esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
Comparing the results of the numerical simulation combining large-eddy simulation and the FW-H equation for determining the aerodynamic noise of high-rise buildings with the acoustic wind tunnel experimental results, we can make conclusions as follows:  
1. The numerical simulation of the aerodynamic noise of the high-rise building is in good agreement with the localization results for noise sources in an acoustic wind tunnel, and thus, a noise source on the surface of a high-rise building can be located by numerical simulation.
2. Because of limited experimental conditions, wind tunnel experimental results in the low-frequency band are greatly influenced by background noise, resulting in the deviation of sound pressure level spectrum from the simulated results in the low-frequency band.
3. The numerical simulation results are in good agreement with the acoustic wind tunnel experimental results in the middle- and high-frequency bands, which are more significantly influenced by the aerodynamic noise radiated from the surface of the building model.
4. The numerical simulation method can be used to calculate aerodynamic noise intensity at various points on the surfaces of high-rise buildings and reasonably predict and evaluate the influence of wind-induced aerodynamic noise on the environment.  
Figure 20: The wind angle 270\({}^{\circ}\). (**a**) Wind speed 25 m/s; (**b**) Wind speed 30 m/s; (**c**) Wind speed 35 m/s; (**d**) Wind speed 40 m/s.  
(5) The numerical simulation method can reproduce the results of acoustic wind tunnel experiments with high accuracy.  
This paper can provide a theoretical basis for research on the localization of aerodynamic noise radiation sources in the full-scale model and the methods for controlling aerodynamic noise on the surfaces of high-rise buildings. The combination of numerical simulation and acoustic wind tunnel experiments was used for the first time in the study of aerodynamic noise in high-rise buildings. The method of numerical simulation for locating noise sources was first applied to the study of locating noise sources on the surfaces of high-rise buildings. Numerical simulation research on the surface aerodynamic noise of building models can optimize the appearance of high-rise buildings in the architectural design stage and locate the existing high-rise building surface aerodynamic noise sources, allowing for more accurate and effective local noise reduction research processing, saving resources, reducing urban environmental noise pollution, and optimizing the living environment.  
Conceptualization, Z.L. and J.L.; methodology, Z.L.; software, J.L.; validation, Z.L. and J.L.; formal analysis, J.L.; investigation, J.L.; resources, Z.L. and J.L.; data curation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, Z.L.; visualization, J.L.; supervision, Z.L.; project administration, Z.L. and J.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Fast Coherent Video Style Transfer via Flow Errors Reduction
## 3 Methods
### Loss Functions for Temporal Consistency  
#### 3.5.1 Rgb-Level Coherent Loss  
The flickering artefact is actually represented by texture and colour discontinuities in RGB-level regions between consecutive frames, such as disoccluded regions and motion boundaries. Pixel values in these areas change in adjacent frames, and the optimizer [9] or feed-forward network [10] transforms them differently in a particular style as well. To detect these disoccluded regions and motion boundaries, we apply optical flow methods (e.g., Flownet2 [63]) to estimate these flow traceable areas between coherent frames. Let \(f_{s}^{t-1}\) and \(f_{s}^{t}\) denote two adjacent per-frame stylized results, \(\widehat{x}_{out}^{t-1}\) denote the previous output, \(\mathcal{W}(\cdot)\) denote the function to warp image and \(w^{t}\) denote the warped image using the previous output image \(\widehat{x}_{out}^{t-1}\) and the optical flow \(F^{t}\) from \(f_{s}^{t}\) to \(f_{s}^{t-1}\) (backward direction). The warped image \(w^{t}\) is then given by:  
\[w^{t}=\mathcal{W}(\widehat{x}_{out}^{t-1},F^{t}) \tag{5}\]  
