esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Test
### Instrument Setup  
Different types of measurement instruments were employed, as shown in Figure 5a. The load-displacement (_F-u_) curves can be obtained from the MTS system. The end of the FAS specimen that is far from the MTS is defined as the distal end and the end of the FAS specimen close to the MTS is defined as the proximal end.  
A displacement observation point (_a_) was set at the distal end of the FAS specimen, as shown in Figure 5a. The vertical displacement of point \(a\) (\(v_{\text{a}}\)) (upward is defined as the positive value) can be measured through observation window \(O_{2}\). The horizontal angle (\(\theta\)) of the sand slip surface can be measured through observation windows \(O_{1}\) and \(O_{2}\). The deformation position (\(x\)) (away from the MTS is defined as the positive value) and the vertical deformation (\(v_{\text{s}}\)) (upward is defined as the positive value) of the sand surface can be measured through observation window \(O_{3}\).  
An inclinometer (\(R_{\text{a}}\)) was installed at the mid-length point of the specimen's top surface to measure the rotation angle of the FAS specimen (\(\alpha_{\text{a}}\)) (counterclockwise rotation is defined as the positive \(\alpha_{\text{a}}\)). Five earth pressure cells (\(S_{1}\) to \(S_{5}\)) were arranged. The earth pressures are negative values. \(S_{1}\) to \(S_{3}\) were arranged from low to high on the back surface of the lower part of the front wall each with a height interval of 20 cm. \(S_{3}\) was 25 cm lower than the bottom surface of the FAS specimen. \(S_{4}\) was installed at the mid-thickness point of the distal end surface of the FAS specimen. \(S_{5}\) was arranged at the mid-height point of the back surface of the upper part of the front wall. The values of the MTS system, inclinometer, and earth pressure cells were set to zero before the commencement of the test.
# Adjacency-Information-Entropy-Based Cooperative Name Resolution Approach in ICN
## 3 Model and Theory Fundamental
### Adjacency Information Entropy
This means that this node's position in the network is more critical, and the probability that this node contains a large amount of information is higher. Adjacency information entropy takes into account the node's properties as well as the impact it has on its neighbors, and it has a low computational overhead since only the local properties of the node are utilized. [PERSON] et al. also used the adjacency information entropy as a metric for identifying vital nodes in the network and showed good performances [46]. Based on the above reasons, we believe that the adjacency information entropy has a good fit with the scenario of name resolution neighbor cooperation in this paper. It can help us discover the latency neighbors in complex networks efficiently and can preferentially select nodes with critical location as well as high information content to improve the success rate of name resolution services.
## 3 Results  
The average survival time (\(\pm\)SE) was longest in 3% KNO\({}_{3}\) mice group (241 \(\pm\) 34 days) and followed by 2% KNO\({}_{3}\) group (227 \(\pm\) 52 days), with one mouse surviving for 408 days. While shorter average survival times were recorded in the control (0% KNO\({}_{3}\)) and 1% KNO\({}_{3}\) treatment groups: 213 \(\pm\) 17 and 159 \(\pm\) 27 days, respectively (Figure 1A). The shortest survival time was registered in the 1% KNO\({}_{3}\) and in the 2% KNO\({}_{3}\) treatment groups (both 126 days). However, the differences in survival of different KNO\({}_{3}\) treatment groups were non-significant (log-rank test: \(\chi^{2}(3)\) = 6.334, \(p\) = 0.096).  
Ordinal regression revealed a significant relationship of treatment groups with the pathological changes (\(\chi^{2}(3)\) = 15.11, \(p\) = 0.002). The regression model explained 55.4% (Nagelkerke R\({}^{2}\)) of variance of the pathological changes. The experimental treatment groups had significantly fewer pathological changes in comparison to the control group (1% KNO\({}_{3}\) group: Wald \(\chi^{2}(1)\) = 4.096, \(p\) = 0.043; 2% and 3% KNO\({}_{3}\) groups, both: Wald \(\chi^{2}(1)\) = 8.336, \(p\) = 0.004). Moreover, mice from the control group and 1% KNO\({}_{3}\) group were in the worst condition. Mice that were fed 2% and 3% KNO\({}_{3}\) were able to maintain a good condition the longest (Figure 1B).  
