esa-sceva/satcom-chunk-collection huggingface dataset:
# A Primer on CFD-DEM for Polymer-Filled Suspensions
## 1 Introduction
The dependence of shear-thinning on \(\phi\) suggests that its origin lies in the micromechanics of interparticle interactions. However, there is a gap in the scientific literature regarding the dependence of the yield stress of the suspension on the particle volume fraction. For this reason, in this work we will develop a numerical approach to capture the influence of the particle volume fraction on the yield stress of a suspension flow. To model the continuum and dispersed phases, one must use governing equations that describe the phase behavior on a particular time and length scale. Then a suitable coupling scheme must be used so that the influence of one phase on the other can be captured. Various coupling schemes can be used, each with its own advantages and disadvantages [9]. Particle-laden flows are widely described in the literature by two popular coupling schemes: the continuum-continuum approach at a macroscopic level represented by the so-called two-fluid model (TFM) [10], and the continuum-discrete approach at a multiscale level that  
Figure 1: Parallel-plate rheometer with two plates of radius \(r\) separated by a distance \(H\), with \(r\ll H\). The upper plate can rotate with an angular velocity \(\Omega\). results from the combination of the Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM), the so-called CFD-DEM approach [11; 12]. In this paper, we evaluate the CFD-DEM method by simulating suspensions in a parallel-plate rheometer. We consider the CFD-DEM solver [13; 14] using OpenFOAM for CFD and LIGGGHTS for DEM. These general approaches aim to significantly extend and generalize the capabilities of numerical investigation of the behavior of particle-filled systems. The aim is to show that the numerical results agree either qualitatively or quantitatively (depending on the availability of experimental data for comparison) with the experimentally measured results [15; 16]. The interested reader should consult references [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. For a brief explanation on the evolution of CFD-DEM, please consult [34]. The remainder of this work is structured as follows. In Section 2, we describe the governing equations that model the incompressible single-phase flow of Newtonian and power law fluids inside a parallel-plate rheometer. In addition, the numerical implementation and validation of the single-phase algorithm are performed for torque calculations. Section 3 is devoted to particle-laden flows. We start by presenting the incompressible volume-averaged Navier-Stokes equations and we then explain the CFD-DEM numerical procedure. In Section 4, we assess the CFD-DEM numerical model for simulations of suspensions involving several particle volume fractions. Finally, in Section 5, we summarize the main conclusions of this work.
## 3 Results
### Antimicrobial Activity of Oak Bark Extracts  
As can be observed in Table 3, the oak bark extracts exerted antibacterial activity against the tested bacteria strains. The MIC values for gram-positive bacteria ranged from 0.3 to 1.25 mg/mL. The MBC values ranged from 0.3 (5. _aureus_) to 5 mg/mL. The MBCs of QREM was significantly higher (0.3 mg/mL) compared to the other samples. Among the gram-negative bacteria, those most sensitive to the oak bark extracts were _K. pneumoniae_ (MBC\(-\)0.6 mg/mL) and _P. aeruginosa_ (MIC\(-\)0.6 mg/mL). The MICs for _E. coli_ were not determined, since they were above the maximum tested concentrations.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Code Sample** & **Extraction Yield (\%)** & **TPC (mg GAE/g DW) \(\pm\) SD*** & **TTC (\% _m/m_)*** \\ QREM & 5.66 & 347.74 \(\pm\) 8.66 1 & 26.02 \(\pm\) 8.88 \\ QRAM & 4.57 & 323.16 \(\pm\) 3.17 2 & 37.16 \(\pm\) 4.48 \\ QREUS & 5.32 & 240.99 \(\pm\) 1.49 2 & 33.88 \(\pm\) 5.53 \\ QRAUS & 2.72 & 267.04 \(\pm\) 4.21 2 & 33.85 \(\pm\) 4.77 \\ \hline \end{tabular}  
* TPC\(-\)total polyphenol content; DW\(-\)dry weight; TTC\(-\)total tannins content; GAE\(-\)galic acid equivalent; SD\(-\)standard deviation (n = 3); QREM\(-\)hydroalcoholic extract obtained using MAE; QRAM\(-\)aqueous extract obtained using MAE; QREUS\(-\)hydroalcoholic extract obtained using UAE; QRAUS\(-\)aqueous extract obtained using UAE. The different letters in the same column note significant statisticaldifferences at \(p<0.05\).  
