esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Adsorption Mechanism  
#### 3.5.1 SEM-EDS Study  
The morphology of MZP-PM before and after the adsorption during experiments is presented in Figure 7. The SEM images indicate that NH\({}_{4}\)+-N and TP were adsorbed onto MZP-PM, leading to the distribution of spherical particles onto the surface, and the protuberances formation (a stacking structure) reduced the intra-particle void size. The morphology of MZP-PM become changed after the adsorption of phosphate into crystallization onto the active sites, which increased the particle size. EDS analysis confirmed the existence of O, Ca, La, Na, Al, and Si as the major elements in the composition of MZP-PM, indicating the possible formation of the La-P complex. The given observations were in accordance with the results of phosphate speciation onto loaded MZP-PM. From another point of view, the weight of Na+ decreased from 9.5% to 4.5% upon the adsorption of NH\({}_{4}\)+-N and TP. Thus, this phenomenon decreased the Na+ and the existence of N in the EDS image demonstrated the exchange of Na+ with NH\({}_{4}\)+-N.
## 1 Introduction  
Humankind is experiencing changes in the climatic system globally, with more changes to occur in the future [1]. According to the UN Environment Programme [2], this change is reputed to be one of the most significant environmental challenges. It is essential to bear in mind that no region of the world is spared from the effects of climate change, and its consequences are now more substantial than ever. As a result of anthropogenic climate change and natural climate variability, climate extremes can worsen, which vary in frequency, intensity, duration, and spatial extent [3]. Climate change occurs because of human (anthropogenic) and natural factors; some of the human factors are industrial activities leading to the burning of fossil fuels, also deforestation, agriculture, transportation, and waste management, while the natural causes include volcanic eruptions, ocean currents, natural greenhouse gas emissions, orbital changes, and wildfires. Climate extremes can be defined as abnormal, severe climatic states associated with meteorological conditions that can culminate in catastrophic events with consequential impacts on society and the ecosystem [3].  
Apart from climate extremes, the world is plagued by other challenges, such as economic upheavals, inflation, global wars, and the outcome of the pandemic, which have consequences for food production. Agricultural production depends on ecosystem services, and this is in many facets, like soil structure and fertility, nutrient cycling, pollination, and pest control [4]. Ecosystem services are crucial to humans because they provide food, climate and disease regulation, freshwater, soil formation, aesthetic purposes, educational values, spiritual services, and other benefits [5]. They can be likened to the benefits of nature and natural ecosystems. The interaction between ecosystem services and agriculture has attracted the attention of agricultural ecologists, who have promoted agroecosystem services [6]. Agroecosystems combine producers,consumers, and decomposers that can independently enable complete material circulation and energy transformation [7]. They have the characteristics of both natural and artificial ecosystems, which can encourage agricultural production based on human and natural factors.  
An agroecosystem is also part of the natural capital superintended by human activities with a simplification and selection of biodiversity to support ecosystem function by generating food, feed, timber, fibres, and other necessary products for commercial value [8; 9]. Ecosystems shaped by agricultural practices provide critical ecosystem services that help maintain environmental stability and support human well-being. When managed for various benefits, agroecosystems have the potential to contribute to multiple sustainability aspects in climate, land, water, and biodiversity for food and hunger [10; 11]. Consequentially, this research will explore the critical nature of the agroecosystem for ecological balance and human society.
## 2 Basic Theories
### Efficiency Calculation of SBG
#### 2.1.3 Lubrication Model  
Contact behavior of SBG is typical point contact (shown in Figure 2) and the pressure distribution in elliptical contact area could be calculated by the Reynolds equation considering the angle between movement direction and contact elliptical axis direction [29; 30]:  
\[\frac{\partial}{\partial x}(\frac{\rho}{12\pi}h^{3}\frac{\partial p}{\partial x })+\frac{\partial}{\partial y}(\frac{\rho}{12\pi}h^{3}\frac{\partial p}{ \partial y})=U\cos(\theta_{u})\frac{\partial(ph)}{\partial x}+U\sin(\theta_{u })\frac{\partial(ph)}{\partial y}+\frac{\partial(ph)}{\partial t} \tag{5}\]  
Figure 2: The contact ellipse model.  
where \(h\), \(p\), \(\rho\), \(\overline{\eta}\) represent lubricant film thickness, contact pressure, lubricating oil density, and lubricating oil viscosity, respectively.  
