esa-sceva/satcom-chunk-collection huggingface dataset:
# Superimposed Training Combined Approach for a Reduced Phase of Spectrum Sensing in Cognitive Radio
###### Abstract  
This paper presents an approach to exploit the superimposed training (ST)-based primary users' (PUs) transmissions in the context of spectrum sensing for cognitive radio. In the low signal-to-noise ratio (SNR), the proposed scheme splits the spectrum sensing phase into two sample processing periods, allowing a secondary user (SU) to carry out a training sequence synchronization (with a small probability of error) before the implementation of a robust spectrum sensing algorithm that enhances the detection, based on the deterministic signal components embedded in the ST PU's signals along with the unknown data signal. The overall sensing performance is improved using a reasonable number of samples to achieve a high probability of detection, resulting in a reduced spectrum sensing duration. Furthermore, a low computational complexity version of the proposed ST combined approach for a reduced phase (SCAR-Phase) of spectrum sensing is presented, which attains the same detection performance with a smaller number of real operations in the low SNR. In the practical consideration of imperfect training sequence synchronizations, the results show the advantages of exploiting the ST sequence to perform spectrum sensing, thus quantifying the significant improvement in detection performance and the maximum SU's achievable throughput.  
spectrum sensing; superimposed training; cognitive radio 1 Facultad de Ciencias, Universidad Autonoma de San Luis Potosi (UASLP), San Luis Potosi 78290, Mexico; [EMAIL_ADDRESS] (L.L.-L.); [EMAIL_ADDRESS] (E.S.-N.); [EMAIL_ADDRESS] (U.P.-R.)  
1 Facultad de Ciencias, Universidad Autonoma de San Luis Potosi (UASLP), San Luis Potosi 78290, Mexico; [EMAIL_ADDRESS] (Centro de Investigacion y de Estudios Avanzados del Instituto Politecnico Nacional (CINVESTAV-IPN), Ciudad de Mexico 07360, Mexico; [EMAIL_ADDRESS] (Correspondence: [EMAIL_ADDRESS]; Tel.: +52-444-826-2300 (ext. 5667) 24 May 2019; Published: 28 May 2019
## 4 Description of PODESCA and SBDMT
### Structure of SBDMT
#### 4.2.1 Sensitivity-Based Indices
\begin{array}{l}IS^{+}=\left\{WS_{N_{1}^{+}},\;\ldots,WS_{N_{M}^{+}}\right\} \\ IS^{-}=\left\{WS_{N_{1}^{-}},\;\ldots,WS_{N_{M}^{-}}\right\}\end{array} , \tag{21}\]  
where  
\[ \begin{array}{l}WS_{N_{j}^{+}}=\left\{max_{i}WS_{Nij}\big{|}j\in J,min_{i}WS _{Nij}\big{|}j\in J^{\prime}\right\}\\ WS_{N_{j}^{-}}=\left\{min_{i}WS_{Nij}\big{|}j\in J,max_{i}WS_{Nij}\big{|}j\in J ^{\prime}\right\}\end{array} , \tag{22}\]  
where \(J\) denotes the set of benefit attributes and \(J^{\prime}\) is the set of cost attributes. Step 5: find the distances \(D_{i}^{+}\) and \(D_{i}^{-}\) from \(IS^{+}\) and \(IS^{-}\), respectively. \[ \begin{array}{l}D_{i}^{+}=\sqrt{\sum_{j=1}^{M}\left(WS_{N_{j}^{+}}-WS_{Nij} \right)^{2}}\\ D_{i}^{-}=\sqrt{\sum_{j=1}^{M}\left(WS_{N_{j}^{-}}-WS_{Nij}\right)^{2}}\end{array} , \tag{23}\]  
Step 6: calculate the relative closeness of alternatives to the ideal solutions \(RC_{i}^{+}\) by  
\[RC_{i}^{+}=\frac{D_{i}^{-}}{D_{i}^{-}+D_{i}^{+}}, \tag{24}\]  
where \(RC_{i}^{+}\in[0,1]\), and the relative closeness of the best solutions should be the closest to 1 [43]. Furthermore, the utilization of SBDMT for reducing the OPSC problem's search space is shown in the flowchart of Figure 2.
