esa-sceva/satcom-chunk-collection huggingface dataset:
# A Joint Denoising Learning Model for Weight Update Space-Time Diversity Method +
## 2 Related Works
However, it does not apply to radiation noise suppression with nonlinear characteristics. Structurally, RKHS and KAPA have converged very closely to the radial basis function neural network (RBFNN) [53], both possessing the core mechanisms for online learning. Although RBFNN is a simple form of the DNN, it can still be parameterized by three implicit layers. It can perform adaptive feature extraction on the data at the input side, which are features that KBM algorithms do not possess. The literature [54] mentioned an improved RBFNN for the image filtering structure, and the results showed superiority over the linear adaptive noise-reduction algorithm. They further optimized the structure of the filtering process by replacing the radial basis kernel function with a convolutional kernel based on the literature [55]. A GAN-based controlled learning noise-reduction method was also proposed in the literature [56]. It trains its model by generators and adjudicators and is more effective than the CNN methods for noise-reduction. The literature [57] pointed out that the acoustic signal can be expanded into a two-dimensional image in both time-frequency dimensions. This theory is one of the bases for image noise-reduction methods for acoustic signals. Due to the complex-type nature of the UWA signal in the STD structure, the above methods are not applied to UWA array communication directly. The literature [58] proposed to characterize the acoustic signal by representing it as real and imaginary parts for speech enhancement. The literature [59] mentioned a DNN structure that can be used for complex-type input data by constructing complex convolutional modules. Although the above studies collectively showed that it is feasible to use the DNN method as a UWA array communication denoising and handle the complex-type characteristics of UWA signals, performing noise-reduction processing and MAI suppression in the STD structure is still challenging.
# Bluetooth Low Energy Mesh Networks: A Survey
## 6 Discussion and Open Issues
### Open Issues
#### 6.2.3 Multicast  
Multicast is an important paradigm for use in BLE mesh networks. Some applications require multicast such as turning on a specific group of lights in a building or polling a specific group of sensors. Efficient communication with a group of destinations is crucial in this type of environment.  
However, since the BLE Link Layer does not support multicast, transmitting data to a subset of neighbors in a BLE mesh network translates into unicasting data as many times as needed. If the group of multicast destinations is large compared with the network size, flooding-based solutions could be suitable. Nevertheless, the efficiency of this scheme will decrease as the number of multicast group members deviates from the network size.  
Another approach for multicast in BLE mesh networks is leveraging layer three multicast, e.g., IPv6 multicast in IPv6-based BLE mesh networks, jointly with a filtering tool. The latter may benefit from a table in each node containing information on which specific neighbors of the node need to receive a multicast packet (for further forwarding or as actual recipients of the packet). This solution would reduce inefficiency due to the lack of BLE Link Layer multicast, although it will not be possible to avoid the specific issue of sending a multicast packet from a node to some or all of its neighbors byunicasting it to as many of these neighbors as needed. The overhead for maintaining the multicast infrastructure should also be considered. In this regard, an interesting proposal called constrained-cast has been recently adopted by the IETF Routing Over Low power and Lossy networks (ROLL) working group in order to enable efficient multicast in RPL networks, based on use of bloom filters [57].
## 3 System Overview  
### Power Model  
The power consumption of the processor is directly proportional to the frequency. Hardware-based solutions to the problem of power consumption have reached a saturation point. The energy efficiency of multi core systems is entirely dependent on the workload. Cores that have no activity will experience static power loss, which is directly related to the supply voltage. Running a core at maximal workload means it will use the highest frequency and voltage, resulting in high power consumption. By distributing the workload among the processors, the work is completed in the same amount of time with less power consumption. In multi core systems, the only way to address this problem is to maintain the optimal CPU frequency with the minimum energy consumption ratio by distributing the workload. Operating the processor at minimum frequency is a sensible and more reasonable model for achieving minimum power requirements. There is a collective impact on P-states and workload activity on processor temperature [47]. A linear relationship exists between power consumption and the temperature of a processor in a well-cooled environment. DVFS is used to scale the supply voltage and frequency to prevent power wastage. As DVFS has a direct influence on the power consumption and temperature, it can be used as a workable thermal and power control mechanism.
