esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 6 Results and Discussion
### Analysis of the Results of the Fixed Equipment Edge Node Placement Algorithm
Figure 11b illustrates the deployment costs of various methods as the number of terminal devices increases. The figure reveals an upward trend in deployment costs for all four methods as the number of devices increases. Among the four algorithms, IBGA consistently demonstrates superior performance and lower costs. The randomized algorithm displays significant cost fluctuations due to its unrestricted nature. This is attributed to the growing number of devices, resulting in increased task volume and higher demand for computing resources. Consequently, more expensive nodes are selected to meet the higher resource requirements, resulting in escalated deployment costs.
###### Abstract  
In rock mass engineering, stress balance changes often cause the relative slip of fractures along a wall surface, impacting the seepage behavior of fluid in the fractures. Using computer tomography (CT) scanning, spatial models of fractures with dislocations ranging from 0 to 10 mm were created to explore the relationship between changes in fracture dislocation and changes in fluid flow behavior, respectively. The spatial fractal dimension of cavity distribution within the fractures was calculated using a thin-plate filling approach to characterize the complexity of the fracture cavity distribution. The fluid flow within the dislocation fractures was then simulated using COMSOL, and the effect of cavity alterations in the form of dislocation on the fluid seepage behavior was analyzed using the spatial fractal. The results show that the values of mechanical aperture after dislocation of the fracture obtained by a CT test are normally distributed, the distribution range of mechanical aperture gradually widens with an increase in the dislocation distance, and the average mechanical aperture increases on a logarithmic curve. The relative spatial fractal dimension decreases gradually with an increase in dislocation distance, and the interconnected pathways within the fracture decrease; in addition, it is observed that the change in the relative spatial fractal dimension is closely correlated with the change in the mean mechanical aperture. Numerical simulations of dislocation fracture seepage found that the permeability increases nonlinearly with increasing dislocation distance. When the dislocation distance reaches 5 mm, nonlinear behaviors such as eddy currents occur, and the influence range of eddy currents gradually expands with the increase in dislocation distance under the influence of the boundary. Moreover, the inertia coefficient B in the Forchheimer equation and the critical hydraulic gradient \(Jc\), which can describe the nonlinear seepage characteristics, show a power function decreasing trend with increasing dislocation distance, and the fluid in the fracture is more likely to produce nonlinear flow.  
fracture shear dislocation; CT scans; seepage numerical simulation; void fractal dimension; nonlinear seepage +
Footnote †: journal: Applied Sciences
## 2 Problem Description and Model Formulation
### Description of Multi-Stage Lifelong Multi-AGV Path Finding  
To better reflect real-world manufacturing scenarios, where transportation tasks are assigned dynamically, spanning multiple floors and workshops, this paper defines the multi-stage lifelong multi-AGV pathfinding problem. In this problem, AGVs continuously execute transportation tasks assigned by the central system, plan their paths accordingly, and collaborate to complete multi-stage transportation while avoiding known and unforeseen obstacles. The objective is to minimize overall transportation costs and maximize transportation efficiency.  
The multi-stage lifelong multi-AGV pathfinding problem can be described as a tuple \((G,A,T)\), expressed as follows:  
\(G=(N,E)\): A directed graph where nodes \(n\in N\) represent traversable locations for AGVs, and edges \((n,n^{\prime})\in E\) indicate feasible transitions between nodes;  
\(A=\{a_{1},a_{2},a_{3},\cdots,a_{k}\}\): The set of AGVs, where \(k\) denotes the number of AGVs. Each AGV \(a_{i}\) has a recorded current position and other status information;  
\(T=\{t_{1},t_{2},t_{3},\cdots,t_{m}\}\): The set of multi-stage transportation tasks, where \(m\) is the total number of tasks. The number of tasks \(m\) is usually greater than the number of AGVs \(k\), so an AGV can perform multiple tasks. Each multi-stage task consists of the following subtasks:  
\[t_{i}=\{s_{1},s_{2},\cdots,s_{n}\} \tag{1}\]  
where \(n\) is the number of subtasks in a multi-stage task \(t_{i}\). Each subtask \(s_{i}\) is defined by a set of key waypoints, denoted as follows:  
\[s_{i}=\left\{n_{1},n_{2},\cdots,n_{goal}\right\} \tag{2}\]  
AGVs must visit these key waypoints sequentially to complete the subtask.Each multi-stage task can be collaboratively executed by multiple AGVs, with each AGV handling one or more subtasks. A subtask \(s_{i}\) is always executed by a single AGV.  