\end{table}
Table 1: The extraction yield, total content of phenolic compounds and tannins in oak bark extracts.  
\begin{table}
\begin{tabular}{c c c} \hline
**Code Sample** & **IC\({}_{\text{50}}\) DPPH (\(\mu\)g/mL)*** & **IC\({}_{\text{50}}\) ABTS (\(\mu\)g/mL)*** \\ QREM & 11.99 \(\pm\) 0.34 & 8.68 \(\pm\) 0.31 \\ QRAM & 1.94 \(\pm\) 0.17 & 6.06 \(\pm\) 0.00 \\ QREUS & 2.43 \(\pm\) 0.13 & 2.76 \(\pm\) 0.12 \\ QRAUS & 2.89 \(\pm\) 0.83 & 2.77 \(\pm\) 0.42 \\ \hline \end{tabular}  
* ABTS\(-\)2,2’-azino-bis(3-ethylbenzothidine-6-sulfonic acid; DPPH\(-\)2,2-diphenyl1-1-picrylhydrazyl; SD\(-\)standard deviation (n = 3); QREM\(-\)hydroalcoholic extract obtained using MAE; QRAM\(-\)aqueous extract obtained using MAE; QREUS\(-\)hydroalcoholic extract obtained using UAE; QRAUS\(-\)aqueous extract obtained using UAE.  
\end{table}
Table 2: The values of IC\({}_{\text{50}}\) for antioxidant activity of the oak bark extracts.  
The oak bark extract that was obtained using MAE showed good activity against _Candida krusei_ (MIC\(-\)2.5 mg/mL) and weak activity against _C. parapsilosis_ (MIC\(-\)5 mg/mL), (Table 4). The MICs were not recorded for _C. albicans_.
## 5 Experimental Results and Analysis
### Ablation Study  
To demonstrate the effect of each module, ablation experiments are conducted on four datasets in this section, and the experimental results are shown in Table 4. \"-\" means the removal of a part, \"syntax\" indicates semantic feature extraction, \"topic\" indicates extraction of topic information, \"dialogue graph\" indicates the dialogue interaction module, including the processing of speaker information and the construction of dialogue graph. We also conducted experiments using only BERT for sentiment classification to better demonstrate the effectiveness of the model.  
It is shown from the experimental results that removing each module will lead to different degrees of decline in the experimental results, indicating that each part of our framework contributes effectively to the emotion recognition task. We can also find that compared with removing a single module, removing the syntax module and topic module at the same time leads to more model drop. This proves that the combination of syntactic structure information and topic feature information improves the accuracy of classification, and their processing of sentence representation is complimentary. We found that the dialogue interaction module did not perform as well as expected in DailyDialog, which is because DailyDialog is a two-speaker dialogue dataset and its dialogue content is relatively concise. The speaker interaction distance of this dataset is relatively close. Existing methods, such as BERT, have been able to obtain the contextual information of the dialogue very well so the improvement of our method is limited. Even so, our model still maintains a good result. In contrast, because the dialogues of IEMOCAP and EmoryNLP are long and complex, the interaction module performs well on both datasets. We observed that there are as many as 302 speakers in IEMOCAP, so it is crucial to model the speaker information for this dataset, and our model does this well.
## 3 Research Methodology
### Performance Metrics
#### 3.1.3 Data Drop Rate  
Data drop rate means total data dropped by the MAC layer, which comes from a higher layer as a consequence of consistently failed re-transmissions. Data dropped values lower, which indicates that the network path is stable and has better transmission capability. It is an important performance measurement criteria for real-time communication systems. The data drop rate can be calculated as follows in a transmission time frame:  
\[DDR=\frac{N^{t}-N^{r}}{N^{tx}}\times 100\% \tag{3}\]  
where \(DDR\) is the data drop rate. \(N^{t}\) is the total number of transmitted data, and \(N^{r}\) is the total number of received data.
# Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering
## 4 Previous Works
### Obtaining Recommendations
#### 5.1.4. Validation of KNN, SVM and SVD Implemented in the CF  
The mean square error (MSE) method is used to obtain the degree of error when predicting unknown ratings (Equation (4)). The precision, recall and FScore measures (Equations (5), (6) and (7), respectively) were used to evaluate the efficacy of the methods in obtaining relevant recommendations [23; 28; 29].  
\[MSE=\frac{\sum_{j=1}^{n}{(r^{{}^{\prime}}_{ui}-r_{ui})^{2}}}{n} \tag{4}\]  
where \(r^{{}^{\prime}}_{ui}\) is the predicted rating, \(r_{ui}\) is the real rating, and \(n\) is the number of predictions.  
