esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Response and Rock Damage of Different Shapes of Cavities under Blasting Loads
###### Abstract  
In order to investigate the dynamic response and rock mass damage characteristics of cavities with different shapes under blasting loads, this paper, through a combination of model tests and numerical simulations, studies the stress distribution, strain, failure modes, and blasting fragment size distribution of cavities with different shapes subjected to blasting loads. The results show that under the action of blasting loads, the presence of cavities with different shapes significantly affects the blasting effects and rock mass damage. Spherical cavities exhibit excellent blast resistance, whereas rectangular and triangular cavities are prone to stress concentration at their tips, which in turn promotes rock mass damage and failure. Subsequent analysis of the blasting fragment sizes reveals that rectangular and triangular cavities yield more favorable blasting results than spherical cavities. The research findings provide important theoretical foundations and practical guidance for the design and construction of underground engineering blasting, contributing to enhancing engineering safety and promoting the sustainable development of the underground engineering industry.  
cavity; cross-sectional shape; blasting load; failure mode; fragment size distribution Article
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 3 Energy Management Proposal for 5G Network Slicing
### Energy Management Strategies for Adaptive Consumption
#### 3.2.2 Use of Time Shifting Capability for Service Execution  
To efficiently use the energy resource and adapt consumption to generation, different strategies can be applied optionally to services belonging to a network slice. The application of the strategies on services can seek several objectives such as: (i) maximization in energy utilization, as shown in Figure 1a; (ii) maximization in service processing (acceptance rate); (iii) multi-metric objectives, e.g., maximize consumption and also service processing; or (iv) it may be linked to a scheme that prioritizes the processing of one type of service over others. These objectives, as well as priority in network slices (Section 3.2.1), can be established automatically by the MNO or can be defined in contractual agreements with consumers.  
In the context of energy efficiency, a strategy that adapts consumption to existing supply is the temporal displacement in the service execution time [33]. In the proposal, this strategy is defined as _time shifting_ and is useful to encourage the anticipated consumption (i.e., performing a _backward time shifting_ for service execution) or a deferral in the use of energy (i.e., performing a _forward time shifting_ for service execution, as shown in Figure 1b for service \(S_{1}\)), within a finite time horizon and according to the energy availability. In the proposed management model, the _time shifting_ strategy, similar to the others described in this section, can be implemented because the service processing (and corresponding energy consumption) is not carried out immediately upon customer requests, but rather there exists a calculation process executed by the MNO (specifically at network slice management and orchestration domain), in which the configuration of network slices and services is performed by applying management strategies and considering energy generation and consumption conditions. This procedure enables the MNO to find the efficient distribution of services (service scheduling) that optimizes energy consumption, as shown in the example in Figure 1a. A detailed explanation of the operation of the architecture and the proposed energy management model is discussed in Section 3.3.
## 3 Nitrate and Bacterial Contamination in Dairy Cattle Drinking Water
### Current Manure Handling Practices  
Throughout the United States, rural farm operations typically handle manure by flushing water into lagoons intended to pretreat and settle out solids. Alternatively, solids can be separated mechanically. After recycling the liquids for flushing, they are then applied to nearby fields for irrigation [24]. The solid manure is commonly applied to nearby lands but can also be further treated for other uses such as composting, anaerobic digestion, or cattle bedding [25; 26]. Liquids have a higher proportion of nitrate (as compared to the solids) and have high variability in concentration but have been measured to range from around 100 mg/L to 1170 mg/L [27]. Dairy farm manure loads from land application, lagoon leaching, corrals, etc., can lead to elevated groundwater nitrate concentrations. A study surveying nitrate concentrations surrounding dairy farms throughout California's San Joaquin Valley found that nitrate-N concentrations in shallow wells were 64 mg/L (around 283 mg/L nitrate) as compared to 24 mg/L (106 mg/L nitrate) in shallow wells upgradient from the dairy farms and source of manure [28].  
In addition to dairy farms themselves being large contributors to local groundwater nitrate concentrations, nearby croplands subject to synthetic fertilizer inputs are another risk factor of high nitrate concentrations in ground and surface waters. In fact, cropland contributes approximately 96% of leached nitrate present in groundwater basins [29].  
