esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Transmit Power Allocation and Splitting for SWIPT Aided OFDM-IDMA in Wireless Sensor Networks
## 4 Simulation Results and Discussions
### Performance Versus EH Requirement  
We then focus on the impact of EH Requirement on the performance of the proposed algorithm. Figure 4 depicts the minimum transmit power of various schemes versus \(E_{c}\). We let \(K=12\) with the  
Figure 3: Transmit Power comparison versus target SINR (\(E_{c}=5\)\(\mu\)W, \(\sigma^{2}=-112\) dBm). (**a**) \(K=8\); (**b**) \(K=16\).  
BER target of each RN equals to \(10^{-5}\). We can find the proposed algorithm always outperforms the other alternatives regardless of the \(E_{c}\) value. In fact, the minimum required harvest energy of each RN is determined by \(Q_{k}E_{c}\). It would provide more accurate expectation on the EH demands if we track the variation of \(\{Q_{k}\}\) during the optimization process. It is of great importance in **EH Dominated Case**, where \(E_{c}\) is relatively large and turns out to be the stronger constraints to the final transmit power.  
We further verify our findings by set different BER targets among RNs, as illustrated in Figure 5. We let \(K=12\) RNs to be evenly divided into 3 groups, with the BER target of each group equals to \(10^{-2}\), \(10^{-4}\) and \(10^{-6}\), respectively. We can find the proposed algorithm is still advantageous over other schemes with any \(E_{c}\) value. In addition, we observed that when \(E_{c}\) is large, the total transmit power of each algorithm is smaller than the corresponding ones illustrated in Figure 4. It makes sense since the desired SINR of RNs with lower BER requirement has already been achieved, and less energy is needed.  
In Figure 6 we plot the transmit power of the proposed algorithm with different number of RNs. Obviously, a larger number of RNs leads to higher transmit power. In addition, the slope of curves is getting smaller as \(E_{c}\) increases, indicating that the power optimization problem shifts from the **ID Dominated Case** to the **EH Dominated Case**. Note that it would cause severer multi-user interference when more RNs are introduced into the system. Consequently the desire BER performance is harder to be achieved. Hence such transition process, along with the increase of \(E_{c}\), is slower when the number of RNs is larger.  
Figure 4: Transmit Power versus the harvest energy \(E_{c}\) for the proposed algorithm (\(\sigma^{2}=-112\) dBm and \(K=12\), the BER target is \(10^{-5}\) for each RN).
## 2 Transient Stability Assessment by Co-Simulation
### Transient Stability Assessment Methods for Monolithic and Co-Simulations  
Performing a monolithic simulation for transient stability studies consists of different steps, as shown in Figure 1. After preparing the power system model in a typical power system analysis tool, a power flow calculation is performed providing an (possibly optimised) set of nodal voltages and active and reactive power injections. The power flow results are used for calculating the initial conditions for the differential equations. This is usually carried out in reverse order by calculating the algebraic variables and states upstream. After a successful initialisation, a time-domain simulation is executed. Using the appropriate integration method and starting algorithm after events, the resulting system of differential-algebraic equations are solved numerically in time. Finally, the simulation results are exported for post-processing. Since all simulations are performed in one environment, a monolithic simulation has a relatively linear workflow.  
The steps that generally need to be taken for co-simulation-based transient stability assessment are shown in Figure 1. As an example, two different simulators are considered for the co-simulation: the power system simulator and the controller simulator. After model preparations, the initial conditions for power system equations are calculated based on its power flow results. Afterward, these initial condition equations are used for calculating the initial conditions for the converter controller. After the successful initialization, the two simulators are ready to be connected. Interfacing and synchronization between two simulators is a significant step, and, as a rule, the interfacing sequence needs to be provided separately by the user. Next, the dynamic simulation is executed and the simulation results are obtained and exported to a common data format.  
As can be seen in Figure 1, a transient stability assessment by co-simulation needs more steps, which is commonly manual and error prone engineering work. This adds a level of complexity compared to monolithic simulations. On the other hand, by applying co-simulation, a large converter-dominated power system can be divided into smaller and possibly simplified models, eventually decreasing the computational burden. The flexibility offered by such approach is an important weighing factor in deciding which modelling and simulation approach to take.
# Emulating Software-Defined Disaggregated Optical Networks in a Containerized Framework
## 2 Proposed Architecture for SDN-DON Emulation
### Legacy Work  
In the previous work presented in [36], we reported our preliminary advancements towards an optical emulation framework for automatic creation of agents in SDONs.  
As seen in Figure 1a, this architecture comprises three main elements: the ONOS SDN controller [39]; a pool of software agents that emulate the interaction with optical devices, in this case, Cassini transponders; and the API gateway.  