## 3 Application to the Shuangjiangkou Hydropower Station  
### Measured Results of In Situ Stress in Underground Cavern Zones  
#### 3.1.1 General Situation of Engineering Geology  
The Shuangjiangkou hydropower station in Jinchuan County, Sichuan Province, is the fifth power station in the Dadu River basin. Figure 3 shows the site of the Shuangjiangkou hydropower station, which has the highest dam in the world. The underground cavern is deeply buried on the left bank with a horizontal depth of 300-500 m and a vertical depth of 42 m, and its axial azimuth is N10\({}^{\circ}\)W. F1 is the main fault distributed around the underground cavern area, which has a width of 0.5 to 0.6 m, and the occurrence is about  
Figure 2: Flow chart of the PSO-BP in situ stress inversion method.  
N79\({}^{\circ}\)W/SW\(\angle\)48\({}^{\circ}\). A lamprophyre also occurred about N35\({}^{\circ}\)-50\({}^{\circ}\)W/SW\(\angle\)72\({}^{\circ}\)-75\({}^{\circ}\) at the cavern area, with a width of 0.8-1 m. In general, the Shuangjiangkou underground cavern area mainly comprises porphyritic granite with different degrees of weathering. Currently, empirical systems, such as rock-mass rating (RMR), tunneling quality index (Q) system, and geological strength index (GSI), are mainly utilized in international rock engineering projects [28; 29; 30]. In the geological survey report of Shuangjiangkou Hydropower Station, the (basic quality index of rock mass) BQ method [31] is employed to classify the rock mass into four types of materials, namely, class II (fresh granite), class III (moderately weathered granite), class IV (strongly weathered granite), and class V (geological structures), as listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Type of Rock** & **Rock** & **Density** & **Young’s** & **Poisson’s Ratio** & **Cohesion** & **Friction Coefficient** \\
**Mass** & **Classification** & **(kg/m\({}^{3}\))** & **\(E\) (GPa)** & **\(\mu\)** & **\(c\) (MPa)** & **\(f\)** \\ \hline Fresh granite & II & 2600 & 32.9 & 0.25 & 1.5 & 1.3 \\ Moderately & & III & 2550 & 25.0 & 0.30 & 1.0 & 1.0 \\ weathered & & & & & & \\ \hline \end{tabular}
\end{table}
Table 2: The mechanical parameters.  
Figure 3: Dam site of Shuangjiangkou Hydropower Station.
# Two-Stage Metaheuristic Algorithms for Order-Batching and Routing Problems
## 4 Proposed Algorithm
### Order Batching  
Using the existing number corresponding to each order to conduct neighborhood searching can reveal whether more effective solutions are available. Mutation and swap procedures can be applied in the search process; adopting mutation procedures can effectively reduce the total number of batches. The original batch can be emptied, and thus, eliminated by mutating batches that exhibit only one order to another batch. In contrast to the mutation procedure, which is used to reconstruct overall batches, the swap procedure improves the batch arrangement and is the most commonly applied method in neighborhood design. The swap procedure is a type of optimization procedure, and it cannot be used to reduce the number of batches; however, it facilitates reassigning the orders to optimal batches. When the aforementioned two methods are used, vehicle capacity must be considered during neighborhood search processes.
# Immobilization of Levocyetririzine on Mesoporous Silica for Antiallergenic Gel Formulation
## 3 Results and Discussions
### In Vitro Levo Releasing  
After the morphological and physicochemical tests of Levb attached to Syloid, we determined the active ingredient content of _sample SyLev8_, and then investigated the releasing of Levb from the silica, which was followed by the UV-VIS spectrophotometric method [24]. The active substance content of the sample was 23.2%. The sample _SyLev8_ was first mixed in distilled water at an ambient condition. The releasing test shows that a third (36.9%) of the active substance released very quickly, within 5 min. After that, a gradual dissolution could be observed until the 120 th min., when it reached 85% (Figure 11A). The dissolution rate apparently gradually slowed down from 345 mg min\({}^{-1}\) to approx. up to 1 mg min\({}^{-1}\).  
The rapid releasing of Levo from the silica is partly due to the very good solubility of the active ingredient, which can be seen in Figure 11A. The dissolution of Levb was almost complete in 15 min. The dissolution also depends on the experimental conditions; the process of dissolution is faster in water than, e.g., in an acid solution [39].  
Figure 11: Dissolution of levocetriazine base in water and release of levocetriazine base from silica MSM at 20 \({}^{\circ}\)C (**A**) and 37 \({}^{\circ}\)C (**B**).  
Figure 10: X-ray diffractograms of samples _SyLev7_ and _SyLev9_.  
