esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Continuous-Discontinuous Multi-Field Numerical Model for Rock Blasting
## 2 Numerical Method  
### Continuous-Discontinuous Element Method  
The continuous-discontinuous element method (CDEM) is a numerical analysis framework with explicit integration built based on the Lagrange equation [31; 32; 33]. The elements adopted in this work include deformable block and interface elements, see Figure 1. In Figure 1, the solid black lines represent the real interface such as joints and other initial cracks. The dashed black lines represent the virtual interfaces, which will become activated when damage happens. Normal and tangential springs are arranged in the virtual interface. When these springs reach their strengths, they will break and the interfaces will open. The governing equation of CDEM method is as follows:Block element The governing equation is established based on the Lagrangian energy system. \[\frac{d}{dt}(\frac{\partial L}{\partial u_{i}})-\frac{\partial L}{\partial u_{i}}= Q_{i}\] (1) \[L=\Pi_{m}+\Pi_{e}+\Pi_{f}\] (2) The energy function of element is \[L=\frac{1}{2}\int_{V}\rho\dot{u}_{i}^{2}dV+\int_{V}\frac{1}{4}\sigma_{ij}(u_{i, j}+u_{j,i})dV-\int_{V}f_{i}u_{i}dV\] (3) The system external force includes damping and boundary external forces: \[\left\{\begin{array}{l}Q_{\mu}=\int_{V}\mu\dot{u}_{i}dV\\ Q_{T}=-\int_{S}\bar{T}_{i}dS\end{array}\right.\] (4) Hence, Equation (1) can be rewritten as: \[-\left(\int_{p}\rho\bar{u}_{i}dV+\int_{V}\partial_{ij}\frac{\partial_{u_{i,j}} }{\partial u_{i}}dV-\int_{V}f_{i}dV\right)=\int_{V}\mu\dot{u}_{i}dV-\int_{S} \bar{T}_{i}dS\] (5) Finally, the block element dynamic function is \[\mathbf{M}\ddot{u}(t)+\mathbf{C}\dot{u}(t)+\mathbf{K}u(t)=\mathbf{F}(t)\] (6)
* Interface element An interface locates between two block elements. The normal and tangential contact forces of the two elements are the spring forces as \[\left\{\begin{array}{l}F_{n}=-K_{n}*\Delta u_{n}\\ F_{\tau}=-K_{\tau}*\Delta u_{\tau}\end{array}\right.\] (7) The relative displacements between adjacent elements are \[\left\{\begin{array}{l}\Delta u_{n}=\frac{(\sigma_{n1}+\sigma_{n2})A}{2K_{ n}}\\ \Delta u_{\tau}=\frac{(\sigma_{\tau1}+\sigma_{\tau2})A}{2K_{\tau}} \end{array}\right.\] (8) The Mohr-Coulomb criterion is adopted; see Equation (9). When the normal stress of the spring is greater than the tensile strength, the normal spring will break and  
Figure 1: Constitutive model in the CDEM.  
tensile failure will occur. Similarly, when the tangential spring reaches its strength, the tangential spring will break and shear failure will occur:  
\[\left\{\begin{array}{ll}F_{n}=0,&\sigma_{n}\geq\sigma_{t}\\ F_{\tau}=F_{n}\tan\phi,&\sigma_{\tau}\geq c+\sigma_{n}\tan\phi\end{array}\right. \tag{9}\]
# Multifunction RF Systems for Naval Platforms
## 4 MFRF System Prototype Development Programs
### M-Aesa
#### 4.3.2 Technical Description
The HPAs were GaAs MMIC devices, with their characteristics indicated in Table 4 for two different models that were developed. HPA 1 was aimed at use in transmit modules for the wideband arrays in the second antenna configuration, while HPA 2 was intended for T/R modules that would be employed for the radar array in that configuration. While the operating frequency range of HPA 2 was somewhat less than that of HPA 1, it was still large enough to accommodate a C-band or X-band radar function. The obvious advantage of using HPA 2 for radar functions was the higher output power. These HPA specifications compared favourably to those of similar devices on the market in the 2010 time frame that the M-AESA HPAs were developed. However, the emergence of GaN technology since that time has resulted in currently available HPAs that are significantly superior, as indicated by comparisons to the HPA specifications in Table 2. Lastly, the wideband LNA used in the T/R module design yielded an overall measured noise figure for the module of less than 4.1 dB over the entire Rx operating frequency range of 2-18 GHz, and less than 2.9 dB over typical radar operating frequencies of 6-13.5 GHz. The M-AESA design utilized subarray-level waveform generation and digitization. Consequently, the total time delays required for TTD beamforming were achieved by a combination of digital domain implementation at the subarray level, and analog delays at the element level. Specifically, the design called for the capability to provide analog delays of up to 1144 ps (equivalent to 34 cm in free space) for the signals from/to each array element. As mentioned previously, a maximum of 124 ps of delay was available in each T/R module, where this number was likely constrained by the available space on the core chip for the delay line implementation. Thus, to meet the analog delay requirement, a separate analog TTD board that serviced each subarray provided up to an additional 1020 ps of delay for each array element, with the delay controlled by an 8 bit word with the least significant bit equivalent to 4 ps. The TTD board contained the same core chip as in the T/R module to provide the first 5 bits of delay, and then microstrip transmission lines were employed to implement the higher order 3 bits. Switches were also included on the board to select between Rx and Tx modes.
