esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 5 Conclusions  
In this survey, various existing routing protocols that are proposed in WBANs were categorized and detailed analyzed. It was seen that the routing protocol plays an important role in the design of energy efficient, reliable and low cost WBANs. Based on the method and the design target, the routing protocols for WBANs were categorized as postural-based, temperature-based, cross-layer, cluster-based, and Qos-based routing. Moreover, a comparison of different protocols has been analyzed so that an appropriate protocol can be selected, according to the targeted application. This survey will benefit the researchers to study the energy efficient routing protocols for WBANs in the field of healthcare systems.  
**Funding:** This work was supported by the National Natural Science Foundation of China under Grant 61772175, 61801170, 61501405, U1504619; National Thirteen Five Equipment Research Fund under Grant 6140311030207, National Key Research and Development Plan under Grant 2018 YFB0904905; Science & Technology Major Program of Henan Province of China under Grant 161100210900; Natural Science Foundation of Henan Province under Grant 162300410098, 162300410096; Program for Science & Technology Innovation Talents in the Universityof Henan Province(17 HASTIT025); Science and Technology Research Project of Henan Province under Grant 182102210285; Postdoctoral Science Foundation of China under Grant 2018M632772, 2018M633351.  
The authors declare no conflict of interest.
## Materials and Methods
### Data Sources and Preprocessing
#### 2.2.1 GPP Data  
Two GPP datasets were used: the flux site and MODIS. Eight representative stations across China were selected for the selection of station datasets: two grasslands (Inner Mongolia and Dangxiong), four forests (Changbai Mountain, Dinghushan, Qianyinzhou, and Xishuangbanna), one shrub (Haibei), and one farmland (Yucheng) station. Notably, the time range for the two grassland stations was 2004-2010, while the remaining stations covered 2003-2010 [35]. Subsequently, flux site data from multiple grassland and forest stations were averaged and used to construct an estimation model.  
The second GPP dataset employed here originates from MOD17A2 (available at [[https://ladsweb.modaps.eosdis.nasa.gov/search/order/1/MOD17A2H-6](https://ladsweb.modaps.eosdis.nasa.gov/search/order/1/MOD17A2H-6)]([https://ladsweb.modaps.eosdis.nasa.gov/search/order/1/MOD17A2H-6](https://ladsweb.modaps.eosdis.nasa.gov/search/order/1/MOD17A2H-6)), accessed on 20 April 2024), which leverages various surface parameters derived from NASA's earth-observing satellites (Terra and Aqua) equipped with MODIS sensors which estimate surface parameters at a spatial resolution of 500 m. The GPP dataset has been extensively used in agriculture, ecology, and climate research [36]. We subsequently aggregated from an 8-day to a monthly scale, spanning the entire period from 2003 to 2010.
# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
## 3 Methods
### Feature Sets  
#### 3.2.1 Texture Features  
Texture feature is an important feature to improve the classification accuracy of SAR data, which can be quantified by a series of different statistical measures, and is widely used for crop classification [7; 20; 23; 49]. In this paper, the gray level co-occurrence matrix (GLCM) proposed by [PERSON], which is widely used in spatial and texture extraction [50], was used to extract texture features. Finally, the following eight texture measures were extracted: mean, variance, entropy, angular second moment, contrast, correlation, dissimilarity, and homogeneity.