ONOS is chosen to play the role of the SDN controller, and is configured with the required modules and YANG drivers to interact with the optical devices or agents in the data plane via NETCONF protocol. A pool of agents emulates a realistic environment of a DON data plane; those agents are implemented in docker containers and they are created automatically by the API gateway REST-based interface according to the topology specification given by ONOS.  
The other key point of this work is the structure of the optical network agent, depicted in Figure 1b. The software agents in the pool are virtualised entities within their respective Docker container. In addition, each agent includes four components: (i) the OpenConfig YANG model of Cassini transponders, (ii) the connection manager (i.e., the Netopeer2 NETCONF server) that enables connectivity with the controller, (iii) a database to store the configuration files and (iv) a software engine to detect and interact with changes in the configuration files. It is relevant to mention that the last three elements belong to the same subsysem, a Sysrepo framework aimed at automatic creation of YANG-based agent configuration. The Netopeer2 server is built in the Sysrepo framework and it provides the adequate NETCONF-based server for interacting with the SDN client. The Netopeer2-Sysrepo symbiosis provides benefits in the automatic creation of YANG agents but they have a closed and short catalogue of YANG-based optical device models that limits the scalabilty of a SDN DON emulation framework.
# Prediction of Structural Type for City-Scale Seismic Damage Simulation Based on Machine Learning
## 4 Semi-Supervised ML Solution
### Structural Type Prediction for Tongzhou Downtown  
The GIS data of building footprints of Tongzhou downtown are also from the department of urban planning in the local government. According to the GIS data, Tongzhou downtown has 37,463 buildings.  
By the building sampling investigation, the structural types of 3% of the total buildings in Tongzhou downtown were determined. According to the recommended sampling fraction in this study, 1% of the total buildings was used for the training model, while the remaining 2% for assessing the model.  
Figure 26: Seismic damage statistics by (**a**) structure type and (**b**) construction year.  
Figure 25: Distribution of seismic damage in Daxing downtown.  
Using the designed semi-supervised ML solution, four self-trainings were performed, and the optimal training results are shown in Figure 27. As shown in Figure 27, the overall accuracy of the prediction results is 99.5%, and the accuracy of each structure type is beyond 97.9%. Obviously, the trained model exhibits a high prediction accuracy. Besides, the corresponding precisions and recalls are beyond 99.0%, as shown in Figure 27.  
Using the trained model, the structural type of all buildings in Tongzhou downtown were predicted. The ratios of different structural types are shown in Figure 28. It is clear that most buildings in Tongzhou downtown are masonry buildings, which is similar to Daxing downtown. The distribution of structural type in Tongzhou downtown is shown in Figure 29.
# Radiomics in Gynaecological Imaging: A State-of-the-Art Review
## 3 Endomet Cancer
### CT-Based Radiomics  
To the best of our knowledge, only one study explored the performance of texture analysis based on CT imaging for EC assessment. [PERSON] et al. developed a model to predict DMI, cervical stroma invasion (CSI), and LNM by analysing the tumour texture features from 155 preoperative pelvic contrast-enhanced CTs. They found that high tumour entropy independently predicted DMI and CSI with an AUC of 0.71 and 0.67, respectively. Another CT feature (Kurtosis5) correlated with LNM (AUC = 0.69). Furthermore, high tumour kurtosis tended to independently predict reduced recurrence- and progression-free survival [59].
# DOSS Protocol for Data Caching and Sharing Security in a 5G Network
## 4 The Proposed Data Caching and Sharing Security (DCSS) Protocol  
This section presents the proposed Data Caching and Sharing Security (DCSS) Protocol that leverages the [46], security [1] and Service-based Architecture (SBA) [47] of 5G with Federated Identity Management (FldM) and enhanced access controls supporting the provision of network services and slicing [2]. This protocol relies on primary authentication protocols such as 5G-AKA [3] to authenticate the UE on the HN and federated authorization protocols such as [4,32] to authorize the UE access the SP services. The federated security process is controlled by IdP via trust and Public Key Infrastructure (PKI), after this process, the UE is assigned federated ID (\(Fld\)) and SSO. Therefore, the UE will be able to use those credentials during the run of DCSS protocol.  
The proposed 5G DCSS protocol has following advantages:  
* It uses the security context from other service authorization protocols [4] that provides the UE with access to the data such as keys and IDs. Hence encouraging security integration as specified in [2] without compromise the security.
* It provides two options to the UE in terms of how to use the data that is caching and sharing it with other UEs.
* It can be used for caching or sharing or for both.
* It allows the UE delegate its access permission, if it was permitted by the SP.
* The data security is considered by use of subject and object labels that gives the UE and the data capabilities aligned with user's access rights.
