esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Intelligent Core Network for Tactile Internet and Future Smart Systems
## 4 Simulation and Results
### Simulation Environment and Experiment Set Up  
There are a number of efficient simulation environments and frameworks that are used to simulate and evaluate the performance and attributes of SDN and MEC based networks. One of the most efficient, reliable, and powerful frameworks is the CloudSim environment and its related extension projects. CloudSim is a Java based simulation framework that enables simulation, experimentation, and modeling for cloud based networks [48]. CloudSimSDN is a Java based simulation framework that is built on top of CloudSim [49]. This framework is developed mainly for simulation purposes of SDN and cloud based systems.  
In CloudSimSDN, the SDNC is programmable and it allows for the testing of VM management policies and workload scheduling algorithms [50]. Furthermore, the centralized controller is responsible for dynamically managing resources in data centers. Based on [49], the authors prove that CloudSimSDN simulator provides much features and facilities with better or at least the same performance as Mininet simulation environment. Also, it gives the ability for the modifications and extensions.  
We consider two simulation cases with the topology illustrated in Figure 5b,c. In the first case, the two hosts belongs two different base stations (eNBs), but the two eNBs are connected to the same Mini-cloud. In the second case, the two hosts are in different cells and the cells are connected to different Mini-cloud units. The round trip delay is considered to be the performance of our system. The simulation process is repeated multi-times, with different bandwidth for each time but with the data size is the same in each case. All important simulation parameters used are illustrated in Table 4.  
Figure 5: **(a)** Evolved Packet Core cellular network; **(b)** First case of the proposed system; **(c)** Second case of the proposed system.
# Preliminary Propulsion and Power System Design of a Tandem-Wing Long-Range eVTOL Aircraft
## 5 Results and Discussion of the Power System Design
### Sensitivity Analysis of the Power Design Methodology  
Figure 14 displays the relation between the capacity of a single cell and the increase in the number of cells due to the division in cells in series and in parallel, with respect to the number of cells purely required for energy storage for that capacity. The saw-like pattern comes from the fact that the number of total cells in parallel has to be a multiple of the number of batteries. When the capacity increases, the energy stored in each cell, and thus the total energy stored, increases. At certain values for the cell capacity, the total number of cells in parallel can be reduced, while still meeting the minimal energy requirements. This can be seen in Figure 14 as a sudden jump downwards. With an increase in capacity comes an increase in amplitude. The total number of cells decreases when the cell capacity increases, and thus, the percentage of increase in the cell count increases.  
Figure 15 shows the relation between the percentage of cells increase and the nominal cell voltage. It can be seen that a small change in nominal voltage can have a major effect on the number of cells. For example, using a nominal cell voltage of 3.71 V instead of 3.70 V turns the change in the number of cells from 1.27% into 20.75%. This is caused by the implementation of the required voltage from a battery pack. This is now exactly 500 V, and the number of cells is chosen to fit this number. Especially when working with low nominal voltages, many spikes are observed, which is not entirely representative of a real-world situation, where: small production differences result in different cell voltages and capacities; and the motors do not exactly require 500 V, but instead have a window of design voltages for which they work as intended. The method can be improved by accounting for ranges for cell voltage, capacity, and the required voltage for the motors. This reduces the peaks in the sawtooth-like shape shown in Figure 15.  
Figure 14: Relation between cell capacity and the increase in the number of cells with respect to the number before taking the configuration into account.
## 3 Materials and Methods
### Description of Simulated Datasets

Table 4: Range of MODTRAN input variables for the _reference_toa_ subset according to Latin Hypercube sampling. MODTRAN fixed geometric variables: solar zenith angle: 30\({}^{\circ}\); viewing zenith angle: 0\({}^{\circ}\). Remaining MODTRAN parameters were set to their default values. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Model Variables** & **Units** & **Minimum** & **Maximum** \\ \hline O3C & O\({}_{3}\) column concentration & [amt-cm] & 0.25 & 0.35 \\ CWV & Column Water Vapour & g\(\cdot\)cm\({}^{-2}\) & 0.4 & 4.5 \\ AOT\({}_{550}\) & Aerosol Optical Thickness at 550 nm & unitless & 0.05 & 0.5 \\ G & Asymmetry parameter & unitless & 0.6 & 1 \\ \(\alpha\) & Angstrom exponent & unitless & 0.05 & 2 \\ SSA & Single Scattering Albedo & unitless & 0.85 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Range of MODTRAN input variables for the _analysis_ dataset according to Latin Hypercube sampling. MODTRAN fixed geometric variables: solar zenith angle: 30\({}^{\circ}\); viewing zenith angle: 0\({}^{\circ}\). Remaining MODTRAN parameters were set to their default values. Both the _reference_toc_ and the _reference_toa_ subsets were convolved by S2 instrument spectral response function (ISRF, \(f_{c}\)) for each spectral channel \(c\) (1 to 13) [42] following Equation (2):  
\[L_{c}=\frac{\int L\cdot f_{c}d\lambda}{\int f_{c}d\lambda} \tag{2}\]  
The third subset (_reference_atm_) aims to represent surface reflectance spectra obtained after a non-perfect atmospheric correction as would be from Sen2 Cor algorithm. Instead of implementing an atmospheric correction process on the _reference_toa_ subset, the _reference_toc_ subset was perturbed in order to reproduce the expected error propagation from the Sen2 Cor algorithm [50]. Accordingly, the _reference_atm_ surface reflectance spectra (\(\rho_{atm}\)) is created from the _reference_toc_ spectra (\(\rho_{toc}\)) following Equation (3):  
\[\rho_{atm}=\rho_{toc}+\varepsilon_{\rho} \tag{3}\]  
where \(\varepsilon_{\rho}\) is the expected wavelength-dependent error from Sen2 Cor shown in Figure 4.  