The equation of lubricant film thickness could be written as:  
\[h(t)=h_{0}(t)+\frac{x^{2}}{2r_{x}}+\frac{y^{2}}{2r_{y}}+v(x,y,t)+\delta_{1}(x,y,t )+\delta_{2}(x,y,t) \tag{6}\]  
where \(h_{0}(t)\) and \(t\) are initial oil film thickness and time variable; \(r_{x}\) and \(r_{y}\) are the radius of curvature along the minor and major axis direction of contact ellipse; \(\delta_{1}\) and \(\delta_{2}\) are the surface roughness of the pinion and gear; \(v(x,y,t)\) is the local contact deformation that could be obtained by Boussinesq integration:  
\[v(x,y,t)=\frac{2}{\pi E^{\prime}}\iint\frac{p(\xi,\zeta)}{\sqrt{\left(x-\xi \right)^{2}+\left(y-\zeta\right)^{2}}}d\xi d\zeta \tag{7}\]  
where \(E^{\prime}\) represents the equivalent modulus of elasticity.  
According to previous studies, the viscous pressure equation and density pressure equation could be written as:  
\[\eta=\eta_{0}e^{\alpha p} \tag{8}\]  
\[\rho=\rho_{0}\bigg{(}1+\frac{0.6\times 10^{-9}p}{1+1.7\times 10^{-9}p}\bigg{)} \tag{9}\]  
where \(\rho_{0}\) and \(\eta_{0}\) are the initial density and viscous, respectively, \(\alpha\) is pressure viscosity exponent.  
The equations could be solved after dimensionless analysis based on the finite difference method and progressive mesh densification (PMD) method, which is detailed in reference [29; 30; 31; 32].
## 4 Simulation  
### Simulation on ISSA Performance  
In the simulation tests, eight standard benchmark functions (F1 to F8) were utilized to evaluate the performance of the ISSA. These functions are designed to test different aspects of the algorithm to ensure its efficiency, accuracy, and robustness. Among them, F1, F2, and F4 are unimodal functions, which are primarily used to test the convergence speed and efficiency of optimization algorithms. They help us to understand the algorithm's performance as it approaches the optimal solution, as well as the algorithm's speed and stability. On the other hand, F3, F5, F6, F7, and F8 are multimodal functions, which contain multiple local optima within their domains. This complexity makes it more challenging to locate the global optimum. Therefore, multimodal functions are employed to test the global search capabilities of optimization algorithms, specifically assessing whether the algorithm can effectively escape local optimal values and ultimately locate the global optimum. In the simulation, we utilized six different optimization algorithms, namely the sparrow search algorithm (SSA), the improved sparrow search algorithm (ISSA), particle swarm optimization (PSO), chimpanze optimization algorithm (Chimp), the grey wolf optimizer (GWO), and the whale optimization algorithm (WOA). The parameters for the algorithms were set as follows: the maximum number of iterations was set to 500, and the population size was set to 40. For the SSA and ISSA algorithms, the warning value was set to 0.6, the proportion of explorers was 0.7, and the proportion of sparrows aware of the danger was 0.3. In the PSO algorithm, the inertia factor was 0.9, and the learning factor was 2. In Chimp, the exploitation factor was 2. For the GWO and WOA methods, the dynamic adjustment parameter initial value was set to 2. Each function was run independently 10 times. The average value, optimal value, and worst value were recorded for the SSA, ISSA, PSO, GWO, Chimp, and WOA techniques. The simulations were performed using MATLAB R2021a on a system running Windows 10 (64-bit) with an 11 th Gen Intel (R) Core (TM) i5-11400F (Intel, Santa Clara, CA, USA) processor at 2.60 GHz. Table 1, as regards F7, \(u\) represents the penalty function, defined as \(u=k\cdot((x\text{-}a)^{m}\cdot 1_{x\text{-}a}+(-x-a)^{m}\cdot 1_{x\text{ -}a})\), where \(\mathbf{k}=100\), \(\mathbf{a}=5\), and \(\mathbf{m}=4\). Here, \(1_{x\text{-}a}\) is an indicator function that takes the value 1 if \(x\text{ -}a\) and 0 otherwise, and \(1_{x\text{ -}a}\) is another indicator function that takes the value 1 if \(x<-a\) and 0 otherwise. In F8, let \(c_{i}=[0.1957,0.1947,0.1735,0.16,0.0844,0.0627,0.0456,0.3424,0.323,0.235,0.246]\) and \(d_{i}=\left[\frac{1}{0.25},\frac{1}{0.5},1,\frac{1}{2},\frac{1}{4},\frac{1}{ 6},\frac{1}{8},\frac{1}{10},\frac{1}{12},\frac{1}{14},\frac{1}{16}\right]\) for \(i=1,2,...,11\). Table 2 presents the experimental results for six different algorithms across various functions. To illustrate the optimization performance of the algorithms, Figure 3 displays the iteration curves for each function.
