esa-sceva/satcom-chunk-collection huggingface dataset:
In this paper, we provide an extensive study of ridged apertures (i.e., open-ended ridge waveguides) to assess their performance as array elements. We present results for square and hexagonal waveguides in square [14] and triangular lattices [17; 18], respectively (such geometries are sketched out in Figure 1). Differently from [5], we only consider the element at the aperture level, i.e., without the matching section in order to find the intrinsic peculiarities and limitations of each configuration. In this way, this study offers a starting point to design more complex elements integrating element as well as cascaded discontinuities to match improvement and polarization conversion.  
The novelty aspect of this paper is the study of square and hexagonal ridged waveguides in an array environment covering a bandwidth of 13.2%, i.e., the downlink Ka-band in terms of ARC, scan loss and cross-polar discrimination (XPD). For all configurations, we provide cut-off, parametric and complete performance analyses for the optimum parameter sets using both linear and circular polarization. Even if the system operates using circular polarization, linear polarization provides additional physical insights, i.e., it clearly shows that the reduced performance at high-scan angles is due to the scan blindness phenomena.  
All the optimum geometrical parameters are reported as a reference for future design activities. The provided cut-off and parametric analyses can be used as design curves to investigate different operative frequencies and manufacturing constraints.  
To the best of the author's knowledge, this is the first paper showing the performance of the quadridge element within a 13.2% bandwidth up to a \(\pm 60^{\circ}\) scan angle. This is also the first paper providing results on a hexagonal ridged waveguide in a triangular lattice to overcome the limits of quadridge.  
The paper is organized as follows: Section 2 describes the main parameters for a LEO array design, such as periodicity and configuration. Section 3 presents the cut-off analyses for both quadridge and hexagonal waveguides. Section 4 discusses and compares the performance of the proposed radiating elements in terms of S-Parameters, scan loss and XPD. Section 5 validates the results using both infinite and finite array approaches. The conclusion is reported in Section 6.
# Physical-Layer Security Analysis over M-Distributed Fading Channels
## 1 Introduction
However, the secure performance over M fading channels has not been completely revealed. In this paper, a wireless communication network with one transmitter ([PERSON]), one legitimate receiver ([PERSON]) and one eavesdropper ([PERSON]) is considered. The main and eavesdropping channels experience independent M-distributed fadings. The PLS over the M fading channel is investigated. The main contributions are listed as follows:  
* Under the M-distributed fading channel, the exact expression of the SOP is first derived. To reduce computational complexity, a closed-form expression for the lower bound of the SOP is obtained. It is shown that the performance gap between the exact SOP expression and its lower bound is small in the high signal-to-noise ratio (SNR) regime. * The closed-form expression for the probability of the SPSC over the M-distributed fading channel is derived. It is found that when the target secrecy rate is set to be zero, the lower bound of SOP has the same expression as the probability of the SPSC. * Over the M-distributed fading channel, the exact expression of the ESC with a double integral is obtained. To obtain a closed-form expression, the lower bound of ESC is then derived. It is shown that the performance gap between the exact expression and the lower bound of the ESC is small. * As special cases of the M fading channel, the secure performance over the K, exponential, and Gamma-Gamma fading channels is derived, respectively. The accuracy of these performance analysis is verified by simulations. The remainder of this paper is organized as follows. In Section 2, the system model is introduced. Section 3 focuses on the secure performance analysis. For some special cases, the secrecy performance is further investigated in Section 4. Section 5 presents numerical results before Section 6 concludes the paper. _Notations_: \(E(\cdot)\) denotes the expectation operator, \(\text{Var}(\cdot)\) denotes the variance operator. \(\Gamma(\cdot)\) denotes the Gamma function. \(K_{\text{\tiny{p}}}(\cdot)\) denotes the modified Bessel function of the second kind with order \(v\). \(G_{p,q}^{m,n}[\cdot|\cdot]\) denotes the Meijer's G function [22]. \(\mathcal{N}(a,b)\) denotes a Gaussian distribution with mean \(a\) and variance \(b\). \(f_{X}(x)\) and \(F_{X}(x)\) denote the probability density function (PDF) and the cumulative distribution function (CDF) of a random variance \(X\). \(\{x\}^{+}\) denotes \(\max\{x,0\}\).
