esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Dynamic Power Control with Cell Range Expansion for Small-Cells in 5G HetNets
###### Abstract  
In 5G heterogeneous networks (HetNets), small-cell base stations (SBSs) are deployed in the coverage of macro base stations (MBSs) to improve the system performance. However, some macro user equipments (MUEs) have strong interference from neighboring SBSs and thus the performance of MBSs decreases. Thus, in this paper, we propose a novel intelligent dynamic power control (DPC) with cell range expansion (CRE) to improve the downlink performance of both small-cell user equipments (SUEs) and CRE user equipments (CUEs) in 5G HetNets. That is, in the proposed DPC scheme, each MUE first collects the received signal strength indicator (RSSI) measurements from neighboring SBSs and sends them to the serving MBS. Then, the MBS finds MUEs with strong interference from neighboring SBSs based on a given target threshold of CRE and offloads a fraction of MUEs from MBSs to SBSs. In addition, SBSs divide their SUEs and CUEs into two groups, i.e., inner and outer groups, to assign different subchannels and dynamically allocate the appropriate transmission power to increase the performance of both SUEs and CUEs. Through simulation results, it is shown that the proposed DPC scheme outperforms others in terms of the capacity and outage probability of SUEs and CUEs.  
5G; heterogeneous networks; dynamic power control; cell range expansion; small-cell +
Footnote †: journal: Applied
# Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
## 2 Related Work  
### Subspace Clustering  
The k-means [22] and GMM [23] algorithms have been widely used to solve the clustering problems due to their computational efficiency. However, it is a challenge to achieve satisfactory results when applied these methods on high-dimensional data. The concept of self-expression is that any data point in a dataset can be represented as a linear combination of other data points in this dataset [24]. Conventional subspace clustering based on the idea of self-expression such as SSC [24], LLR [25], SSSC [26] and KSSC [27] are designed to build an affinity matrix for spectral clustering, while DSC-Net [20] incorporates a self-expressive layer in the autoencoder network, which improves the clustering results significantly. [PERSON] et al. [28] designed a network called DASC, which incorporated the idea of generative adversarial network [29] to guide the learning process and improved the accuracy of clustering results. To improve the feature representation ability, [PERSON] et al. [30] proposed an overcomplete convolutional encoder and an undercomplete convolutional encoder fusion approach to extract more meaningful representations. Some methods add a prior [31; 32] or redesign constraints and regularizations [33; 34; 35] to the network. On the other hand, a few works aim to optimize clustering representation learning [36; 37; 38; 39; 40] to perform joint optimization of feature learning and clustering.
# Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks
###### Abstract
The non-Gaussian CNN model can handle Gaussian denoising with the unknown noise level. Non-Gaussian eliminates the latent clean image in the hidden layers, which constructively remove noise. It increases flexibility and capacity for exploiting image characteristics, which ensures the CT scan image's fidelity, aiming for better classification accuracy. We trained the deep learning model with a labeled lung dataset to produce a minimum error rate. This paper is a continuation of our work [17]. This paper improved the algorithm and computed the difference to reduce training time and analyze the performance. We implemented the proposed algorithm in various platforms, Apache Spark, GPU, and CPU. General deep learning models in real-time are being trained by GPUs [18] and parallelized GPUs [19]. The training time of a complex model with many hidden layers would take even several days to converge. To reduce the converging time and to fine-tune the hyper-parameters, distributed training is the solution. Many researchers are trying to integrate deep learning and Spark to diminish the computation expense and to distribute the process [20]. Hence, we did the performance analysis by comparing the platforms, as mentioned earlier. Overall, our contributions in this paper are listed as follows:  
* The proposed NG-CNN model shows a great accuracy percentage in detecting the lung nodule even when the nodule's size is less than 1.5 mm as we applied a noise filter as an extensive pre-processing technique. Measurement of correntropy is integrated with the autoencoder based deep neural network. * We designed Apache Spark deep learning framework for our proposed NG-CNN model. Training time and performance are analyzed using detailed experimentation on various platforms, such as Apache Spark, GPU, and CPU. * Large labeled lung CT scan dataset of size 150 GB has been used for evaluation, making it perfect for CNN training. The cumulative number of cases is more than 1600, with multiple slices per patient. * Classification accuracy, sensitivity, specificity, and area under the ROC curve (AUC) of the model performance are compared with various combinations of CNN parameters and other deep neural networks. The rest of the paper is organized as follows: Section 2 exhibits the literature survey related to our research work. Our proposed methodology is illustrated in detail in Section 3. Section 4 demonstrates the results of the experiments and discussion. Section 5 concludes the paper along with possible future work.
