esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive BBU Migration Based on Deep Q-Learning for Cloud Radio Access Network
###### Abstract  
The efficiency of the current cellular network is limited due to the imbalance between resource availability and traffic demand. To overcome these limitations, baseband units (BBUs) are deployed on virtual machines (VMs) to form a virtual pool of BBUs. This setup enables the pooling of hardware resources, reducing the costs associated with building base stations (BSs) and simplifying both management and control. However, extreme levels of server resource use within the pool can increase physical maintenance costs and impact virtual BBU performance. This study introduces an adaptive, threshold-based dynamic migration strategy for virtual BBUs within the iCanCloud framework by setting upper and lower limits on the servers' resource usage in the pool. The proposed method determines whether to initiate a migration by evaluating resource usage on each compute node and identifies the target node for migration if required. This aims to balance server load and cut energy consumption, and also to avoid unnecessary migration because of too high or too low server load, and effectively determine the time to trigger migration and not depend only on a certain instantaneous peak of server resource utilization. This paper used a deep Q-network learning method to predict resource utilization and make an accurate migration decision based on a history dataset. Experimental results show that as compared with Kalman filter prediction and other traditional methods, this model can effectively lower the cost of VM migration by decreasing the migration time and occurrence of it to enhance overall performance while reducing energy consumption.  
CRAN; BBU cloud migration; load balancing; deep Q-network Article
## 2 Power System Model
### Solar Collector System  
The SC system is equipped with the HP which has the function of heating source. The water temperature in the storage tank is adjusted by diluting it with cooling water, which is then provided to the shower users. If the water temperature in the storage tank is lower than 40 \({}^{\circ}\)C in the utilization time, the water in the storage tank is preliminarily heated by the HP so that it does not fall below 40 \({}^{\circ}\)C. In order to calculate the operation time of the HP, thermal model which dynamic characteristic of the water temperature is considered, must be needed in the SC system. In this paper, we utilize a thermal model which the thermal dynamic characteristic including the temperature change is expressed by mathematical model, which have been consolidated by the authors in our previous work [1,3,18].  
The operation time of the HP is decided as follow. At first, the amount of heat collected by the SC is calculated by the thermal model based on insolation forecast data. If the thermal energy supplied from solar radiation is not enough and the water temperature is below the goal temperature, then the operation time which HP should do, is calculated by the thermal model [1,3,18] where the thermal dynamic characteristic such as the heat loss caused by the temperature difference between inside of the tank and outside of it, is considered in detail. The thermal energy supplied by the HP can be obtained by the following equation.  
\[Q_{e}=c\rho v_{w}\beta(T_{e}-T_{h})\qquad\qquad[\text{W}] \tag{3}\]  
where, \(c\) (J/(kg\(\cdot\)K)) is specific heat of water (\(c\) = 4.19 \(\times\) 10\({}^{3}\) J/(kg\(\cdot\)K)), \(\rho\) (kg/m\({}^{3}\)) is density of water (\(\rho\) = 975 kg/m\({}^{3}\)), \(v_{w}\) (m\({}^{3}\)/h) is the quantity of hot water heated by hour, \(\beta\) (Wh/J) is parameter for unit conversion (\(\beta\) = 1/3600 Wh/J), \(T_{e}\) (K) is the goal temperature of the hot water in the storage tank, \(T_{h}\) (K) is the water temperature in the storage tank.  
Regarding specification of the HP, we assumed that the rated power consumption of the HP is 1 kW with on-off type in reference to the literature [19] and the COP is constant value as can be seen in the literature [20] for the simplification of the model. Generally, it is known that the COP of HP changes according to outside temperature and decreases at low temperature in winter season. In this paper, the simulation is conducted in summer season with using data of Okinawa in Japan which the climate is relatively warm throughout one years. Specification of the SC system including the HP, is shown in Table 1[16].  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Solar Heater Collector** & & \\ Heat collection efficiency & \(\eta_{h}\) & 60 \% \\ Heat collection area of one panel & \(A_{c}\) & 1.655 m\({}^{2}\) \\ Number of panels & \(n_{sc}\) & 3 panels \\ \hline
**Heat Pump** & & \\ Rated power consumption & \(P_{RHP}\) & 1 kW \\ COP & \(C_{cop}\) & 4.0 \\ Capacity of water storage tank & \(V_{w}\) & 370 L \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of solar collector (SC) system.