## 3 Results
### Relationship between the Dietary Vitamin D Intake and MetS
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**V Vitamin D Intake (\(\mu\)g/day)**} \\ \cline{2-10}
**Variables** & & \multicolumn{3}{c}{**All (\(n=2513\))**} & \multicolumn{3}{c}{**Men (\(n=1145\))**} & \multicolumn{3}{c}{**Women (\(n=1368\))**} \\ \cline{2-10}  & **\(\beta\)** & **(95\% CI)** & \(p\)**-Value** & **\(\beta\)** & **(95\% CI)** & \(p\)**-Value** & **\(\beta\)** & **(95\% CI)** & \(p\)**-Value** \\ \hline Height (cm) & 0.037 & (\(-0.010 0.0038\)) & 0.126 & 0.055 & (\(-0.011 0.121\)) & 0.103 & 0.032 & (\(-0.060\) 0.067) & 0.917 \\ Weight (kg) & 0.084 & (\(-0.017 0.174\)) & 0.069 & 0.142 & (0.028 0.257) & 0.015 & \(-0.043\) & (\(-0.114 0.209\)) & 0.242 \\ BMI (kg/m\({}^{2}\)) & 0.015 & (\(-0.010 0.041\)) & 0.237 & 0.029 & (\(-0.040 0.063\)) & 0.089 & \(-0.019\) & (\(-0.039 0.022\)) & 0.078 \\ Waist circumference (cm) & 0.061 & (\(-0.010 0.131\)) & 0.090 & 0.107 & (0.013 0.200) & 0.026 & \(-0.030\) & (\(-0.094 0.034\)) & 0.356 \\ Systolic blood pressure (mmHg) & \(-0.031\) & (\(-0.115 0.053\)) & 0.465 & \(-0.024\) & (\(-0.131 0.082\)) & 0.655 & \(-0.050\) & (\(-0.177 0.078\)) & 0.443 \\ Elastic blood pressure (mmHg) & \(-0.033\) & (\(-0.105 0.040\)) & 0.378 & \(-0.046 & (\(-0.148 0.055\)) & 0.371 & \(-0.023 & (-0.100 0.055\)) & 0.565 \\ Tagblood glucose (mg/dL) & 0.062 & (\(-0.027 0.151\)) & 0.170 & 0.026 & (\(-0.063 0.114\)) & 0.571 & 0.127 & \(-0.040 0.294\) & 0.135 \\ HbA1C (mg) & 0.000 & (\(-0.003 0.003\)) & 0.914 & \(-0.001 & (\(-0.005 0.022\)) & 0.475 & \(-0.003\) & (\(-0.003 0.009\)) & 0.290 \\ Serum triglycerides (mg/dL) & \(-0.440\) & (\(-1.071 0.192\)) & 0.172 & \(-0.873\) & (\(-1.799 0.052\)) & 0.064 & \(-0.115\) & (\(-0.699 0.469\)) & 0.699 \\ Serum total cholesterol (mg/dL) & \(-0.171\) & (\(-0.382 0.041\)) & 0.113 & \(-0.198 & (\(-0.488 0.022\)) & 0.106 & \(-0.186\) & (\(-0.553 0.181\)) & 0.319 \\ Serum HDL cholesterol (mg/dL) & 0.000 & (\(-0.068 0.068\)) & 0.996 & 0.015 & (\(-0.077 0.107\)) & 0.747 & \(-0.031\) & (\(-0.153 0.091\)) & 0.616 \\ Serum LDL cholesterol (mg/dL) & \(-0.684\) & (\(-1.172 -0.196\)) & 0.006 & \(-0.397\) & (\(-0.967 0.172\)) & 0.170 & \(-1.751\) & (\(-3.193 -0.309\)) & 0.018 \\ Serum 25(OH)D (mg/mL) & 0.010 & (\(-0.039 0.059\)) & 0.688 & \(-0.009\) & (\(-0.075 0.057\)) & 0.794 & 0.060 & (\(0.001 0.119\)) & 0. The relationship between vitamin D intake and MetS was then analyzed using composite sample logistic regression, as detailed in Table 6. There were no significant risk factors of MetS associated with a vitamin D intake among all participants. As expected from Table 5, a higher vitamin D intake was related with greater abdominal obesity and increased TG in men. In women, there was no relation between vitamin D intake and any MetS-related factor.
## 4 Combining AAT and Glucocorticoid Therapies
### Reduction in the Bacterial Burden  
Although it may seem unlikely given the anti-inflammatory nature of AAT, one of its intriguing properties is its capacity to decrease the bacterial burden. A clinical AAT treatment for patients with a genetic AAT deficiency resulted in a significant reduction in bacterial colonization across several clinical studies [60; 61; 62]. In one such clinical trial, cystic fibrosis patients were treated with inhaled AAT and, as a result, exhibited a marked reduction in pulmonary _Pseudomonas aeruginosa_ numbers [63]; this outcome is consistent with animal studies [64; 65]. In a peritoneal sepsis model, mice transgenic to human AAT (h AAT\({}^{+/+}\)) exhibited significantly lower bacterial titers within 1 day after the induction of bacterial sepsis alongside a lower incidence of leukopenia, a lower occurrence of multi-organ dysfunctions and a superior 24 hour survival rate [10]. This outcome was highly unexpected as native AAT does not inhibit bacterial growth in cultures. However, in an environment rich in nitric oxide (NO), AAT is nitrosylated and gains antibacterial properties (Figure 2) [66]. S-NO-h AAT is an inflammatory agent as it transfers the nitrite to the signaling proteins of innate immune cells, thus nitrosylating them and altering the signaling cascade towards macrophage activation; the cells in turn upregulate the production of iNOS and generate more nitric oxide, reaching high bactericidal concentrations. Upon nitrosylation, the enzymatic inhibition profile of AAT changes towards inhibiting cysteine proteases such as caspase-3, thus preventing the apoptosis of neutrophils and prolonging their stay in the infected tissue. The nitrosylation of AAT is reversible, allowing the molecule to continue with local tissue protection after it participates in safely reducing the bacterial burden.
