esa-sceva/satcom-chunk-collection huggingface dataset:
# Latent Dimensions of Auto-Encoder as Robust Features for Inter-Conditional Bearing Fault Diagnosis
## 4 Proposed Method
### K-Nearest Neighbors Classifier  
The main assumption of this work is that the latent features inferred using an MLCAE trained with one condition share an approximately similar distribution to other conditions' data. Thus, proximity-based classifiers are preferred to boundary- or threshold-based classifiers. To test our hypothesis of latent features across conditions sharing similar distribution, we trained K-Nearest Neighbors (KNN). KNN is a well-known algorithm that predicts class label based on the proximity of the input features. It measures the distance to the \(K\) number of known observations in a multi-dimensional feature space and votes for its class accordingly. Using above inferred latent features from the MLCAE as inputs for a KNN classifier, we tested its performance for various parameters of K and distance function.  
To benchmark the performance of KNN with different hyper-parameters, we investigated the effect of varying K and distance functions on data from the two bearing fault datasets. After splitting the data into 80% and 20% (training and validation, respectively) of one randomly chosen condition from each dataset (C0 and P0), we first trained an MLCAE and then used its latent features to train and validate various KNNs for classification accuracy.  
For the K value, the range investigated was between 1 and 30 in steps of 3. For the distance metric, which plays a major role in the performance of the KNN, we chose Chi-square, Euclidean, Manhattan and Chebyshev distances. Chi-squared and Euclidean were the best performing distance functions for numerical datasets such as ours according to [22], while Euclidean, Manhattan and Chebyshev distances were among the top 10 performing distance metrics for 28 datasets in a previous investigation [23]. It is to be noted that the Hassanat distance performed well when the data were not normalized. If the inputs are normalized, this distance metric may not perform at a similar level to the other known metrics. Thus, in our benchmark study to find optimal hyper-parameters for KNN, we omitted Hassanat.  
As can be seen from Figure 6, the results of one of the iterations show that Chi-square distance performs the worst for both bearing fault datasets, whereas Euclidean distance  
Figure 5: GNE-based clustering results of features from MLCAE for Raw and FFT data of both the datasets. CWRU, raw and FFT features (**a**) and Paderborn, Raw and FFT features (**b**). Prefix A in Inner Race and Outer Race fault labels of PU data refer to artificial faults.  
works well in both experiment cases (C0 and P0) and shows better performance over the other considered distance functions. Euclidean distance is measured using the equation mentioned in (4). In addition, multiple values of K have produced good results on the validation data, making it a free choice within the range of 1 to 15. An absolute value of K chosen for the robust learning experiments will be discussed in later sections.  
\[d_{\textit{euclidean}}(q,p)=\sqrt{\Sigma_{i=1}^{n}(q_{i}-p_{i})^{2}} \tag{4}\]
## 2 Preliminaries
### Adversary Model  
The Dolev-Yao (DY) model, which portrays the capabilities of the adversary, has been widely used in formal and heuristic analyses with respect to the security of authentication protocols [14]. Currently, the latest research [15] has taken this a step further by consolidating adversary capabilities in order to comprehensively evaluate the authentication protocol. In this refined threat model, an adversary \(\mathcal{A}\) possesses six capacities (A-), which are as follows:  
1. (A-1) \(\mathcal{A}\) can intercept, modify, insert, and delete any messages that are being transmitted through the open channel.
2. (A-2) \(\mathcal{A}\) can systematically enumerate all elements within the Cartesian product of the identity space and password space, which is denoted as \(D_{id}\times D_{pw}\).
3. (A-3) \(\mathcal{A}\) is capable of obtaining previously established session keys between the physician and BASN.
4. (A-4) \(\mathcal{A}\) possesses the capability to acquire the secret key of the GWN in situations where the system eventually experiences failure.
5. (A-5) \(\mathcal{A}\) can breach some specific BASNs, extracting sensitive data stored within them. Furthermore, \(\mathcal{A}\) can manipulate the compromised BASN so that it can participate in subsequent communications involving the GWN, other users, and body area sensor nodes.