# An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs
## 4 Our Proposal
### Improving the Ant Clustering Algorithm for Sensor Node Clustering
#### 4.2.1 Initialization Step
Each node's property is represented by the Euclidean distance of the sensors, which is defined as follows:  
\[\text{d}(\text{S}_{i}\,\ \text{CH}_{j})=\sqrt{\big{(}\sum_{L=1}^{\text{Natt}} \left(\text{S}_{i}\cdot\text{att}_{L}-\text{CH}_{j}\text{att}_{L}\right)^{2}} \tag{10}\]  
where \(\text{d}\left(\text{S}_{i}\,\ \text{CH}_{j}\right)\) is the distance between the sensor \(\text{S}_{i}\) and the \(\text{CH}_{j}\), \(\text{S}_{i}\)att\({}_{L}\) is the \(L^{\text{th}}\) attribute of sensor \(\text{S}_{i}\), and \(\text{CH}_{j}\text{att}_{L}\) is the \(L^{\text{th}}\) attribute of the \(j^{\text{th}}\) Cluster Head. Node \(\text{S}_{i}\) with a distance to \(\text{CH}_{j}\) less than their communication range \(\text{C}_{\text{range}}(\text{S}_{i}\,\ \text{CH}_{j})\) is assigned to the cluster via \(\ \text{CH}_{j}\). The CN of \(\text{S}_{i}\) is changed to \(\text{CH}_{j}\)'s CN. Alternatively, the \(\text{S}_{i}\) that failed to be assigned to any cluster is placed in a random free grid cell, and \(\text{S}_{i}^{\prime}\text{CN}\) is updated. The main clustering step then begins after the initialization process.
# Finite Element Analysis of Proposed Self-Locking Joint for Modular Steel Structures
## 5 Parametric Study on the Proposed Connection
### Effect of Bolt Pretension  
Bolt pretension variability is considered since it may also be a possible issue in the practice of modular steel construction. Hollow steel section (HSS), where through bolts are used, are liable to inward point load, which can result in distortion of walls under the pretension force of bolt depending on the thickness of HSS. Due to the high flexibility of HSS walls, it is suggested to install bolts by means of snug tightening [35]. Three different pre-tensioning force levels for snug tightening were selected; 10%, 20%, and 30% of the ultimate strength of bolt \(f_{u}\), represented by model Cp6, Cp1, and Cp7, respectively. To study the bolt pretension, other parameters such as the size of the beam and column were kept constant as in Cp1. The initial stiffness and slip almost endured the same, or inelastic range pretension force did not significantly affect the behavior of joint. Bolt pretensions force up to 20% of the ultimate strength of bolt, which showed an increase in the moment carrying capacity of the joint. Further, an increase in pretension force results in decreasing moment capacity, as shown in Figure 10a. A further increase in pretension force caused the decrease in moment carrying capacity of joint. This was because of the local deformation in beams due to applied bolt load.
## 4 Results and Discussions
### Grnn  
Here, five different _spread_ values (0.01, 0.1, 1, 10, 100) were utilized to find the best performance. The training results of these different _spread_ values are shown in Table 1.  