## 3 Results and Discussion
### Corrosion Susceptibility Assessment Tests
#### 3.3.2 Potentiodynamic Linear Polarization
(4.8 \(\times\) 10\({}^{-5}\) mm \(\times\) year\({}^{-1}\)), which possess a greater electropositive corrosion potential (\(-\)123 mV) and the highest polarization resistance value (7.3 \(\times\) 10\({}^{3}\) K\(\Omega\)\(\times\) cm\({}^{2}\)). These values are consistent with the observations made on the polarization plots. The improvement of the corrosion rate by almost two orders of magnitude can be explained by the appearance in the first stage of a mixture of chromium oxide and aluminum oxide with an underlying Al\({}_{2}\)O\({}_{3}\) (in the fast stage of the mechanism of the oxidation process) [40; 42; 43]. The slower decrease in the corrosion rate for the samples oxidized for 1440 h or 2160 h, compared to samples oxidized for 720 h, can be explained by the predominantly slow growth of the Al\({}_{2}\)O\({}_{3}\) layer, according to the second stage of the proposed oxidation mechanism. Furthermore, in the case of this sample, the highest protective efficiency value (99.55%) and the lowest porosity value (1.67 \(\times\) 10\({}^{-4}\)%) indicate the formation of more protective films on the surface of these samples. To better illustrate the dependence of the corrosion speed calculated from the potentiodynamic polarization curves on the coating thickness, the graph in Figure 11 was made. It can be noted that a rapid decrease in the value of the corrosion speed for the samples coated with a layer of NiCrAIY and oxidized under conditions of high temperature and pressure are comparable to the non-oxidized sample. At the same time, it can be observed that, for the samples tested under supercritical conditions, the increase in the thickness of the coating leads to a decrease in the value of the corrosion speed, indicating good corrosion behavior of the oxidized samples. Porosity, a measure of the density of the oxide defect, and its corrosion behavior, could be related. Several studies in the literature [56; 57; 58] have found that samples with lower porosity, lower corrosion current densities and rates, higher polarization potentials, and higher polarization resistivities exhibit the best oxide protective efficiencies (and can prevent corrosion the best). These findings are supported by the results.