Studies have also shown that livestock wastewater can contribute to elevated indicator bacteria concentrations in groundwater (i.e., two to three orders of magnitude higher than background bacteria levels). This is especially true following rainfall events, which can increase the infiltration of bacteria from soil retention to groundwater [30]. Another study conducted in Argentina sampled groundwater surrounding dairy farms and found levels of total coliform between 0 and 1600 most probable number (MPN)/100 mL and levels of fecal coliform between 0 and 140 MPN/100 mL. While the upper limits of those measured ranges are above recommended levels for cattle, about 50% of the samples, however, were determined to be suitable for human consumption with concentrations close to 0 MPN/100 mL [31]. These results are consistent with observations in the literature that while dairy farms contribute bacteria to groundwater and consequently trough water, a major source of bacterial pollution in cattle drinking water troughs could be from the cows themselves via cud, dust, manure, etc. [9]. Figure 2 outlines major pathways for nitrate and bacterial inputs to reach dairy cattle drinking water and some of the short-term and acute impacts as well as longer-term or indirect impacts.
## 6 Theoretical and Security Analysis
### Theoretical Analysis
#### 6.1.3 Comparative Analysis with Existing BOSMs  
We compare the proposed NexoNet system with several existing blockchain social media platforms, including Steemit (available online: [[https://steemit.com](https://steemit.com)]([https://steemit.com](https://steemit.com)) (accessed on 10 September 2024)), Mastodon (available online: [[https://joinmastodon.org](https://joinmastodon.org)]([https://joinmastodon.org](https://joinmastodon.org)) (accessed on 10 September 2024)), Minds (Available online: [[https://www.minds.com](https://www.minds.com)]([https://www.minds.com](https://www.minds.com)) (accessed on 10 September 2024)), and Diaspora (available online: [[https://diasporafoundation.org](https://diasporafoundation.org)]([https://diasporafoundation.org](https://diasporafoundation.org)) (accessed on 10 September 2024)), across key dimensions such as decentralization, data storage, incentive mechanisms, and scalability. The comparison results are summarized in Table 1.  
From the comparative analysis, it is evident that NexoNet outperforms other platforms in several key areas. In terms of decentralization, NexoNet achieves a higher degree through its PoAP consensus mechanism, allowing users to vote for maintainers and take part in platform governance, unlike Steemit and Minds, which maintain partial decentralization. NexoNet also excels in data storage by leveraging Swarm distributed storage technology, ensuring data security and traceability, and avoiding single points of failure, unlike the more centralized approaches of Steemit and Minds, and the federated structure of Mastodon and Diaspora.  
In terms of incentive mechanisms, NexoNet introduces a multiple incentive model that fairly distributes rewards based on user participation and contributions, offering a more comprehensive system compared to Steemit's token-based rewards. Platforms like Mastodon and Diaspora lack built-in economic incentives altogether. Additionally, NexoNet's scalability is a major advantage, supporting large-scale participation and efficient data processing, whereas Steemit, Minds, Mastodon, and Diaspora face limitations due to blockchain performance or node-based configurations. Overall, NexoNet demonstrates significant improvements across these dimensions, making it a promising solution for decentralized social media platforms.
## 5 Performance Evaluation of FVCOM  
FVCOM is a community numerical ocean circulation model developed by UMASSD-WHOI and it solves 3-D primitive free-surface equations using a finite-volume method. The horizontal grid is composed of unstructured triangular grids for horizontal space and terrain-following sigma grids for vertical space. This model has demonstrated excellent performance in coastal studies characterized by complex coastlines such as the western and southern coast of Korea. FVCOM is considered to be a good balance of computational efficiency and accuracy because it combines the best features of finite-element method (grid flexibility) and finite-difference methods (numerical efficiency and code simplicity) [15].