According to the results of Table 1, the best performance was achieved when the _spread_ value was 0.1. Choosing a _spread_ value smaller than 0.1 will not change the training results. Choosing a _spread_ value larger than 0.1 will continuously reduce the success of the training results. As seen in Figures 2 and 6a, the results of the network are very close to the experimental results. The results of the 276 Gy and 320 Gy experiments do not fully match the expected predictions. Figure 6b is a general image showing the GRNN structure. In the 'Pattern layer' layer of the network, neurons are automatically generated--as many as the number of rows of input data (in this study, 3069). The transfer function used in the neurons in the 'Hidden layer' is Radbas(n). The transfer function used in neurons in the 'Output Layer' layer is Purelin(n). The 'Summation layer' is always one more than the number of neurons in the 'Output Layer'. These are the characteristic features of this network. Figure 6c is the general view of GRNN in MATLAB. This figure is automatically produced in MATLAB. The number of input/output data types and the number of neurons in the layers are automatically displayed below the picture.  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & _Spread_ = **0.01** & _Spread_ = **0.1** & _Spread_ = **1** & _Spread_ = **10** & _Spread_ = **100** \\ \hline Training R\({}^{2}\) & 1 & 1 & 0.9992 & 0.9975 & 0.5958 \\ Testing R\({}^{2}\) & 0.9563 & 0.9563 & 0.9558 & 0.9547 & 0.5550 \\ Training RSE & 0 & 0 & 163.48 & 330.99 & 6645.55 \\ Testing RSE & 738.81 & 738.81 & 756.07 & 738.88 & 3897.21 \\ Training MAE & 0 & 0 & 0.0397 & 0.1828 & 2.1079 \\ Testing MAE & 0.2793 & 0.2793 & 0.2854 & 0.2668 & 1.4342 \\ \hline \end{tabular}
\end{table}
Table 1: Performance comparisons of spread values for the GRNN method.  
Figure 5: ANFIS test results and network structure. (**a**) Gaussmf 66 test results. (**b**) Gaussmf 66 network structure in MATLAB. (**c**) ANFIS general representation.  
Figure 6: _Cont._
## 3 Data Enhancement Methods
### WGAN-GP Data Enhancement
\[L_{WGAN}=E_{\widetilde{x}\sim P_{g}}[D(\widetilde{x})]-E_{\widetilde{x}\sim P _{r}}[D(x)] \tag{5}\]  
\[GP=\lambda E_{\widetilde{x}\sim P_{\widetilde{x}}}[(||\
abla_{\widetilde{x}}D (\widetilde{x})||_{2}-1)^{2}] \tag{6}\]  
\[L_{WGAN-GP}=E_{\widetilde{x}\sim P_{g}}[D(\widetilde{x})]-E_{\widetilde{x}\sim P _{r}}[D(x)]+\lambda E_{\widetilde{x}\sim P_{\widetilde{x}}}[(||\
abla_{ \widetilde{x}}D(\widetilde{x})||_{2}-1)^{2}] \tag{7}\]  
With respect to the WGAN-GP data enhancement experiments, the network model and the related hyperparameters of the _WGAN_ discriminator are presented in Table 1, and the network model and the related hyperparameters of the _WGAN_ generator are listed in Table 2. The activation functions of both the discriminator and the generator are Leaky ReLU functions, the same as in the original _GAN_ model. The main purpose of using this function was to prevent the output of the activation function from generating zeros. The sigmoid function was used in the last layer of the generator model because the generated feature values had to be normalized to have the same value range as the real data, which were distributed in the interval [0, 1]. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Discriminator Model**} \\ \hline
**Layer** & **Hyperparameter Values** \\ \hline Input Layer & Shape: 6, 7, 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _WGA-GP_ discriminator model.
# Wearable 256-Element MUX-Based Linear Array Transducer for Monitoring of Deep Abdominal Muscles
## 2 Materials and Methods  
### System Design  
The system design consists of a compromise between contradictory requirements relating to the ultrasound transducer and the electronics. First, a wide field of view is required to ensure easy identification of the muscle structure of interest. The frequency must be in the range of 5 MHz or more, which is a spectral range often used for musculoskeletal (MSK) ultrasound. Typically, frequencies up to 15 MHz are used for imaging of superficial muscles, tendons and joint capsules. However, since the TrA is a deep muscle whose depth below the skin furthermore can strongly vary depending on the presence of an abdominal fat layer, a frequency at the lower end of the range that is typically used in MSK applications was chosen. With a pitch in the range of \(\lambda/2\) and a footprint of \(\sim\)4 cm, such as many clinically used linear arrays, this results in \(>\)200 elements. The electronics needed to drive all array elements should ideally be wearable but, at the same time, provide access to all data types (from pre-beamformed RF channel data to image data) for later processing. A multiplexer is typically used to drive a large element count linear array with a significantly smaller number of TX/RX channels. Since no HV MUX (high voltage multiplexer) with the needed specifications could be identified, we specified a system based on LV MUX integrated into the receive path, inducing the need for different TX-only and RX-only transducer elements.  