Finally, the third dataset (further referred to as _retrieval_) is used to train the retrieval algorithms for each of the biophysical variables (see Section 3.5). The _retrieval_ dataset consists of two subsets of surface reflectance (_retrieval_toc_) and TOA radiance (_retrieval_toa_), both generated with the same process as for the construction of the _reference_ dataset. Regarding the _retrieval_toc_ subset, this is constructed from the remaining 50% samples from the _analysis_ dataset that were not used in the _reference_toc_ subset. The TOA radiance subset uses a new set of 5000 MODTRAN simulations with the same input variables as in Table 4 but only using MODTRAN's rural aerosol type. In this way, the retrieval of biophysical variables from TOA will carry along errors due to uncertainties in aerosol optical properties.
###### Abstract
It is well known that an effective disaster management cycle is composed of four main phases [86; 87; 88; 89]: mitigation and preparedness, which occur before a disaster, and response and recovery, which happen after the occurrence of a disaster. The availability of spatially distributed data concerning the potential conditions of risk and the knowledge of the technologies used to recover these data is strategic for making societies more resilient to natural, anthropogenic, and technological disasters. In this framework, the need to collect, process, and interpret large amounts of data to obtain synthetic but comprehensive indicators of the state of a society that is subject to multi-hazard risks is crucial [90; 91]. Spatial Data Infrastructures (SDI) [92; 93] have been established to overcome some of the shortcomings related to a specific data source, improve data sharing, and develop technologies and standards used by disaster management officers. EO technologies play a significant role in all disaster phases, especially in the mitigation and preparedness ones, by guaranteeing the development of extended investigations on the state of the observed scenes, searching for precursor signals of potential disasters, and forecasting future evolutions of natural phenomena, and for identifying critical conditions. Constellations of satellite sensors working from microwave to optical wavelengths are systematically used to monitor changes in the Earth system and obtain valuable information about the critical needs. The interest in the analysis of Earth's environment through EO systems is also testified by the increased number of publications over the last 25 years which address the use of EO methods for disaster risk management, especially for coastal regions. This paper addresses current state-of-the-art RS technologies that can be exploited to detect changes in coastal environments and study their interactions with sea surface characteristics and global ocean circulations and dynamics. Particular emphasis is on investigations carried out over the most extended river deltas worldwide. Although land areas of river deltas only comprise 5% of the global area, they are intensively developed and densely populated areas; river delta regions are where thirteen of the world's biggest cities are found. The broad investigations performed by multiple studies demonstrate that river delta regions are seriously affected by SLR and other natural disasters, justifying the need for extended analyses of natural and human-made events pertinent to coastal areas [94].
# An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach
## 2 Related Research
### New Customer Churn Model Variables  
Next, we show how our customer churn variables have been chosen. These data and parameters are presented here as gathered from three sources, sequentially: literature review, questionnaire and interviews with the telecom company experts (Table 2) and the customer satisfaction rate obtained from customer tweet mining. We can further divide the variables into two types: independent variables (predictors), which are all the variables collected as inputs for the prediction model, and dependent variable, which represents the model outcome of the churn status variable. This section explains in detail where and why we use these specific variables based on literature. Using customer demographics (age and gender) as churn predictors in the churn prediction model is common in the literature [6; 7; 25; 29; 30; 31; 32; 33; 34; 35; 36] found that young people below forty-five years of age are more likely to churn. The similar results were found by [33; 36]: customers between forty-five and forty-eight years old are more likely to churn. Many researchers have studied the impact of a family or a friend leaving the same telecom company on a customer's churn decision [8; 33]. That is because of the increase in call price between two customers with different voice provider. Consistent with this result, reference [37] showed that a customer is more likely to churn if they have a social relationship with another customer, who intends to, or has already churned from the telecom company. This finding denotes that a company is at risk of churning if a customer's relationship leaves the company. Moreover, references [38; 39; 35], used calling behaviour and network interaction (call length and number of calls) as churn predictors. Some studies have realised the impact of social network information on churn prediction. For instance, reference [8] predicted customer churn by using customer information and their social network information. Their dataset was from the Pokec social network ([[http://snap.stanford.edu/data/soc-pokec.html](http://snap.stanford.edu/data/soc-pokec.html)]([http://snap.stanford.edu/data/soc-pokec.html](http://snap.stanford.edu/data/soc-pokec.html)), accessed on 23 June 2021) and the call details of customers issued from the network over an interval of six months. They found that combining social network information with call log details improved the churn prediction. The same results were obtained by [40] who studied the impact of the social network on the prediction of customer churn.