# A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography
## 2 Materials and Methods
### Radio Frequency Propagation for RF Acoustic Computed Tomography (RACT)  
Radio frequency radiation is modeled by [PERSON]'s equations, which when modeled at a single frequency can be reduced to the following wave equation:  
\[\
abla\times\
abla\times E(x)-\omega^{2}\mu(x)\varepsilon(x)E(x)=q(x)\ \ x\in\Omega. \tag{4}\]  
In this equation, \(E\) is the complex electric field, \(\omega=2\pi f\) is the frequency, and \(\mu\) and \(\varepsilon\) are the permeability and permittivity of the medium, respectively. In a material with electric loss, the permittivity is complex valued, with \(\varepsilon=\varepsilon_{\text{real}}-j\frac{\varepsilon}{\omega}\), and \(\sigma=0\) for a medium with no loss. When the simulation is restricted to two dimensions (assuming a \(TM_{z}\) polarization), Equation (4) can be reduced to a scalar Helmholtz equation:  
\[\
abla^{2}E_{3}(x)+k(x)^{2}E_{3}(x)=q(x)\ \ x\in\Omega, \tag{5}\]  
where \(k=\frac{\omega}{c}\) is the wavenumber, and can be complex in a tissue with energy loss. For boundary conditions, a Dirichlet boundary condition similar to Equation (2) is implemented. However, this would not prevent the electromagnetic waves from reflecting off the boundaries of the domain, causing numerical artifacts. Therefore, while there is a Dirichlet boundary condition, there is also a non-physical space occupied by a perfectly matched layer (PML) to absorb the outgoing waves and prevent reflection [34]. Once the electric field is calculated, the initial pressure is given by three contributions [35]. The first contribution is due to the conductivity of the medium, \(p_{cond}=\int_{V}\xi\|E|^{2}dV\). The second due to the permittivity \(\int_{V}\frac{\varepsilon_{0}\varepsilon_{r}}{2}|E|^{2}dV\), and the third the permeability \(\int_{V}\frac{\varepsilon_{0}\mu_{0}t}{2}|H|^{2}dV\). In practice, for the tissue media of interest in thermoacoustic tomography, the contribution to the pressure due to the conductivity dominates over the permittivity and permeability, and so these terms aregenerally ignored. Therefore, similar to the optical absorption in Equation (3), the initial pressure due to the electric field is given as:  
\[p_{0}(x)=\Gamma(x)\frac{\sigma(x)}{2}|E(x)|^{2}. \tag{6}\]
# Development of a Wideband Slotted Antenna Array with Low Profile and Low Sidelobe
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency** & **_E-Plane HPBW_** & **_H-Plane HPBW_** & **_E-Plane First SLL_** & **_H-Plane First SLL_** \\
**(GHz)** & **(Degree)** & **(Degree)** & **(dB)** & **(dB)** \\ \hline
17.9 & 7.01 & 6.87 & \(-28.9\) & \(-26.1\) \\
20 & 5.46 & 5.74 & \(-28.3\) & \(-24.8\) \\
22.3 & 4.94 & 4.69 & \(-28.5\) & \(-20\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Radiation characteristics of the proposed antenna. which indicates that the simulation results can generally well reflect the actual performance of the antenna array. Therefore, the proposed slotted cavity antenna array is expected to feature wideband, low SLL, and high aperture efficiency in actual applications, if a proper fabrication process is used. In order to avoid the potential electromagnetic leakage, the proposed antenna array is planned to be fabricated by involving the additive manufacturing technology [39]. Though there are currently still some technical problems, a well-fabricated antenna can be expected in the near future. In addition, for wider applications, a suitable circular polarization is also planned in the near future. As the distance between the radiation slots in the proposed antenna array is very small, it is challenging to realize a general hexagonal radiating aperture [40]. Thus, a new circular polarization structure will be explored.
# Mathematics-Driven Analysis of Offshore Green Hydrogen Stations
## 2 Materials and Methods
### PHASE III: Modeling of an Electrolyzer: Type ALK and PEM  
The mathematics-based technical model used to estimate the energy consumption required to produce a given number of \(\frac{\mathrm{mol}}{\mathrm{s}}\) of hydrogen (\(N_{mol}\)) through the dissociation of the hydrogen molecule--water electrolysis--is expressed as (\(PW_{i}\)) (Equation (29)).  