# Design, Modeling, and Analysis of IEEE Std 80 Earth Grid Design Refinement Methods Using ETAP
## 2 Literature Review  
The IEEE Std 80-2013 substation earth grid design specification [1] outlines various refinement methods that can be employed during the design phase of earth grids to improve design compliance or methods that can be applied on brownfield projects to improve the integrity of existing earth grids. These methods include decreasing the total grid resistance, using closer grid spacing, diverting a greater part of the fault current to other parts, limiting the total fault current, barring access to energized areas, and increasing the tolerable touch and step voltage [1]. From these methods, some are easily applicable and more feasible to employ than others. For this reason, this research study only assessed the three commonly used refinement methods in the medium-voltage and low-voltage industry, and these methods are limiting the fault current, increasing the tolerable touch and step voltages, and the fault current diversion method.
## 2 Study Area and Methods
### Methods
#### 2.2.2 Ecosystem Classification and Quality Assessment  
Based on the climate, topography, and other bio-geographic parameters in Ganzi [(26)], Ganzi's ecosystems were classified into seven types: forest, shrub, grassland, wetland, farmland, urban, and barren land. According to the first step of the framework, ecosystem changes were first estimated, and in this study the ecosystem types and their associated changes were also analyzed between 2000 and 2015 in Ganzi in terms of area and quality.  
Analysis of the changes in forests, shrubs, and grasslands were carried out, since the area of these three ecosystem types account for 90.5% of the total area of Ganzi. The relative biomass density (\(RBD\)) is used as the ecosystem quality indicator of forest, shrub, and grassland ecosystems. First, the pixel binary model was used to obtain the vegetation coverage data of Ganzi's forest and shrubs by MODIS (i.e., Moderate Resolution Imaging Spectroradiometer) image inversion. Second, the aboveground biomass of the forests and shrubs in Ganzi was obtained [(27; 28)]. Third, Ganzi's grassland ecosystem quality was evaluated by using the cumulative Net Primary Production (NPP) method [(29)] to calculate the aboveground biomass of the grasslands within the grass-growing period [(30)].  
Forest, shrub, and grassland ecosystem quality index (Equation (1)) was calculated as follows:  
\[RBD_{i}=\frac{B_{i}}{B_{cc}}\times 100\% \tag{1}\]  
where \(RBD\) means relative biomass density; \(B_{i}\) means the biomass of ecosystems (forest, shrub, or grassland) in pixel \(i\); and \(B_{cc}\) means the biomass of ecosystems (forest, shrub, or grassland) in one  
Figure 2: Framework for a regional ecological risk warning system based on the ecosystem services approach.  
pixel in the climax community. The ecosystem quality was classified into excellent, good, medium, low, and poor, in terms of \(RBD\geq 80\%\), \(60\leq RBD\) < 80, \(40\leq RBD\) <60, \(20\leq RBD\) < 40, and \(RBD\) < 20, respectively [29].
## 3 Methods  
### Methodology  
The present research methodology comprises six main steps: (1) Obtaining sentinel-1 and Sentinel-2 images for each landscape and preprocessing. (2) Calculating spectral indices and texture information. (3) Adopting the SVM-RFE method to find the most valuable features (among the spectral band, spectral indices, and texture information). (4) Implementing the G-SMOTE method to rebalance the acquired reference datasets. (5) Employ ML methods to generate LC maps with the selected features from step 3. (6) Analyze the results and recommend the most helpful method and features for every landscape.
# On Alpha-Expansion-Based Graph-Cut Optimization for Decoder-Side Depth Estimation
## 4 Experimental Results  
To evaluate the proposal, we compare it with state-of-the-art decoder-side depth estimation encoding. In total, six different configurations were tested:  
1. basic alpha-expansion,
2. hierarchical alpha-expansion with optimization step \(n=2\) (current state of the art and reference method in MIV experiments)
3. as in (3), but with \(n=4\),
4. proposed hierarchical alpha-expansion with second-cycle label offset with optimization step \(n=2\),
5. as in (4), but with \(n=4\),
6. proposed hierarchical alpha-expansion with second-cycle label offset and neighboring segments label examination (with \(n=4\)).