# Polycyclic Aromatic Hydrocarbons in Popcorn Corn Varieties and Popcorns
## 3 Results and Discussion
In other research, PAH concentrations determined in maize were below the permissible limit established by the EFSA, and the maize plant has potential for the degradation of phenanthrene and pyrene [37; 38]. Some research even describes the use of the maize plant as an alternative technology for the bioremediation of PAH-contaminated soils [39]. However, since popcorn grains contained low amounts of PAHs, it is clear that most of the PAHs in the studied popcorns formed during popping. This is probably due to the exposure to heat during popping, as popcorns are exposed to heating and subsequently the cooking of starch in the kernel until it expands. Although the expansion of the corn kernel is a relatively quick process, it requires a certain amount of energy to be absorbed by the starch in order to expand. PAH formation takes place while food is being exposed to direct heating at high temperatures, resulting in food combustion [40]. This is when free radicals g during food combustion at high temperatures recombine and form light PAHs, followed by heavy PAHs [41]. Ant was present in all samples, which indicates that it is omnipresent. It is a light PAH and can end up in samples via air, water, and soil. Considering that popcorns are very light, it is possible that these PAH concentration values cannot significantly affect health, but it should be noted because popcorn is a widespread snack consumed by all age groups and very often. For example, the average consumption of popcorns in the USA is 60 quarts per year, which is approximately 56 kg per person per year [42].
###### Abstract  
(1) Background: Since multiple sclerosis (MS) is a neurological pathology known for its disabling effects across many domains, the introduction of virtual reality (VR) usage has been attempted, as it represents a new method of approach to rehabilitation and treatment of chronic neurological pathologies. Encouraging research has explored the use of video game consoles and VR-assisted Robot-Assisted Gait Training (RAGT) to address balance disturbances in this population. (2) Methods: The search involved two databases, Web of Science and PubMed, utilizing a selection of terms including \"Virtual reality\", \"Multiple sclerosis\", \"Balance\", and \"Rehabilitation\". Two reviewers initiated and performed the search for articles, subsequently selecting and extracting data from the studies. The PEDro scale was the tool for evaluating the quality of the articles that we introduced in our research. (3) Results: After rigorous scanning, nine articles remained eligible for our study. VR interventions, particularly compared to standard balance training, consistently improved balance in multiple sclerosis. Robotic-assisted technology with 2D VR yielded superior results in balance rehabilitation. VR interventions had varied effects on walking speed. They have shown promise in decreasing the risk of falls and improving patients' daily lives while reducing fatigue in multiple sclerosis. (4) Conclusions: VR offers comparable or superior benefits to classical exercise and no intervention for balance but does not significantly improve functional mobility. However, it shows the potential to improve quality of life and fatigue in MS patients. Investigation of VR alongside RAGT is important to be performed with larger sample sizes and comprehensive results are needed to fully understand its efficacy in MS rehabilitation.  
balance; multiple sclerosis; rehabilitation; gait; quality of life; fatigue; risk fall; virtual reality
## 4 Analysis and Discussion  
In this study, the Intel RealSense D435 depth camera was utilized to perform real-time 3D reconstruction of underground pipes. This camera serves as a crucial data source for our reconstruction process. Additionally, the core hardware environment of the UAV acquisition system used in this experiment consists of an i5-1145G7 with Radeon Graphics 2.60 GHz and 8 GB RAM. The experiment employed an onboard mini-computer running on a 64-bit Ubuntu 20.04 operating system. The system can process thirty frames per second during operation. The time consumed during data acquisition and the experimental process demonstrates that the current efficiency is sufficient for real-time 3D reconstruction. Importantly, the device's stability ensures the high quality of the video during data acquisition.  
Pipe 3D modeling in pipeline internal environments presents unique challenges such as complex pipeline structures, lack of natural light, inconspicuous pipe in-wall texture,  
Figure 10: Comparison of 3D reconstruction point clouds: (**a**) RTAB-Map; (**b**) ORB-SLAM2; (**c**) Manhattan-SLAM.  
