esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Problem Setup for CFD Model
#### 3.5.1 Space Convergence on Flow Past a Circular Cylinder  
The accuracy of the present method was assessed in this section. In fact, the spatial key parameters of the AMR features are the maximum level of refinement \(M^{*}\) giving the smallest grid size defined as \(\Delta=L_{0}/2^{M^{*}}\), where \(L_{0}\) is the domain size and the refining criteria \(c^{*}\) such that the threshold \(u_{\text{stw}}/c^{*}\) is necessary to locally refine the grid. In the case of this study, we chose to refine the grid based on the change of the velocity fields. Naturally, in the following, we define the number of discretisation points along a twine diameter as \(\delta^{*-1}=d_{t}/\Delta\) and the dimensionless time as \(t^{*}=u_{\text{stw}}t/d_{t}\). Furthermore, the test consisted of computing the drag coefficient for a single circular body immersed in a constantly moving fluid. The impulsively started flow past a unit-diameter circular cylinder problem is a good candidate to help us in choosing the right numerical parameters to solve the problem of complex boundary layer separation over the circular cylinder. As outlined in Figure 12, the case was based on the experiments of [PERSON] and [PERSON] [47], and noteworthy early numerical simulations were the works of [PERSON] and [PERSON] [48] and [PERSON] and [PERSON] [49], hereafter, respectively, referred to as K&L and SIM. In Figure 13a, we show a space convergence with Basilisk on the chosen test case. Additionally, Figure 13c depicts the necessary effort to compute the drag force according to the refinement criteria \(c^{*}\). Finally, we show in Figure 13e the effect for the refinement criteria \(c\) on the drag coefficient.  
Hereafter, it is clear to us to use a set of \((\delta^{*-1},c^{*})=(128,20)\) to sufficiently capture the unsteady behavior of the flow behind the cylinder width a relatively modest effort.
## 3 Numerical Results and Discussion
### Simulation Settings  
We conducted several simulation experiments to validate the proposed dual-driven learning-based MIMO symbol detection method. The simulation settings used in these experiments are presented in Table 3.  
In our setup, there are \(N_{\mathsf{t}}=16\) UAVs, each equipped with a single transmitting antenna. The GS has \(N_{\mathsf{r}}=64\) receiving antennas. The receiving antennas are dual-polarized and arranged in a uniform planar array (UPA) with an antenna spacing of \(\lambda/2\) between adjacent elements.  
For the propagation scenario, we consider an urban environment where the main scatterers are buildings, trees, and moving vehicles. The GS is located at a height of 25 m,while the heights of the UAVs are randomly and uniformly distributed between 1 m and 100 m. We assume that all UAVs are moving in a straight line at a speed of 30 km/h.  
The UAV communication system operates in the millimeter-wave frequency band with a center frequency of 73 GHz. The frequency band has a width of 100 MHz. We use a total of 1024 subcarriers, with a spacing of 60 kHz between adjacent subcarriers.  
The channel matrix \(\mathbf{H}_{n,k}\) for (\(n=1,2,\ldots,N_{\text{t}}\)) and (\(k=1,2,\ldots,N_{\text{c}}\)) is generated based on the 3 GPP TR 38.901 V16.1.0 [14] and 3 GPP TR 36.777 V15.0.0 [15] specifications. We employ a 4 QAM modulation scheme. Although the constellation set \(\chi\) contains only four constellation points, we can generate any number of training sample combinations \(\left\{\overline{\mathbf{s}}^{(j)},\overline{\mathbf{y}}^{(j)},\overline{ \mathbf{H}}^{(j)}\right\}\) with complex Gaussian noise vector \(\mathbf{n}_{k}\). Here, the superscript \(j\) denotes the \(j\)-th sample combination in the dataset. The dataset consists of 16,000 training samples, 2000 validation samples, and 2000 testing samples. We use a mini-batch size of 100.  