In the releasing experiment at a higher temperature, such as the human body's, of 37 \({}^{\circ}\)C, the dissolution of Levb was faster, exceeding 50% in 5 min and reaching 95% in 120 min (Figure 11B). The initial dissolution rate was also much higher (525 mg min\({}^{-1}\)) than at room temperature. The dissolution of Levb was very quick, 99.5% in 5 min; therefore, this is not depicted in Figure 11B.  
The active substance release is also influenced by the applied DDS [40]. Depending on the DDS, the diffusion rate of Levo is slowed [41]. Thus, the extended Levo active substance release obtained in our releasing experiment has already been described by several people in the literature. The Levo dissolution-active agent release is slower from the Levo-chitosan nanoparticle [42], as well as from the Levo-polymer system; however, its rate depends on the polymer used [39].
# Semantic Traffic Sensor Data:
## 2 Related Work  
Publishing open data has become an increasingly pressing need within government bodies and public administrations. The principles of sharing public information have been defined by the International Open Data Charter [1] and are the following: (1) Open By Default, (2) Timely and Comprehensive, (3) Accessible and Usable, (4) Comparable and Interoperable, (5) For Improved Governance and Citizen Engagement, and (6) For Inclusive Development and Innovation. Opening up data often happens in an ad-hoc manner, and in many cases thousands of datasets are published without adhering to commonly-agreed standards and without reusing common identifiers. Hence, finding, reusing, and integrating data from different sources is a real challenge. Linked Data can respond to these challenges and can lead to smarter and more efficient government services and applications. Therefore, a crucial aspect when sharing open data is to follow the Linked Data principles [12].  
Moreover, to publish high-quality, semantically annotated Open Data, it is crucial to identify the ontologies that better describe the domain of interest [13]. Ontologies provide a formal representation of the domain of interest and constitute the component with which the Linked Open Data (LOD) consumers (both humans and software programs) interact. Then, also the mapping between ontologies and data is important, since it is used to translate the operations on the ontology in terms of concrete actions on the data.  
In the following subsections, related approaches for publishing smart city traffic data are described and different types of ontologies related to traffic data are analyzed. The identification of the most relevant ontologies and concepts and the mapping between data and the selected ontologies, instead, is described in Section 4.1.
# Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence
## 2 Materials and Methods
### Data Acquisition
#### 2.2.2 Cotton Biometric Parameters and Yield Data Acquisition  
Experimental data collection and measurements were taken at ten-day intervals starting from the budding period for a total of seven collections. Three cotton plants with uniform growth were collected as samples from each plot. The following biometric parameters were measured:  
1. Leaf area index (LAI): The leaf area data were obtained by using an LI-3100c bench leaf area instrument (LI-COR, Inc., Lincoln, NE, USA) to measure the leaf area of all the leaves of each cotton plant. The formula for calculating LAI is as follows: m is the number of plants measured; A is the leaf area of all leaves of plant m; \(p\) is the planting density; \[\text{LAI}=0.75\;\rho\;\frac{\sum_{i=1}^{\text{m}}\text{A}}{\text{m}}\] (1)
2. Aboveground biomass (AGB) (g\(\cdot\)m\({}^{-2}\)): Once the measured plants were collected, the plants at the upper part of the cotyledon node were cut into pieces and packed in bags. The plants were defoliated at 105 \({}^{\circ}\)C for 30 min and dried at 80 \({}^{\circ}\)C to a constant weight. AGB is calculated by the following formula: B is the dry weight of a cotton plant and S is the area occupied by a cotton plant; \[\text{AGB}=\frac{\text{B}}{\text{S}}\] (2)
3. Seed cotton yield (kg\(\cdot\)ha\({}^{-1}\)): Yield estimation using sample method harvesting. During the cotton fluffing period, representative sample squares (2.28 m \(\times\) 1 m) were selected from each plot for yield determination; the number of plants, number of bolls per plant, and single boll weight were determined, and the weight of seed cotton was weighed in each sample square. The formula is as follows:  
\[\text{Seed cotton yield (kg\(\cdot\)ha\({}^{-1}\)) = Harvest density (number of plants\(\cdot\)ha\({}^{-1}\)) \(\times\) Number of bolls per plant (number of individuals\(\cdot\)plants\({}^{-1}\)) \(\times\) Single boll weight (g)/1000 \(\times\) Measurement yield correction factor (90%)
###### Abstract  
Effective management of airport pavements is essential for maintaining safety and operational efficiency in air travel. An airport pavement management system (APMS) operates at two levels: the network level, which monitors overall pavement performance across the airport, and the project level, which conducts detailed inspections of individual pavements. However, pavement assessments are often costly and labor intensive, necessitating the development of cost-effective and practical models. This paper introduces the Airport Pavement Integrated Risk Management (APIRM) model, which integrates pavement condition assessment criteria with safety risk management (SRM) methodologies. The model was applied at Istanbul Airport. By using APIRM, airports can prioritize high-risk areas, optimizing resource allocation and enhancing safety. The model encourages coordination among various airport departments, offering a holistic approach to pavement management that integrates maintenance requirements with safety considerations.  