Time is discretized into timesteps; at each timestep, each AGV selects a discrete action from its action space to move along an edge from its current node to the next node. The path of an AGV from its current location to a key waypoint is represented as follows:  
\[P_{n_{start}\to n_{key}}=\left\{n_{curr},n_{mid1},n_{mid2},\cdots,n_{key}\right\} \tag{3}\]  
The AGV moves step by step until it reaches the key waypoint.  
\[P_{s_{i}}=\left\{P_{n_{start}\to n_{1}},P_{n_{1}\to n_{2}},P_{n_{2}\to n_{3}}, \cdots,P_{n_{goal-1}\to n_{goal}}\right\} \tag{4}\]  
For a multi-stage transportation task with n subtasks, the overall pathfinding solution consists of the following set of paths:  
\[P_{t_{i}}=\left\{P_{s_{1}},P_{s_{2}},\cdots,P_{s_{n}}\right\} \tag{5}\]  
The final transportation plan for all AGVs is the collective set of planned paths for all tasks.  
\[P_{total}=\left\{P_{t_{1}},P_{t_{2}},\cdots,P_{t_{m}}\right\} \tag{6}\]
## 1 Introduction
### Improvement and Structure  
In this paper, by using an IRS-assisted UAV as the air cellular data network base station, a system model is constructed; based on this, the system performance is analyzed and optimized. Firstly, two cascaded downlinks of the system are constructed: BS-UAV as a line-of-sight (LoS) link and BS-IRS-UAV as a non-line-of-sight (NLoS) link [23]. Then,the SNR formula of the system is derived by assuming a noise type using simulated data. Finally, according to the coding principle of the communication systems, the SER of the system is further deduced [24]. We use the distance between the base station and the IRS, the height of the UAV, the distance between the UAV and the IRS, the number of components, and other parameters to analyze the relationships between them and the SER, and we use a numerical diagram to analyze and discuss the key values of some parameters. In the latter part of the paper, we build a spatial model of UAV deployment at a low altitude. We synthesize all of the parameters as the influence factors of the system SER and deduce a formula for the spatial geometry for a numerical analysis in order to generate the best position for UAV deployment at a low altitude so as to achieve the improvement and optimization of the whole communication system's performance.  
Unlike in the previous system models, this paper reestablishes a cellular data network transmission model in a building area by taking advantage of the advantages of flexible deployment of UAVs and portable items, as well as the characteristics of an IRS that can be used to adjust the amplitude and phase of signals.This model not only avoids the signal-blocking problem caused by occlusion due to buildings, but also solves the problem of the small coverage of the down-tilt signal of the base station.
# Fully Automated Dispensing System Based on Machine Vision
## 2 Dispensing Positioning Information Extraction
### Image Processing of PCB Boards
#### 2.2.3 Binarization of the Dispensing Target  
The grayscale histogram shows a large difference in grayscale between the dispensing object and the background image of the PCB board, which requires a lot of work to extract the object features; thus, the matching efficiency will be affected. In multi-objective acquisition matching, threshold segmentation removes the area outside the dispensing object and thus reduces the search area, i.e., to segment the dispensing object from the background. In this study, the Otsu algorithm is used for image segmentation: let an image have L different gray levels, and the number of pixels corresponding to a gray value of \(i\) is \(n_{i}\), then the total number of pixels is obtained as:  
\[N=\sum_{i}^{L}=n_{i}. \tag{4}\]  
The probability of each gray level is:  
\[p_{i}=\frac{n_{i}}{N}. \tag{5}\]  
The pixels in the image are divided into two groups according to the gray value using the threshold \(T\). \(C_{0}\) consists of pixels with a gray value within \([1,\ T]\), and \(C_{1}\) consists of pixels with a gray value between \([T+1,\ L]\).  