The precision (Equation (5)) is the number of ratings predicted as being relevant (\(t\)) from among all recommended ratings (\(n\)).  
\[Precision=\frac{t}{n} \tag{5}\]  
_Recall_ (Equation (6)) is the number of ratings predicted as being relevant (\(t\)) divided by the total ratings considered to be relevant for a user (\(m\)).  
\[Recall=\frac{t}{m} \tag{6}\]  
The _FScore_ is a single value combining the _Precision_ and _Recall_ values. If the _FScore_ is 1, _Precision_ and _Recall_ have the best values.  
\[FScore=\frac{2(Precision*Recall)}{Precision+Recall} \tag{7}\]  
These measures were used to compare the recommendation methods implemented in the hotel ratings data set. Predicted ratings with a value greater than 4 were considered relevant for calculating _Precision_ and recall. Table 2 presents the values of the evaluation measures of the three methods.  
Table 2 presents a performance analysis of the three analyzed methods. The results obtained with the SVD method were better than those from the other methods. SVD achieved the lowest MSE value, as well as a relatively good _FScore_, _Recall_ and _Precision_, which shows that the matrix factorization in our data set benefited the CF results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **MSE** & **Precision** & **Recall** & _FScore_ \\ \hline SVM & 0.657 & 0.741 & 0.763 & 0.751 \\ KNN & 0.578 & 0.787 & 0.806 & 0.796 \\ SVD & 0.456 & 0.836 & 0.851 & 0.843 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation results of CF methods.
## 2 Antenna Design
### Proposed Design Strategy  
The design goals were as follows: the first goal was to design a compact dual-band monopole meander line antenna with a flexible truncated ground, incorporating textile materials. The second goal was to show reliable on-/off-body communication with wide radiation patterns in both frequency bands and small SAR values. The design of this antenna started with a meander line attached to a microstrip line on a truncated ground plane and separated by a felt substrate. The antenna yielded a wide impedance BW. The antenna size was reduced by asymmetrical inverted slots loading. The meander line was fabricated using copper tape with a thickness of 0.035 mm and resistance of 0.04 \(\Omega\)/sq. The ground was made of e-textile (ShieldIt(tm)) with a thickness of 0.17 mm, a weight density of 230 g/m\({}^{2}\), and a resistivity of 0.05 \(\Omega\)/sq. Moreover, in our CST MWS simulations, lossy material with a conductivity (\(\sigma\)) of 1.18 \(\times\) 10\({}^{5}\) S/m was considered to model the ShieldIt material. Table 3 illustrates the important parameters of the proposed design.
# Study on Shear Performance of Web-Embedded Composite Shear Connector
## 3 Numerical Analysis
### Finite Element Model Verification  
#### 3.2.1 Load-Slip Curve Verification  
The load-slip curve based on the test results was compared with that of the finite element simulation (Figure 17). The simulated curves of all specimens increased linearly in the elastic stage and gradually decreased in the nonlinear stage. The trend of the simulated curve was consistent with the test curve. A comparison of the curves revealed that the simulated overall stiffness was slightly less than the measured stiffness owing to the presence of many unbalanced forces in the nonlinear analysis process. With the continuous calculation of the model, certain concrete elements began to gradually fail, rendering it difficult to achieve convergence in the iterative calculation. Once the iteration number reached the upper limit, ABAQUS provided the calculation result, which was generally slightly lower than the true value. Further analysis was performed on the error between the finite element simulated and measured USBCs (Table 7).  
As shown in Table 7, the overall simulated USBC was less than the measured value, and there were errors between the simulated and measured values. The comparison results showed that the errors ranged as 4.0-8.2%, and the average error of the finite element simulation was 5.9%. This indicated that the simulation results were numerically accurate and reliable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Vield Strength \(f_{y}\)** & **Ultimate Strength \(f_{u}\)** & **Elastic Modulus \(E_{s}\)** & **Poisson’s Ratio** \\ \hline Perforated steel plate & 345 & 490 & 210 & 0.3 \\ \hline Rebar & 426 & 646 & 206 & 0.3 \\ \hline Stud & 340 & 480 & 206 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: ABAQUS steel parameter setting.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen Number** & **Test Value/kN** & **Simulated Value/kN** & **Error/\%** & **Average Error/\%** \\ \hline P-0 & 223.05 & 214.2 & 4.0 & \\ \hline P-18 & 694.26 & 657.4 & 5.3 & \\ \hline P-18-S & 821.43 & 787.5 & 5.4 & \\ \hline P-14-S & 745.79 & 701.7 & 5.9 & \\ \hline P-22-S & 952.37 & 892.6 & 6.3 & \\ \hline P-18-S\# & 1077.52 & 98
## 2 Generation of Precision Timing Information
### Time-Scale Algorithm  
Figure 3 shows the schematic of the system setup. It consists of all the hardware discussed in the previous section. As shown by the schematic, the MMS captures the time differences between a reference clock and the input clocks, with the H-maser serving as the reference in our case. Therefore MMS records two sets of differences, \(x_{r}-x_{1}\) and \(x_{r}-x_{2}\), differences between H-maser and cesium clock 1 and H-maser and cesium clock 2, respectively. These recorded differences are then used by a time-scale algorithm to calculate the ensemble time, \(x_{re}\). For this paper, the _AT1_ algorithm is implemented.  