# Balancing Distributed Key-Value Stores with Efficient In-Network Redirecting
## Motivation
### Existing Solutions for Skew Mitigation  
Figure 1b,c depicts two widely used approaches to skew mitigation: caching and replication.  
**Caching:** The basic idea of cache-based approaches involves the use of a dedicated cache server to absorb hot item queries. In Figure 1b, all queries for hot item \(Z\) are processed by the dedicated cache. Since a small set of hot items are responsible for most queries, the skew-filtered queries should be balanced for back-end servers. These caching approaches suffer from two key limitations. First, these techniques usually target traditional flash-based or disk-based KVSs, since they are much slower than in-memory caches. Thus, a dedicated node with an in-memory cache is sufficient to keep up with the hot item load. However, this method does not work for in-memory KVS, as there is little difference in performance between the back-end servers and the cache node. Secondly, caching approaches are not viable at larger scales. In these situations, the load of hot items increases significantly, such that the caching method is fundamentally limited by the ability of the cache server.  
**Replication:** As shown in Figure 1c, it is natural to copy hot items to multiple servers so as to achieve load balance. Accordingly, \(Z\) is replicated to another two servers so that the queries for it can be dispersed across these servers. Existing replication approaches typically rely on using SLBs to realize the redirect process for hot item queries [1; 12]. However, lots of SLBs are required due to its low processing rate; updating replica information for larger numbers of SLBs is also complex. In order to simplify the updating process, Alibaba's KVS chooses to replicate all hot items to a hot zone on all servers. The price of this is the production of too many replicas in a cluster, increasing server memory usage significantly.
## 3 Design Optimization
### Multi-Objective Function, Constraints, and Design Variables  
The multi-objective function should minimize the demagnetization rate and maximize the average torque as follows:  
\[Minimize\ F_{obj}=\frac{Demag\_ratio^{w1}}{T_{ave}^{w2}} \tag{3}\]  
where \(F_{obj}\) is the multi-objective function, \(T_{ave}\) is the average value of the torque, and \(w1\) and \(w2\) are the weight coefficients. The efficiency and torque ripple were selected as constraints. The efficiency was set to 91.55% or more based on the value of the initial model, and the torque ripple was set to less than 10%, which is the level allowed in the traction motor for electric vehicles [20]. The design variables to satisfy the multi-objective function and constraints are shown in Figure 1, and the range of the design variables is shown in Table 2[8]. The size of the permanent magnet has a great influence on the demagnetization properties. The larger the size of the permanent magnet, the better the demagnetization property, but the material cost will also increase. Therefore, the size of the permanent magnet is not generally considered as an optimal design variable.
###### Abstract  
The ability to recognize learning emotions facilitates the timely detection of students' difficulties during the learning process, supports teachers in modifying instructional strategies, and allows for personalized student assistance. The detection of learning emotions through the capture of convenient, non-intrusive signals such as photoplethysmography (PPG) and video offers good practicality; however, it presents new challenges. Firstly, PPG-based emotion recognition is susceptible to external factors like movement and lighting conditions, leading to signal quality degradation and recognition accuracy issues. Secondly, video-based emotion recognition algorithms may witness a reduction in accuracy within spontaneous scenes due to variations, occlusions, and uneven lighting conditions, etc. Therefore, on the one hand, it is necessary to improve the performance of the two recognition methods mentioned above; on the other hand, using the complementary advantages of the two methods through multimodal fusion needs to be considered. To address these concerns, our work mainly includes the following: (i) the development of a temporal convolutional network model incorporating channel attention to overcome PPG-based emotion recognition challenges; (ii) the introduction of a network model that integrates multi-scale spatiotemporal features to address the challenges of emotion recognition in spontaneous environmental videos; (iii) an exploration of a dual-mode fusion approach, along with an improvement of the model-level fusion scheme within a parallel connection attention aggregation network. Experimental comparisons demonstrate the efficacy of the proposed methods, particularly the bimodal fusion, which substantially enhances the accuracy of learning emotion recognition, reaching 95.75%.  