It can be derived that the probability of generating \(C_{0}\), \(C_{1}\), respectively, is:  
\[w_{0}=\frac{\sum_{i=1}^{T}n_{i}}{N}=\sum_{i=1}^{T}p_{i}. \tag{6}\]  
\[w_{1}=\frac{\sum_{i=T+1}^{L}n_{i}}{N}=1-w_{0}. \tag{7}\]  
where the average grayscale of the \(C_{0}\), \(C_{1}\) group is:  
\[u_{0}=\frac{\sum_{i=1}^{T}n_{i}\times i}{\sum_{i=1}^{T}n_{i}}=\frac{\sum_{i=1} ^{T}p_{i}\times i}{w_{0}}. \tag{8}\]  
Figure 3: Image processing comparison. Note: (**a**) original figure (**b**) Mean filtering; (**c**) Median filtering; (**d**) Gaussian filtering.  
\[u_{1}=\frac{\sum_{i=T+1}^{T}n_{i}\times i}{\sum_{i=T+1}^{T}n_{i}}=\frac{\sum_{i=T+1 }^{T}p_{i}\times i}{w_{1}}\;. \tag{9}\]  
The average grayscale of the whole image is:  
\[u=\sum_{i=1}^{L}p_{i}\times i\;. \tag{10}\]  
where the grayscale average at a threshold value of \(T\) is:  
\[u(T)=\sum_{i=1}^{T}p_{i}\times i\;. \tag{11}\]  
In summary, it can be seen that the sampled grayscale averages are:  
\[u=u_{0}\times w_{0}+u_{1}\times w_{0}\;. \tag{12}\]  
Analogous variance:  
\[\delta^{2}=w_{0}(u_{0}-u)^{2}+w_{1}(u_{1}-u_{2})^{2}=w_{0}w_{1}(u_{0}-u_{1})^{2} \tag{13}\]  
Then take a suitable value of \(T\) (\(1<T<L\)) to make \(\delta^{2}\) maximum, at which time \(T\) is the best threshold for the maximum interclass variance method to get the best segmentation effect.  
Then Blob analysis with morphological processing, etc., is used to extract a single dispensing object, as shown in Figure 4.  
The overall binarization diagram of 20 dispensing PCB boards is shown in Figure 5. The white area in the diagram is the dispensing area. Through the binarization segmentation process, each dispensing area is successfully segmented out and visible, laying the foundation for the edge extraction of the dispensing area in the later step.
###### Abstract  
In this paper, we propose a novel approach to realize a compact and highly isolated dual-polarized holographic antenna using a pillbox feeding structure. The proposed antenna feeds dual orthogonal surface waves with low distortion phase distribution and high isolation through a compact three-layer pillbox feeding structure. This antenna also consists of a shared aperture dual-polarized hologram pattern calculated to radiate the objective wave in the desired direction without increasing the antenna size. As a result, the proposed holographic antennas (HA) have a compact size and support forward-to-backward continuous scanning with minimal gain degradation. The simulated and measured results are in good agreement, validating the efficiency of the proposed antenna design, which has the ability to scan the beam direction from \(+18^{\circ}\) to \(-25^{\circ}\), passing through the broadside within the frequency range of 21-27 GHz. Finally, the proposed antenna has a broadside gain of 18.5 dB in each polarization and a gain variation of less than 2 dB within the operating bandwidth.  
holographic antenna; metasurface antenna; continuous beam scanning; dual-polarized antenna; pillbox feeding structure
## 3 Method  
### Model Size and Material Parameter  
ABAQUS 6.14 and MARC 2022 were used for finite element simulation in our work. As reported in our previous work [37], the effect of welding structure on the structural stress distribution of the flue-side wall was carried out by ABAQUS 6.14. Meanwhile, the dependence of residual welding stress and deformation on the fin size and welding parameters was investigated using MARC 2022. The finite element model, as shown in Figure 4, was established based on the actual working conditions and structural parameters provided in Table 1. The left side of the model represented the horizontal flue-side wall with a pipe length of 11,180 mm, while the right side represented the tail flue-side wall with a pipe length of 19,410 mm. The connecting fins for both pipe groups had a length of 10,520 mm. The entire model was made of 12 Cr1 MoV steel, and the material parameters such as heat capacity \(C\) (J/Kg-K), thermal conductivity \(\lambda\) (W/m\(\cdot\)k), linear expansion coefficient \(\alpha\) (10\({}^{-6}\)/\({}^{\circ}\)C), elastic modulus \(E\) (10\({}^{3}\) MPa), shear modulus \(G\) (10\({}^{3}\) MPa), [PERSON]'s ratio \(\
u\), and yield strength \(\sigma_{\mathrm{s}}\) (MPa), which vary with temperature \(T\) (\({}^{\circ}\)C), are listed in Table 3. The meshing strategy adopted in this study was manual meshing, and the meshing type was hexahedral meshing. In the welding simulation process, the encrypted mesh was used in the weld seam and heat-affected zone, where the temperature gradient greatly changed. The temperature gradient in the area far from the weld seam was much smaller, so a relatively sparse grid was used. The final number of simulated elements reached 195,727.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Species** & **Value** \\ \hline Outer diameter of wall tube D (mm) & 32 \\ Thickness of water-cooled wall \(\delta\) (mm) & 8 \\ Pitch x (mm) & 49.5 \\ Horizontal flue-side wall cladding wall pipe length L1 (mm) & 11,180 \\ Tail flue-side wall package wall pipe length L2 (mm) & 19,410 \\ Connecting fin length L3 (mm) & 10,520 \\ Weld fin angle \(\alpha\) (\({}^{\circ}\)) & 31 \\ Weld radia radius R (mm) & 5 \\ Size of welding toe at connecting fin k1 (mm) & 3.33 \\ Size of welding toe at wall pipe k2 (mm) & 2.86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structural parameters of water-cooled tubes and connecting fins in a membrane water-cooled wall.  