The most common pathology in all groups was pulmonary hyperemia and edema (Table 2). Mice that were fed 2% and 3% KNO\({}_{3}\) solution revealed only pulmonary hyperemia and edema, while in other groups more morphologic changes in organs were detected (Table 2). One more frequent pathology that was detected during the autopsy was pulmonary hyperemia, edema, and hepatic insufficiency; it was registered in groups that were fed 0% and 1% KNO\({}_{3}\) (Table 2). Pulmonary hyperemia, edema, as well as hepatic and kidney insufficiency were registered only in the control group (Table 2). Post-mortem tissue histology did not show any early-stage lesions of cancerogenic origin.  
Figure 1: Survival (**A**) and status scores (**B**) of mice in groups with different KNO\({}_{3}\) concentrations.
## 3 Challenges in Terahertz-Based FSO Communication  
Free-space optical (FSO) communication systems based on terahertz (THz) technology face several technical and environmental challenges that impact their deployment and reliability. One of the most critical challenges is atmospheric absorption, where water vapor and oxygen absorption cause severe attenuation at higher THz frequencies, exceeding 100 dB/km beyond 500 GHz, making long-distance communication impractical [1; 2]. However, a lower-loss transmission window at 680 GHz offers a more viable option for THz-FSO links in specific applications [3]. The power loss due to atmospheric absorption follows the Beer-Lambert law, where the received power diminishes exponentially with distance and is influenced by environmental factors such as humidity, temperature, and pressure, leading to frequency-dependent attenuation [4; 5]. Apart from absorption, line-of-sight (LOS) alignment is another major hurdle, as THz signals require precise beam alignment, and even slight misalignments can result in substantial power loss, which is further exacerbated by atmospheric turbulence and environmental vibrations [6; 7]. Additionally, signal distortion due to scattering and refraction affects the efficiency of THz-FSO communication, for which the Kolmogorov turbulence model quantifies refractive index fluctuations and the Mie scattering theory explains airborne particle interference with THz waves [8; 9]. Hardware constraints are also significant; existing quantum cascade lasers (QCLs) and photonic mixers both exhibit low power efficiency and require cryogenic cooling, increasing their operational costs and complexity [10; 11]. Similarly, THz photodetectors suffer from low sensitivity and responsivity, limiting their large-scale deployment [12]. The development of high-efficiency THz components, including black phosphorus-based photodetectors and metamaterial-enhanced antennas, is crucial for improving communication systems'performance, but their scalability remains a challenge due to high fabrication costs and precision engineering requirements [13]. To mitigate these challenges, software-defined networking (SDN) and multi-band adaptive frameworks are used to dynamically select the most efficient frequency bands for transmission [6]. Hybrid optical-THz-RF systems provide enhanced reliability by switching between THz, millimeter-wave, and optical frequencies, depending on the atmospheric conditions [14]. Moreover, artificial intelligence (AI)-driven network management is emerging as a promising approach to optimizing resource allocation, beam alignment, and error correction in real time [15]. Despite these issues, THz-FSO communication remains a promising solution for ultra-high-speed, secure, and low-latency networks, and addressing these limitations through frequency selection, low-loss material integration, the use of adaptive optics, and AI-driven optimizations will be critical for 6G, satellite, and high-speed terrestrial communication deployments [16; 17].
# Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model
## 3 SINR Coverage Probability and Area Spectral Efficiency
### SINR Coverage Probability
#### 3.1.3 \(\zeta=1\)  
The NLOS coverage probability expression in (12) simplifies to the following expression for \(\zeta=1\).  
\[P_{c,NLOS}(T)=\prod_{k=1}^{4}\exp\left(\frac{2\pi\lambda}{\kappa^{2}}b_{k}Li_{2 }\left(-Ta_{\
u_{k}}\right)\right)\times\left(1-\frac{2\pi\sqrt{\lambda}}{ \kappa}b_{k}y(Ta_{\
u_{k}})e^{\frac{\pi a_{k}b_{k}^{2}\gamma(Ta_{\
u_{k}})}{ \kappa^{2}}}Q\left(\frac{\sqrt{2\pi\lambda}}{\kappa}b_{k}y(Ta_{\
u_{k}}) \right)\right) \tag{16}\]  
where \(y\left(Ta_{\
u_{k}}\right)=ln\left(1+Ta_{\
u_{k}}\right)\) and \(\text{Q}()\) is the Q-function.  
Proof.: The proof is given in Appendix A.3
# Influence of Rock Slide Geometry on Stability Behavior during Reservoir Impounding
## 2 Materials and Methods  
A series of naturally occurring deep-seated rock slides were selected to determine the impact of the geometry on the stability evolution during reservoir impounding by means of limit-equilibrium calculations. With the exception of two generically created models, representing extreme geometries, seven models were taken from the literature, where the location of the basal shear zone is well explored by geological mapping, seismic investigations, geodetic surveys, drillings, or other investigation methods. The selection was made in such a way as to cover a wide range of rock slide geometry types. Only five of the selected rock slides are situated in the surroundings of a dam reservoir. However, the main objective was to investigate the effect of the rock slide geometry on the change in the factor of safety during reservoir impounding, independent of a real or hypothetic situation, and it has to be stated that this study does not provide any information for the corresponding case study concerning the in situ stability or movement behavior. Thus, the FoS determined herein cannot be transferred to the real case studies. A brief description of the selected rock slide case studies including their profiles is given in Section 4.1.  
GNU Octave Version 8.4.0 [16], which is a free software licensed under the GNU General Public License (see GNU General Public License for more details: [[http://www.gnu.org/licenses/](http://www.gnu.org/licenses/)]([http://www.gnu.org/licenses/](http://www.gnu.org/licenses/)) (accessed on 1 December 2023)), was used to calculate the solutions for the block model and to plot the results. Exemplarily, selected scripts of the analyses are provided as Supplementary Materials within this article. Diagrams showing the relationship between the height of the reservoir water level and the corresponding factor of safety were also created using GNU Octave. Geometrical profiles for the analytical block model and the limit-equilibrium analyses of the different case studies were drawn by AutoCAD [17]. In addition, profile sections of published rock slides were taken to perform 2D limit-equilibrium analyses using the software Slide2 Version 7 [18] to study the impact of the rock slide geometry on the factor of safety of the slope. The General Limit Equilibrium (GLE)/Morgenstern-Price method was chosen for the calculation as this method is described as rigorous and satisfies both equilibrium moment and equilibrium force [19]. The calculations were carried out with a gradual increase in the reservoir level, resulting in 15 to 26 water level stages depending on the case study.
# Real-Time Interaction for 3D Pixel Human in Virtual Environment
## 2 Related Works
### Human Matting and Segmentation  
Similar to the function of replacing the background in a 2D video stream, we must also remove the background from user video stream inputs when forming a virtual avatar to obtain a clean video stream containing only a human. Commonly used methods for extracting humans from real-time video streams during video conferencing include video matting and semantic segmentation. The methods in [15; 16; 17] are based on semantic segmentation methods. In addition to humans, they can also segment other subjects. However, the segmentation results typically contain hard edges and artifacts; therefore, there is a clear boundary between the human image and the background, which is inappropriate for a smooth transition with the background image or virtual environment. In contrast, the results of matting methods had soft boundaries. Methods such as BGM [18] and BGMv2 [19] use a fixed monocular webcam to accomplish real-time high-precision human matting. However, background-based matting methods require an additional pre-captured background image as an advanced input. Trimap-based matting methods, such as those proposed by [PERSON] et al. [20] and [PERSON] et al. [21], rely on additional input trimaps for foreground and background prediction. [PERSON] et al. [22] proposed a method for automatically generating a trimap for matting, but the trimap generation process is relatively complex. MODNet [23] is an auxiliary-free matting method that operates on frames as independent images, but has difficulty handling fast-moving body parts. [PERSON] et al. [24] and [PERSON] [25] proposed multi-person part segmentation methods that can segment human body parts and determine the range of the arms, but they are not sufficiently robust and are relatively time-consuming.  