To optimize the entire dual-driven network and minimize the loss function, we employ the adaptive moment estimation (Adam) optimizer. The Adam optimizer utilizes a variable learning rate of \(1\times 10^{-3}\), \(5\times 10^{-4}\), and \(1\times 10^{-5}\).
## 1 Introduction
At lower (subcritical) temperatures and/or pressures, CO\({}_{2}\) can be used in two different ways, a liquid, or a gaseous state, as well as two-phase mixtures of these states. An additional important parameter is the Joule-Thomson coefficient (see [18; 19]). The Joule-Thomson effect describes the change in temperature of a real gas or liquid when it flows through a valve or porous plug without heat and work interactions with the environment. For CO\({}_{2}\), the Joule-Thomson coefficient can be up to 10 times higher than that of air for the range of pressures up to 5 MPa at temperatures of about 40 \({}^{\circ}\)C. Thus, a flow of CO\({}_{2}\) at much lower temperature can be generated to remove more heat as compared to that from an air stream. To obtain a rough estimate for the amount of CO\({}_{2}\) that can be stored and utilized, we hypothetically consider a steam power plant where the steam at 540 C (813 K) and 10 MPa expands through a turbine to become a saturated-vapor steam at 0.008 MPa (saturated temperature is 41.5 C (314.5 K)). The conditions used here might be found in a typical power plant, but it is not for any specific power plant. To increase the overall plant efficiency, exhaust steam needs to be condensed in low-pressure tubes (about 10 to 20 kPa). If supercritical CO\({}_{2}\) is used, the tubes carrying low-pressure steam have to be exposed to a high-pressure environment (>7.3 MPa). Thus, using supercritical CO\({}_{2}\) to remove the condensation heat might not be suitable. There are very few studies related to the power plant cooling using CO\({}_{2}\) as the cooling fluid; in this paper, we propose that CO\({}_{2}\), from various sources, is first captured where excess solar (or wind) energy is used to compress the captured CO\({}_{2}\). This is then stored in a high-pressure container. When it is needed, the compressed CO\({}_{2}\) can be expanded at constant enthalpy into multiple lower-pressure (0.1 MPa) flow paths that circulate, removing the excess heat from the various components of a thermal power plant. The flow paths and the high-pressure container are designed in a closed-loop arrangement so that the exhaust low-pressure CO\({}_{2}\) streams cannot escape but are collected and compressed again. Thus, using CO\({}_{2}\) for cooling purposes in power plants can offer a novel approach for CO\({}_{2}\) storage and utilization. Since we need to explore the possibility of using CO\({}_{2}\) instead of air, in the following sections, we discuss the thermophysical properties of dry air and CO\({}_{2}\) under subcritical states.
## 4 Experimental Results  
### Experimental Hardware  
The signal acquisition unit consisted of a sensor module, AD acquisition module, and a laptop computer (Figure 6). The sensor module included seismic and acoustic sensors. The seismic sensor had a natural frequency of 4.5 Hz and a sensitivity of 100 \(\mathrm{m}\mathrm{v}\mathrm{/}\mathrm{m}\mathrm{/}\mathrm{s}\). The sound sensor consisted of an analog MEMS microphone with a sensitivity of \(-42\) dBV, and a 110 dB amplifier circuit. The AD acquisition module had an AD acquisition card with 24-bit acquisition accuracy, and the sampling frequency was set to 10,000 Hz. The laptop computer was a Lenovo Saver R7000K with an i710875H CPU and a GeForce RTX2070 Super GPU.