## 3 Methods  
### Signal Preprocessing  
Prior to the application of the algorithm to distinguish humans from mechanical vibrations, five signal preprocessing steps were implemented to reduce the computational complexity and filter clutter. These included range accumulation, normalization along the slow time dimension, direct current (DC) removal, low-pass (LP) filtering at a cutoff frequency of 2 Hz, and least-mean square (LMS)-based adaptive filtering. A flow chart of the signal preprocessing steps is presented in Figure 2.  
Range accumulation was implemented to considerably reduce the computational complexity by compressing the number of range points. It is illustrated by  
\[R_{RA}(x,\ n)=\frac{1}{10}\sum_{m=10(x-1)+1}^{10x}R(m,n). \tag{1}\]  
After range accumulation, the range points were compressed from 2048 to 200, namely, \(x=1,2,3,\ldots,X,\)and \(X=200\). Normalization along the slow-time dimension was then designed to make the energies of the signals from humans and mechanical vibrations comparable, as described by  
\[R_{N}(x,\ n)=2\frac{R_{RA}(x,\ n)-\min_{n}R_{RA}(x,\ n)}{\max_{n}R_{RA}(x,\ n)- \min_{n}R_{RA}(x,\ n)}-1. \tag{2}\]  
Subsequently, the DC components and baseline drift were eliminated by DC removal, as implemented by  
\[R_{DC}(x,\ n)=R_{N}(x,\ n)-\frac{1}{100}\sum_{n}^{n+99}R_{N}(x,\ n). \tag{3}\]  
The high-frequency clutters were then removed using LP filtering with a cut-off frequency of 2 Hz, according to  
\[R_{LP}(x,\ n)=R_{DC}(x,\ n)*h(t), \tag{4}\]  
where \(h(t)\) denotes the impulse function of the finite impulse response filter [14], and \"\(*\)\" denotes the convolution operation.  
Finally, the human-respiration-like clutter was suppressed by adaptive filtering based on the least-mean square algorithm. This process was described in detail in [15; 16]. All the subsequent methods were based on the preprocessed radar data, which are denoted by \(R_{AF}\left(x,n\right)\). The matrix size is 200 \(\times\)\(N\), and \(N\) depends on the length of the slow time.  
Figure 2: Flow chart of the signal preprocessing steps (DC, direct current; LP, low-pass).