## 4 Results and Discussion
### Analysis of Organic Compound Concentrations
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Product**} & **Protocatechu Acid** & **Neochlorogenic Acid** & **Chlorogenic Acid** & **Caffeic Acid** & **4-Feruloylquinic Acid** & **Isochlorogenic Acid** \\ \hline \multicolumn{5}{c}{**mg/g d.w.**} \\ \hline P-1 & 0.5493 \(\pm\) 0.0153 & - & - & - & - & - \\ \hline P-2 & - & 2.6869 \(\pm\) 0.1033 f & 1.9787 \(\pm\) 0.0210 i & 1.7938 \(\pm\) 0.1225 a & 1.1955 \(\pm\) 0.0095 e & 1.7116 \(\pm\) 0.0305 s \\ \hline P-3 & - & 6.0020 \(\pm\) 0.0505 e & 4.6736 \(\pm\) 0.0270 h & 0.1709 \(\pm\) 0.0051 e & 0.830 \(\pm\) 0.0047 g & 5.9957 \(\pm\) 0.0126 f \\ \hline P-4 & - & 16.9913 \(\pm\) 0.2093 d & 14.7412 \(\pm\) 0.0273 a & 0.5362 \(\pm\) 0.0036 b & 2.4146 \(\pm\) 0.0350 a & 13.153 \(\pm\) 0.2333 a \\ \hline P-5 & - & 5.9032 \(\pm\) 0.1702 e & 5.6598 \(\pm\) 0.0207 g & 0.3493 \(\pm\) 0.0073 d & 1.7904 \(\pm\) 0.0374 b & 2.6328 \(\pm\) 0.1638 b \\ \hline P-6 & - & 19.7390 \(\pm\) 0.1671 c & 11.3490 \(\pm\) 0.0401 b & 0.3132 \(\pm\) 0.0152 d & 1.7453 \(\pm\) 0.0233 b & 20.6498 \(\pm\) 0.0959 c \\ \hline P-7 & - & 16.8391 \(\pm\) 0.1578 d & 9.1499 \(\pm\) 0.0467 f & 0.2566 \(\pm\) 0.0031 d & 1.0652 \(\pm\) 0.0604 f & 20.7914 \(\pm\) 0.3593 c \\ \hline P-8 & - & 17.6460 \(\pm\) 0.1180 d & 10.1720 \(\pm\) 0.1032 d & 0.1491 \(\pm\) 0.0104 e & 1.1760 \(\pm\) 0.0330 e & 16.2011 \(\pm\) 0.1676 d \\ \hline P-9 & - & 23.9750 \(\pm\) 0.5405 a & 9.6597 \(\pm\) 0.0828 e & 0.1881 \(\pm\) 0.0139 e & 1.3148 \(\pm\) 0.0313 d & 31.6961 \(\pm\) 0.8483 a \\ \hline P-10 & - & 21.1462 \(\pm\) 0.6872 b & 10.8487 \(\pm\) 0.0828 c & 0.3989 \(\pm\) 0.0098 c & 1.6283 \(\pm\) 0.0321 c & 27.5268 \(\pm\) 0.6656 b \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Concentration of organic compounds in analyzed yerba mate products after methanol extraction. Each analysis was conducted three times. The letters next to values represent [PERSON]’s HSD post hoc results, different letters (a–i) indicate significant differences between homogeneous groups of yerba mate products for each organic compound (_p_ < 0.05).
## 3 Benefits of an Optimized Enterprise Network System
### Cost Minimization  
Reduced operational expenses are a direct result of an optimized enterprise network, since it drastically minimizes expenses for operations, services, software, hardware, and maintenance [56]. In addition, implementing cloud-based structures promotes efficient data management, improved corporate procedures, and smooth device interoperability [57]. Large enterprises may find it easier to benefit from network optimization, even though it is crucial for all kinds of businesses. Nonetheless, network optimization is very important for small organizations, as it will help them reduce cost, better manage revenue, and provide high-quality services.
# Field Emission from Carbon Nanostructures
## 2 Field Emission from Carbon Nanostructures  
### Field Emission Technology Development  
Due to the exponential dependence of the emitted current on the local electric field as well as on the work function (see Equation (1)), small variations of the chemical state of the surface or of the shape of the emitters strongly influence the emission properties. Since the early sixties, a huge effort has been focused on reducing the applied electric field/voltage necessary to enable electron emission. This has been carried out by sharpening the emitter surface thereby profiting from the enhanced field factor at the tip apices. On the other hand, reducing the curvature radius of an emitter apex to obtain higher \(\beta\), also corresponds to a reduction in the effective emitting area, which limits the maximum current emitted from a single structure. For instance, the current from a single tip can rarely exceed \(10^{-4}\) A (despite high current density being easily obtained). This implies that emitters with a very small curvature radius are suitable when a narrow-spectrum rather than a high-current electron source is required, such as in high-resolution scanning electron microscopy. At the same time, materials for FE should have high electronic conductivity, high melting points, chemical inertness, and mechanical stiffness.  