## 1 Introduction
Apart from total movement distance, which is used to measure the logistics efficiency, a closeness rating chart is often used in assessing the HFLP to measure non-logistical factors such as safety, cleanness, noise, and so forth [25; 28]. In this paper, we propose a novel multi-objective double-row model for the multi-floor HFLP of a working hospital in Shanghai, China. In this model, both logistical factors (movement distance and path) and non-logistical factors (closeness rating) are taken into account. Additionally, some fixed facilities, the locations and dimensions of which are unchangeable according to the hospital's requirements, are considered. Because the model is a nondeterministic polynomial problem, a novel heuristic has been designed to search for Pareto-optimal solutions. Data from the hospital is used for computational experiments to test the model and the algorithm we present. In conclusion, the main contributions of this paper are as follows:  
1. The paper presents a novel multi-objective double-row model suitable for a multi-floor HFLP which minimizes the movement distance of patients and maximizes the closeness relationships between departments by considering the patient's medical experience and the relationship between various departments of the hospital. Obtained results outperform actual layout in both of the objectives. 2. An NSGA-II (nondominated sorting genetic algorithm II) algorithm with improved local search ability has been developed to solve the multi-objective model, the effectiveness of which is positively evaluated. The remaining part of the paper is organized as follows. In Section 2, we present a detailed review of previous research on HFLPs. In Section 3, the problem under investigation is described in detail. In Section 4, the multi-objective double-row model is presented. The detail of our proposed algorithm is illustrated in Section 5. In Section 6, real data from the hospital is used to examine the effectiveness of the model and the performance of the algorithm that we propose, and the paper is finalized with conclusions and discussions in Section 7.
# Push/Pull Parts Production Policy Optimization in the ATO Environment
## 4 Case Study
Final PUSH/PULL policy definition. In this stage, according to the breakeven optimal values of a** and EPQ**, it is possible to define the optimal PUSH/PULL policy to adopt for each part i. Figure 10 reports the case study values of a** and EPQ** compared with that of the different parts ai and EPQi, with related optimal PUSH/PULL policy to apply. The application of the proposed procedure to the plate parts of the case company moved 29 parts of 425 from Push to Pull policy (about 7%). In this hybrid Push/Pull scenario, compared with that of the previous pure Push policy, the total number of stocked pallets at the warehouse decreases by about 27%, while the total setup time increases by about 3.5%. The results demonstrate that the application of the proposed bi-objective optimization model reduces the stocked pallet. Figure 10: Case study final optimal PUSH/PULL policy definition for different parts. Figure 9: Case study bi-objective optimum point (in red) within Pareto frontier (in blue) compared to Utopia point (in yellow). On the other hand, the increase in setup time is lower than in the parts moved from Push to Pull Policy (3.5% versus 7%). To validate the proposed approach, the case study data are used, comparing the obtained solution toward a milestone MTO/MTS production method: the economic production quantity [34]. The economic production quantity model (EPQ) determines the quantity a company needs produce to minimize the total inventory costs by balancing the inventory holding cost and average fixed ordering cost. The formulation of the EPQ is known and not reported in the paper for the sake of brevity. Moreover, the single-objective solutions \(P_{TOT}\) and \(T_{TOT}\) are reported as well. The single objective solutions aims to minimize only one of the objective functions. Figure 11 shows the two-basic function \(P_{TOT}\) and \(T_{TOT}\) calculated for the two methods. (EPQ versus the proposed bi-objective method). The Scenario proposed by the EPQ model is a dominated solution respect the Pareto Frontier. As consequence, both the objective functions are in this case greater than the optimal solution proposed but the bi-objective model. In fact, the case study is composed by many large parts, with even low demand. As a consequence, the EPQ model has a certain number of items with production batch equal to 1 with a high impact on the setup times. On the other hand, EPQ proposes items with large production batches and high space utilization. As shown in Figure 11 the proposed bi-objective methodology performs better than EPQ for the considered case study. Looking at the single-objective functions, it was reported that solutions lie on the Pareto frontier. There can be solutions with the same values of one objective function but greater values of the other. The proposed bi-objective methodology performs better by considering both objective functions at the same time. As shown in Figure 11, with a small increase in one objective function, it is possible to obtain a large decrease in the other.
