esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust UWSN Handover Prediction System Using Ensemble Learning
###### Abstract
When this occurs, successful and timely handover is vital to guide the sensor node to the nearest sea buoy in order to guarantee efficient and uninterrupted data transmission. However, existing acoustic handover mechanisms are not feasible due to their long propagation time, unreliable sensor node location, and poor transmission rates. Since machine learning techniques are rarely used in handover prediction in UWSNs, we are motivated to utilize ensemble learning (e.g., gradient boosting) to predict handover events in UWSNs, as ensemble learning has not been used to address this problem. In this work, we simulate the movement of UWSN sensor nodes using real open ocean marine data such as water current speed and direction. We utilize real-time data provided by the Korea Hydrographic and Oceanographic Agency ([[https://www.khoa.go.kr/eng/](https://www.khoa.go.kr/eng/)]([https://www.khoa.go.kr/eng/](https://www.khoa.go.kr/eng/)) (accessed on 26 August 2021)) for this purpose. This agency monitors the southeastern coasts of the Korean Peninsula [1,5]. The simulation captures the handover events, which are used as a dataset for the proposed work. Finally, we utilize several machine learning methods to predict the future coverage cell for the sensor nodes in handover events. Finally, we utilize several machine learning methods, including DT, GNB, KNN, and gradient boosting to predict the future coverage cell for the sensor nodes in handover events. The major contributions of this paper can be summarized as follows. * To the best of our knowledge, this is the first work that provides a handover dataset created from real open ocean marine data ([[https://github.com/[PERSON]/UWSN](https://github.com/[PERSON]/UWSN)]([https://github.com/[PERSON]/UWSN](https://github.com/[PERSON]/UWSN)) (accessed on 26 August 2021)). This is also the first work to provide the utilized datasets and source code in the field of UWSN handover prediction; thus, the reported results are reproducible. * To the best of our knowledge, this is the first effort to employ an ensemble learning approach in a UWSN that explores the handover prediction of sensor nodes. * The proposed handover prediction models outperform the existing state-of-the-art methods with a huge performance gap. The remainder of the paper is organized as follows. The background of this work is discussed in Section 2. Section 3 discusses the state-of-the-art methods. In Section 4, we explain the proposed handover prediction system. Section 5 discusses the proposed experiments and obtained results. Finally, the paper is concluded in Section 6.
# An Investigation on Radiomics Feature Handling for HNSCC Staging Classification
## 2 Materials and Methods
### Image Pre-Processing  
Patients' FDG-PET and CT raw images (DICOM format) have been converted into NIFTI format for the radiomics approach. For each patient, tumor contours have been segmented on both FDG-PET and CT images and have been made available in RT-Struct files. Among the masks contained in RT-Struct files, only \"GTV\({}_{primary}\)\" (Primary Gross Tumor Volume) ones have been taken into account for radiomics feature extraction. CT images have been kept in Hounsfield Units (HU), and PET images have been converted to Standard Uptake Value (SUV) maps using an in-house algorithm.
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 5 Resource Orchestration  
In recent years, resource management literature has reported approaches that orchestrate resources in NSL by using techniques such as optimization [156; 157; 158; 159] and (meta)heuristics [160; 161; 162]. Figure 5 presents an architecture using RL and DRL agents to orchestrate network slices. In this architecture, the resource orchestrator module composes RAN, CN, and edge network resources to form an end-to-end slice that follows the structure of admitted and scheduled NSRRs by employing a RL/DRL agent, a RAN composer, an edge composer, and a CN composer. The agent determines normalized weight values for composing services according to NSRRs of 5G/6G use cases. These values determine the orchestration priority, the (re)composition of service chains in RAN, CN, and edge networks, and traffic paths between the elements forming the end-to-end service (network slice in operation). Moreover, those values should lead to achieving an optimization goal, for instance, maximizing network utilization while meeting XLA. In the raised example, the Agent selects an action that, if taken, permits meet XLA and maximize network utilization. The agent learns to select actions that increase network utilization to avoid resource waste and allow meeting experience requirements by considering the information on states and rewards from interaction with the environment by using, for example, TD3 or online DQN. In the example, the composers use the values defined by the agent to build up and manage the life cycle of end-to-end network slices.