aircraft pavement management system; performance evaluation; FAA Paser; friction; roughness; safety risk management; Istanbul Airport
## 5 Evaluation and Validation of the Developed System  
The performance of proposed fall detection system was tested in a real home environment. An external camera is used for recording the real video sequence with the image size of 280 \(\times\) 280. The recorded video sequence is processed by using OpenCv and Keras for the CNN training.  
For evaluating this fall detection system, each volunteer (student) is asked to simulate different falling and non-fall activities in different directions. Fall is detected when the lying posture is classified as falling by the CNN, see Table 1. The proposed CNN based fall detection systems could accurately detect falls (75%) with a low false alarm rate (25%). The misclassifications are attributed to the reason that the bend posture in some directions is viewed similar to the lie posture and we can adopt multiple cameras to capture different posture views for ameliorating it.  
The accuracy of the CNN is bellow 75%, some preprocessing effort could be made to optimize CNN structure and improve the results.Yu et al. [46] obtains a better performance but the images need to be preprocess in order to generate a silhouette. From a recorded video stream, background subtraction is applied to extract the human body silhouette. Extracted silhouettes corresponding to daily activities are applied to construct a convolutional neural network, which is applied for classification of different classes of human postures and detection of a fall event.  
Figure 4: An example of CNN architecture. The way in which the layers are structured, which elements are used in each layer and how they are designed will often affect the speed and accuracy with which it can perform various tasks.  
Rates (72.2%-75%) have been the maximum accuracy rate reached considering the standard CNN topology and a variable data set split factor (0.15-0.28). Results suggest that with these numbers of images the accuracy might not be able to improve considerably. Thus, more training images would be needed to ensure a more reliable system. Nevertheless, considering the number of training observations, and the outcome of using the cross-validated test, this module clearly adds value to the entire system.  
In reality, only part of falling human body is included in some images, this technique detect the falling events provided a 80% of body is shown in images. A future research includes information from multi-modality sensors together with deep learning techniques for developing a more robust fall detection system in case the whole human body is not present in the images.
## 3 Results and Discussion
### ABTS Radical Scavenging Activity  
The ABTS radical scavenging ability IC\({}_{50}\) values of well-known BHT and Trolox are 1.3 ug/mL and 2 ug/mL, respectively [38]. Figure 4 displays the results of the ABTS radical scavenging activity and the low experimental IC\({}_{50}\) value of Trolox validates this assay. Peel pulp extracted with DI water exhibited better ABTS radical scavenging activity at a temperature of 80 \({}^{\circ}\)C, whereas 50% ethanol exhibited better activity at 60 \({}^{\circ}\)C. The seeds extracted with DI water exhibited better activity at 60 \({}^{\circ}\)C, and that extracted with 50% ethanol exhibited better activity at 80 \({}^{\circ}\)C. Overall, the peel pulp had better ABTS radical scavenging ability than the seeds did for both DI water and 50% ethanol extraction, corresponding to the result of DPPH radical scavenging activity in Figure 3. The peel pulp and seeds  
Figure 3: DPPH IC\({}_{50}\) of jujube peel pulp and seeds extracted using DI water and 50% ethanol at different temperatures. The error bar represents the standard error of the mean. Temperatures in the histogram with the same letter were not significantly different at the 5% level, according to the results of the LSD test.  
Figure 2: IC\({}_{50}\) of jujube peel pulp extracted using various extraction solutions at 60 \({}^{\circ}\)C. The error bar represents the standard error of the mean. Solvents in the histogram with the same letter were not significantly different at the 5% level, according to the results of the least significant difference (LSD) test.  
extracted with 50% ethanol had better radical scavenging ability than did those extracted with DI water. Among all of the tested conditions, peel pulp extraction with 50% ethanol at 60 \({}^{\circ}\)C demonstrated the best scavenging ability (0.5 \(\pm\) 0 mg/mL).