6. (A-6) \(\mathcal{A}\) could potentially register as either a legitimate user or even the role of the GWN administrator only if the security of the physician's password is evaluated  
Figure 2: System model of the proposed scheme.  
during the registration phase. Carrying out formal and heuristic analyses in Section 4, based on the DY adversary's capability, we can quantify the advantage of the adversary relative to their ability to bypass semantic security, and via heuristic analyses, we prove that the protocol can resist all kinds of attacks issued by the DY adversary.
# DTV Essential Hidden Area Decoder for DTV Broadcasting
## 3 The Proposed DEHA Decoder for DEHA Utilization
### Video Encoder and Decoder for DEHA Utilization
#### 3.1.1 Proposed Decoder  
Similar to a conventional HDTV receiver, the proposed HDTV receiver comprises several essential components, including a tuner, a transport stream de-multiplexer, a video decoder, and a panel driver. These components work together to facilitate the utilization of DEHA. To incorporate DEHA functionality into the existing HDTV broadcasting systems, a DEHA overlay block is inserted, providing a visual representation of how DEHA is utilized. DEHA images or block data are overlaid onto the decoding image using the Picture-in-Picture (PIP) mode.  
This overlay is achieved by employing 240 DEHA blocks positioned at the bottom of the encoded stream. Figure 8 illustrates the composition of the proposed HDTV receiver and the integration of DEHA for enhanced broadcasting capabilities.
# Plasmonic and Dielectric Metasurfaces: Design, Fabrication and Applications
###### Abstract  
Two-dimensional metasurfaces are widely focused on for their ability for flexible light manipulation (phase, amplitude, polarization) over sub-wavelength propagation distances. Most of the metasurfaces can be divided into two categories by the material type of unit structure, i.e., plasmonic metasurfaces and dielectric metasurfaces. For plasmonic metasurfaces, they are made on the basis of metallic meta-atoms whose optical responses are driven by the plasmon resonances supported by metallic particles. For dielectric metasurfaces, the unit structure is constructed with high refractive index dielectric resonators, such as silicon, germanium or tellurium, which can support electric and magnetic dipole responses based on Mie resonances. The responses of plasmonic and dielectric metasurfaces are all relevant to the characteristics of unit structure, such as dimensions and materials. One can manipulate the electromagnetic field of light wave scattered by the metasurfaces through designing the dimension parameters of each unit structure in the metasurfaces. In this review article, we give a brief overview of our recent progress in plasmonic and dielectric metasurface-assisted nanophotonic devices and their design, fabrication and applications, including the metasurface-based broadband and the selective generation of orbital angular momentum (OAM) carrying vector beams, N-fold OAM multicasting using a V-shaped antenna array, a metasurface on conventional optical fiber facet for linearly-polarized mode (LP\({}_{11}\)) generation, graphene split-ring metasurface-assisted terahertz coherent perfect absorption, OAM beam generation using a nanophotonic dielectric metasurface array, as well as Bessel beam generation and OAM multicasting using a dielectric metasurface array. It is believed that metasurface-based nanophotonic devices are one of the devices with the most potential applied in various fields, such as beam steering, spatial light modulator, nanoscale-resolution imaging, sensing, quantum optics devices and even optical communication networks.  
plasmonic; dielectric; metasurface; vector beams; orbital angular momentum; coherent perfect absorption; Bessel beams +
Footnote †: journal: Applied Physics Letters  
16 June 2016 10 August 2016 14 September 2016
## 4 Results  
### Mesh Dependency Study  
Figure 7 shows the load-displacement curves of the PFA results with different mesh cases explained in Section 3.3. The crack band (CB) model applied PFA (CB-PFA) shows less influence with mesh dependency. This is because the elements of the model are fractured, and the fracture energies are the same in each failure mode (fiber tension/compression, matrix tension/compression) [7]. Figure 8 shows the normalized maximum load variation according to the total number of elements of the PFA model conducted with the CB and MLT model, respectively. The MLT model implemented PFA (MLT-PFA) [1] is the most commonly known PFA model, and this has been used in various studies [2; 31; 32; 33]. The normalized maximum load was calculated by dividing the predicted load by the experimental load. As shown in Figure 7, despite the difference of 11 times the number of elements, only 8.5% of the maximum load error was observed in the CB-PFA model. For the quantitative study, this was compared to the results obtained from the MLT-PFA model. The results of the MLT-PFA model showed an error up to 10% in comparison to the CB-PFA model even though the number of elements was similar to the CB-PFA model. As the results converged towards the experimental results, the CB-PFA model results displayed lower errors than the MLT-PFA model. The small mesh dependence of the CB-PFA model results from the damage variable were calculated using the CB model. In the damage variable, the equivalent displacement at the final failure state was calculated in consideration of the fracture energy of the material (Equation (9)), and the fracture energies at each failure mode had a constant value. Therefore, each element failed when considering the constant fracture energy regardless of the element size. As a result, the PFA model of this study has less mesh dependency. The small errors were induced from the difference in the element aspect ratio in CB-PFA model [7; 8].  