## 2 Methods
### Inverse Laplace Transform Algorithm  
#### 2.2.1 Vertical Component of Magnetic Field in the (\(k_{y}\), \(s\)) Domain  
For 2.5D forward modeling in the FEM, the electromagnetic field is first analyzed in the Laplace and Fourier domain, i.e., the (\(k_{y}\), \(s\)) domain, where \(k_{y}\) is the wavenumber of the along-strike field; and \(s\) is the Laplace transform variable. It is then recovered in the time domain. For a layered geoelectric model, the vertical component of the magnetic field under the trapezoidal waveform excitation can be written as  
\[\hat{H}_{z}=f_{s}.H, \tag{2}\]  
\[H=\left\{\begin{array}{ll}G_{0}^{\infty}\Big{(}\frac{u_{0}R_{1}-u_{1}}{u_{0} R_{1}+u_{1}}e^{-2u_{0}z_{0}+u_{0}z}+e^{u_{0}z}\Big{)}\frac{k_{x}^{2}+k_{y}^{2}}{u_{0}k_{ x}}\sin\left(k_{x}a\right)\cos\left(k_{x}x\right)dk_{x},\text{ for }z<0\\ G_{0}^{\infty}\Big{(}\frac{u_{0}R_{1}-u_{1}}{u_{0}R_{1}+u_{1}}e^{-2u_{0}z_{0}+u _{0}z}+e^{-u_{0}z}\Big{)}\frac{k_{x}^{2}+k_{y}^{2}}{u_{0}k_{x}}\sin\left(k_{x} a\right)\cos\left(k_{x}x\right)dk_{x},\text{ for }0<z<z_{0}\end{array}\right., \tag{3}\]  
\[G=\sqrt{\frac{2}{\pi}}\frac{l_{0}\sin(k_{y}b)}{\pi k_{y}}. \tag{4}\]  
For a homogeneous half-space, \(R_{1}=1\), and for a layered model, \(R_{1}\) is defined as follows:  
\[R_{1}=\frac{u_{1}+u_{2}\text{tanh}(u_{1}h_{1})}{u_{2}+u_{1}\text{tanh}(u_{1}h _{1})}\, \tag{5}\]  
\[\begin{array}{ll}u_{0}=\sqrt{k_{x}^{2}+k_{y}^{2}+k_{0}^{2}},k_{0}^{2}=\mu s (\varepsilon s+\sigma_{0}),\\ u_{1}=\sqrt{k_{x}^{2}+k_{y}^{2}+k_{1}^{2}},k_{1}^{2}=\mu s(\varepsilon s+ \sigma_{1}),\\ u_{2}=\sqrt{k_{x}^{2}+k_{y}^{2}+k_{2}^{2}},k_{2}^{2}=\mu s(\varepsilon s+ \sigma_{2}),\end{array} \tag{6}\]  
where \(z_{0}\) is the height of the transmitter loop; \(k_{x}\) and \(k_{y}\) are the wavenumbers along the x-and y-directions, respectively; \(h_{1}\) is the depth of the first layer; \(\sigma_{0}\), \(\sigma_{1}\), and \(\sigma_{2}\) are conductivities of the air, seawater, and seabed layer, respectively; \(l_{0}\) is the amplitude of the incident current; the current waveform \(f_{s}\) is considered in the Laplace domain; \(a\) and \(b\) are the values for half of the loop side length.  
In Equation (2), the field is divided into two parts; one is the current waveform \(f_{s}\), and the other is \(H\), which only has a relation with parameters of the transmitter instrument and the geoelectric model. In addition, the Laplace form of the trapezoidal wave is divided into four parts, as shown in Equation (1). When solving the Laplace inversion of Equation (2), the combination of \(H\) and the first  
Figure 1: Schematic of airborne transient electromagnetic (TEM) system.  
part of \(f_{s}\) can be solved; then, the other parts can be obtained by the translation property of the inverse Laplace transform.