All time-scale algorithms share a common foundation, expressed by the general equation depicted in Equation (1).  
\[x_{je}(k)=x_{je}(k-1)+y_{je}(k-1)\tau+0.5d_{je}(k-1)\tau^{2} \tag{1}\]  
The equation shows the fundamental relationship used to estimate the behavior of a clock \(j\) with respect to an ensemble \(e\), at time stamp \(k\) given the condition of clock at \(k-1\). Here, \(x_{je}\) is the phase difference, \(y_{je}\) is the frequency difference, and \(d_{je}\) is the frequency aging of clock \(j\) with respect to the ensemble \(e\). The _AT1_ algorithm is a recursive algorithm that can be used in the estimation of clock behavior. The procedural steps involved in the realization of the _AT1_ algorithm have been described in our earlier paper [22].
# Watermarking Protocols: A Short Guide for Beginners
## 3 Security Primitives
### Homomorphic Encryption
The watermark insertion can be directly carried out into the encrypted domain by exploiting an additively homomorphic cryptosystem, thus obtaining [36; 37]  
\[E[\bar{x}]=E[x]\cdot E[b]^{2 as}\cdot E[\alpha s]^{-1} \tag{1}\]  
Homomorphic encryption can also be applied to the class of data hiding schemes defined as \"informed embedding\". Such schemes hide signal-dependent watermarks using as embedding rules the quantization of some content features. For example, consider a watermark insertion based on quantization index modulation (QIM) [38]. It is carried out according to the following expression [37]:  
\[\bar{x}=f(x)+b\Delta(x)\]  
where \(x\), \(\bar{x}\), and \(b\) maintain the signification reported above, while \(f(x)\) and \(\Delta(x)\) denote a function of the original signal features and a signal-dependent quantization step, respectively. In particular, \(f(x)\) and \(\Delta(x)\) can be expressed as:  
\[f(x)=Q_{\delta,0}^{2\Delta}(x)\qquad\qquad\Delta(x)=\Delta\cdot sgn(x-Q_{ \delta,0}^{2\Delta}(x))\]where  
\[E_{k}(X)=E_{k}(x_{1},x_{2}\ldots x_{l})=(E_{k}(x_{1}),E_{k}(x_{2})\ldots E_{k} (x_{l}))\]0  
In particular, \(Q\) is a quantizer associated with the following codebook:  
\[E_{k}(X)=E_{k}(x_{1},x_{2}\ldots x_{l})=(E_{k}(x_{1}),E_{k}(x_{2})\ldots E_{k} (x_{l}))\]1  
where \(\Delta\) is the quantization step and \(\delta\) is the dithering value. In fact, both parameters are specific to a binary dither modulation with uniform scalar quantizers, such as the QIM modulation [38]. By directly operating in the encrypted domain and by assuming an additively homomorphic cryptosystem, the watermark insertion based on QIM can be obtained by applying the following expression [37; 39; 40]:  
\[E_{k}(X)=E_{k}(x_{1},x_{2}\ldots x_{l})=(E_{k}(x_{1}),E_{k}(x_{2})\ldots E_{k} (x_{l}))\]2  
The two examples reported above show that homomorphic encryption results in a kind of commutativity between encryption and some data-processing operations, such as those concerning watermark insertions. This makes it possible to decouple content encryption from watermark insertion: a party involved in a watermarking protocol can receive an encrypted watermark and can directly insert it into an encrypted content without knowing anything about it. Such a property is crucial in the development of protocols able to meet the security requirements reported in the previous sections.