Mature technology was achieved before the nineties with the development of micro-gated field-emission arrays (FEAs) called Spindt emitters [43], in which large arrays are created by micro-fabrication technology employed in the electronics industry (Figure 2) to be employed for flat panel displays [44; 45].  
FEAs are typically fabricated by means of a lithographic process to realize an ordered two-dimensional arrangement of individual molybdenum micrometric conical emitters, in gated configuration, on a Si-substrate. Typically, molybdenum is the material of choice because of its good thermal, mechanical and electrical properties, having \(\phi=4.6\) eV, low resistivity and a high melting point, which allows high currents. The main drawbacks of such a technology are related to the complex and expensive fabrication process, the critical lifetime in vacuum, and the still relatively high operating voltages.  
Figure 2: Schematic of Spindt-type emitters.
## 6 Strengths and Limitations  
The main strength of this study is that it is current and relevant to the SSA community. The findings of this study provide new knowledge that can form a basis to inform the policy and practice of STI and HIV diagnosis, prevention, and management. However, the study may be limited in that only those publications and documents accessible to the researchers were used, and there may be new findings since research is always taking place.  
There are data inadequacies due to a lack of consideration or absence of certain characteristics, such as not explaining the effects of missing sociodemographic attributes of the participants, behavioral characteristics, and not emphasizing certain health issues. In addition, some of the studies had procedural limitations, including using smaller samples, focusing on very few algorithms, insufficient assessment of model performance, and limited scope. There were limitations on study designs that restricted the generalizability of the study findings, such as the use of secondary data without the integration of AI approaches in primary healthcare settings.  
The implications of right-skewed distribution for meta-analysis results are noteworthy. Larger studies may have a disproportionate influence on the pooled effect size because of their greater weight in the analysis. Meanwhile, smaller studies may contribute less to the overall findings, even though they can represent important subpopulations. In future scenarios where a right-skewed distribution occurs, it would be beneficial to conduct a sensitivity analysis. This analysis can assess whether excluding the largest studies significantly alters the results, helping to determine if the findings are robust against the influence of potential outliers.
# Reinforcement Learning Based Query Routing Approach for P2P Systems
## 3 RLQR Approach
### K-NS Algorithm  
We introduce here a generic K-NS algorithm that relies on the proposed 1-NS algorithms (EGNS, UCBNS, and TSNS) to select the \(k\) relevant neighbors to forward the query to. The main idea is summarized in Algorithm 4. It consists simply of running one of the three introduced 1-NS algorithms (EGNS, UCBNS or TSNS) \(K\) times. As shown in Algorithm 4, the inputs of K-NS are the search query \(q_{t}\) at trial \(t\), the set of all neighbors \(N=\{n_{1},\ldots,n_{m}\}\) and the number of neighbors to be selected \(k\). The output is a set \(S\) of relevant neighbors to forward the query \(q_{t}\) to. At each iteration \(i=1\ldots k\)K-NS relies on EGNS, UCBNS or TSNS to select from the set \(N=\{n_{1}\dots n_{m}\}\) the neighbor \(n_{j}\) with the highest expected probability of success according to the used 1-NS algorithm (see line 3 Algorithm 4). The selected neighbor \(n_{j}\) at the iteration \(i\), is then added to the set of relevant neighbors \(S\) and removed from the set of neighbors \(N\) (see lines 5-7 Algorithm 4).