Figure 9: Trajectories in three axes.  
and poor security. To address these challenges, the use of RTAB-Map, a graph-based SLAM system, is proposed. RTAB-Map combines loop closure detection and graph optimization methods, while also providing memory management for large-scale and long-term online operations. This system demonstrates advantages in scenarios where the camera moves quickly, surrounding objects reflect light, or illumination is unstable. Additionally, RTAB-Map's memory management mechanism ensures the system's stability over extended periods. To evaluate the feasibility of RTAB-Map in pipeline internal complex environments, this study conducted experiments using a UAV to collect video data and extract high frame rate RGB and depth images. The results confirm that RTAB-Map is a viable solution for navigation and modeling in such challenging environments.
## 5 Results and Discussion
### Economic Results  
The results of the Discounted Cash Flow (DCF) financial analysis are illustrated through Table 6. Furthermore, Figure 17 illustrates the cash flow of the project throughout its lifespan for this study.  
All estimated values can increase or reduce when the project will become true. This scenario (grant and debt financing) of the study is deemed as feasible. However, there are various parameters and conditions that affect the decision and should be taken in NEPCO's consideration. These parameters are:  
* Availability of the initial percentage of the grant.
* Availability of the lending institute (bank) providing the long-term loan estimated interest rate for the initial cost.
* The technical capability of the technical team in NEPCO to operate and maintain the system.
* Stability of the selling energy price for the entire project lifetime.
* The cost of the spare parts if needed during the lifetime of the project.
## 2 Methodology
### Counterfactual Transport Emission Scenarios  
Putting together these sources of information (real-world emission factors, EDGAR emissions, EMEP air quality model), three simulations were performed:  
1. A Reference Case (REF): Road transport emissions correspond to the state of the fleet in the year 2015, i.e., a mixture of Euro 1 to Euro 6. The light-duty vehicles exhaust emission factors for all pollutants are those contained in the EDGAR inventory, using Tier 2 exhaust emission factor and Tier 3 fuel economy indicators presented in the EEA guidelines released in 2019 and based on the COPERT emissions model [30].
2. Scenario 1 (SC1): This counterfactual scenario assumes that all passenger and light-commercial vehicles in the simulated domain were approved under the Euro 6b standard, that is, vehicles approved between 2015 and 2018 following the NEDC test procedure which did not include on-road testing. Exhaust PM, NOx, NH\({}_{3}\), and non-methane VOCs (NMVOCS) emission factors of all pre-Euro 6b gasoline light-duty vehicles are changed to the corresponding COPERT emission factors of Euro 6b vehicles, and equivalently for diesel vehicles. The emission factors of vehicles propelled with fuels other than gasoline and diesel (e.g., natural gas, liquified-petroleum gas) are kept unchanged. Emission factors for heavy-duty vehicles, motorcycles, and mopeds also remain unchanged. In short, SC1 aimed at simulating the effect in air quality of renovating the fleet of all light-duty vehicles in Europe to Euro 6b vehicles.
3. Scenario 2 (SC2): For this counterfactual scenario, all passenger cars and light-commercial vehicles are assumed to comply with the most recent and stringent type approval regulation applicable to Euro 6d-TEMP or Euro 6d vehicles (EC Regulation 2017/1151 and amendments). Euro 6d-TEMP and 6d vehicles, available in the market since late 2017, need to comply with emission limits in the updated laboratory test procedure (WLTP) and on the road following the Real Driving Emissions (RDE) regulations. In SC2, the exhaust emission factors of PM, NOx, NH\({}_{3}\), and NMVOCs for gasoline and diesel light-duty vehicles are the ones estimated by JRC STU, as described earlier. Again, in this scenario, emission factors of light-duty vehicles using fuels other than gasoline and diesel are left unchanged as compared to the reference scenario. SC2 is designed to provide evidence of the effect on air quality of the new WLTP and RDE emissions regulations in Europe.
## 2 Spectrum Sensing Problem
### Spectrum Sensing as a Statistical Decision  
The PU's signal detection by the SU can be modeled as a binary hypothesis testing problem, given as [26]:  
* Hypothesis \(\mathcal{H}_{0}\): PU's signal is absent
* Hypothesis \(\mathcal{H}_{1}\): PU's signal is present
* Let \(y(n)\) be the signal observed by the Secondary User (SU): \[y(n)=hs(n)+b(n)\] (1) where \(h\) is the channel gain and \(b(n)\) is the channel noise. The noise samples \(b_{k}\) are assumed to be independent and identically distributed (i.i.d). The noise \(b(n)\) is a Complex Circular Gaussian variable \(\left(\mathrm{E}[b(n)]=0;\mathrm{E}[|b(n)|^{2}]=\sigma_{b}^{2}\right);\mathrm{ E}[.]\) denotes the mathematical expectation.  