Figure 7: Load-displacement curves for different mesh cases.
## 2 Related Work
The classical JPS algorithm builds on the framework of the A* algorithm and further optimizes the way of finding successor nodes through the neighbor-pruning rule and forced neighbors, which improves the search efficiency of the A* algorithm [35] and, thus, greatly reduces the time consumption in the search process [36]. From another perspective, the JPS algorithm based on discrete grid maps also has many problems. During the pathfinding process performed by the JPS algorithm, the planning usually starts from the center of the grid and only allows expansion to the centroids of neighboring grids, as shown in Figure 2. This will result in a fixed direction of search for the JPS algorithm, ignoring path selection at any angle [37], which leads to a longer resultant path than the actual path. Secondly, the JPS algorithm is mainly used for pathfinding in static space. For dynamic threats and changes in the path-planning environment, JPS algorithms can only be solved using replanning, which will significantly increase the time cost of path planning [38] and is not conducive to drones' mission execution. Based on the above issues and inspired by previous related work, this proposal takes into account the time cost of path planning for drones, the degree of path smoothing (path length and total path-turning angle), and obstacle avoidance strategies for static obstacles and dynamic threats. First, this paper extends the traditional JPS to the 3D space and implements the any-angle path planning of the 3D JPS algorithm by the parent node transfer law, which ensures that the resultant path of the JPS algorithm is optimal in termsof length and total turning-angle measures. Secondly, this paper introduces the polynomial-interpolation method based on minimum snap to achieve the continuity and smoothness of the position, velocity, acceleration, and jerk function at the drones' path nodes while ensuring the shortest path. Finally, this paper uses the 3D Bresenham's line algorithm and the virtual-target gravitational field to solve the disadvantages of the artificial-potential-field method which is easy to fall into the minima, and combines the 3D JPS algorithm to realize the drones' real-time dynamic-obstacle avoidance. The main contributions of this paper are listed below. First, this paper achieves efficient autonomous pathfinding for drones in a static environment through JPS 3D extension. Secondly, the generation path of the JPS algorithm is further optimized by the parent node transfer law and polynomial-interpolation optimization. Finally, a dynamic-obstacle avoidance scheme based on the improved artificial-potential-field method is proposed to realize the safe flight of drones in the dynamic environment of low-altitude airspace.
## 4 Experiment 3
### Results and Discussion  
In total, 61 participants reported seeing the unexpected, unmarked teammate (noticers 64%) and 34 reported not seeing him (non-noticers, 36%). Thus, our results were slightly higher than those in a previous handball study [9] (44% failed to notice the unguarded player). We found a significant relationship between the players' playing positions and their likelihood of showing inattentional blindness, \(\chi^{2}(3)=10.02\), \(p=0.018\). Post-hoc pairwise comparisons indicated that the detection rate differed significantly between the defenders and the midfielders, \(\chi^{2}(1)=6.23\), \(p=0.013\), OR = 4.2, 95% CI [1.3, 13.5]. The defenders (46% notices) saw the unexpected object less often than the midfielders (78% notices; Table 3). There was no significant difference in the detection rate for all other group comparisons: goalkeepers vs. midfielders, \(\chi^{2}(1)=5.35\), \(p=0.021\), OR = 5.0, 95% CI [1.2, 20.7]; goalkeepers vs. defenders, \(\chi^{2}(1)=0.06\), \(p=0.813\), OR = 1.2, 95% CI [0.3, 4.8]; goalkeepers vs. strikers, \(\chi^{2}(1)=3.79\), \(p=0.052\), OR = 4.0, 95% CI [1.0, 16.8]; midfielders vs. strikers, \(\chi^{2}(1)=0.13\), \(p=0.716\), OR = 0.8, 95% CI [0.2, 2.7]; defenders vs. strikers, \(\chi^{2}(1)=4.25\), \(p=0.039\), OR = 3.4, 95% CI [1.0, 11.0] (Bonferroni-adjusted post-hoc comparisons had an adjusted alpha of 0.013).  