The value of the hydrogen heating value constant (\(HV\)) depends on the design of the green hydrogen station and its energy recovery capacity. Therefore, the electrolyzer model allows for the use of both the higher heating value of hydrogen (\(\mathrm{HHV}=285.8\ \frac{\mathrm{kJ}}{\mathrm{mol}}\)) and the lower heating value (\(LHV=241.8\ \frac{\mathrm{kJ}}{\mathrm{mol}}\)). The actual efficiency of the electrolyzer is modeled by dividing the theoretical calculation by an efficiency coefficient that depends on the model and type of electrolyzer selected (\(K_{electrolyte\_i}\)). In both cases, \(i\) represents the type of technology used in the electrolyzer--ALK or PEM. The electrolyzer's energy consumption is expressed in \(\frac{\mathrm{kJ}}{\mathrm{s}}\) or kw.  
\[PW_{i}=\frac{N_{mol}\times HV}{K_{electrolyte\_i}} \tag{29}\]  
According to recently published ALK and PEM electrolyzer models [42], the authors estimate the value of \(K_{electrolyte\_i}\) (Equations (30) and (31)) based on the commissioning _year_ of the station, respectively.  
\[K_{electrolyte\_ALK}=\frac{1}{(555.25-0.25\times year)} \tag{30}\]  
\[K_{electrolyte\_PEM}=\frac{1}{(1570.75-0.75\times year)} \tag{31}\]As part of the model presented in this article, equations (Equations (30) and (31)) were used to determine the relationship between the efficiencies associated with each type of electrolyzer (\(R_{ALK\_PEM}\)):  
\[R_{ALK\_PEM}=\frac{K_{electrolytez\_ALK}}{K_{electrolytez\_PEM}}=\frac{(1570.75-0.75\times year )}{(555.25-0.25\times year)} \tag{32}\]  
Additionally, in the same article [43], the authors also present two other mathematical models to characterize the relationship between the commissioning year of the station (\(year\)) and the hydrogen pressure at the output of each type of electrolyzer.  
In this way, the model was obtained for the ALK electrolyzer type (\(P_{ALK}\)) (Equation (33)) and for the PEM electrolyzer type (\(P_{PEM}\)) (Equation (34)). Both results are expressed in bar.  
\[P_{ALK}=-3528.75+1.75\times year \tag{33}\]  
\[P_{PEM}=-7533.75+3.75\times year \tag{34}\]
## 2 Materials and Methods
### Quad-M-SHIME(r) Setup  
A Quad-M-SHIME(r) configuration was used in this study. This configuration utilizes fewer compartments per condition than the standard SHIME(r) model [33] but allows for the comparison of four different conditions (i.e., test products) in parallel. Each parallel arm consisted of a compartment to simulate the stomach and small intestine, one to simulate the PC, and one to simulate the DC. The stomach/small intestine compartment operated according to the fill-and-draw principle and peristaltic pumps added 140 mL of SHIME feed at pH 3.0 (to simulate the stomach), which was then incubated for 1.5 h and followed by the addition of pancreatic and bile liquid (60 mL) at pH 6 (to simulate the small intestine) and an additional 1.5 h incubation. Next, the intestinal suspension was pumped into the PC compartment (pH 5.6-5.9; retention time, 20 h; volume, 500 mL) then to the DC compartment (pH 6.6-6.9; retention time, 32 h; volume, 800 mL). The mucosal compartment was simulated in both the PC and DC by the inclusion of mucin-coated beads, as described by [PERSON] et al. [38; 39]. Two parallel Quad-M-SHIME(r) configurations were set up; each configuration was inoculated with microbiota isolated from a fecal sample originating from a single healthy adult volunteer who had not been treated with antibiotics within the 6 months prior to sample collection. Rather than pooling the fecal microbiota from the two donors, this approach was employed to allow us to account for interindividual variability when comparing the effects of the four test products.  