# Suppressing Salt Transport through Composite Pervaporation Membranes for Brine Desalination
## Materials and Methods
### Composite Membrane Preparation  
The commercial supporting membrane was firstly pre-treated with 1 M KOH solution at 65 \({}^{\circ}\)C water bath for different length (0.5, 1, 2, 6 and 8 h) [29; 30]. The KOH treatment led to reduction in water contact angle due to the hydrophilization effect. In this work, 2 h pre-treatment time was selected as longer treatment time did not lead to the increase of surface hydrophilicity (Figure S1). Subsequently, the membrane was rinsed with Milli-Q water and dried at room temperature. For the casting solution, aqueous PVA solution (10 wt. %) was prepared by dissolving PVA powder in Milli-Q water at 80 \({}^{\circ}\)C water bath with mechanical stirring for at least 6 h. Then the PVA solution was cooled down to room temperature. A certain amount of glutaraldehyde, and quencher methanol were added to the aqueous PVA solution and the mixture was magnetically stirred for another 30 min, which was followed by the addition of catalyst HCl and stirring for another 5 min. The molar ratio of glutaraldehyde/PVA repeat unit (denoted as MR value) was varied, while the ratio of methanol/HCl/PVA was maintained invariant, i.e., 1 g PVA was mixed with 2 mL of 10% methanol and 0.4 mL of 1 M HCl. To prepare the composite membrane with MR value of 0.2, for 1 g PVA prepared for the casting solution, the glutaraldehyde used (25% in water) was 1.72 mL. The PVA concentration in the final casting solution was maintained at 5 wt. % by adjusting the amount of Milli-Q water in the casting solution. The casting machine (Sheen 1133N automatic film applicator, Sheen Instruments, Surry, UK) was used to cast the PVA membrane on PVDF supports with a casting speed of 50 mm/s under 50% humidity. Then the coated membrane was dried at room temperature overnight, followed by oven drying at 80 \({}^{\circ}\)C for 30 min.  
For the GO-containing PVA composite membrane fabrication, a certain amount of GO was evenly dispersed into the PVA casting solution containing glutaraldehyde and methanol. To initiate the cross-linking, HCl was then added to the above casting solution and stirred for 5 min. The membrane casting and drying procedures are identical to the previous pure PVA composite membranes. The weight ratio of GO to PVA (0.1, 0.2, 0.3 wt. %) was used to denote different samples, e.g., PVA0.1 GO/PVDF denoted composite membrane with 0.1 wt. % of GO in PVA matrix. For the PVA-GO composite membranes, the molar ratio of glutaraldehyde to PVA repeat unit was kept invariant of 0.2.  
In order to understand the interfacial interactions between GO and PVA, the freestanding PVA-GO membranes were prepared by pouring the above-mentioned casting solution in Petri dishes, followed by the same drying and post-heat treatment procedure as the cast coated membrane.
## 4 Discussion
### Fabrication of the NPs
#### 4.1.4 The Influence of an Organic Solvent  
Four water-miscible organic solvents--Three highly polar and less volatile, DMI, DMF, and DMSO; and one semi-polar and volatile acetone, which could easily be removed from nanosuspensions by evaporating under reduced pressure [42]--Were used in the present study to form the organic phase containing an AABB polymer. The polar solvents dissolved all four AABB polymers (**1F6**, **1L6**, **4F4** and **8L6**), while acetone dissolved the leucine-based polymers, **1L6** and **8L6**. The results of the nanoprecipitation experiments for the organic solvents used are listed in Tables 5-8 in terms of MPD and PDI. As one can see from these data, the nature of the organic solvent influenced both NPs size (MPD) and distribution (PDI). DMSO was found as the best solvent from which the NPs of MPD 56-249 nm were formed depending on the nature of the AABB polymer and the surfactant used. (It has to be taken into consideration that DMSO is a nontoxic organic solvent used in the medical field [46].) The smallest NPs from DMSO were fabricated in the presence of Tween 20, Tween 80, Kolliphor P188, and Brij 010; polyvinyl alcohol (Mowiol 4-88), as above, led to NPs of bigger size in almost all the studied cases. The polydispersity of the NPs fabricated from the DMSO solution varied from mean (0.04 \(\leq\) PDI \(\leq\) 0.16) to wide (PDI \(>\) 0.16). DMI was also found to be a good solvent in terms of the MPD of the fabricated NPs. Among the highly polar organic solvents tested in the nanoprecipitation method, DMF demonstrated the worst characteristics, resulting in NPs of bigger MPD. From the acetone solution the NPs of bigger size were fabricated regardless of the AABB polymer and surfactant used (MPD 172-323 nm); in this case the PDI also varied from mean to wide (mostly) values. However, acetone is a volatile solvent and is very convenient to be removed from the nanosuspensions. This solvent can be recommended for fabricating NPs from the PEA **8L6** using Kolliphor P188, Tween 20, or Brij 010 as the surfactants; the MPDs obtained with these emulsifiers were 172, 192, and 203 nm, respectively, vs. the 229 nm obtained by [PERSON] et al. [42] from the acetone solution of poly-(D,L-lactide) using two emulsifiers--A mixture of phospholipids (Epikuron 170) and poloxamer (Pluronic F68).