While Experiment 2 showed a difference in the inattentional blindness rate between the defenders and the strikers, in Experiment 3, we found a difference in the rate of inattentional blindness between the defenders and the midfielders. However, it should be noted that 22 (69%) of the 32 midfielders reported being offensive midfielders and only 10 (31%) reported being defensive midfielders. Therefore, we performed a further analysis comparing the group of all the defensive players (including all the goalkeepers, all the defenders, and the 10 defensive midfielders) with a group of all the offensive players (including the 22 offensive midfielders and all strikers). The group of the offensive players detected the unexpected player more often (86% notices, 14% non-noticers) than the defensive players (41% notices, 59% non-noticers), \(\chi^{2}(1)=20.36\), \(p<0.001\), OR = 8.5, 95% CI [3.2, 23.0]. It can, therefore, be concluded that our results replicate the findings in Experiment 1 and 2 in a more representative sport-specific situation that the defensive players show more inattentional blindness than the offensive players. While in Experiment 3, a central offensive midfield player finally got the ball in front of the opposing defense line, future studies could use videos presenting game situations with more defensive situations in order to explore if the game situation itself does affect the players' inattentional blindness rate.
## 3 Results and Discussion
### Recovery of Lycopene from Tomato Peels
#### 3.1.1 Model Fitting
These results align with the present study, which also identified microwave power and the solid/liquid ratio as critical determinants of the lycopene content and extraction yield. Given the scarcity of research exploring the combined impacts of microwave and ultrasound, the findings of this study are primarily compared with previous studies that utilized either UAE or MAE techniques independently. For instance, [PERSON] et al. (2014) utilized UAE for lycopene extraction from tomato waste and found that ultrasound power increases both the lycopene content and yield, particularly when lower solid/liquid ratios are used [17]. Similarly, [PERSON] et al. (2015) employed MAE for lycopene recovery from tomato peels and reported that this method efficiently disrupted the cell structure, thereby improving the lycopene yield. The optimal lycopene extract obtained through MAE had an extraction yield of 10.362 mg/100 g [29]. The highest lycopene content reported in this study is likely due to the combined effects of ultrasound and microwave power.
# Pushing Mechatronic Applications to the Limits via Smart Motion Control
## 5 Pilot Applications and Key Results
### Generic Substrate Carrier
#### 5.1.2 Application of Project BBs to GSC (Pilot 1)  
The overall scheme of BBs application is shown in Figure 9. Firstly, As GSC is a multi-input multi-output (MIMO) system, it was necessary to check, whether there are significant cross-relations between I/O signals and consequently if decoupling filters are necessary to use. This kind of identification procedure is a part of BB8. The procedure at the system level is depicted in Figure 10.  
Figure 8: Pilot 1—Generic Substrate Carrier (GSC) motion axis configuration.  
Next, high quality sensors signals must be delivered to ensure optimal control. Figure 11 below shows results that were created during one of the auto-calibration routines. The upper picture shows detected \"voids\" in a measured sensor signal that needs to be filtered out. The bottom picture shows the result of the same sensor after the auto-calibration, measured in a validation measurement that is part of the routine.  
Finally, repetitive control was the technology (BB9) that brought the biggest impact to machine performance improvement. The key idea is a \"memory buffer\" in addition to the classical feedback loop, which allows to adapt the system to periodic disturbances of known period while the periodic part of the tracking error tends to zero. Although the method is theoretically well explored, the real implementation always brings a lot of problems namely in designing additional filters L (learning filter) and Q (robustness filter) ensuring the stability of the main feedback loop (Figure 12). The performance improvement of GSC is shown in Figure 13 in the context of tracking error. When depicting in the form of cumulative power spectral density for the X-axis, one can observe a 40% improvement at higher frequencies (Figure 14).  