# Influence of Monolith Length on Temperature Field of Concrete Gravity Dams
## 1 Introduction
[14] conducted an analysis on the influence of the start of concreting on the thermal stresses of a CGD made of RCC. Solar radiation was taken into account by increasing the ambient temperature by 1.0 \({}^{\circ}\)C. The results showed that the start of concreting during warm periods of the year can cause thermal cracks that can spread through the body of the dam, especially in the area of the foundation of the dam. [PERSON] et al. [15] performed thermal stress tests for different monolith lengths (15.0, 30.0, and 45.0 m). [PERSON] et al. [16] conducted a thermal stress analysis during construction with and without the influence of surface thermal insulation during the winter. [PERSON] et al. [17] performed a thermal parametric analysis by varying the material thermal parameters, the pouring height of the concrete layers, and the initial temperature of the concrete mixture. [PERSON] et al. presented a spatial numerical model for a phased calculation of the TF as a part of the research in [18,19], where verification of the model was performed based on the results of measuring the concrete temperatures in the dam's body. [PERSON] et al. [20] carried out a thermal stress analysis for two monolith lengths (15.0 m and 20.0 m) and presented a methodology for optimizing the monolith length by controlling the thermal tensile stresses during the construction and service of a CGD. [PERSON] et al. [21] conducted an analysis of the influence of extremely low temperatures on the behavior of rock-filled concrete (RFC) dams located in cold regions. The TF was calculated during the construction and exploitation process. The presented results showed that the use of RFC gives lower temperatures and thermal stresses than CVC, and the installation of an insulation layer on the surface of the dam body can significantly reduce the thermal tensile stresses. As part of the research in [22], the application of modern types of cements in the area of concrete dams was considered. The aim of the research conducted in this paper was to show the influence of monolith length on the temperature field of CGDs built using the block method over a period of about five years. In the numerical models, a phased TA was performed for four different monolith lengths (20.0 m, 15.0 m, 12.5 m, and 10.0 m), taking into account the corresponding initial and boundary conditions that define heat transfer. Model verification was carried out by controlling the amplitudes in characteristic nodes with the help of a simplified calculation method, which is presented in [4; 23]. As a result of the calculation, the TF, the temperatures in characteristic nodes, and their variations with changes in the monolith length during the construction and service phases are shown. The presented results show a significant sensitivity to changes in the monolith length.
## 2 Methodology
### Feed-Forward, Multilayer ANN-Based NIALM  
A feed-forward, multilayer ANN can be used to learn and distinguish from aggregated, extracted NIALM feature data for load disaggregation. In this paper, a comparative study where a feed-forward, multilayer ANN against the GA described in the previous subsection is used to address the same NIALM/load disaggregation problem, Equation (1), is conducted. As seen in Figure 5, a feed-forward, multilayer ANN contains an input layer, a number of intermediate hidden layers, and an output layer. The size of the input layer depends on the number of independent variables (extracted representative features) of considered feature data to be learned. The number of intermediate hidden layers with their size (the number of hidden neurons) specified in each hidden layer, can be determined experimentally through hyperparameter tuning where hyperparameters including the learning algorithm and the number of epochs for iterative rounds of learning affect how well the connectionism can represent the considered feature data (the hyperparameters are a set of parameters whose value is specified and used to control the learning process). The size of the output layer depends on the number of dependent variables (mutually exclusive classes) of considered feature data to be fitted.  
Figure 4: A chromosomal design encoding \(x_{i}(t)\) (at time \(t\)) in Equation (2).  
Figure 3: A workflow of the parallel computing accelerated GA used to solve Equation (2) for load recognition in the NIALM in this paper, where the evolutionary cycle is parallelized.  
In this paper, \(F_{1}\) score described in detail in the following subsection is conducted and used as the performance metric to indicate the effectiveness of the two parallel computing-accelerated AI approaches in load recognition for load disaggregation.
## 3 Insights into Algal-Bacterial Biomass
### The Feasibility and Application of Systems Utilizing MABAs  
The effluent utilized for microalgae cultivation harbors organic pollutants, which microalgae absorb as cellular constituents like lipids and carbohydrates [61]. Among the genera of microalgae employed in MABA systems, _Chloella_ sp. and _Scendesmus_ sp. exhibit superior performance, attributed to their heightened capacity to absorb nitrogen, phosphorus, and carbonaceous organic matter, alongside achieving elevated growth rates [62; 63]. Despite numerous studies aimed at enhancing microalgae-based technologies, extracting value-added products such as biofuels from these organisms remains a challenge.  