# Magnetoelectric Nanoparticles for Wireless Peripheral Nerve Stimulation: A Computational Study
## 1 Introduction
MENPs can be administered to the desired site using different strategies, e.g., intranasally or intravenously, in the case of the central nervous system, and navigated via the application of a magnetic field gradient [7; 16], or directly placed into the target region, e.g., by stereotactic injection into a specific region [7; 15]. Subsequently, MENPs can be activated through the application of a proper DC or, as in case of neural stimulation, an AC magnetic field at a specific low frequency, eliciting a localized electric field at a frequency equal to twice the frequency of the stimulating AC magnetic field [6; 9]. Finally, post-treatment, MENPs can be pulled away from the localized area by the application of a reversed magnetic field gradient [9] or can be independently cleared from the body. In fact, as shown in a mouse model, nanoparticles are excreted from the body within a two-month period, depending on their size and on the specific organ in which they are [17]. This study aims to describe and characterize the use of core-shell CoFe\({}_{2}\)O\({}_{4}\)-BaTiO\({}_{3}\) nanoparticles (hereafter referred to MENPs) for the electrical stimulation of the peripheral nervous system. The electromagnetic physical quantities generated by the presence of MENPs and their interactions with the dynamics of the nervous system were quantified by a computational approach. Starting from the results obtained by previous multiphysics models of such nanoparticles [18; 19], which included the investigation of the magnetostriction and piezoelectric effect multiphysics coupling, this work presents the modeling of the electric behavior of MENPs when placed into a uniform AC magnetic field at a frequency of 50 Hz and amplitude above magnetic saturation. Furthermore, this study focused on the quantification of the electric field distribution induced by MENPs in the human body, the thresholds needed to elicit action potentials, the impact of pulse shape and the selectivity of axons' activation inside the neuronal fibers, and, finally, the complex interplay between the two [20; 21]. For this purpose, the finite element method (FEM) and axons dynamics model were combined. This study underlined the ability to use MENPs as stimulating tools and, through a quantitative analysis, enabled the investigation of the influence of MENPs' concentration, orientation and distance on the effectiveness of MENPs' stimulation.
## 3 RSMA-Assisted Integrated Sensing and Communication Systems  
In this section, we first provide the RSMA system model and then investigate the RSMA-assisted ISAC architecture. Finally, we study the performance metrics of RSMA-assisted ISAC systems.
## 2 Methods
### Flow Cytometry  
Bacterial cell numbers were quantified using flow cytometry. Cells were stained using the fluorescent dyes SYBR Green I (SG-I; 10,000\(\times\) stock; cat.nr. S-7567; Life Technologies Ltd., Paisley, UK) and propidium iodide (PI; 1 mg mL\({}^{-1}\), cat.nr. P3566; Life Technologies Ltd.). SG-I was diluted to a working stock concentration of 100\(\times\) using dimethylsulfoxide (DMSO; Sigma Aldrich, St. Louis, MO, USA) and stored at \(-\)20 \({}^{\circ}\)C until use. In a first step, aliquots (250 \(\mu\)L) of water samples were transferred into 96-well plates (cat. nr. 601808, HJ-Bioanalytik GmbH, Erkelenz, Germany). To determine total cell concentrations (TCC), 200 \(\mu\)L sample aliquots from this plate were transferred into the wells of a second 96-well plate with pre-aliquoted 2 \(\mu\)L of the 100\(\times\) SG-I working stock solution, followed by thorough mixing by pipetting up and down several times with a multichannel pipette. To determine intact cell concentrations (ICC), 200 \(\mu\)L sample aliquots from this plate were transferred into the wells of a second 96-well plate with pre-aliquoted 2.4 \(\mu\)L of a mixture of 100\(\times\) SG-I (2 \(\mu\)L) and PI (0.4 \(\mu\)L), followed by thorough mixing. Staining was performed at 37 \({}^{\circ}\)C for 13 min in an air incubator. Data were collected using two Novocyte(r) ACEA benchtop instruments (OLS OMNI Life Science, Bremen, Germany) equipped with a 488 nm laser and an autosampler to enable analysis on a 96-well plate. The trigger was set on the green fluorescence (recorded in the FL1-channel), and the detection threshold was set to 800 fluorescence units. The data were analyzed using instrument-specific software and a gating procedure similar to the one described by [PERSON] et al. [26]. Graphical data visualization was performed using Microsoft Excel. In cases where the total signals exceeded approx. 5000 signals/sec, the water samples were diluted with 0.1 \(\mu\)m filtered mineral water (Evian, Evian-les-Bains, France). The TCC and ICC values were the basis for calculating the relative proportions of intact and membrane-damaged cells. For selected data sets, the relative proportion of high-nucleic-acid (HNA) bacteria is shown. The distinction between HNA and low-nucleic-acid (LNA) bacteria was carried out in the FL1/FL3 fluorescence plot of samples stained with SG I and PI. The instrument was regularly calibrated using NovoCyte QC particles (cat. nr. 8000004; Agilent Technologies, Santa Clara, CA, USA) to ensure that the instrument was operating within acceptable limits as stated by the manufacturer. QA also included regular assessments of differences between the two instruments using drinking water and bacterial suspensions. The standard deviations for repeated measurements of the same sample (technical repeats) were typically \(\leq\)5%, in line with [PERSON] et al. [27].
