. The first model consists of all the conventional IMs (Model 1), while in the second one, the proposed IMs are also included (Model 2). The discriminate analysis performance verification is performed using 90% of the total data as the train set, while the remaining 10% is the test set.  
The average performance metrics of the classification analysis are presented in Table 5 for both the train and test sets. Based on the confusion matrix, the accuracy, precision, recall, and F1 score are calculated. [80]. Accuracy is the ratio of correctly predicted observations to the total ones. On the other hand, precision is the percentage of correctly predicted positive observations to the total predicted positive observations. The correctly predicted positive observations divided by the actual observations of a specific class defines the recall metric. Finally, the F1 score is a complex metric that combines precision and recall. In Table 5, the performance metrics of both models are provided comparatively. Where the values inside the parentheses are referred to as Model 2, based on these metrics, it is obvious that the HHT-based IMs should be combined with conventional IMs to classify the examined structural system's seismic response reliably. This fact is especially highlighted by the classifier's performance in the test set.
# A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios +
###### Abstract  
The massive amount of traffic required by the emerging Internet of Things (IoT) paradigm can be supported by the imminent arrival of 5G next-generation networks. However, the limited capacity of resources in IoT nodes, e.g., battery lifetime or buffer space, opens a challenge to be taken into account when proposing new routing solutions on IoT scenarios with intermittent connectivity. In this paper, we propose the concept of Opportunistic Context-Virtual Networks (OCVNs). The novelty of this approach is to create virtual groups of nodes that share interests in common for routing purposes. Therefore, only the nodes that are interested in the content of the messages that are flowing throughout the network are used as relaying nodes, providing their own resources for the sake of the communication. By leveraging the use of store-carry-and-forward mechanisms, a novel routing algorithm is proposed and evaluated over two realistic scenarios. Experimental results reveal that our solution outperforms other well-known opportunistic routing algorithms in terms of delivery probability and overhead ratio, while resource usage of relaying nodes is significantly reduced.  
Keywords: opportunistic networks; Internet of Things; routing algorithms; contextual information +
Footnote †: 28 February 2019; Accepted: 16 April 2019; Published: 19 April 2019
# Application of Machine Learning to Express Measurement Uncertainty
## 3 Experimental Data, Comparative Analysis, and Discussion
### Discussion  
The used ML model that implements the DART algorithm to predict the value of the \(l_{sr}\) contributed to the uncertainty budget of the non-iterative algorithm by \(u=u_{B}=0.06\) m (Table 2). The improvement to the trivial calculation of the mean value of \(l_{sr}\) was between four and five times (\(R^{2}>0.75\)). The DART algorithm performed better than the random forest, which was expected. The results showed that most predictions (>97%) fell into the standard measurement uncertainty interval for both the whole considered dataset and the 10% \"unseen\" dataset.  
The cube-shaped area with a side length of 0.60 m was considered in the experiment. In the case of standard measurement uncertainty of the ML model, the contribution to the uncertainty budget of the non-iterative algorithm accounts for 10% of the side length value, while expanded measurement uncertainty accounts for approximately 18%.  
The statistical value of >97% for the standard uncertainty of the ML model can find its usefulness only when put in the appropriate context. For example, the standard measurement uncertainty can be used for the periodical testing of power equipment performed on demand or annually. On the other hand, for the online monitoring of power equipment where the number of PD occurrences and temperature oscillations in mineral oil can be very high, the expanded measurement uncertainty of the ML model could be more appropriate to use as a contribution to the measurement uncertainty budget of a non-iterative algorithm.
###### Abstract  
Hyperspectral imaging technologies in the food and agricultural area have been evolving rapidly over the past 15 years owing to tremendous interest from both academic and industrial fields. Line-scan hyperspectral imaging is a major method that has been intensively researched and developed using different physical principles (e.g., reflectance, transmittance, fluorescence, Raman, and spatially resolved spectroscopy) and wavelength regions (e.g., visible (VIS), near infrared (NIR), and short-wavelength infrared (SWIR)). Line-scan hyperspectral imaging systems are mainly developed and used for surface inspection of food and agricultural products using area or line light sources. Some of these systems can also be configured to conduct spatially resolved spectroscopy measurements for internal or subsurface food inspection using point light sources. This paper reviews line-scan hyperspectral imaging techniques, with introduction, demonstration, and summarization of existing and emerging techniques for food and agricultural applications. The main topics include related spectroscopy techniques, line-scan measurement methods, hardware components and systems, system calibration methods, and spectral and image analysis techniques. Applications in food safety and quality are also presented to reveal current practices and future trends of line-scan hyperspectral imaging techniques.  