## 5 Scalability
### Strong Scalability  
CHTKC achieves ideal speedup on both H.1 and H.2 in the strong scalability test (Figure 7 and Supplementary Table S8). In contrast, the speedup ratio of KMC3 tends to remain unchanged after the thread number exceeds 8, which shows relatively poor strong scalability. Since CHTKC's hash function calculation is more computationally expensive than the partitioning and counting scheme of KMC3, more threads can speed up the lookup of the hash table. Because CHTKC adopts a chain hash table and lock-free CAS operation, the negative effect of race condition among threads is limited and does not limit the strong scalability. On the contrary, the low CPU utilization of KMC3 indicates the computational resources are not the bottleneck (Figure 1b), and more threads do not significantly contribute to the strong scalability. In addition, the limited scalability of the Raduls algorithm restricts the KMC3 algorithm cannot have better scalability. Since the impact of IO on the KMC3 algorithm is more noticeable, the strong scalability of KMC3 is relatively poor. Overall, KMC3 relies less on computing power, whereas CHTKC can make better use of computing resources.  
Figure 7: Strong scalability comparison of KMC3 and CHTKC. (**a**) counting k-mers with various lengths in H.1; (**b**) counting k-mers with various lengths in H.2.
## 3 Results and Discussion  
### DLS Analysis  
In order to estimate the size of the particles obtained, they were analyzed by DLS. One milliliter of an aqueous solution of particles with a concentration of 1 mg/mL was used, obtaining a concentration independent measure. In Figure 2 the analysis results are reported. For CS material (Figure 2a), the largest number of particles had an average diameter in the range from 580 nm to 600 nm, while for SDR (Figure 2b) most particles had a diameter in a range from 50 to 70 nm, which means ten times smaller than those produced by simple colloidal solution reaction.  
Figure 2: (**a**) Particle size distribution of sulfur particles produced by precipitation from colloidal solution;(**b**) particle size distribution of sulfur particles produced by SDR.
# Preparation and Chemical Protective Clothing Application of PVDF Based Sodium Sulfonate Membrane
## 1 Introduction
Compared with PVDC, polyvinylidene fluoride (PVDF) has more advantages in high chemical stability and excellent barrier and solution-casting properties. Therefore, PVDF has broad application prospects as a matrix resin used in a CPC selectively permeable membrane and is chosen for this study [23; 24; 25; 26; 27; 28]. PVDF can usually be modified by using high-energy radiation, inorganic strong base to obtain modified PVDF films containing active sites or carbon-carbon double bonds [29; 30; 31; 27]. However, radiation sources are required in the high-energy radiation method. Moreover, inorganic alkali is difficult to permeate into the PVDF membrane. Both methods can only modify the surface of the PVDF membrane and lead to a low water vapor transmission rates (WVTR). Additionally, PVDF can also be grafted with functional polymers in organic solvents by using ozone-induced graft copolymerization and atom transfer radical polymerization (ATRP). However, for ozone-induced graft copolymerization, an ozone generator is required and leads to safety hazards [32; 33]. For the ATRP method, transition metal complex inorganic substances are required in the preparation process and difficult to be removed, lead to low blockage of chemical agent simulants. Most importantly, the ATRP method requires a strict anhydrous and oxygen-free environment, which is difficult to satisfy in a large-scale application [34]. Tetramethylammonium hydroxide (TMAH) is a strong organic alkali, which can attack alkane halide to generate carbon-carbon double bonds by the elimination of hydrogen halide through elimination reaction. Moreover, the TMAH methanol solution and its decomposition products (methanol and trimethylamine) have a low boiling point, which can be completely removed at a specific temperature without affecting the modification effect. Thus, the PVDF modification of using TMAH is an ideal method to take into account the cost, simplicity, and post-treatment for CPC membrane preparation. In this study, a functional polymer, poly(vinylidene fluoride)-graft-poly(sodium p-styrene sulfonate)(PVDF-\(g\)-SSS) was synthesized via one-pot reaction involving PVDF modification and sodium p-styrene sulfonate (SSS) grafting. The synthetic procedure for the PVDF-\(g\)-SSS was synthesized under convenience and started from low costing materials. The ion exchange capacity (IEC, molar of the sulfonic acid groups per gram of dried membranes) could be finely tuned in the range of 0 to 2.29 mmol/g. After membrane preparation, the obtained PVDF-\(g\)-SSS was evaluated as the membrane material for CPC application. The effect of IEC on mechanical properties, contact angles, permeations, and selectivities were systematically investigated. It was found that the IEC is the dominating factor that decides the selectivity and mechanical properties [35]. At an optimized IEC value of 1.5-2 mmol/g, the PVDF-\(g\)-SSS showed good CPC performance.