## 3 Results  
### Task Completion Time  
The task completion times varied significantly by the zones in the QWERTY soft keyboard (\(F(14,448)\) = 4.84, \(p\) < 0.0001, partial eta2 = 0.13) (Table 1). As shown in Figure 3, the Tukey test determined that zones 1, 3, 4, 6, 7, 9, 11, 12, 14, and 15 showed statistically longer task completion times than the other zones--in particular, the longest task completion times were found in zone 11. Meanwhile, zones 2, 3, 4, 5, 7, 8, 9, 10, 13, and 14 were classified into the shortest group of the task completion times.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Zone** & **Task Completion Time (s)** & **Touch Error Frequency** & **Subjective Satisfaction** \\ \hline
1 & 1.04 \({}^{\alpha,\beta}\) (SE: 0.05) & 0.05 (0.05) & 4.59 \({}^{\delta-\zeta}\) (0.27) \\
2 & 0.92 \(\gamma\)s (0.04) & 0.02 (0.02) & 5.29 \({}^{\alpha-\gamma}\) (0.21) \\
3 & 0.98 \({}^{\alpha-\delta}\) (0.04) & 0.03 (0.03) & 5.35 \({}^{\alpha-\gamma}\) (0.22) \\
4 & 0.96 \({}^{\alpha-\delta}\) (0.05) & 0.02 (0.03) & 5.35 \({}^{\alpha-\gamma}\) (0.20) \\
5 & 0.94 \({}^{\beta-\delta}\) (0.05) & 0.01 (0.02) & 4.61 \({}^{\delta-\zeta}\) (0.25) \\
6 & 1.04 \({}^{\alpha,\beta}\) (0.09) & 0.02 (0.02) & 4.68 \({}^{\gamma-\zeta}\) (0.24) \\
7 & 0.96 \({}^{\alpha-\delta}\) (0.08) & 0.01 (0.02) & 5.42 \({}^{\alpha,\beta}\) (0.21) \\
8 & 0.95 \({}^{\delta-\delta}\) (0.03) & 0.01 (0.01) & 5.67 \({}^{\alpha}\) (0.21) \\
9 & 0.95 \({}^{\alpha-\delta}\) (0.03) & 0.02 (0.02) & 5.55 \({}^{\alpha,\beta}\) (0.21) \\
10 & 0.90 \({}^{\delta}\) (0.03) & 0.00 (0.00) & 4.98 \({}^{\beta-\epsilon}\) (0.25) \\
11 & 1.08 \(\alpha\) (0.05) & 0.03 (0.03) & 3.88 \({}^{\zeta}\) (0.28) \\
12 & 1.00 \({}^{\alpha-\gamma}\) (0.05) & 0.03 (0.03) & 4.43 \({}^{\zeta,\zeta}\) (0.26) \\
13 & 0.92 \({}^{\gamma,\delta}\) (0.04) & 0.03 (0.03) & 5.03 \({}^{\beta-\delta}\) (0.22) \\
14 & 1.00 \({}^{\alpha-\delta}\) (0.04) & 0.01 (0.02) & 4.76 \({}^{\gamma-\epsilon}\) (0.23) \\
15 & 1.07 \({}^{\alpha,\beta}\) (0.04) & 0.00 (0.00) & 4.36 \({}^{\zeta,\zeta}\) (0.22) \\ \hline \end{tabular} The Greek letters indicate statistically different groups at a 95% confidence level (\(\alpha>\bar{\beta}>\gamma>\delta>z>\Diamond\)); the superscripts indicate the statistical groups to which the values belong (e.g., \(\alpha\)-\(\gamma\) indicates that the value belongs to three groups: \(\alpha\), \(\beta\), and \(\gamma\)).  