hyperspectral imaging; spectroscopy; visible; near infrared; reflectance; fluorescence; Raman; scattering; food safety; food quality
# A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning
## 8 Discussion
### Research Challenges
#### 8.2.5 IDS for IoT  
There is a need to develop IDS specifically for IoT. One of the main challenges with IDS for IoT is that IoT devices generally use wireless networks. Most of these devices contain sensor nodes and they collect a tremendous amount of data. However, most of these nodes have limited computing power. Thus, to implement IDS for IoT, the IDS needs to be lightweight, require less computing power and utilize a smaller amount of data to detect threats. However, most of the reviewed papers in this study proposed solutions for wired networks, therefore, one of the biggest challenges for researchers will be to develop lightweight IDS that would have a high detection rate in a wireless environment.
## 5 V-HOE Based Solar Concentrators
### Holographic Solar Deflector  
In 2010, [PERSON] et al. [9] designed and characterized a holographic grating, recorded on dichromatic emulsion, able to deflect the direct sunlight on a PV cell with the higher energy efficiency possible. A detailed study of the effects of incident spectra that vary hourly, daily and seasonally was performed, and, in order to maximize the energy collection efficiency per year, the authors proposed the structure illustrated in Figure 4a. The designed cell is composed by two cascaded holographic  
Figure 3: (**a**) A schematic representation of the experimental set-up used in Reference [52] for the doubly slanted layer structure. Red and blue arrows are the incident laser sources emitting at 632 nm and 488 nm, respectively, used to record the double superimposed slanted reflecting hologram. Diffraction patterns evaluated taken from behind (**b**) and above (**c**). Here, the arrow represents the incident light.  
grating on each side of the PV cell (holograms A and B), that are conjugated (i.e., A and A' or B and B') to provide peak energy collection at different seasons.  
The optical crosstalk of the V-HOEs was reduced by designing the two cascaded holograms to diffract light in opposite directions with the incident angles in different quadrants of the Bragg circle (Figure 4b). To guarantee that maximum of the diffracted rays of the sunlight within the solar responsivity spectrum of PV cell can reach the surface of the cell independently of the incident angle, the geometrical parameters of the system (i.e., the hologram width and the distance hologram-PV cell) have been optimized, too. Results reported an average daily energy increase of 147% due to the concentrator and approximately 50% of the total energy that reaches the hologram areas can be collected by PV cells without the need of tracking.  
An interesting application of V-HOE-based solar concentrator for solar control of domestic conservatories and sunrooms was published in 2005 [55]. In particular, selective use of HOE with given working angles is predicted to maintain the daytime temperatures to an acceptable level when only 62% of glazed the area is used with HOE. Additionally, thanks to the angular selectivity of the V-HOEs, during the winter months theoretically none of the incident beam radiation is attenuated allowing to obtain a comfortable temperature.
# An Optimal Resonant Frequency Band Feature Extraction Method Based on Empirical Wavelet Transform
## 6 Discussion and Conclusions
### Discussion  
(1) Under actual operating conditions, the mechanical vibration signals will inevitably be exposed to noise pollution. Based on [18], this paper analyzes the influence of selecting fixed scale parameter on the feature extraction ability when the background noise intensity changes. At the same time, the strong noise increases the difficulty of feature extraction method design and greatly affects the uncertainty of the experimental results. Therefore, the noise preprocessing mechanism will play a positive role in the use of the method.  
(2) The randomness of the noise leads to many narrow-band modes in the decomposition result. When the center frequency is selected, the fusion of the correlation coefficient and ASN is used as the judgment index, and good results are obtained. However, the quantitative influences of the squared envelope, square envelope spectrum and correlation coefficient on the selection results when the background noise intensity changes have not been analyzed. Therefore, the selection of each parameter should be further optimized to improve the effectiveness of this method.  
(3) Compared with the traditional binary frequency band segmentation method, the scale-space histogram segmentation method adopted in this paper has more physical significance. In the mode adjacent to the center frequency, the mode whose correlation coefficient is greater than 0.25 is combined to construct the optimal resonance demodulation frequency band. In other words, adjacent modes with default correlation coefficients less than 0.25 are noise, to achieve the purpose of noise elimination. In actual engineering, this parameter deserves further optimization.