Robust video matting (RVM) [26] is a robust, real-time, high-resolution human video matting method that exploits the temporal information in videos. RVM uses the backbone proposed in MobileNetV3 [27] and uses a deep guided filter [28] to upsample video resolution. Because we intend to mat humans from backgrounds in real-time when forming a virtual avatar and hope that our application can be used directly without additional preparation or inputs, in this study, we adopt the RVM method to segment humans from backgrounds.
## 4 Discussion
Therefore, it is assumed that it acts as a regulator of homeostasis. NO has also been reported to play crucial roles in wound healing [50]. NO induces angiogenesis [51; 52] and the proliferation and migration of ECs [53]. Furthermore, CAPE has been proposed to enhance NO activity and prevent reactive oxygen species from degrading NO [54]. Moreover, investigations have suggested that sufficient NO production is needed for efficient wound healing and that topical NO treatment advances the closure of the excisional wounds in diabetic rats [55]. Additionally, worsened angiogenesis was observed in eNOS-deficient mice [56]. Besides, VEGF has been indicated to induce the upregulation of eNOS expression and endothelial NO production [57; 58]. Similarly, NO influences vasorelaxation, which symbolizes increased blood flow. Increased blood flow to the limb accelerates the wound healing rate in the limb delayed by diabetes-derived ischemia [38]. Likewise, this study demonstrated that CAPE induced endothelium-dependent vasorelaxation in isolated porcine coronary artery rings. The inhibitor of NO synthase and guanylyl cyclase, but not the potassium channel blocker and inhibitor of cyclooxygenase, strongly inhibited this vasorelaxant effect of CAPE. This result suggests that CAPE caused an endothelium-dependent vasorelaxation mainly by mediating the NO-sGC-cGMP pathway. Taken together, CAPE can contribute to improved wound healing, at least in part, via the blood flow increase caused by NO-mediated vasorelaxation. Nevertheless, further studies are needed to demonstrate how CAPE induces the expression of VEGF. Follow-up studies on the detailed mechanism are also required.
## 4 Results and Discussion
Throughout the entire measured launched optical power range from \(-5\) dBm to \(+5\) dBm, the number ofSVs of CS M-ary NLE is always lower than that of M-ary NLE. Figure 10: Q factor versus launched optical power for 120 Gb/s PS-64 QAM coherent optical communication system with a transmission distance of 375 km; (**a**) the constellation diagram of PS-64 QAM detected by CS M-ary SVM NLE; (**b**) the constellation diagram of PS-64 QAM without employing NLE. In addition, the CPU running time of the two NLE schemes is depicted in Figure 11b. The results show that the CPU running time is the lowest when the launched optical power is \(-3\) dBm. Throughout the entire measured launched optical power range from \(-5\) dBm to \(+5\) dBm, the CPU running time of CS M-ary NLE is always lower than that of M-ary NLE.
## 5 Results and Analysis
### GMM Clustering Results  
#### 5.2.1 Cluster Separation  
The GMM application for the engine dataset classified each engine into one of the clusters. Every cluster corresponds to one of the engine health states: Healthy, Middle, or Unhealthy. Eventually, 57% of engines fell into the first class, 36% into the middle, and 7% into the third.  