\end{table}
Table 1: Task completion time, touch error frequency, and subjective satisfaction as functions of zones.  
Figure 3: Task completion times in fifteen zones (the darker colors represent longer task completion times).
###### Abstract  
An analytical conversion method was developed for the self-balanced test results of monopile bearing capacity in layered soils to realize the better applicability of the self-balanced test theory for the bearing capacity test of foundation piles. To the additional settlement of the lower pile bottom brought on by the negative friction of the upper pile soil during the loading process in layered soils, the interaction effect between the upper piles and lower piles is first taken into account. To accurately convert the results of the self-balanced test pile into the traditional static load test curve form and solve the ultimate bearing capacity, the displacements and internal forces at micro-segment piles in each layer of soil were obtained using the finite difference method. Then, for verification, conventional static test piles and indoor model tests were conducted in a multi-layered ground foundation. The outcome demonstrates that the simplified conversion method's bearing capacity of the test pile is greater than that of the traditional static pressure test, the analytical conversion method's **Q-\(s\)** curve is relatively similar to the results of the conventional static load test, and the accuracy of the analytical conversion results is increased by about 9.3 percent. At the same time, the analytical conversion method was applied to the self-balanced test project of bored cast-in-place piles in Wutong Garden, Laibin, Guangxi, China, and the accurate bearing capacity and internal force deformation characteristics were obtained. The accuracy of the calculation result is improved by 12% compared with that of the simple conversion calculation result. Therefore, it can be widely promoted and applied in self-balanced pile bearing capacity test projects.  
self-balanced test pile; model test; analytical conversion method; ultimate bearing capacity; engineering application Article
# Impacting Robustness in Deep Learning-Based NIDS through Poisoning Attacks
## 4 Experimental Setup
### Performance Evaluation  
To assess the performance of the model, we employ the Confusion Matrix (CM), a pivotal tool used for binary classification purposes to identify the differences between normal (benign) and malware (threat) data samples. CM provides a complete malfunction regarding the performance of the ML model, and it contains four primary elements: True Positive (True-P) refers to the accurate classification of normal instances, True Negative (True-N) denotes the correct identification of deceitful threats, False Positive (False-P) implies the misclassification of normal activity in the model as malware, and False Negative (False-N) represents the incorrect labeling of data attacks as normal tasks. Such elements are fundamental in order to successfully evaluate the accuracy of the ML model. Moreover, we incorporated different measurements, in which each has been designed for a specific task. Among these, the primary measurements utilized include:  
1. Accuracy (Acc): Represents the percentage of successfully classified records in the whole dataset after training the algorithm.
2. Positive Predictive Value (PPV): Refers to the percentage of successfully recognized threat samples from all predicted threats.
3. False Positive Rate (FPR): Signifies the proportion of incorrect distinguished threat samples to the total number of actual threats.