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 6 Future Directions and Real-World Challenges
### Data Fragmentation  
The use of FRSs is becoming increasingly popular due to the fact that they enable organizations to manage and store data in a distributed manner without compromising privacy. However, when data are stored in a distributed manner, there is a risk of data fragmentation. Data fragmentation occurs when the data are split into pieces and stored in different locations. This can cause problems with accessing and utilizing the data, as well as with data consistency:  
* **Future directions**: To address the issue of data fragmentation, research should focus on approaches to facilitate seamless data transfer between different locations and to ensure that the data are consistent and up-to-date. This could include the development of distributed databases, distributed query processing, and distributed data analytics. Moreover, research should focus on developing methods for federated learning with fragmented data. These methods should aim to reduce the computational cost of training models and improve the accuracy of the models.
* **Real-world challenges**: Data fragmentation can be a significant challenge in FRSs, as it can lead to inconsistent data and can make it difficult to access and utilize the data. In addition, privacy concerns may prevent the data from being shared across different locations, further exacerbating the problem of data fragmentation. As such, organizations will need to develop policies and technologies to facilitate data transfer and to ensure that the data are consistent and up-to-date. Moreover, organizations will need to ensure the security of the data and protect the privacy of users.
###### Abstract  
In this study, an ultra-wideband actively tunable terahertz absorber composed of four identical arc-shaped structures made of phase transition material vanadium dioxide (VO\({}_{2}\)) is presented. A metal ground plane is placed at the bottom and an insulating spacer (quartz) as the middle dielectric layer. Simulation results demonstrate 90% absorption with a broad bandwidth spanning 3 THz (2.7 THz-5.7 THz) under normal incidence. The proposed structure transforms from a reflector to an absorber by changing the conductivity from 200 S/m to 2 \(\times\) 10\({}^{5}\) S/m; the absorbance at peak frequencies can be consistently tuned from 4% to 100%. Absorption spectra demonstrate that the polarization angle does not affect the response of the proposed structure. Power loss density (PLD) and impedance-matching theory are further analyzed to learn more about the physical origin of ultra-wide absorption. The ultra-wide operating bandwidth, high absorption efficiency, active tunability, and independence of polarization make the proposed structure an excellent candidate for integration into profound THz applications such as sensors, modulators, and optic-electro switches.  
Keywords:ultra-wideband; terahertz; absorption spectra +
Footnote †: journal: Applied Sciences
## Emergency Navigation without an Infrastructure  
**[PERSON] * and [PERSON]**  
Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering,  
Imperial College London, London SW7 2 BT, UK; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +44-207-594-6342; Fax: +44-207-594-6274.  
_Received: 6 May 2014; in revised form: 3 July 2014 / Accepted: 8 August 2014 / Published: 18 August 2014_  
**Abstract:** Emergency navigation systems for buildings and other built environments, such as sport arenas or shopping centres, typically rely on simple sensor networks to detect emergencies and, then, provide automatic signs to direct the evacuees. The major drawbacks of such static wireless sensor network (WSN)-based emergency navigation systems are the very limited computing capacity, which makes adaptivity very difficult, and the restricted battery power, due to the low cost of sensor nodes for unattended operation. If static wireless sensor networks and cloud-computing can be integrated, then intensive computations that are needed to determine optimal evacuation routes in the presence of time-varying hazards can be offloaded to the cloud, but the disadvantages of limited battery life-time at the client side, as well as the high likelihood of system malfunction during an emergency still remain. By making use of the powerful sensing ability of smart phones, which are increasingly ubiquitous, this paper presents a cloud-enabled indoor emergency navigation framework to direct evacuees in a coordinated fashion and to improve the reliability and resilience for both communication and localization. By combining social potential fields (SPF) and a cognitive packet network (CPN)-based algorithm, evacuees are guided to exits in dynamic loose clusters. Rather than relying on a conventional telecommunications infrastructure, we suggest an _ad hoc_ cognitive packet network (AHCPN)-based protocol to adaptively search optimal communication routes between portable devices and the network egress nodes that provide access to cloud servers, in a manner that spares the remaining battery power of smart phones and minimizes the time latency. Experimental results through detailed simulations indicate that smart human motion and smart network management can increase the survival rate of evacuees and reduce the number of drained smart phones in an evacuation process.  