To investigate the impact of such a system design with reduced transmit element count (and consequently larger TX pitch), we acquired data from a commercial ultrasound phantom (CIRS multipurpose phantom Model 040 GSSE, Sun Nuclear, Melbourne, FL, USA) in different transmit-receive schemes. First, the full aperture of a 128-element 5 MHz linear array transducer was used in plane wave compound (PWC) mode (Figure 1A). Next, 64 elements (even-numbered ones) were used in transmission, and the odd-numbered ones were used as receive elements in the same PWC scheme (Figure 1B). As can be seen, there is little difference (apart from a slightly reduced contrast) between both images acquired with identical voltage and TGC. Accordingly, a system concept based on 32 native RX/TX channels in the backend electronics with a custom ASIC MUX in the RX path was chosen for the next steps. In this concept, 128 elements are used in TX with 4 elements excited with the same delay (accounting for the 32 available transmit channels) and a second set of 128 elements is used as receive. Here, four TX/RX events are required for the acquisition of signals of all RX elements, with the MUX switching between elements after each event. The corresponding system scheme is given in Figure 2.
## 3 Route Determination for Ship Passage Using Capability Plots
### Other Examples of Determined Routes
#### 3.4.3 Navigation Scenario #3  
The final navigation scenario illustrates a situation where the vessel is within the waypoint grid when determining the passage route. In this case, the vessel does not start from the edge of the waypoint grid, but from the nearest adjacent point, as shown in Figure 17. The environmental conditions at the anchorage have been taken into account when determining the passage route:  
* wind speed: 10 m/s.
* true wind direction: 75\({}^{\circ}\).
* sea current speed: 0.5 m/s.
* true sea current direction: 20\({}^{\circ}\).  
In the above scenario, the transition weights shown in Figure 7 (for a wind speed 10 m/s) should be used to determine the route cost function. Table 12 shows the calculated route cost function.  
The cost of the route function for the first scenario, when the ship travels straight to the destination, is about 598, which is approximately about 12.4% more than for the route determined by the algorithm.  
Figure 17: The ship passage route—true wind direction 75\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Waypoints’ Edge** & **d [m]** & **Course [\({}^{\circ}\)]** & **k [-]** & _d-k_ \\ \hline
34 \textgreater{} 46 & 452 & 45 & 0.1762 & 79.64 \\
46 \textgreater{} 58 & 452 & 45 & 0.1762 & 79.64 \\
58 \textgreater{} 70 & 452 & 45  
The energy consumption analysis for the third case is shown in Tables 13 and 14. As in the previous two scenarios, the start point (A) is the red point on the waypoint grid, while the end point (B) is the green point.  
During the route directly to the destination, energy consumption was 3174.81 [kWh].  
In Scenario 3, the energy consumption for the route determined by the algorithm was 2642.28 kWh, resulting in a 16.7% reduction in energy consumption compared to the direct route.
## 5 Conclusions  
This study analyzed a sector capacity model based on an improved WITI index under the influence of bad weather with the aim of identifying the sector capacity for different types of bad weather. Weather-radar echo charts and the controller's call records were taken as the research objects, and the WITI model was optimized by extracting weather information data from these charts, so that it can quantify the adverse effects of different types of bad weather. By combining a route-availability model and AIRTOP software modeling, the aircraft operations that need to be diverted under bad weather conditions were obtained through simulations. Finally, the DNN algorithm was used to calculate the influence of bad weather and the number of diverted flights, so as to obtain the controller workload in the presence of bad weather, and the sector capacity was calculated according to the controller workload model.  
This research paper has described a theoretical model for calculating the sector capacity in bad weather. Most importantly, the model can be used to evaluate the sector capacity more accurately for different types of bad weather. Providing data support for making flight plans in bad weather will help to improve the punctuality rate of flights and travel efficiency.  