The Test Statistic \(T\) should be compared with a predetermined threshold \(\lambda\) for decision making. In this case, the probability of false alarm \(P_{fa}\) and the probability (Pr) of detection \(P_{d}\)[29] are defined as follows:  
\[P_{fa} \triangleq Pr\{T\geq\lambda|\mathcal{H}_{0}\} \tag{2}\] \[P_{d} \triangleq Pr\{T>\lambda|\mathcal{H}_{1}\} \tag{3}\]  
where \(\triangleq\) stands for equal by definition.  
For good performance, \(P_{d}\) should be increased as much as possible while keeping \(P_{fa}\) under a small preselected value. In the noncooperative spectrum sensing context, the detection algorithms should be able to find out intrinsic features of the communication signal to enhance a spectrum sensing task. One of the main features can be the recurrence of internal states in the communication signals. Hereinafter, we develop a robust algorithm based on RQA.
# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 4 PEAR Solution: Model Design and System Implementation
### PEAR Algorithm Description
For this, a topology change is required where the selected children reroute their packets through another parent device having the appropriate energy margin to accept additional energy load. Firstly, the procedure creates a list with all the children of the overconsuming device arranged in descending order of the Tx rate. The Tx rate indicates the load that a field device adds to the parent. The aim is to find a new device to act as parent for a child device prioritizing the most consuming ones. For each child device, the first parent candidate is considered the backup parent. The VR950 Gateway [10] dynamically allocates backup communication links to all nodes such that the backup link can be used in the communication opportunity following a primary link failure. This preserves the network topology since these redundant communication links are allocated at network formation stage to enhance the network reliability. If the backup parent device is not suitable as the new primary parent, PEAR considers other neighbours as candidates. Among these candidates, the ones belonging to a higher consumption profile are prioritized such that, whenever possible, inter-cluster balancing is preferred over the intracluster balancing. If none of the neighbours represents a suitable new primary parent candidate PEAR repeats the new parent search for the next child device. The PEAR post-network-formation pseudocode algorithm is described in Figure 3.
## 3 Spatial Grid Structure Modeling Analysis  
For the convenience of calculation, a rectangular flat grid is selected for numerical simulation calculation. The rectangular slab grid structure is shown in Figure 2. The length of the long side of the upper chord plane is 18 m, the length of the short side is 12 m, the length of the long side of the lower chord plane is 16 m, and the length of the short side is 10 m. The grid length is 2 m, the grid height is 1.5 m, and the lower chord is a fixed hinge support. The members are made of Q235 steel with an elastic modulus of 2.06 \(\times\) 10\({}^{11}\) N/m\({}^{2}\), the Poisson's ratio is 0.3, the upper chord cross section is P139.8 \(\times\) 3.6, the lower chord cross section is P101.6 \(\times\) 3.2, and the web member cross section is P48.6 \(\times\) 2.8.  
The first six orders of theoretical frequencies and vibration shapes of the structure obtained from the modal analysis calculations using Midas are shown in Figure 3.  
The stationary white noise is multiplied by the amplitude adjustment function \(C(t)=\mathfrak{A}(e^{-0.02t}-e^{-0.04t})\) to obtain the non-stationary white noise [22], and three uncorrelated non-stationary white noises are used for excitation. Using the structural dynamics method of \(Wilson-\theta\)[23, 24], the displacement response signal in each degree of freedom is obtained. Figure 4 shows the applied non-stationary white noise signal.  
Figure 3: Theoretical frequencies and vibration shapes of the first six orders.  
Figure 2: Rectangular slab grid structure.  
As can be seen from Table 1 and Figures 5 and 6, under non-stationary excitation of white noise, the maximum error of frequency identification of the SSI-COV method is 3.501%, and that of the improved RDT-SSI method is 1.351%, which is an improvement in accuracy. The damping ratio identifications are generally similar, with large differences in individual orders; there is a local deviation in the measured shape of the vibration pattern at the fifth order, and the rest of the measured vibration patterns are in good agreement with the theoretical shape of the vibration pattern. This is because the system response is usually more sensitive to these modal parameters than to the frequencies, and modes with lower energy contribute less to the system response. Therefore, modes with lower modal energy are less effectively identified compared to those with higher modal energy.  
The improved RDT-SSI method takes the trigger level at equal intervals and then performs amplitude modulation averaging on the attenuation curves. This reduces the signal-to-noise ratio of the response signal and improves the accuracy of identification.  
Figure 6: Vibration mode identification results.