**Keywords:** coordinated emergency navigation; infrastructure-less; cloud computing; _ad hoc_ cognitive packet networks
## 5 Results and Analysis
### Urban (Scenario I)
#### 5.1.3 Mixed Cell Structure  
However, we achieved a coverage percentage of 95.77% using MPA algorithms, but still, there is no access for 4.23% of users due to coverage holes so far. We have introduced a two-tier RAN implementation using both micro-RRUs and macro-RRUs to have full coverage. We implemented the algorithms for macro-RRUs to obtain full coverage and Figure 17 shows the best coverage obtained so far by MPA and is 99.41%. Figure 18 shows the best-optimized location of macro-RRUs and coverage in hexagonal cell.  
The trade-off between coverage-enriched macro-RRUs and capacity-enriched micro-RRUs is optimized by calculating coverage and capacity dimensioning, and 66 micro-RRUs and 8 macro-RRUs are implemented to have full coverage and capacity. Figure 19 shows the optimized deployment of RRUs, and Figure 20 shows the optimized deployment of RRUs in the satellite map of mixed cell deployment for greenfield.  
Figure 16: Best optimized location urban 5G.  
Figure 14: ALO.  
Figure 15: Coverage percentage.  
Figure 19: Mixed cell 5G greenfield.  
Figure 17: Coverage urban macro-RRUs.  
Figure 20: Mixed cell in the field.  
Figure 18: Coverage cell macro-RRUs.  
For urban brownfields, we achieved a coverage percentage of 95.09% using MPA algorithms, but still, there is no access for 4.91% of users due to coverage holes. We have introduced a two-tier RAN implementation using both micro-RRUs and macro-RRUs for full coverage as in Case I. We implemented the algorithms for macro-RRUs to obtain full coverage and Figure 17 shows the best coverage obtained so far by MPA and is 99.41%. Figure 18 shows the best-optimized location of macro-RRUs and coverage in hexagonal cells. The trade-off between coverage-enriched macro-RRUs and capacity-enriched micro-RRUs is optimized by calculating coverage and capacity dimensioning, and 71 micro-RRUs and eight macro-RRUs are implemented to have full coverage and capacity. Thus, using the existing infrastructure of 4G, we can save the cost of 31 sites. Figure 21 shows the optimized deployment of RRUs, Figure 22 shows the optimized deployment of RRUs in the satellite map of mixed cell deployment for 5G deployment using existing 4G infrastructure.
## 3 Numerical Tests
### Examples of Three-Layer Model  
We used a three-layer model to test our method. Figure 3a illustrates the true velocity model. The model grid was 150 \(\times\) 300 and the mesh spacing was 12.5 m. A total of 60 shots were evenly distributed on the surface, and the shot interval was 62.5 m. There were 300 receivers evenly distributed on the surface, and the receiver interval was 12.5 m. The source wavelet was selected as the Ricker wavelet with a peak frequency of 15 Hz. The time sample interval was 1 ms. Figure 3b illustrates the true reflectivity model, which can be a comparison of the LSRTM image.  
Here we choose the data of the 30 th shot. The recorded data is shown in Figure 4a. Figure 4b shows the predicted internal multiples after five iterations. The predicted internal multiples are used for computing the weighting matrix to separate the primary and internal multiples in data residual. The data residual after five iterations is shown in Figure 4c. Figure 4d,e shows the primary and internal multiples of the data residual after using the weighting matrix, respectively. Due to the amplitude of the internal multiple being too small, the boundary reflection still exists in the shown data. The red arrows indicates the place of internal multiple. The internal multiple is about 1.5 s when the \(x\)-axis is 0 km. We can find the internal multiple in Figure 4a-c, e, which shows that it has been estimated and is separated from the data residual.  
Figure 5 shows the images of the migration. As seen in Figure 5a, there are the artifacts in LSRTM image, and its depth is 1.4 km. These artifacts are suppressed in RWLSRTM image of the primary and RWLSRTM image of the primary and internal multiples, as shown in Figure 5b,c. Comparing Figure 5b,c, there are no other artifacts introduced. Figure 5d,e is the comparison of the 150 th trace and its extract of the depth of 1.25 km and 1.875 km. We can see the artifacts at a depth of about 1.4 km only exist in the LSRTM and is eliminated when the internal multiple is separated.  