# Shear-Wave Velocity Model from Site Amplification Using Microtremors on Jeju Island
## 4 Results
### Mrd  
Figure 12 illustrates the observed and best-inverted HVSR curves for the small islands situated south of the Jeju region, as depicted in Figure 1. The observed HVSR curve featured a prominent peak at a frequency of 0.43 Hz, with corresponding peak amplitudes of 3.36, as indicated in Table 4.  
For the variance bounds from 0.25 to 2.5, the misfit sizes of the inversion using the MC simulations ranged from 80.41 to 2.22. The SA algorithm also produced misfit sizes for the best-fit inversion model within the same variance bounds. The misfit sizes, as well as the variations in amplitudes across the different frequency ranges, decreased with increasing the variance bounds for both algorithms.  
Figure 13 displays the Vs profiles at the MRD site, portraying all the inverted and optimal Vs models. In determining the optimal shear velocity profile, the darker segments, which represent more frequently mapped areas, were given more weight. From the surface to depths of 60-80 m, the first layer exhibited velocities of 900-1100 m/s. Employing the site classification method based on the surface shear-wave velocity, as outlined in Table 5, the outcrops at the MRD sites (900-1100 m/s) were categorized as \(\rm S_{B}\). The second layer, with velocities of 1200-1400 m/s, extended from 130-150 m depth. The third layer, the LVL, displaying velocities of 600-800 m/s and a significant velocity reduction of around 600 m/s, extended to depths of 290-310 m. This layer aligns with the typical LVL found throughout the Jeju region, as referenced in previous studies. With the further increase in depth, rapid velocity escalations were observed twice, at depths of 490-510 m and 690-710 m, with the latter marking the onset of the deep basement [45].  
At the MRD site, as outlined in Table 11, there were four large positive RC values (0.13, 0.22, 0.21, and 0.17) and one large negative RC value (\(-\)0.30). The negative RC value denotes that the LVL, or the third layer, extended up to depths of 290-310 m with a velocity reduction of 600 m/s. This reduction was consistent with the \(-\)0.3 RC value, representing a very large absolute value. The deep basement initiated at depths of 690-710 m, with velocities ranging between 2200 and 2400 m/s.  
Figure 12: Observed and synthetic HVSRs of MRD site; the red line indicates the observed curve, and the black dotted line indicates the synthetic curve.  
The final velocity model at the MRD site was interpreted as comprising six layers, including the deep basement. This interpretation was based on an analysis that included both the models frequently represented in darker colors and the average RC values.
# Integrated SDN-NFV 5G Network Performance and Management-Complexity Evaluation
## 2 Related Work
### Underlay and Overlay Network Concept  
Spine-leaf architecture as shown in Figure 1 has the ability to answer the needs of modern data centers. It provides low latency and minimum packet loss by enabling the addition and deletion of network nodes on demand. This also has an impact on the evolution of the data center (DC) architectural design and network infrastructure, which also plays an important role in that change, as mentioned in [9]. Several aspects of this new paradigm need to be considered:  
1. Flexibility: Allows mobility from anywhere.
2. Resilience: Maintain service even in damaged conditions.
3. Multitenant Capacity: Better segmentation of workloads in the network.  
One of the main problems when implementing modern network infrastructure, both physical and virtual, is the limited number of IP addresses that leads to the search and reuse of available IP addresses when they are not in use, or on network devices that are no longer in use. Another limitation of traditional networks is the limitation of virtual  
Figure 1: Spine-leaf architecture.  