At present, this paper studies the capacity of a single airport within a sector, and the research on severe weather currently focuses on thunderstorms and floating dust. In future work, we will consider airports with different levels of busyness in different geographical locations, such as Shanghai Hongqiao International Airport and Chengdu Shuangliu International Airport, and explore the impact of different bad weather types on sector capacity.  
[PERSON] determines the research direction of the paper and core concepts of the algorithm. [PERSON] provided refinements and performed data acquisition and generation as well as further supplemental programming. [PERSON] was responsible for continuous monitoring of simulation process and elimination of system errors. [PERSON] was responsible for suggestions paper revision. [PERSON] was responsible for text proofreading, finding key problems, and putting forward modification suggestions. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the financial support from the National Natural Science Foundation of China [grant no. 71874081], and Innovation Project from the Air Traffic Management Bureau, Civil Aviation Administration of China [grant no. IPATM202106].  
Not applicable.  
The data sampled from Air Traffic Management Bureau, Civil Aviation Administration of China, used to support the findings of this study, are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# Review of Soft Actuators Controlled with Electrical Stimuli: IPMC, DEAP, and MRE
###### Abstract  
Soft actuators have been developed for a variety of applications, including soft grippers, artificial muscles, wearables, tactile devices, and medical devices. In this review, we will discuss a group of chemical materials and their robotic applications in soft actuators controlled with electrical stimuli. Soft actuators provide a deformable body and allow interaction with the environment to achieve the desired actuation pattern. We will also discuss the principles of operation and functionality and focus on important real-life applications of three groups of soft actuators: ionic polymer-metal composites (IPMCs), dielectric electroactive polymers (DEAPs), and magnetorheological elastomers (MREs). This review article aims to provide researchers interested in the field of soft robotics with a guide to various state-of-the-art chemistry methods used in electrically activated soft actuators, as well as the application areas for such devices.  
soft actuators; ionic polymer metal composite; magnetorheological elastomers; dielectric electroactive polymers +
Footnote †: journal: Applied Sciences
## 3 Finite Element Analyses
### Slot Depth to Width Ratio Analysis  
Rectangular Slot  
The geometry simulated is characterized by wide tooth, thus the widest slot opening guarantees the minimal additional losses will be obtained coherently with the tooth width analysis. The slot depth to slot width ratios chosen were: 1.5, 2, 2.5 and 3. The percentage total power losses reduction between the ratio \(R=3\) and the ratio \(R=1.5\) is very important: 62.67%, from 51.12 W to 19.07 W, Figure 23. Furthermore, a ratio of 1.5 guarantees a low \(\Delta\)Z%: 14.94%, if the narrowest opening is chosen. As well as for the slot opening width analysis, a narrow opening leads to a smaller difference between the impedance.  
Even the current density distribution is enhanced: in fact, even if it remains uneven, especially for high operating frequencies, the average current density value inside the conductors gets closer to the maximal one, see Figure 24. That is assumable from the colour plot as well; the \"best case\" is presented in Figure 25.  
Figure 23: Total losses comparison—different ratios.
# No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
## 1 Introduction
### Related Work
#### 1.1.2 HVS-Based Methods  
In contrast to NSS-based methods, HVS-based approaches try to utilize one or more computational aspects of the HVS to devise an NR-IQA algorithm. For example, [PERSON] et al. [16] employed the free-energy principle [17] and considered the visual quality assessment of an image as an active inference process. Similarly, [PERSON] et al. [18] applied the free-energy principle but it was improved by structural and gradient information. [PERSON] et al. [19] utilized the observation of previous studies [20,21] that the HVS is conformed to extract structural information from images. Thus, the authors introduced a special variant of local binary pattern [22] operators on the luminance map to describe the structural information. In [23], the authors took the same assumption, but first- and second-order image structures were extracted to characterize the structural information of an image. Namely, Prewitt filters [24] were applied for first-order image structure extraction, while local contrast normalization [25] was used for second-order image structure extraction. On the other hand, [PERSON] and [PERSON] [26] built on redundancy and scale invariance properties of natural images [27].