# Uncertainty Analysis Based on Kriging Meta-Model for Acoustic-Structural Problems
## 4 Surrogate Modeling Techniques
### Kriging Meta-Model
\begin{bmatrix}1&x_{1}^{(1)}&\dots&x_{m}^{(1)}&x_{1}^{(1)}x_{2}^{(1)}& \dots&x_{m-1}^{(1)}x_{m}^{(1)}&(x_{1}^{(1)})^{2}&\dots&(x_{m}^{(1)})^{2}\\ 1&x_{1}^{(2)}&\dots&x_{m}^{(2)}&x_{1}^{(2)}x_{2}^{(2)}&\dots&x_{m-1}^{(1)}x_{ m}^{(2)}&(x_{1}^{(2)})^{2}&\dots&(x_{m}^{(2)})^{2}\\ \vdots&\vdots&\ddots&\vdots&\vdots&\ddots&\vdots&\vdots&\ddots&\vdots\\ 1&x_{1}^{(n)}&\dots&x_{m}^{(n)}&x_{1}^{(n)}x_{2}^{(n)}&\dots&x_{m-1}^{(1)}x_{ m}^{(n)}&(x_{1}^{(n)})^{2}&\dots&(x_{m}^{(n)})^{2}\end{bmatrix}  \tag{17}\]  
In order to be able to construct the Kriging surrogate model, we first need to determine the correlation parameters of the vector \(\mathbf{\theta}\) in Equation (14) or Equation (15), the regression coefficients of the vector \(\mathbf{\beta}\) in Equation (11) and the variance of the stochastic model \(\sigma^{2}\) in (Equation (12)). The method, currently the most used to determine all these parameters, is based on the statistical framework of Kriging and in particular that of Gaussian processes. Indeed, it proposes to determine these parameters by maximizing the likelihood function. \[L(\mathbf{\beta},\sigma^{2},\mathbf{\theta})=\frac{1}{\sqrt[5]{2\pi\sigma^{2}}\sqrt{| \mathbf{R}(\mathbf{\theta})|}}\exp\left[-\frac{(\mathbf{y}_{S}-\mathbf{F}^{\mathrm{T}}\mathbf{ \beta})^{\mathrm{T}}R^{-1}(\mathbf{\theta})(\mathbf{y}_{S}-\mathbf{F}^{\mathrm{T}}\mathbf{ \beta})}{2\sigma^{2}}\right] \tag{18}\]In practice, we apply the natural logarithm on the expression of the likelihood and we obtain:  
\[\ln(L)=-\frac{n}{2}\ln(2\pi)-\frac{n}{2}\ln\Bigl{(}\sigma^{2}\Bigr{)}-\frac{1}{2 }\ln(|\mathbf{R}(\mathbf{\theta})|)-\frac{(\mathbf{y}_{S}-\mathbf{F}^{\mathrm{T}}\mathbf{\beta})^{ \mathrm{T}}\mathbf{R}^{-1}(\mathbf{\theta})(\mathbf{y}_{S}-\mathbf{F}^{\mathrm{T}}\mathbf{\beta})}{2 \sigma^{2}} \tag{19}\]  
The likelihood is maximal when its derivatives with respect to \(\mathbf{\beta}\) and \(\sigma^{2}\) are setting to zero, thus gives:  
\[\hat{\mathbf{\beta}}=\Bigl{(}\mathbf{F}^{\mathrm{T}}\mathbf{R}^{-1}\mathbf{F}\Bigr{)}^{-1}\mathbf{ F}^{\mathrm{T}}\mathbf{R}^{-1}\mathbf{y}_{S}, \tag{20}\]  
and  
\[\hat{\sigma}^{2}=\frac{(\mathbf{y}_{S}-\mathbf{F}^{\mathrm{T}}\mathbf{\beta})^{\mathrm{T}} \mathbf{R}^{-1}(\mathbf{\theta})(\mathbf{y}_{S}-\mathbf{F}^{\mathrm{T}}\mathbf{\beta})}{n} \tag{21}\]  
The optimal values of \(\mathbf{\theta}\) are determined by solving the nonlinear optimization problem of the maximization of the likelihood function:  
\[ \begin{array}{ll}\underset{\theta}{\text{max}}&L(\theta)=-\frac{1}{2}[n(\ln (2\pi)+\ln(\hat{\sigma}^{2}))+\ln(|\mathbf{R}|)]\\ \text{s.t.}&\theta_{i}>0,\ i=1,\ldots,m\end{array}  \tag{22}\]  
Thus, by solving this optimization problem, the estimators \(\hat{\mathbf{\beta}}\) and \(\hat{\sigma}^{2}\) can be calculated and then the model \(\hat{y}(\mathbf{x})\) is completely defined by Equation (16).