LANs (VLANs) which can only be capable of covering 4094 VLANs. This makes it difficult to implement networks on a large scale, such as telecommunications service providers and data center networks. When using virtual extensible VLAN (VXLAN) technology, we can address this shortcoming because VXLAN can cover approximately 16 million IDs. A VXLAN can also optimize logical addressing because it connects different sites with the same IP addressing block. A multitenant DC allows the allocation of service provision, and is isolated to several different users by providing a specific segment for each customer [20].  
The use of multitenant DCs has expanded as a result of the virtualization platform. They are now found not only in large service providers, but also as part of network infrastructure. One way to achieve this is to implement an overlay network architecture that encapsulates packets. The idea is to implement a network \"on top\" of others that have been created by tunneling between links through an established network infrastructure, commonly called an underlay network.
## 3 Results
### Emission Rate of Ammonia
Furthermore, the variation in NH\({}_{3}\) emission rates among vehicles can be attributed to the use of oxidation catalysts in diesel vehicles, while gasoline and LPG vehicles undergo both oxidation and reduction processes. EURO4 vehicles, equipped with EGR and DOC aftertreatment devices, exhibit NH\({}_{3}\) emissions at similar levels to EURO6 vehicles. Similar to EURO6, NH\({}_{3}\) emissions are only detected during the initial startup, with no NH\({}_{3}\) emissions detected under normal driving conditions. It is known that the precursor CO in exhaust gas contributes most to the formation of NH\({}_{3}\). Previous studies showed a high correlation (r\({}^{2}\) = 0.76, 0.97) between NH\({}_{3}\) and CO from EURO6 (WLTC mode) and EURO5, 6 (NEDC mode) [24; 41]. A correlation analysis between the ammonia released from each regulatory mode and the ammonia precursor CO was performed. Gasoline and LPG show a high correlation (r\({}^{2}\) = 0.96), and diesel shows a low correlation with NH\({}_{3}\) ER less than 0.02 mg/km. In the current regulatory framework, legal standards are in place for controlling exhaust gas pollutants such as HC, CO, and NOx. To simultaneously reduce these pollutants, the installation of TWC aftertreatment devices is mandatory for gasoline and LPG vehicles. This device utilizes catalysts such as platinum (Pt), palladium (Pd), and rhodium (Rd) to facilitate the reduction of regulated exhaust emissions [42]. However, a challenge arises as these aftertreatment devices also generate a significant precursor to secondary pollutants, namely NH\({}_{3}\). NH\({}_{3}\) formation occurs through the reaction of NOx with H\({}_{2}\), which is generated from the reaction of CO and H\({}_{2}\)O [43; 44]. [PERSON] et al. [43] reported NH\({}_{3}\) concentrations of 10.9 mg/km in the NEDC mode and 46.9 mg/km in the WLTC mode when measured after the TWC. Furthermore, the sulfur content present in the fuel can contaminate the TWC converter, affecting the production and conversion of NH\({}_{3}\) and NOx [26]. However, previous studies have primarily focused on measuring NH\({}_{3}\) solely at the vehicle tailpipe after the TWC without confirming whether the NH\({}_{3}\) is actually generated by the catalyst. Hence, this study aimed to measure NH\({}_{3}\) both at the vehicle tailpipe and at the front of the TWC to determine the catalyst's role in NH\({}_{3}\) generation. GDI vehicles were utilized for experimental purposes, conducted under the NEDC mode. The findings revealed trace amounts of NH\({}_{3}\) detected at the TWC front and substantial amounts detected at the TWC tailpipe, confirming the TWC as the primary source of NH\({}_{3}\) production. These results underscore the necessity of developing strategies to mitigate NH\({}_{3}\) emissions from gasoline and LPG vehicles, aiming to reduce PM\({}_{2.5}\) pollution in the atmosphere.