# 5G Beyond 3 GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts
## 4 Cross-Border Use Case Implementations
\begin{table}
\begin{tabular}{c c c} \hline \hline
**5G Technology** & **Implementation** & **Deployment** \\ \hline Roaming & Availability of common APIs & Continuity on IP address \\ Multi-radio & Aggregate traffic & Multiple network interfaces \\ Transform across radio technologies & Dedicated infrastructure \\ Multi-SIM & Perform techniques for payload merge & Accurate clock distribution \\  & and payload discard based on timestamps & technique granting common time basis \\  & Apply sync to clock and timestamp signalling & Dedicated infrastructure \\ NR positioning & Local master/slave clocks & Hierarchical clock sync setup \\  & Manage discovery & Edge system to boost discovery \\  & Negotiate handshake \& security & Edge system to enable trust \\ Broadcast \& & Employ compatible UE & Aggregate dedicated system \\ multicast & Remove dependency on feedback messages & to the Base Stations \\  & High-performance bus & High-throughput equipment \\ mmWave comm. & High-capacity storage & High-capacity cache \\  & Employ compatible UE & Short-range deployment \\  & Policies implementation & Policies harmonisation across domains \\  & Interfacing provisioning/virtualization system & Open 3 rd party APIs \\ Edge & Mobility-based session continuity & Listen neighbour activity updates \\ Computing & Geo-paremelling for instance subscription & Geo-pinned deployment \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Implementation and deployment aspects of 5G technologies in cross-border CAM services. perspective as it enables decentralised communication in areas where ad hoc infrastructure is not available yet. Furthermore, the deployment of advanced techniques which boost handover and roaming is imperative to keeping latency low and realising the communication with external systems, as they are onboard while travelling. In regard to applications or services wherein the audience volume is big, common information awareness is required immediately and explicit acknowledge messaging is not suitable, broadcast and multicast communication turns into a primary option. Widely studied techniques to accommodate traffic demands to network assets and to prioritise specific flows can make the difference when traffic with different severity levels is delivered. Here the harmonisation of different operator policies is a complex challenge. Last but not least, capillarity, zero latency and local privacy from edge computing systems will bring environmental understanding to the next level comprising real-time computer vision and network analysis as a perfect example of incoming service and network symbiosis.
## 1 Introduction
Moreover, given the significant advantages of T-PSUs, it is necessary to establish the optimal scheduling model of T-PSUs and explore the integration of both FS-PSUs and T-PSUs in PS to effectively participate in the day-ahead optimal power system scheduling. The main contributions of this paper are threefold:  
(1) The mathematical model of T-PSUs participating in the day-ahead optimization scheduling of a power system is constructed for the first time. On this basis, the hybrid pumped storage model which combines FS-PSUs and T-PSUs is further developed. (2) A set of comprehensive indicators, which are utilized for the flexibility assessment of the pumped storage, are proposed to quantify the wind power curtailment rates, the operational costs of thermal power plants, the depth of thermal power peak regulation, and the levels of thermal power output volatility, thus better satisfying the requirements of environmental protection, economic efficiency, and operational smoothness. (3) Sensitivity analysis conducted from three aspects, which are wind power capacity, load rate, and pumped storage capacity, thoroughly investigates the flexibility of various pumped storage types in day-ahead optimization scheduling. The rest of this paper is organized as follows: In Section 2, the models for various types of pumped storage plants to participate in optimization scheduling are constructed. In Section 3, a day-ahead optimization scheduling model of the wind-thermal-pumped storage bundled transmission system is formulated. In Section 4, a set of comprehensive evaluation indicators are defined. In Section 5, extensive experiments are conducted to validate the flexibility of the T-PSUs model presented in this paper. In Section 6, some important conclusions are drawn.
## 3 Multi-Model Modeling of the CO\({}_{2}\) Capture Process
### Nonlinearity Analysis of the Monoethanolamine-Based Post-Combustion CO\({}_{2}\) Capture Process
Generally when the load of the power plant changes, the mass flow rate of flue gas will change accordingly while the components of the flue gas, that is, the concentration of CO\({}_{2}\), N\({}_{2}\), etc., exhibit minor change [48]. In such a case, to maintain the designed 80% CO\({}_{2}\) capture rate, the mass flow rate of lean MEA change almost proportionally to the mass flow rate of flue gas, as seen in Table 3. Also note that the mass fraction of MEA and H\({}_{2}\)O in the lean solvent remains unchanged owing to the make-up of water and MEA. Therefore, at the working points of different power plant loads and the same CO\({}_{2}\) capture rate, the inlet flow of the absorber not only has almost the same concentrations of the reactants but also the same liquid/gas (L/G) ratio. This indicates that the solutions of model equations around these working points are similar, which results in similar dynamics and weak nonlinearity. On the other hand, at the working points of same power plant load and different CO\({}_{2}\) capture rates, the L/G ratio undergoes a significant change, which has a great influence on the mass balance in the packed column and consequently changes the reaction rate of CO\({}_{2}\). At the working point with a high CO\({}_{2}\) capture rate, increasing the mass flow rate of lean solvent will lead to a smaller increase of the CO\({}_{2}\) capture rate than at the working point with a low CO\({}_{2}\) capture rate, because the low concentration of CO\({}_{2}\) gradually becomes the dominant factor of the reaction rate. Therefore, the process nonlinearity becomes increasingly strong as the CO\({}_{2}\) capture rate grows.