```
0:\(q_{t}\): search query at trial \(t\). \(N=\{n_{1},\dots,n_{m}\}\): set of all neighbors \(k\): number of neighbors to be selected for \(q_{t}\)
0:\(S\): set of neighbors to be selected Initialization:\(S=\emptyset\):
1:for\(i=1\dots k\)do
2: select neighbor \(n_{j}\) using a 1-NS learning algorithm (EGNS, UCBNS or TSNS)
3:\(S=S\bigcup\{n_{j}\}\)
4:\(N=N\setminus\{n_{j}\}\)
5:endfor
```  
**Algorithm 4** K-NS algorithm
###### Abstract  
The experimental and numerical modeling of thermal enhanced oil recovery (EOR) requires a detailed laboratory analysis of core properties influenced by thermal exposure. To acquire the robust knowledge on the change in rock saturation and reservoir properties, the fastest way is to examine the rock samples before and after combustion. In the current paper, we studied the shale rock properties, such as core saturation, porosity, and permeability, organic matter content of the rock caused by the combustion front propagation within the experimental modeling of the high-pressure air injection. The study was conducted on Bazhenov shale formation rock samples. We reported the results on porosity and permeability evolution, which was obtained by the gas pressure-decay technique. The measurements revealed a significant increase of porosity (on average, for 9 abs. % of porosity) and permeability (on average, for 1 mD) of core samples after the combustion tube experiment. The scanning electron microscopy showed the changes induced by thermal exposure: the transformation of organic matter with and the formation of new voids and micro and nanofractures in the mineral matrix. Low-field Nuclear Magnetic Resonance (NMR) was chosen as a primary non-disruptive tool for measuring the saturation of core samples in ambient conditions. NMR T\({}_{1}\)-T\({}_{2}\) maps were interpreted to determine the rock fluid categories (bitumen and adsorbed oil, structural and adsorbed water, and mobile oil) before and after the combustion experiment. Changes in the distribution of organic matter within the core sample were examined using 2D Rock-Eval pyrolysis technique. Results demonstrated the relatively uniform distribution of OM inside the core plugs after the combustion.  
Keywords:thermal EOR; high-pressure air injection; oil shale; Bazhenov Formation; NMR relaxometry; Rock-Eval pyrolysis; SEM +
Footnote †: journal: Applied Soft. Phys. Rev. Lett.
## 1 Introduction
### Model Accuracy in Heterogeneous Industrial IoT with Non-IID Dataset  
In heterogeneous industrial IoT environments with non-IID datasets, FL encounters the challenge of maintaining model accuracy. The non-IID data characterize situations in which the data distribution across IoT devices is uneven, unbalanced, or displays distinct features, a common occurrence in real-world IoT deployments. Additionally, IoT devices within extensive networks may possess varying resources for data processing, further contributing to heterogeneity. Consequently, these scenarios result in heterogeneity challenges stemming from diverse data collection rates, a variety of device types, and unique data patterns across devices, which can impact the performance of federated learning systems.  
Specifically, the issue of model accuracy in heterogeneous industrial IoT environments arises from federated learning's reliance on aggregating locally trained models to create a global model. In the presence of non-IID data, local models trained on individual devices may exhibit satisfactory performance on their respective local data but perform inadequately on data from other devices. As a result, when aggregating these local models, the ensuing global model may acquire lower accuracy or slow convergence due to discrepancies in data distributions and available computational resources.
## 4 Discussion on Storage and Maintenance Methods for Ni-MH Battery Modules  
### New Storage and Maintenance Method  
From the perspective of industrial applications, both the maintenance cost and effect need to be considered comprehensively. Higher efficiency of a single maintenance can lead to less total maintenance time and lower maintenance cost. Therefore, a long maintenance period, short rest time, short charge-discharge time, and low storage SOC should be employed for an appropriate maintenance plan. Based on the test results and the above discussion, a new storage and maintenance method for Ni-MH battery modules is proposed.  
1. The maintenance period is selected as 120 days (set to T). When the storage time does not exceed 1T, maintenance is required once before the battery modules are put into use. If it exceeds 1T but not more than 2T, it needs to be maintained twice before being put into use, and so on.
2. During the storage period of the Ni-MH battery modules, the _FDC_ mode and _DDC_ mode are utilized alternatively. In other words, the _DDC_ mode is used for the odd numbers of maintenances, while the _FDC_ mode is employed for the even numbers of maintenances. Taking three times of maintenance during storage as an example, the first time uses _FDC_, the second uses _DDC_, and the third uses _FDC_ again.
3. The rest time is set to 5 min, the charge and discharge rates are both 1 C, and the storage SOC is 30%. The specific method is shown in the Table 11.