The silhouette performance metric was used to validate GMM clustering performance. It quantifies how well each data point fits into its assigned cluster compared to other clusters, balancing two key concepts: cohesion and separation. The first one defines how close a data point is to other points in its own cluster. The second one explains how distinct a data point is from points in other clusters.  
For the real-life engine operational dataset and GMM clustering, the achieved silhouette performance was equal to 0.12. This silhouette score (0.12) implies weak but meaningful separation. In real world engine signals characterized by measurement noise and gradual degradation, such modest separation is expected. This low value stems from gradual degradation transitions in real engines, unlike sharp failure boundaries in simulated data (silhouette = 0.39).  
One of the most significant differences between GMM and K-means clustering is that the latter is a hard clustering method. It means that it works by associating each point with one and only one cluster. There is no uncertainty or probability measure of how much each data point is associated with a specific cluster.  
That was the reason why it was decided to calculate the average clustering probability and posterior probabilities for each engine. The first metric shows how confident the model is in its cluster assignments on average, and the achieved result was 0.99. The posterior probabilities for each engine were calculated and are presented in Figure 3. As a result of posterior calculations, a matrix was returned where each row corresponds to an engine, and each column gives the probability of belonging to the respective cluster (Healthy, Middle, Unhealthy). Unfortunately, only for a few engines, clustering confidence is easy to recognize in the diagram. Still, for all the engines, cluster membership probability never reaches 1. There are always some residual probabilities for other engine health states.  
Figure 3 displays the GMM cluster posterior probability distributions for individual engines across the three identified health states. Each row represents an engine, and the color intensity indicates the probability of that engine belonging to a specific cluster (Healthy, Middle, or Unhealthy). While the average clustering probability was high (0.99), suggesting overall confidence in assignments, the figure reveals that for most engines, cluster membership probability does not reach 1. This indicates that there are often residual probabilities for other health states, reflecting the inherent ambiguity and gradual transitions between degradation stages in real-world engine data. Only for a few engines is the clustering confidence distinctly clear in the diagram.
###### Abstract
To overcome these problems, there is a need for a robust method that is based on a lightweight deep learning-based model, which involves a small number of learnable parameters so that it can be learned with limited data, avoiding overfitting. Motivated by the ensemble learning approach, we propose an ensemble-like method to scan EEG signals for the detection of schizophrenia. We propose a lightweight 1D CNN model based on deep learning theory as a backbone model for this method. Its simple architecture involves a few learnable parameters and can be trained with limited data. It automatically learns and extracts discriminative features relevant to schizophrenia. Though the proposed lightweight 1D CNN involves a small number of parameters, it needs enough data to learn these parameters and avoid overfitting, but the available dataset is very small. To tackle this issue, we introduce a simple data augmentation scheme that creates EEG trials from the available limited dataset. The main contributions of this study are as follows:  
* A lightweight one-dimensional convolution neural network (1D-CNN) model based on the pyramid approach is introduced, which is suitable for embedded systems. It involves a small number of learnable parameters and can be learned with a small amount of data without suffering from overfitting problems. * An efficient and automated ensemble-like method based on the proposed 1D-CNN model and a simple ensemble approach for classifying people with schizophrenia and healthy controls. It splits an input EEG signal for analysis into smaller segments, where the same backbone model analyses each segment. In this way, it makes decisions after scanning an EEG signal of any length without increasing the complexity, i.e., it scales well with an EEG signal of any length. * A data augmentation scheme to solve the data scarcity problem that is encountered in the training of the deep (1D-CNN) model. The rest of the paper is organized as follows. Section 2 reviews the recent studies on schizophrenia classification based on EEG. In Section 3, the proposed method is described to perform binary classification between people with schizophrenia and healthy controls. In Section 4, the dataset and data augmentation scheme are discussed. In Section 5, the implementation details and results are presented. Section 6 discusses the results obtained and compares them with state-of-the-art methods. In Section 7, we conclude our work.