## 4 Methodology
### Regularization Techniques  
One of the challenges presented by DL-based models is overfitting. This occurs when the model is overparameterized or very complex; consequently, it learns the statistical noise from the training data, resulting in a model that overfits these data excessively. Moreover, when evaluating the model, it does not generalize correctly on the test data [29; 78].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Activation Function** & \multicolumn{2}{c}{**Strengths**} & \multicolumn{2}{c}{**Weaknesses**} \\ \hline \multirow{3}{*}{ReLu} & \multirow{3}{*}{\(\bullet\)} & Fix vanishing gradient problem. & \(\bullet\) & The components are not updated, due to the “Dead ReLu” problem. \\  & & \(\bullet\) & Low computational cost. & \(\bullet\) & It does not avoid the exploding gradient problem. \\  & & \(\bullet\) & Transforms negative values linearly. \\ \hline \multirow{3}{*}{ELU} & \multirow{3}{*}{\(\bullet\)} & Avoid the “Dead ReLu” problem. & \(\bullet\) & High computational cost. \\  & & \(\bullet\) & Allow negative outputs. & \(\bullet\) & It converges slower due to the exponential. \\  & & \(\bullet\) & Fix vanishing gradient problem. & \(\bullet\) & It does not avoid the exploding gradient problem. \\ \hline \multirow{3}{*}{Leaky ReLu} & \multirow{3}{*}{\(\bullet\)} & Avoid the “Dead ReLu” problem. & \(\bullet\) & It does not avoid the exploding gradient problem. \\  & & Lower computational cost & \(\bullet\) & When differentiating it, it becomes a linear function, causing problems when training. \\ \cline{1-1}  & & \(\bullet\) & Fix vanishing gradient problem. & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Strengths and weaknesses of the ReLu, ELU, Leaky ReLu activation functions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Values** \\ \hline Activation function for LSTM layer & [SeLu, Tanh] \\ Number of neurons for LSTM layer & [50;60,70] \\ Number of neurons for output layer & 96 \\ Iterations or Epochs & 50 \\ Optimizer & Adam \\ Learning rate & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters were evaluated and used to train the model.  
To avoid these drawbacks, we considered implementing dropout and early stopping regularization techniques.
# Molecular Targets of Minor Cannabinoids in Breast Cancer: In Silico and In Vitro Studies
## 2 Results
### Effects of Cannabinoids in Androgen Receptor (AR)  
The activity of the nine minor cannabinoids towards AR was assessed through the AR-EcoScreenTM assay. The cells were treated with the cannabinoids (1-10 uM) for 24 h in the presence (antagonism) or absence (agonism) of R1881 (0.1 nM). The results demonstrated that all the studied cannabinoids caused no AR agonism (\(p\) < 0.05; \(p\) < 0.01; \(p\) < 0.001; Figure 8). On the contrary, all the nine cannabinoids exerted AR antagonistic activity, suggesting that they act as AR antagonists (\(p\) < 0.01; \(p\) < 0.001; Figure 8). **CBG** (\(p\) < 0.001; Figure 8A), **CBN** (\(p\) < 0.001; Figure 8C), **CBDB** (\(p\) < 0.001; Figure 8D), and **CBC** (\(p\) < 0.01; \(p\) < 0.001; Figure 8I) antagonistic activities were verified for all the concentrations studied, while for **CBDV** (\(p\) < 0.001; Figure 8B), **CBDA** (\(p\) < 0.001; Figure 8E), **CBDM** (\(p\) < 0.001; Figure 8F), and **CBCA** (\(p\) < 0.001; Figure 8G), the AR antagonistic activity was only observed at 5 and 10 uM and for **CBGVA** (\(p\) < 0.001; Figure 8H), only at 10 uM. In addition, contrary to **CBGVA** for which an increase in the luciferase activity signal was verified at 10 uM (\(p\) < 0.01; \(p\) < 0.001), which might be due to cell proliferation, all the other treatments slightly reduced cell viability (\(p\) < 0.05; \(p\) < 0.001), mainly at 10 uM (Supplementary Figure S6). As previously reported by our group, **CBD** acts as an AR antagonist, with inverse agonist properties [44].  
Figure 8: Transactivation assays for the determination of AR agonist/antagonist activity induced by the nine minor cannabinoids, **CBG** (**A**), **CBDV** (**B**), **CBN** (**C**), **CBDB** (**E**), **CBDA** (**E**), **CBDM** (**F**), **CBCA** (**G**), **CBGVA** (**H**), and **CBC** (**I**). CHO-K1 cells were treated with the cannabinoids (1, 5, and 10 uM) in the absence (AR agonism) or presence (AR antagonism) of R1881 (0.1 nM) for 24 h. Results are presented as the mean \(\pm\) SEM of at least three independent experiments performed in triplicate.  