## 3 Results and Discussion
### Shelf Life Analysis of Produced Cookies
#### 3.4.2 Texture Analysis
\begin{tabular}{l l l l l l l} \hline \hline
**Cookie Sample** & **Storage Temp** & **R\({}^{2}\)** & **\(H_{o}\) (N)** & **\(a\) (N)** & **\(b\) (Days\({}^{-1}\))** & **\(H_{\mathit{inf}}\) (N)** \\ \hline Control & 25 \({}^{\circ}\)C & 0.9535 & 14.3 \(\pm\) 1.8 \({}^{\mathrm{a}}\)A & 14.2 \(\pm\) 2.4 \({}^{\mathrm{a}}\)A & 0.03 \(\pm\) 0.013 \({}^{\mathrm{a}}\)A & 28.47 \(\pm\) 3.01 \({}^{\mathrm{a}}\)A \\  & 35 \({}^{\circ}\)C & 0.8941 & 14.0 \(\pm\) 4.0 \({}^{\mathrm{b}}\)A & 17 \(\pm\) 5 \({}^{\mathrm{b}}\)A & 0.03 \(\pm\) 0.021 \({}^{\mathrm{a}}\)A & 31.12 \(\pm\) 5.66 \({}^{\mathrm{a}}\)A \\  & 45 \({}^{\circ}\)C & 0.9265 & 14.4 \(\pm\) 2.5 \({}^{\mathrm{b}}\)A & 14.8 \(\pm\) 3.0 \({}^{\mathrm{a}}\)A & 0.05 \(\pm\) 0.032 \({}^{\mathrm{a}}\)A & 29.21 \(\pm\) 3.95 \({}^{\mathrm{a}}\)A \\ \hline
4\% SCG-added & 25 \({}^{\circ}\)C & 0.9868 & 17.0 \(\pm\) 1.4 \({}^{\mathrm{a}}\)B & 19.6 \(\pm\) 2.0 \({}^{\mathrm{a}}\)B & 0.07 \(\pm\) 0.028 \({}^{\mathrm{a}}\)B & 36.35 \(\pm\) 2.17 \({}^{\mathrm{a}}\)A \\  & 35 \({}^{\circ}\)C & 0.9959 & 16.7 \(\pm\) 1.0 \({}^{\mathrm{a}}\)B & 25 \(\pm\) 1 \({}^{\mathrm{b}}\)B & 0.07 \(\pm\) 0.015 \({}^{\mathrm{a}}\)B & 41.58 \(\pm\) 1.53 \({}^{\mathrm{b}}\)B \\  & 45 \({}^{\circ}\)C & 0.9688 & 16.0 \(\pm\) 3.7 \({}^{\mathrm{a}}\)B & 37.8 \(\pm\) 5.8 \({}^{\mathrm{c}}\)B & 0.02 \(\pm\) 0.009 \({}^{\mathrm{b}}\)A & 53.90 \(\pm\) 6.87 \({}^{\mathrm{b}}\)B \\ \hline
7\% SCG-added & 25 \({}^{\circ}\)C & 0.9887 & 16.7 \(\pm\) 2.3 \({}^{\mathrm{a}}\)B & 38.3 \(\pm\) 3.2 \({}^{\mathrm{a}}\)C & 0.03 \(\pm\) 0.006 \({}^{\mathrm{a}}\)A & 54.99 \(\pm\) 9.98 \({}^{\mathrm{a}}\)C \\  & 35 \({}^{\circ}\)C & 0.9984 & 17.0 \(\pm\) 0.7 \({}^{\mathrm{a}}\)B & 32.2 \(\pm\) 1.0 \({}^{\mathrm{b}}\)C & 0.03 \(\pm\) 0.003 \({}^{\mathrm{a}}\)A & 49.28 \(\pm\) 1.24 \({}^{\mathrm{b}}\)C \\  & 45 \({}^{\circ}\)C & 0.9774 & 16.6 \(\pm\) 3.0 \({}^{\mathrm{a}}\)B & 40.0 \(\pm\) 5 \({}^{\mathrm{a}}\)B & 0.02 \(\pm\) 0.008 \({}^{\mathrm{a}}\)A & 56.25 \(\pm\) 5.96 \({}^{\mathrm{a}}\)B \\ \hline \hline \end{tabular}   
* \(\pm\) standard error. Model parameters for the hardness change are calculated by fitting the data in Equation (6). Different superscript letters (a, b, and c) in the same column for each sample (control, 4% SCG-added, and 7% SCC-added) indicate significant differences