## 4 Result and Discussion
### MMC-HVDC Voltage Control and Optimum PV Integration  
Before transferring the renewable energy over the MMC-HVDC network, MMC1 gradually sets the HVDC link voltage. After that, MMC2 gradually sets the HVAC link voltage for renewable energy integration. Figure 8 shows the HVDC link voltage control, whereas Figure 9 illustrates the HVAC link voltage control. Initially, the current limiting resistor is activated to control the charging current of the submodule capacitor. After 0.72 s, the resistor is bypassed, and the controller is activated to gradually control the HVDC link voltage towards 200 kV (1 pu). Similarly, the HVAC link voltage controller regulates the d-component of HVAC voltage while the q-component is regulated at zero. Figures 10 and 11 show the optimum PV energy tracking during irradiance and temperature change in RTDS. It is seen from Figure 10 that 56 units of PV array are injected around 5.6 MW to 100 MW to MMC2 terminal during irradiance change from 100 Wm\({}^{-2}\) to 1000 Wm\({}^{-2}\). It is found from PV array characteristics that the optimum PV energy for one array is 0.1 MW at 100 Wm\({}^{-2}\) and 1.74 MW at 1000 Wm\({}^{-2}\) during constant temperature (25 \({}^{\circ}\)C). Therefore, MPPT is achieved as 56 units yield around 5.6 MW (56 \(\times\) 0.1 MW) and 100 MW (56 \(\times\) 1.74 MW) respectively in RTDS during irradiance change. During temperature change from 25 \({}^{\circ}\)C to 50 \({}^{\circ}\)C, 56 units of PV array inject around 100 MW to 80 MW to MMC2 terminal as illustrated in Figure 11. It is also found from PV array characteristics depicted in Figure 5 that the optimum PV energy for one array is 1.74 MW at 25 \({}^{\circ}\)C and 1.43 MW at 50 \({}^{\circ}\)C during constant irradiance (1000 Wm\({}^{-2}\)). This also confirms the optimum PV energy tracking  
Figure 7: Control strategy for FRT and power levelling of PV and Battery; (**a**) active power curtailment of PV; (**b**) excess energy delivered to battery; (**c**) power levelling during solar radiation and temperature change.  
Figure 11: Optimum PV energy tracking during temperature change in RTDS.  
Figure 12: HVDC voltage regulation of MMC1 in the course of SLG, DLG and 3 LG faults employing conventional dynamic braking resistor (DBR).  
Figure 14: Id and Io current and power of MMC1 for the period of 3 LG fault.  
Figure 13: PCC1 voltage, Y-\(\Delta\) side current of MMC1 for the period of 3 LG fault.  
Figure 16: PCC1 voltage, Y-\(\Delta\) side current of MMC1 for the period of DLG fault.  
Figure 15: PCC voltage, PV voltage and power, MMC1-HVDC voltage for the period of 3 LG fault.  
Figure 17: Id and Iq current and power of MMC1 for the period of DLG fault.  
Figure 18: PCC voltage, PV voltage and power, and MMC1-HVDC voltage for the period of DLG fault.  
Figure 19: PCC1 voltage, Y-\(\Delta\) side current of MMC1 for the period of SLG fault.  
Figure 20: Id and IQ current and power of MMC1 for the period of SLG fault.
## 2 Results
### MCPIP1 Regulates the Expression of XIAP by Reducing Its mRNA Stability  
Because MCPIP1 downregulates both mRNA and protein levels of XIAP, we further investigated the regulatory mechanism of MCPIP1 on XIAP expression. Considering the endonuclease function of MCPIP1, we speculated whether MCPIP1 affects XIAP mRNA degradation by its RNase activity. As expected, similar to IL-17 RA mRNA that has been reported to be degraded by MCPIP1 via its endoribonuclease activity [12] (Supplementary Figure S2E,F), our RT-qPCR analysis showed that the Dox-induced expression of MCPIP1 but not D141N leads to downregulating XIAP mRNA levels in HeLa and SiHa cells (Figure 4A,B), which was in accordance with MCPIP1's regulatory effect on XIAP's protein (Figure 3A,B). Subsequently, we examined MCPIP1's impact on XIAP's mRNA stability. As shown in Figure 4C,D, a Dox-induced expression of MCPIP1 but not D141N leads to a shorter half-life of XIAP mRNA in HeLa and SiHa cells. We also infected the cervical cancer cells with a lentivirus of MCPIP1 shRNAs and determined the XIAP's mRNA levels by RT-qPCR analysis. It was shown that MCPIP1 knockdown results in the upregulation of XIAP mRNA levels in HeLa and SiHa cells (Figure 4E,F). The RNA stability detection assay also demonstrated that compared to the NTC (pLKO.1) control, MCPIP1 knockdown leads to a longer half-life of XIAP mRNA (Figure 4G,H). These findings validate that MCPIP1 can inhibit the expression of XIAP by promoting its mRNA decay. As a result, increased protein expression levels of XIAP, as well as decreased protein levels of cleaved caspase-3 and cleaved PARP1, can be observed in HeLa or SiHa cells with MCPIP1 knockdown (Figure 4I). Therefore, MCPIP1 may decrease the mRNA and protein expression of XIAP by attenuating its mRNA stability, further leading to increased cell apoptosis.