P +
Footnote †: journal: applied sciences  
Article  
A Learning Emotion Recognition Model Based on Feature Fusion of Photoplethysmography and Video Signal
## 4 Experimental Results and Analysis
### Influences of Wet Air Temperature at Dehumidifier Inlet on Experimental Results
#### 4.1.1 Influence of Wet Air Temperatures at Inlet on Outlet of Dehumidifier  
When the volume flow \(V\) of the hot and wet air at the inlet of the dehumidifier is 5.0 m\({}^{3}\)/h and the temperature \(T_{\text{in}}\) is 333.15, 338.15 and 343.15 K, respectively, the variation relationships of the air temperature \(T_{\text{out}}\) at the outlet of the dehumidifier with the time are shown in Figure 7.  
At the initial stage, when the hot and humid air enters the dehumidifier, \(T_{\text{out}}\) increases rapidly with the increase of time, and then \(T_{\text{out}}\) tends to be stable; that is, the heat exchange between the hot and humid air and the PCM tends to be stable throughout the heat pipe. When \(T_{\text{in}}\) = 333.15, 338.15 and 343.15 K, the temperatures when \(T_{\text{out}}\) reaches stability are 327.25, 334.85 and 340.15 K, respectively, and the times \(t_{\text{s}}\) required are 50, 16 and 10 min, respectively. At this time, the temperature difference \(\Delta T\) between \(T_{\text{in}}\) and \(T_{\text{out}}\) decreases to 279.05, 276.45 and 276.15 K, respectively. The reason is that the higher the \(T_{\text{in}}\), the greater the temperature difference between the inlet hot and wet air and PCM, and the stronger the driving force of the heat transfer; At the same time, the higher the \(T_{\text{in}}\), the greater the moisture content and total enthalpy of the wet air, and the moisture content and enthalpy change to a high power with the temperature. For PCM of the same quality, the time \(t_{\text{s}}\) required to achieve a stable heat exchange is shorter, the \(T_{\text{out}}\) is higher, and then, \(\Delta T\) decreases.  
1.2 Effect of Wet Air Temperature at Dehumidifier Inlet on Temperature Field of Phase Change Heat Storage  
In the experiments, when the hot and wet air enters the dehumidifier, its temperature is \(T_{\text{in}}\) = 333.15, 338.15 and 343.15 K, respectively, and the melting states of paraffin on the upper surface of the phase change heat storage photographed at different times are shown in Figure 8. In the early period of the heat exchange, the paraffin exists as a solid; the paraffin around the heat pipes absorbs heat and melts, and the main way for heat exchange between the heat pipes and paraffin is heat conduction. With the increase of the heat transfer time, the liquid paraffin gradually increases. Due to the density of the  
Figure 7: Variations of air temperatures at the outlet of dehumidifier with time at different inlet wet air temperatures.  
liquid being lower than that of the solid, the liquid begins to flow upward. At this period, the main way for heat transfer in the melting zone is natural convection. The higher the temperature of the hot and wet air entering the dehumidifier, the faster the paraffin melts.
## 5 Computational Experiments
### Experiment Results
#### 5.2.1 Effectiveness Validation under Small-Batch Instances  
A total of 48 configurations, each with 30 instances, are solved using the proposed heuristic \(LL\) and algorithm \(SPFH\). Each configuration has \(S\times T\times FillRate\) containers, where \(S\) is the number of stacks of the bay, \(T\) is the maximum number of tiers in a stack, and the FillRate is the occupancy rate of the slots in the bay. In this experiment, the value of \(S\) varies between 5 and 10, and the \(T\) varies between 3 and 6. The FillRate is divided into two cases: 50% and 67%. The batch size of each operation round in each instance is random and varies from \(B=1,\ldots,4\). (1) Comparison of complete time. Tables 1 and 2 summarize the experiment's results as follows. The rows labeled as \"C\" indicate the container number of each configuration, and the rows labeled as \"Solved\" indicates that the number of instances solved optimally is provided in the form \(x\)/30. We can find that all 30 instances for each configuration are solved by \(SPFH\) and \(LL\) in very short time, respectively. The \"MaxTime (s)\" and \"AvgTime (s)\" in the two tables refer to the runtime of the most time-consuming instance of each configuration and the average runtime of 30 instances of each configuration, respectively. As the tiers and the fill rates grow, given instances need longer solution time. Most important, the worst case in Table 1 is that an instance with configuration (five stacks and three layers) and with 67% fill rate has four target containers, and it takes about 0.134 s to solve. For these problem sizes, the solution time of most instances is less than 0.02 s. Since many ports today have a maximum tier requirement of four and need fast solutions, \(SPFH\) and \(LL\) could be used in practices in small-batch cases.