# Direct Spread Spectrum Technology for Data Hiding in Audio +
## 3 Materials and Methods
### Direct Spread Spectrum in Steganography
We used audio covers and experimented with different ways to generate chip codes. We showed that the variety of ways of generating spreading sequences produces different BER values. This is an area for possible optimization of the stegosystem, and we showed which chip codes are really best used to hide information messages in audio containers.
## 4 Software Architecture
### Interface
#### 4.2.4 Classification Performance and Exporting Results  
We have developed GDT to be able to compare between the built tree and an automatically generated one. Once the tree is complete, the users can interact with the classification controls. This area manages the parameters to build the trees implemented by the scikit-learn library. Users can define the split criterion by choosing between entropy or Gini and define the maximum depth from these automatically built trees.  
Once the automatic tree has been executed, users can compare the classification performance between the sci-kit learn tree and the manually built tree. To show the performance of the trees, GDT includes the following indicators for each class present in the dataset: Precision, Recall, F1-Score, and the number of records used to test each class. The Precision value shows the number of correctly classified records of a class among the total number of observations classified as such. The Recall value shows the number of observations classified as one class among the total observations of that class. Lastly, the F1-Score is a general metric used to evaluate the classifier performance in each class, calculated as \(F_{1}=2\times(Precision\times Recall)/(Precision+Recall)\). These results are presented in a table, as shown in Figure 9.  
Once the classifier is built and the user decides that the performance is good enough, both the scikit-learn and the manually built tree can be exported in PDF format by clicking the corresponding button on the interface. These representations employ the Graphviz library [36] to export the PDF. The scikit-learn tree generates a DOT visualization automatically. We color the leaf nodes according to the colormap used in all the visualizations. The manually built tree is exported from a custom function, and we maintain the color map associated with the classes for comparison with the automatically generated tree. The exported trees (see Figure 10) provide detailed information about every split feature, the cutoff value, entropy, the number of samples in relation to the initial trainj  
Figure 8: GDT allows the users to choose the representation for decision support in two classes of nodes. (**a**) SC representation of LDA after applying a jittering based on PCA in the same node. (**b**) Coordinated view of 1D LDA projection, distribution plot of samples, and values of features in linear transformation matrix. In this instance, the focus is on how it distinguishes between the versicolor (2, green) and virginica (3, red) classes within the iris dataset.  
Figure 9: Performance obtained by the automatic and manually built trees in the Iris dataset [33]. Each class consists of 50 balanced records before training and testing sets with a 70:30 ratio.
## 2 Topology and Modelling of the Studied System
### Public Grid Modelling  
The public grid used is considered to be a low voltage three-phase network with the 50 Hz frequency, whose network connection is traditional. Here, \(i^{\prime}\) refers to the inverter current, \(i_{A}\), \(i_{B}\), \(i_{C}\) the currents of the three phases, \(L\) the self-inductance, and \(u_{AC}\) and \(u_{BC}\) the public grid voltages between the phases A and C as well as B and C, respectively. The public grid modelling equations are expressed by Equation (9):  
\[\left[\begin{array}{c}u^{\prime}_{AC}\\ u^{\prime}_{BC}\end{array}\right]=\left[\begin{array}{c}v_{A}-v_{C}\\ v_{B}-v_{C}\end{array}\right];\left[\begin{array}{c}v_{A}\\ v_{B}\\ v_{C}\end{array}\right]=\left[\begin{array}{c}f_{A}\\ f_{B}\\ f_{C}\end{array}\right]v_{C} \tag{9}\] \[\left[\begin{array}{c}v_{A}\\ v_{B}\\ v_{C}\end{array}\right]=\left[\begin{array}{c}f_{A}-f_{C}\\ f_{B}-f_{C}\end{array}\right]v_{C}\right]v_{C}\right]=\left[\begin{array}{c}m_{A }\\ m_{B}\end{array}\right]v_{C}\]  
where \(u^{\prime}_{AC}\) and \(u^{\prime}_{BC}\) are the output's inverter voltages between the phases A and C, and B and C respectively, \(v_{A}\), \(v_{B}\), and \(v_{C}\) are the simple voltages of the three phases, and \(m_{A}\) and \(m_{B}\) are the inverter control variables.