4. Mean Squared Error (MSE): Represents the average squared dissimilarity between the predictions of the ML model and the original monitored outcomes. While MSE is traditionally used in regression problems rather than binary classification tasks, we employ MSE in our study for specific analytical purposes. Our objective is to analyze and monitor the changes in the system's behavior when different ratios of poisoning data are applied. By utilizing MSE, we are able to quantify the deviations between the predicted and original outputs, providing valuable insights into the impact of poisoning attacks on model performance.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & \begin{tabular}{c} **MCI with** \\ **Depression** \\ \end{tabular} & \begin{tabular}{c} **MCI without** \\ **Depression** \\ \end{tabular} & \
\begin{tabular}{c} **F** \\ \end{tabular} &
\begin{tabular}{c} **\(p\) Value** \\ \end{tabular} \\ \hline Digit Span Forward & \(0.20\pm 0.89\) & \(0.16\pm 0.74\) & \(0.04\) & \(0.845\) \\ Digit Span Backward & \(-0.10\pm 0.96\) & \(0.53\pm 0.63\) & \(9.26\) & \(0.004\) \\ TMT A & \(-0.48\pm 1.05\) & \(-0.31\pm 0.82\) & \(0.49\) & \(0.484\) \\ FAB & \(-1.95\pm 1.99\) & \(-2.35\pm 1.71\) & \(0.67\) & \(0.416\) \\ Verbal fluency P & \(-0.51\pm 0.66\) & \(-0.49\pm 0.73\) & \(0.01\) & \(0.945\) \\ Verbal fluency animals & \(-0.81\pm 0.85\) & \(-1.06\pm 0.72\) & \(1.41\) & \(0.240\) \\ TAVEC immediate memory & \(-0.84\pm 1.05\) & \(-1.67\pm 0.62\) & \(14.31\) & \(<\)\(0.001\) \\ TAVEC short-term free recall & \(-0.77\pm 1.07\) & \(-1.81\pm 1.07\) & \(13.25\) & \(<\)\(0.001\) \\ TAVEC memory (short-term cued recall) & \(-0.48\pm 0.93\) & \(-1.71\pm 0.96\) & \(23.22\) & \(<\)\(0.001\) \\ TAVEC long-term free recall & \(-0.86\pm 1.02\) & \(-2.10\pm 1.00\) & \(21.10\) & \(<\)\(0.001\) \\ TAVEC memory (long-term cued recall) & \(-0.65\pm 1.02\) & \(-2.14\pm 0.96\) & \(31.70\) & \(<\)\(0.001\) \\ TAVEC recognition & \(-0.57\pm 0.96\) & \(-1.33\pm 1.12\) & \(7.07\) & \(0.010\) \\ [PERSON]–OCF (memory) & \(-0.58\pm 0.93\) & \(-0.63\pm 0.88\) & \(0.04\) & \(0.838\) \\ [PERSON]–OCF (copy) & \(-0.52\pm 0.76\) & \(-0.08\pm 0.51\) & \(7.11\) & \(0.010\) \\ a-BT praxis & \(-0.69\pm 2.22\) & \(-0.60\pm 1.96\) & \(0.029\) & \(0.865\) \\ a-BT praxis bimanual & \(-0.57\pm 2.11\) & \(-0.67\pm 3.04\) & \(0.02\) & \(0.900\) \\ a-BT command & \(-0.32\pm 1.25\) & \(-0.05\pm 0.84\) & \(0.97\) & \(0.329\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Neuropsychological \(z\)-scores for the two groups studied: MCI with depression and MCI without depression. Data are presented in the following form: mean \(\pm\) standard deviation. Group differences were performed by analysis of variance (ANOVA) with post hoc analysis (Bonferroni), and the significance level was set at 0.001. The degrees of freedom for F values are (1, 95). MCI = mild cognitive impairment; TMT = Trail Making Test; FAB = Frontal Assessment Battery; TAVEC = Test of Verbal Learning Espana-Complutense; Rey–OCF = Rey–Osterrieth Complex Figure; a-BT = abbreviated Barcelona test.