Effective microalgae harvesting is crucial to obtain an effluent with low turbidity. However, studies indicate that harvesting costs range from 20% to 30% of the total production value due to the low sedimentability, small size, and low density of the algal biomass [64; 65]. Dry algal biomass typically constitutes only 0.1% to 1.0% of the culture weight [66]. Consequently, commonly used techniques such as electrical, mechanical, and chemical methods are often limited and significantly escalate operational expenses [64; 67]. A more economical alternative is biofocculation, which involves promoting algae adherence to the surface of flocculent or granular sludge, forming aggregate without the need for chemical additives [68; 69; 70].
# Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models
## 3 Methodology
### The CNN-LSTM Hybrid Model
#### 3.4.4 Hyperparameter Tuning
Models often overfit or underfit when they train continuously to the end of a fixed number of epochs. Two things happen when a fixed number of epochs is set: either the model is interrupted while it is still learning, i.e., underfitting, or it learns so much that it starts memorizing the training data, i.e., overfitting. The literature identifies two methodssuitable for preventing this: early stopping and ModelCheckpoint [63]. We invoked the Keras _early stopping_ call-back and specified the validation loss as the performance metric to monitor and end training. We also set a patience of epochs to 8 to add a delay before stopping. We also added a second call-back called _ModelCheckpoint_ to save the best model achieved during the training process. Finally, deep neural networks were trained using the stochastic gradient descent optimization algorithm, which estimates the error gradient of the model being trained and then updates the model's weights using backpropagation [16; 22; 43]. The amount of the model's weight that is being updated during this training is known as the learning rate. It is often regarded as the most important hyperparameter for deep neural networks [43]. The value is usually between 0.0 and 1.0, and choosing the suboptimal rate is always a challenge. [PERSON] and [PERSON] in [68], in one of their papers, recommend tuning the learning rate after all other parameters. They suggested that even if the learning rate had been tuned earlier, effort should still be made to further reoptimize it at the end of all other parameter tuning because the learning rate tends to interact slightly with other parameters. In this article, we tuned the learning rate twice, one time before tuning the subcomponents of the model and the other time at the very end of all other hyperparameters.
## 4 Results
### Individual FBG Sensor NDT
When the receiver is near the flaw, but not at the same circumferential location, flaw detection is not reliable, due to the use of a point-like sensor. interaction of the initial axisymmetric wave and the flaw has had sufficient time to spiral around the pipe by the time the waves return to the receiving sensor locations. Figure 8 shows F-Scans collected with optical fiber sensors located 6 feet in front of the collar at four separate circumferential positions. In this setup, the axial distance to the two flaws is 1 foot and 4 feet, respectively. This is a relatively small axial distance between the sensors and flaws at 0.6 and 2.3 times the circumference, respectively. In this case, the sensor located on the opposite side of the pipe from the flaw has the lowest reflected wave amplitude from that position. In both cases, the sensor on the opposite side does not effectively detect the flaw, and the reliability of the other sensor locations for flaw detection is intermittent. This may be due to the smaller amount of energy in the scattered wave field travelling at high angles relative to the axis of the pipe, and also the natural angular reception efficiency of the optical sensor which decreases as incident angle increases relative to the pipe axis, with a maximum efficiency at 45\({}^{\circ}\). The exception is for the sensor at the same circumferential location (directly in line) with the flaw. The sensor on the top of the pipe clearly detects the flaw at 10 feet, and the sensor on the right of the pipe clearly detects the flaw at 7 feet. These results suggest that to have 100% detection coverage of the pipe using point-like sensors, including at axial distances close to the sensors, an array of optical fiber sensors is preferred--either circumferentially around the pipe, axially around the pipe, or a combination of the two. An array of sensors can detect flaws regardless of the closeness of the optical sensors to the flaws.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 8 User Study and Real-World Validation
### Study Design and Participants  
The study involved 12 participants, including medical professionals, pharmacists, nurses, students, and non-experts (researchers and engineers), to assess both expert and novice usability. Participants were asked to identify medical vials under three different conditions:  
1. Manual identification-participants visually identified vials without any technological assistance.
2. Barcode-based identification--participants used barcode scanners to identify vials.
3. MR-assisted identification (proposed system)--participants used the MR system with real-time object detection.  
Each participant completed 10 vial identification tasks across the three conditions, ensuring statistical robustness.