Figure 10: Pilot 1 GSC system identification: (**Left**)—traditional way (before the project); (**Right**)—BB8 MIMO identification (at the end of the project).  
Figure 9: Pilot 1 = GSC overall control system structure and utilization of BBs.  
Figure 11: Example of typical BB6 auto-calibration routine results. (**a**) Detection analysis output; (**b**) Validation result after calibration.  
Figure 12: Pilot 1—GSC position-based repetitive control scheme (BB9).  
The list of other BBs applied on GSC is shown in Table 1.  
Figure 14: Pilot 1-GSC cumulative power spectral density (CPSD) of tracking error, 40% improvement based on repetitive control.  
Figure 13: Pilot 1—GSC performance improvement based on repetitive control (BB9). Red line marks time when the controller was switched on. Pilot 1—GSC cumulative power spectral density (CPSD) of tracking error, 40% improvement based on repetitive control.
## 4 Results and Discussion
### Sensor Robot Design as Access Point  
The background information mentioned in Section 3 was used for the derivation of robot design, reflecting the needs of the users. The sensor robot device is an automated input device stationed on the highways/roads, and has communication dependency with other robots as an access point. According to the requirements from the needs-based function list, the architecture of the sensor robot has a sensor assembly comprising recognizing, projection, communication, message library, location information, battery level, state checker, and light units, as shown in Figure 7.  
Figure 7a shows the schematic labeling of each portion of the robot device. The robot includes an upper end (T), body (M), and lower end (B). T is positioned above M and includes a projection module (P) and sensor module (S) for projecting an image and detecting approaching vehicle, respectively. The projection module can be in at least one configuration of the image projection unit and the detection sensor module of the robot device.
## 2 Design Concepts  
Our goal was to develop a novel dual-polarized antenna design that can provide wide impedance bandwidth, high gain with stable broadside radiation, low cross-polarization,and high aperture efficiency. Based on [17], we set the bandwidth (BW) and cross-polarization (X-pol) requirements to be \(\geq\)100 MHz and \(\leq\)\(-\)25 dB, respectively. Since there are multiple desirable qualities, careful coordination is required in order to simultaneously meet all of them. The designed antenna is shown in Figure 1a. The corners of its ground cavity are truncated to form an octagon shape. Figure 1b shows the case without the corner truncation. We will compare the merits of each configuration in Section 3.1.
# An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
## 5 Potential lncRNA-Based Biomarkers
### Heih  
Adding to the understanding of lncRNA functions in BC is High Expression in Hep-atocellular Carcinoma (HEIH), which was found to be upregulated in BC, especially in the TNBC subtype. [PERSON] et al. identified HEIH as a promoter of TNBC progression by inhibiting miR-939-5p, thereby increasing Inducible Nitric Oxide Synthase (NOS2) production. Silencing HEIH enhances the tumor's immunogenic profile by inducing MHC class I polypeptide-related sequence A/B (MICA/B) and suppressing PD-L1, IL-10, and TNF-\(\alpha\). Additionally, higher levels of HEIH were observed in tumor samples with a higher expression of Ki-67 (>14%), compared to those with low Ki-67 (<14%) [67]. Furthermore, TNBC cells' proliferation and inhibition of apoptosis were also determined through the miR-4458/SOCS1 axis, with higher HEIH levels associated with larger tumor size and advanced clinical stage [68]. HEIH promotes BC development by regulating miR-200b and activating the Wnt/B-catenin pathway, with its suppression reducing cell viability, migration, and invasion while promoting apoptosis in BC cells [69]. High expression levels of HEIH were strongly correlated with the TNM stage, LNM, and served as an independent indicator of 3-year OS among 160 patients in a study by [PERSON] et al. [70]. Those insights indicate that HEIH is a key factor promoting BC development and metastasis, suggesting the potential use of HEIH both as a biomarker and therapeutic target in BC management.