Statistically significant differences between the control (cells not exposed to cannabinoids), set as 1, and cells not treated with R1881 are expressed as $ (\(p<0.05\)), $$ (\(p<0.01\)), and $$$ (\(p<0.001\)), while the differences between the control and cells treated with cannabinoids in the presence of R1881 are expressed as \(\underline{E}\) (\(p<0.01\)) and \(\underline{E}\)\(\underline{E}\) (\(p<0.001\)).
## 1 Introduction
On the other hand, pore network modeling [27] involves extracting an idealized and representative pore network from the image, followed by the computation of relevant petrophysical properties and oil and gas flow simulations. With pore-scale modeling and an image-based approach, the processes of reactive transport [28], multi-phase fluid flow [29], and heat transfer [30] were simulated in micro-fractures. However, the micro-fracture model assumptions are too simple. Other researchers used the pore network modeling method to investigate multi-phase fluid flow in horizontal, rough-walled micro-fractures [31]. They disregarded the effects of gravity and viscous forces and also believed that the impact of local fracture wall convergence or divergence could be disregarded. They looked at the combined impact of the two main curvature radii, in plane and aperture induced. Pore-scale imaging and modeling have also been used to explore how contact angles and void structures affect capillary pressure and relative permeability properties. However, there were some shortcomings: (1) The effect of rough-walled micro-fractures on single- and multi-phase flow is not evaluated. (2) The description of micro-fractures roughness in current models is not accurate. (3) The transmission relation between the matrix and the micro-fracture should be considered in the process of heat and mass simulation. Actually, the petrophysical properties are the function of the pore structure of the matrix and micro-fractures. In fact, the real micro-fractures have variable apertures, lengths, and inclinations which create roughness for fluid to flow [32; 33; 34]. It is discovered that non-Euclidian fractal geometry with a self-affine law may adequately explain the intricate geometry of micro-fractures surface. The fractal dimension is a useful parameter to describe the real surface behavior of micro-fractures. This paper is organized as follows: First, X-ray computed tomography (XCT) imaging technology was used to scan the rock sample from tight sandstone reservoirs. The representative elementary volume of digital rock was obtained using image processing, includingfiltering and segmentation. Secondly, the method was introduced to generate a rough surface. The related parameters were determined to reconstruct rough-walled micro-fractures for the next simulation. Thirdly, the approaches to computing pore structure and petrophysical properties were introduced in Section 2. Micro-fracture digital rocks were generated, combining random rough surfaces with the digital rock. In Section 3, the micro-fracture parameters, such as aperture, length, and dip angle, were considered to carry out the effect of the micro-fracture on the pore structure and petrophysical properties. In Section 4, the conclusions were obtained.
## 5 Experiment Results and Analysis  
### Testbed Setup and Parameter Settings  
Here, we used MATpower [60] to simulate the operation of the grid in the following experiments. MATpower is a package of MATLAB M-files for solving power and optimizing power flow problems, designed to give the best performance possible while keeping the code simple to understand and modify, which is a simulation tool for researchers and educators.  
In our experiments, we chose the monitoring power data from the photovoltaic station in Xuhui district (which belongs to the city of Shanghai) government from two typical days on 14 July 2018, and 19 December 2018, corresponding to sunny and cloudy/snowy weather conditions respectively. The monitoring time interval was 10 min and thus 144 data records were collected for one-day simulation.  
Here, we simulated three different scenarios of MATpower in the following experiments:only one datacenter deployed. The datacenter load varied along with the input solar generation power;
2 multiple datacenters deployed. Here we assume there are multiple distributed datacenters put into the grid and the loads are evenly allocated to them;
3 multiple datacenters deployed and running with dynamically allocated loads.  
We selected bus 9, 15 and 25 to integrate the solar station and datacenters respectively in the tested grid system. As parameter settings of GA, we define both the size of the population and the times of iterations as 100 and the fitness function consists of two parts, as follows:  
\[loss1+\alpha*loss2 \tag{7}\]  
where loss1 is to guarantee the loss value is small enough, loss2 is to ensure the residual error between the allocated load and the input solar is small enough, and \(\alpha\) is used to control the two variables. Here, we set \(\alpha=0.25\). Figure 3 shows the input solar generation power on the two days, where Figure 3a,b correspond to sunny and cloudy/snowy weather conditions respectively.