## 4 Outline of the Approach
### Process of Concept Alignment for Interoperability in Heterogeneous Sources
#### 4.5.1 Problem Statement  
Following the work in [38] we define a mapping element as a triple: \(<\!\!c_{s}c_{s}R\!\!>\) where  
* \(c_{s}\) is a source concept expressed as skos:Concept from a source concept scheme \(X\)
* \(c_{t}\) is a target concept expressed as skos:Concept from the ubiquitous user model scheme \(U\)
* R is a semantic relation (e.g., _equivalent_ (=); _related_ (\(\subseteq\),\(\supseteq\),\(\sqcap\)); _independent_: (\(\bot\)) holding between the entities \(c_{s}\) and \(c_{t}\).  
The matching operation determines the alignment (\(A\)?) for the pair of schemas \(X\) and \(U\) where \(X\) can be any concept scheme constructed from a profile supplier/consumer document and \(U\) will always be the ubiquitous user model concept scheme. See Figure 5.  
Figure 5: The matching process.  
\(A\) denotes a previous alignment for this pair of concept schemes (when available) which is to be completed or modified, \(w\) is an external resource used by the matching process (e.g., WordNet in our case).  
Two basic elements are available to find mappings between concepts: concept labels and hierarchical structures. Concept values can be used in conflict resolution when a concept is consumed and to determine if further transformation is necessary for interchangeability.  
We consider a concept scheme as _(C, H\({}_{C}\), V\({}_{C}\))_ where C is a set of concepts arranged in a subsumption hierarchy _H\({}_{C}\)_. V\({}_{C}\) is the set of corresponding concept values if available.  
Each concept _c\({}_{s}\)_ in a set of concept source _C\({}_{S}\)_ is defined by: a label _string1:string2_ where _string1_ is optional and _string2_ is mandatory, and subclass relationships. When namespaces, other than default, are specified in the source document _string1_ is used. The attribute or element identifier corresponds to _string2_ and it is typically described as a simple or compound word in natural language. A subclass relationship sets up a link with other concepts in the source document. The hierarchical structure most be described as a skos:ConceptScheme.  
A concept on the target side _C\({}_{T}\)_ is described by set of labels included in the target skos:Concept consisting of lexical labeling, notation and documentation SKOS properties.
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 7 Challenges and Future Research Directions
### End-to-End and Coordinated Resource Management  
As Tables 1-5 show, on the one hand, it is necessary to propose solutions that face the resource management problem in NSL from an E2E perspective. As most solutions are principally RAN-centered, they disregard one or more resources (mainly CN and edge network resources) needed to build network slices during their modeling. In this way, it is fundamental to investigate how to model, evaluate, and deploy E2E slices considering the three-dimensionality of 6G-RAN (nostatic base stations based on UAV), fog-native architectures, and CNs supported on data center networks. On the other hand, collaborative solutions involving more than one resource management phase also are required to achieve E2E network slices. Notably, we consider that scheduling and orchestration phases in the RL/DRL-based network slicing resource management domain are still in their infancy in 5G/6G and beyond networks. We highlight that it is pivotal to study how to deploy RL/DRL-based solutions to operate E2E slices.
## 3 Artificial Intelligence as Enabler for Adoption of Nuclear Maritime Ships
### Secure Informative Navigation Systems  
The presence of a nuclear reactor on board a ship makes it a potential target for various groups, such as maritime pirates, who may attempt to capture the vessel for its nuclear materials or onboard technologies. These groups are often concentrated in specific regions, posing significant risks of hijacking or trespassing from certain areas [105]. One possible solution is to restrict nuclear ships to routes considered safe, but this severely limits their operational flexibility and marketability. Consequently, unrestricted travel is often the only viable option for such vessels. or the vessel can contain armor and become a hybrid war/commercial ship, subject to appropriate training and equipment.  
In the case of hybrid war/commercial ships AI can play a crucial role recognizing threats and eliminating them, having the captain as the final decision-maker. In addition, it may provide very short-term navigation driven actions in the form of battle maneuvers, which can be implemented via the methods of reinforcement learning.  
Figure 6: AI solutions for supplying electricity to ports from docked nuclear ships include advanced forecasting and efficient management.  
AI can enhance navigation systems by incorporating security considerations alongside other navigational factors. These intelligent systems would assess the risks of potential attacks from groups such as paramilitary or terrorist organizations before determining or adjusting a ship's route [106]. By integrating real-time security information, the system can reroute the vessel as new threats or risks are detected [107]. As a result, nuclear ships could drive the development of a new class of secure, information-driven navigation systems [108], specifically tailored to meet the unique security needs of these vessels [109; 110; 111; 112].