Figure 3: Structural diagram of the tube and connecting fin on the flue-side wall.
## 2 Non-Parameterized Time-Frequency Analysis  
This section introduces three representative examples of non-parameterized time-frequency analysis: the short-time Fourier transform, wavelet transform, and Wigner-Ville distribution. Each method will be systematically examined in terms of its definition, practical applications, and strengths and weaknesses to provide a comprehensive understanding of its role in signal processing.
# Thermal Hydrolysis of Sewage Sludge: A Case Study of a WWTP in Burgos, Spain
## 1 Introduction
Veolia (Exelys(tm)) has four plants of ITHP technology in operation (in this case, known as digestion lysis digestion (DLD)) in France (Marquette-lez-Lille and Bonneuil-en-France), one in Slovenia (Ljubljana) and another one in Denmark (Billund) [32]. Cambi Solidstream(r) is a completely different technology based on the hydrolysis of digested sludge, but unlike ITHP, the hydrolysed sludge is sent to a barometric egg to maintain the temperature and pressure of sludge until dewatering. The dewatered sludge is then cooled down with air using a heat exchanger and sent to a scrubber to eliminate odours [33]. Centrates from the centrifugal dewatering pumps are sent back to digesters because of their high content in chemical oxygen demand (COD) [34], recovering a significant amount of biogas. The soluble chemical oxygen demand (SCOD) originates from the disintegration of sludge (e.g., extracellular polymeric substances (EPS)). The consumption \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & **Commercial Name and** & **Heating System** & **References** \\  & **Company** & & [21] \\ \hline \multirow{2}{*}{Batch process} & CambiTM—Cambi & & [21] \\  & BioThelysTM—Veolia Water & & [21] \\  & Technologies & & [21] \\ \hline \multirow{8}{*}{Continuous process} & HaarslevTM—Haarslev & & [22] \\  & tH\({}_{4}\)+ (Prototype & & [23] \\  & scale)—teCH\({}_{4}\)+ & & \\ \cline{1-1}  & ExelysTM—Veolia Water & & [24] \\ \cline{1-1}  & Technologies & Heat exchangers and & [25; 26] \\ \cline{1-1}  & Turbote®—DMT & steam injection & [27; 28] \\ \cline{1-1}  & Environmental Technology, & & [27; 28] \\ \cline{1-1}  & Sustec & & \\ \cline{1-1} \cline{2-2}  & Lysotherm®—Eliquo Water & Heat exchangers with & [27] \\ \cline{1-1}  & and Energy BV & thermal oil & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Thermal hydrolysis processes at the commercial stage and close-to-commercialisation prototypes. of polyelectrolytes in dewatering is practically zero due to the high operating temperature. A Cambi Solidstream(r) demonstration plant was installed in Amperverband (Germany), and its operation started in 2016 [35; 36]. In this study, different thermal hydrolysis technologies are analysed using data from the WWTP of Burgos as the base scenario when operating with conventional mesophilic anaerobic digestion. The novelty of this case evaluation is the analysis of the expected performance of a WWTP using the electricity obtained from the different processes instead of just digestion improvement as the main comparison parameter. This study considers the additional energy demand associated with the introduction of a whole set of new equipment that is necessary for the implementation of THP in an operating WWTP. The novelty of this study also focuses on evaluating the overall efficiency of the process in addition to biogas enhancement.