## 3 Results and Discussion
### Multivariate Data Analysis
#### 3.2.2 Comparisons Between Groups B and C
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** &
\begin{tabular}{c} **Feature** \\ **Selection Mode** \\ \end{tabular} & **Feature Subsets** & **AUC** & **Accuracy** & **Precision** & **Recall** & **Specificity** \\ \hline \multirow{8}{*}{**Features**} & Full dataset & All 9 laboratory biomarkers & 0.489 & 0.475 & 0.472 & 0.475 & 0.475 \\ \cline{2-9}  & & Lymphocytes, platelets, RBCs, D-dimers, eosinophils, LDH, creatinine, CRP & 0.606 & 0.575 & 0.585 & 0.575 & 0.575 \\ \cline{2-9}  & & Lymphocytes, platelets, RBCs, D-dimers, eosinophils, LDH, creatinine & 0.634 & 0.650 & 0.656 & 0.650 & 0.650 \\ \cline{2-9}  & & Lymphocytes, platelets, RBCs, D-dimers, eosinophils, LDH & 0.613 & 0.600 & 0.610 & 0.600 & 0.600 \\ \cline{2-9}  & & Information gain & Lymphocytes, platelets, RBCs, D-dimers, eosinophils & 0.634 & 0.600 & 0.610 & 0.600 & 0.600 \\ \cline{2-9}  & & Lymphocytes, platelets, RBCs, D-dimers & 0.591 & 0.525 & 0.525 & 0.525 & 0.525 \\ \cline{2-9}  & & Lymphocytes, platelets, RBCs & 0.690 & 0.625 & 0.633 & 0.625 & 0.625 \\ \cline{2-9}  & & Lymphocytes, platelets & 0.684 & 0.650 & 0.679 & 0.650 & 0.650 \\ \cline{2-9}  & & Lymphocytes & **0.708** & **0.725** & **0.730** & **0.725** & **0.725** \\ \hline \multirow{8}{*}{**Features**} & Full dataset & All 34 variables & 0.448 & 0.450 & 0.450 & 0.450 & 0.450 \\ \cline{2-9}  & & Lymphocytes, IL-2R, HGF, platelets, IL-10, eosinophils, IL-7, CXCL10, PARK7, CXCL10 & 0.768 & 0.650 & 0.656 & 0.650 & 0.650 \\ \cline{2-9}  & & Lymphocytes, IL-2R, HGF, platelets, IL-10, eosinophils, IL-7, CXCL10, PARK7 & 0.765 & 0.675 & 0.675 & 0.675 & 0.675 \\ \cline{2-9}  & & Lymphocytes, IL-2R, HGF, platelets, IL-10, eosinophils, IL-7, CXCL10 & 0.765 & 0.675 & 0.675 & 0.675 & 0.675 \\ \cline{2-9}  & & Information gain & Lymphocytes, IL-2R, HGF, platelets, IL-10, eosinophils, IL-7 & 0.758 & 0.700 & 0.708 & 0.700 & 0.700 \\ \cline{2-9}  & & Lymphocytes, IL-2R, HGF, platelets, IL-10 & 0.774 & 0.625 & 0.628 & 0.625 & 0.625 \\ \cline{2-9}  & & Lymphocytes, IL-2R, HGF, platelets, IL-10 & 0.770 & 0.675 & 0.679 & 0.675 & 0.675 \\ \cline{2-9}  & & Lymphocytes, IL-2R, HGF, platelets & 0.746 & 0.625 & 0.628 & 0.625 & 0.625 \\ \cline{2-9}  & & Lymphocytes, IL-2R, HGF & **0.774** & **0.725** & **0.730** & **0.725** & **0.725** \\ \cline{2-9}  & & Lymphocytes, IL-2R & 0.725 & 0.675 & 0.687 & 0.675 & 0.675 \\ \cline{2-9}  & & Lymphocytes & 0.708 & **0.725** & **0.730** & **0.725** & **0.725** \\ \hline \multirow{8}{*}{**Features**} & Univariate & VEGF; Lymphocytes & 0.636 & 0.625 & 0.633 & 0.625 & 0.625 \\ \cline{2-9}  & & & & & & & \\ \cline{1-1} \cline{2-9}  & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 7: Comparisons between prediction models for mortality in the ICU (Group B vs. Group C).