Figure 3: (a) True velocity model. (b) True reflectivity model.  
Figure 4: (**a**) Recorded data. (**b**) Predicted internal multiples after five iterations. (**c**) The data residual after five iterations. (**d**) The primary of data residual after using the weighting matrix (**e**) The internal multiples of data residual after using the weighting matrix.  
Figure 5: _Cont._
## 5 Evaluation
### Experiments on the LIDC-IDRI Dataset  
ASEM-CAD was evaluated using supervised and semisupervised models for nodule malignancy classification using the LIDC-IDRI dataset. ASEM-CAD achieved an AUC of 0.81, which is comparable with the performance of a fully supervised model (with an AUC 0.82). The ROC curves for a fully supervised model and ASEM-CAD are shown in Figure 10. The ROC curves for both models indicate similar accuracy and performance characteristics. It is important to note that ASEM-CAD uses 52% of the data labels, as opposed to 100% of the labels in the fully supervised model. For the LIDC-IDRI dataset, ASEM-CAD achieves comparable AUC performance, and these algorithms outperformed those reported in our previous work on a semisupervised EM (SEM) algorithm [35]. We did not observe an improvement in training LIDC using 3D images of the nodule alone (image only) with a Bayesian experimental design via the Monte Carlo dropout method. To make an improvement, we incorporated additional input variables, known as image biomarkers or image characteristics, assigned by radiologists to the LIDC 3D images and trained the model to predict whether the nodule was malignant or benign. These additional variables include subtlety, internal structure, calcification, sphericity, margin, lobulation, speculation, and texture [80]. The values for these variables are scalar numbers. Subtlety is rated based on the contrast between the lung nodule and its surroundings, indicating the level of difficulty in nodule detection. Its possible ratings are: 1, extremely subtle; 2, moderately subtle; 3, fairly subtle; 4, moderately obvious; and 5, obvious. For more information on the methodology, please refer to our previous publication [34]. As a result, the model was able to achieve an AUC of 0.8822 and a sensitivity of 0.87, which represents an improvement over the use of 3D images only as input, with which an AUC of 0.81 and a sensitivity of 0.79 were obtained. Figure 10 shows the ROC curve.  
Figure 9: Active phase: 10 labels per iteration for the NLST dataset using ResNet34 with (**a**) 25% and (**b**) 50% of initial labels.
# Three-Dimensional Modeling of the Xichang Crust in Sichuan, China by Machine Learning
## 5 Seismotectonic Structure
### Dbscan  
The DBSCAN algorithm can describe the tightness of a sample set based on a group of neighborhoods. Two parameters (\(\varepsilon\), _MinPts_) are used to describe the tightness of the sample distribution of the neighborhood. The \(\varepsilon\) describes the neighborhood distance threshold of the sample. The _MinPts_ describes the neighborhood sample number threshold of a certain sample whose distance is \(\varepsilon\). The biggest advantage of this algorithm is that it does not need to input the number of clusters K and can find clusters of arbitrary shape and eliminate some scattered noise events. Since the density clustering still needs to provide the distance threshold of the sample field and the number of samples, in order to achieve a better clustering effect, this study measures the rationality of clustering by Silhouette coefficient, setting the neighborhood distance range of the samples as 0.1-1 and the step length as 0.02, the threshold range of the number of seismic cluster samples as 10-100 and the step size as 5. Figure 5a shows that with the increase in test thresholds, the Silhouette coefficient also increases rapidly. The Silhouette coefficient tends to rise slowly when the neighborhood distance exceeds 0.26, and the corresponding model parameters are (\(\varepsilon\) = 0.26, _MinPts_ = 25). However, the sample number of some earthquake clusters is small, so it is difficult to extract the corresponding source morphology. On the premise of ensuring the diversity of classification, the clustering effect is the best when the model parameters are \(\varepsilon\) = 0.58, _MinPts_ = 70. Seven earthquake clusters were obtained, and all were distributed near the fault, which can better display the characteristics of the source structure (Figure 5b).