The Quad-M-SHIME(r) experiment had three stages: a two-week stabilization period following the inoculation of the colon compartments with the fecal sample, a two-week control period, and a three-week treatment period. The stabilization period allowed the microbial community to colonize different compartments based on the local environmental conditions. During the control period, samples were collected from the PC and DC compartments to determine the baseline microbial community activity and composition of each region. During the treatment period, samples were collected to evaluate the effects of the test products on the microbial activity and community. The Quad-M-SHIME(r) was fed throughout the study using standard SHIME(r) feed; during the treatment period, the standard feed was supplemented with the predigested test products.
## 1 Introduction
It uses a transformer neural network as a tool to directly fit the mathematical channel model from the measured channel data, avoiding the laborious problem of establishing a mathematical channel model by traditional statistical methods and the interpretability issue caused by using a neural network as a channel model, and can reveal the mathematical relationship and physical mechanism between channel parameters. Figures 1 and 2 show the difference between using neural networks directly as channel model and using deep neural network as a symbolic regression tool to generate channel model. The main contribution of this paper are:  
* To the best of our knowledge, it is the first time to establish a mathematical channel model directly from data by using a symbolic regression method. As a new channel modeling method, it may help researchers to establish a channel model easily. * The PhySO method is improved to be more suitable for channel modeling tasks. The method proposed in this article may have important application prospects in the field of channel modeling. Although this article takes the terahertz frequency band as an  
Figure 1: Deep neural network as channel model. Figure 2: Deep neural network as a symbolic regression tool. example, it has a wide range of applications and can be used as a data-fitting tool for other frequency bands and more complex communication scenarios. This paper is organized as follows: Section 1, introduces the important role of channel modeling in the future inter-satellite terahertz communication systems, expounds on the shortcomings of the traditional channel modeling scheme and the use of neural network as the channel model scheme, and proposes a channel modeling scheme based on symbolic regression. Section 2, introduces the symbolic regression method, especially the PhySO symbolic regression method in detail, and the corresponding improvement to PhySO, which makes it more suitable for the channel modeling task. Section 3, simulates the fitting effect of the improved PhySO symbolic regression method on the free space path loss model and verifies the feasibility of the proposed method. In Section 4, it summarizes the characteristics of the proposed method and points out the next research plan.
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 1 Introduction
The novelty of this research work is assured by the fact that the NOMA simulator is the first of its type to connect different features of SC and MC-NOMA under one platform, where some of these contributions have already been presented in the very recent literature or have not been considered previously. The NOMA simulator is still not an open source because much work is in progress to upgrade, where the following versions comprise various factors such as mobility and massive MIMO setups. Furthermore, this upgradation focuses on different power allocation schemes, channel models, interference cancellation techniques, and channel coding methods other than convolutional coding. To the best of the authors' knowledge, our simulator is the first work to be presented in the context of NOMA link-level and system-level analyses. This work also reinforces the efficient performance of MC-NOMA for both Gaussian and non-Gaussian channels together with intercell interference. The efficacy of the multi-user detection (MUD) approach for SIC to separate multiplexed user information is critical for NOMA-based systems. A small detection error might result in erroneous detection of all users' data, irrespective of the high channel gain. Therefore, the additive white Gaussian noise (AWGN) base ambient noise and impulse noise can lead to deteriorated data recovery. Hence, channel coding is applied in the form of convolutional coding on MC-NOMA to avoid impulse noise, which is a key feature of non-Gaussian channels; thus, it provides a more realistic scenario in this regard. In addition, linear precoding (LP) is suggested to improve the performance of uncoded and coded MC-NOMA by introducing diversity in the system along with a reduction in the peak-to-average power ratio. It is believed that this is the first work wherein the superiority of coded MC-NOMA over uncoded MC-NOMA with LP is highlighted in the presence of AWGN, impulse noise, and intercell interference. The remainder of this article is organized as follows: Section 2 describes the detailed structure of the NOMA link-level simulator, and Section 3 presents the system-level simulator. The Structure of MC-NOMA is given in Section 4, followed by the validation of the presented simulator in Section 5. Coded MC-NOMA in the presence of non-Gaussian channels along with the description of the system model and BER analysis of uncoded and coded MC-NOMA with LP is also presented in Section 6. Finally, the concluding statements are presented in Section 7.