## 3 System Model and Medium Access Control Protocols
### MAC Protocols
#### 3.2.2 Reservation Frame Slotted-ALOHA  
An example of a DCR using RFSA is depicted in Figure 0(b). In every frame, each end-device randomly selects one slot to transmit the first of its \(L\) data packets. When an end-device succeeds in transmitting its first data packet, the slot with success is reserved to that end-device for the next \(L-1\) frames to transmit its other data packets. At the end of each frame, the coordinator broadcasts an FBP to inform about the state of the \(m\) slots: empty, success or failure; and free or reserved. The end-devices that are still contending to transmit their first data packet can only select a slot among the free slots. When an end-device has transmitted the complete sequence of \(L\) data packets, its reserved slot becomes free again in order to be used by other contending end-devices in subsequent frames. An end-of-message flag in the header of the last packet indicates that an end-device releases his reserved slot. These processes are repeated, frame after frame, until the coordinator is able to decode the \(L\) data packets from every end-device.  
The example of Figure 1b shows RFSA with three contending end-devices and three slots per frame. In Frame 1, End-device 2 succeeds in transmitting its first packet in Slot 3. Therefore, Slot 3 is reserved for End-device 2 for the transmission of its sequence of data packets in subsequent frames. Since End-devices 1 and 3 collide in Frame 1, they randomly select a new slot in Frame 2 (among the free Slots 1 and 2) to transmit their first packet. End-devices 1 and 3 succeed in transmitting their first data packet in Slots 2 and 1 of Frame 2, respectively. Therefore, Slots 1, 2 and 3 are reserved for End-devices 3, 1 and 2, respectively, for the transmission of their data packets. The process continues after Frame 3, until every end-device has transmitted its \(L\) packets to the coordinator.  
Note that in both FSA and RFSA protocols, as shown in Figure 1, a guard time, called the inter-frame space (IFS), is left between reception and transmission to compensate for propagation, processing and turn-around times to switch the end-devices' radio transceiver between reception and transmission modes.  
Figure 1: Example of a data collection round considering \(n=3\) end-devices and \(m=3\) slots per frame using: (**a**) frame slotted-ALOHA (FSA); and (**b**) reservation FSA (RFSA). The numbers inside the slots indicate the sequence number of the data packet transmitted by each end-device.
## 4 Discussion and Results
### Cluster Analysis-Based Identification of Favorable Geothermal Energy Zones  
Statistical and unsupervised AI models allow for the integration of all measured chemical elements, beyond U, Th, and K, along with magnetic susceptibility and P-wave velocity data, and RHP estimates, enabling a more effective identification of areas with geothermal energy potential. We used the commonly applied k-means clustering method [53] as the baseline. However, it resulted in a Silhouette Score of \(-0.112\) and a Davies-Bouldin Index of 6.129, indicating poor clustering quality and significant misclassification. Consequently, the k-means method was excluded from further consideration.  
Figure 5: Spatial variations of RHP, estimated from the concentrations of U, Th, and K, from sampling points, denoted by black-filled dots, and the correlative relationship given by [14].  
Clustering results for the analysis of potential geothermal sites from each statistical and AI-model are shown in Figure 6, and the corresponding clustering metrics are presented in Table 1. When earthquake epicenters, production drilling sites, and the Eynal hot spring formed by hot water redirected from nearby drilling are overlaid with the clustering results, overlapping zones of the clusters designate the most promising geothermal energy locations.  
Figure 6: Identification of potential geothermal energy regions using statistical models, including DBSCAN (**a**), GMM (**b**), and ACM (**c**), along with unsupervised artificial intelligence models, including ABDC (**d**), SOM (**e**), and DEC (**f**). For reference, the locations of production drilling sites and the epicenters of significant earthquakes (magnitude \(\geq\) 4) are also displayed on the plots.  
Among the statistical models evaluated, DBSCAN demonstrated the strongest predictive performance by identifying a narrow, well-defined wedge-shaped zone formed by overlapping clusters. This zone encompasses the majority of the production drilling sites as well as thermal springs, which are sustained by discharge from production wells.  
Among the AI models, ABDC emerged as the most promising unsupervised AI model. It identified a relatively compact overlapping cluster zone, also enclosing the drilling sites near the seismically active portion of the study area. Notably, while DBSCAN excluded the Ahmetli region as a potential geothermal zone, ABDC included it within its prediction. Future exploratory drilling in the Ahmetli area could serve as a valuable validation step to assess and compare the predictive reliability of DBSCAN and ABDC.  
Overall, the potential geothermal zones identified based on RHP values calculated using Equation (4), derived from U, TH, and K concentrations, are generally consistent with the results obtained from DBSCAN and ABDC models. However, the statistical and AI-based models delineated more localized and narrowly defined zones. These narrower zones result from the inclusion of additional variables, especially magnetic susceptibility measures and seismic velocities, that were not considered in the RHP formulations but were integrated into the clustering analyses.