## 3 Piperine: Human Clinical Trials
### Clinical Trials on Piperine Effect on Human Health
#### 3.2.4 Clinical Trials on Black and Red Pepper  
There have been many other human clinical trials that investigated the effect of black and/or red pepper (but not pure piperine), either as a food ingredient, in capsules, or in the form of essential oil. One of the early clinical trials [189] investigated the effect of red and black pepper consumption on the gastric mucosa. A single dose of the test meals containing red pepper (0.1-1.5 g) or black pepper (1.5 g) was administered (intragastric) to healthy human volunteers, while aspirin (655 mg) and distilled water were used as positive and negative controls, respectively. It was concluded that a single dose had negative effects on gastric mucosa, causing a significant increase in gastric exfoliation and even mucosal micro bleeding. Therefore, the investigated spices should be used with caution due to negative effects on gastric mucosa, which are comparable to those of aspirin. In a trial of [PERSON] et al. [190], the effects of red pepper and black pepper on small intestinal peristalsis were investigated by measuring orocccal transit time (OCTT). When gelatine capsules containing black or red pepper were given to the healthy subjects, OCTT increased significantly after red pepper consumption, while black pepper failed to show statistically significant efficacy.  
[PERSON] and [PERSON] [191] reported the beneficial effect of vapor from the essential oil of black pepper (BPO) as a cigarette substitute, where the craving for cigarettes, negative affect and somatic symptoms of anxiety were alleviated with a high degree of significance. Another trial evaluated the effect of olfactory stimulation with volatile BPO on risk factors for pneumonia and found improvement of the reflexive swallowing movement in poststroke patients with dysphagia, regardless of their level of consciousness or physical and mental status [192]. In pediatric patients receiving long-term enteral nutrition due to neurological disorders, olfactory stimulation with 100 uL of BPO for three months improved and facilitated oral intake [193]. Some studies reported a lack of black pepper effect on the investigated parameters. After receiving a single brunch meal containing 1.3 g of pepper, there was no statistical significance related to postprandial diet-induced thermogenesis, as well as in appetite or food intake in young male participants [194]. Investigation of the impact of 0.5 g of black pepper as a part of a meal on 24-hour energy expenditure, respiratory quotient, and biochemical markers of metabolism and satiety showed lack of effect to energy expenditure in overweight post-menopausal [184]. Also, [PERSON] et al. [195] reported a lack of activity of a single dose of black pepper capsules (2.0 g) on short-term improvements in sustained attention, motivation to perform cognitive tasks, or feelings of mental energy and fatigue in young adults with low energy.
# Non-Parametric Model-Based Estimation of the Effective Reproduction Number for SARS-CoV-2 +
###### Abstract  
Describing viral outbreaks, such as the COVID-19 pandemic, often involves employing compartmental models composed of ordinary differential equation (ODE) systems. Estimating the parameter values for these ODE models is crucial and relies on accessible data. To accurately represent realistic pandemic scenarios with diverse situations, it is necessary to consider model parameters as time dependent. However, estimating such time-dependent parameters, like transition rates in compartmental models, is notoriously challenging due to the unknown function class of these parameters. In this study, we propose a novel approach by using an Augmented Kalman Smoother (AKS) combined with an Expectation-Maximization (EM) algorithm to simultaneously estimate all time-dependent parameters in an SIRD compartmental model. Our approach can be applied to general ODE systems with time-varying parameters, requiring no prior knowledge of model parameters or additional assumptions on the function class of the ODE time dependencies. A key advantage of our method compared to other methods is that it does not require assumptions about the parameterization of the serial interval distribution for estimating SIRD model parameters. Applying our approach to COVID-19 data in Germany, we adequately describe time-series data with strong fluctuations and multiple waves, obtaining non-parametric model-based time-course estimates for the effective reproduction number.  
SARS-CoV-2; augmented Kalman smoother; SIRD compartmental model; effective reproduction number; estimation of time-dependent parameters; ordinary differential equations +
Footnote †: This paper is an extended version of our paper published in the proceedings of the AIP Conf. Proc. 2872, 030006 (2023).