# Quantification of the Benefits for Power System of Resilience Boosting Measures
## 4 Modeling Framework for the Measures
### Modeling of Threat-Specific Passive Measures
#### 4.3.1 Anti-Torsional Devices  
These devices raise the torsional stiffness of conductors and they are deployed to prevent the beginning of the process which causes the wet snow sleeve to accrete [25]. The effect of anti-torsional devices has been studied in literature for a long time [32], even if further experimentation is needed to fully demonstrate the effectiveness of these devices. The device causes an increase of torsional rigidity, which limits or prevents the conductor rotation, thus the wet snow sleeve accretion. First of all, the model has to realistically quantify the rotation reduction due to these devices. Reference [32] can help in this task, by proposing some quantitative considerations about the observed decrease of ice loading after applying one or four anti torsional devices. Reference [33] proposes an analytical model which quantifies the reduction of ice loading on conductors in line with the findings of the experiments above.  
In particular, the function \(F(x,N)\) in Equation (4) represents the rotation at distance \(x\) from one line terminal in case of \(N\) anti-torsional devices: the curve derives from the superposition of the absolute value of sin function along the line span with a maximum rotation of \(19^{\circ}\) (adequate value at design stage) on the absolute value of a shorter period \(sin\) function.  
\[F(x,N)=max\left(0,(a-bN^{\circ})\times\left|sin\left(\frac{\pi\cdot x(1+N)}{L} \right)\right|+d\times\left|sin\left(\frac{\pi\cdot x}{L}\right)\right|\right) \tag{4}\]  
In Equation (4) L is the span length, while the values for parameters \(a\), \(b\) and \(c\) are derived by trial and error technique from [32]: \(a=71\), \(b=12\), \(c=0.7\), \(d=19\). The reduction factor (\(RF\)) is defined as in Equation (5).  
\[RF=\frac{mean(min(F(x,N),F(x,0)))}{mean(F(x,0))} \tag{5}\]  
\(RF\) is multiplied by the wet snow accretion intensity I\({}_{0}\) in the Makkonen model [26] to derive the derated intensity. The basic assumption is that the accretion intensity reduction is proportional to the reduction of the rotation angle.
# Digital Cloud Environment: Present Challenges and Future Forecast
## 1 Introduction  
Fast growing internet information technologies (IT) based on globally distributed cloud computing (GCC) have allowed radical improvements in the efficiency of internet services as well as capacity and in the performance of virtual machines (VMs). Big international IT companies deploy thousands of VMs with production software in the cloud to provision multiple services for their customers worldwide.  
GCC implementation and operational support has both benefits and challenges, as the following:  
* A cloud service is now an integration of multiple platforms and applications of the internet of things (IoT) [1].
* The well-known problems of big data, including storing client accounts in a database, protocoling user activity in the logs, transferring real time dataflow, and processing big data analytics [2].
* A fast growing and, therefore, unpredictable user workload [3].
* The implementation of mobile applications leads to human activity spikes, not only in peak daytime but also in the late evenings and early mornings.
* Continuous multiservice delivery and operational support in 24/7 mode [4; 5; 6].
* Initially developed in the USA, many International IT companies go global for provisioning GCC services in all regions and time zones worldwide.
* The virtualization of the digital environment and migration from private to public cloud services, such as software as a service (SaaS), platform as a service (PaaS), infrastructure as a service (IaaS), load balancer as a Service (LBaaS), database as a service (DBaaS), etc.  
* Increasing cyber security problems and new sources of threats when moving from private to public clouds.
* State government standards, banking rules, and other local regional limitations.
* Existing monitoring solutions [7] provide only built-in system metrics like memory, CPU, disk space, or network throughput, but not specific business items like HTTP requests per second, the number of actively logged-in users, server connections, calls count, and others.  
These challenges are described in detail and proper solutions are defined in the sections below.