In [23; 24], the Coherent Loss function is supposed to preserve the pixel values of the traceable flow regions in the stabilized outputs, and the flow errors in \(w^{t}\) are then rebuilt through the style transfer process. The straightforward two-frame temporal coherency loss considers the consistency between two adjacent frames; thus, the _two-frame RGB-level Coherent Loss_ is denoted as the mean squared error between the generated image \(\widehat{x}^{t}\) and \(w^{t}\):  
\[\mathcal{L}_{two}^{RGB}(\widehat{x}^{t},w^{t},M^{t})=\frac{1}{D}\sum_{i=1}^{ D}M_{i}^{t}\cdot(\widehat{x}_{i}^{t}-w_{i}^{t})^{2} \tag{6}\]  
where \(D=N\times H\times W\) denotes the dimensionality of \(\widehat{x}^{t}\) and \(w^{t}\). \(N\) denotes the number of image channel and \(H\times W\) is height times width, and \(M^{t}\) denotes the per-pixel flow mask with weights of the coherent loss. This \(\mathcal{L}_{two}^{RGB}\) only considers consistency between two adjacent frames, which causes small errors as the proposed initial image utilizes a mask operating on multiple consecutive frames. To further enhance the coherency, we take consistency between more adjacent frames into account. Let us consider a multi-frame coherency between several adjacent frames, and let \(\mathcal{T}\) (same as Section 3.3) denote the set of indices for video frames which are considered as relative frames. For instance, \(\mathcal{T}=\{1,2,3\}\) denotes that processing frame \(\widehat{x}^{t}\) considers coherency between frame \(f_{s}^{t}\) and frame \(f_{s}^{t-2}\), frame \(f_{s}^{t}\) and frame \(f_{s}^{t-3}\). Then the _multi-frame RGB-level Coherent Loss_ is defined as the combination of three two-frame RGB-level coherent loss \(\mathcal{L}_{two}\)s:  
\[\mathcal{L}_{mul}^{RGB}(\widehat{x}^{t},w^{t-\mathcal{T}})=\sum_{i\in \mathcal{T}:i>i}\mathcal{L}_{two}^{RGB}(\widehat{x}^{t},w^{t-i},M^{t-i}) \tag{7}\]
## 2 Related Works
### The Definition of Concept Drift  
Concept drift is an abnormal phenomenon that appears in data flow. Assume that in a supervised learning environment, the data item arriving in the data flow at time \(t\) is {\(\mathbf{x}_{t}\), \(y_{t}\)}, \(\mathbf{x}_{t}\) is the feature vector, \(\mathbf{x}_{t}=\{x_{1},x_{2},\ldots\}\), and \(y_{t}\) is the label of the data item. The concept of data at time \(t\) is defined as \(P_{t}(\mathbf{x}_{t},y_{t})\), and concept drift can be defined as the concept difference between time \(T\) (\(T>t\)) and time \(t\), as shown in Equation (1) [9]:  
\[\exists\mathbf{x}_{t}:P_{t}(\mathbf{x}_{t},y_{t})\
eq P_{T}(\mathbf{x}_{T},y_{t}) \tag{1}\]  
Concept drift is divided into two types: virtual concept drift and real concept drift [10]. Virtual concept drift refers to the change of \(P(\mathbf{x})\), that is, the decision boundary remains unchanged, and the data distribution within the boundary changes; real concept drift refers to the change of \(P(y\,|\,\mathbf{x})\), which is intended to indicate that the decision boundary of the classifier has changed [11]. Figure 1 illustrates the difference between virtual concept drift and real concept drift, where the dotted line is the decision boundary. The difference between virtual concept drift and real concept drift lies in whether the actual decision boundary undergoes a fundamental change. As shown in Figure 1, the decision boundary for virtual concept drift (Figure 1b) and the decision boundary for the original distribution (Figure 1a) are essentially the same. The distinction is that in virtual concept drift, the internal distribution of the data types has changed compared to the original distribution. On the other hand, real concept drift represents a situation where the decision boundary undergoes a fundamental change, as depicted in Figure 1c. The content of this study falls within the category of real concept drift.  
Real concept drift can be divided into four categories: Abrupt, Gradual, Incremental and Recurring according to different change characteristics [12]. Abrupt drift refers to the sudden generation of a new concept that replaces the original concept at a certain time; Gradual drift refers to the fact that data instances with new concepts slowly occupy the overall sample space within a certain period of time; Incremental drift refers to the gradual transformation of old concepts within a certain period of time. It is a new concept; Recurring drift refers to the regular replacement of two concepts in time. The schematic diagram of the four drift types is shown in Figure 2. Abrupt and gradual concept drift, being the most common types, are the focal points of our research.  
Figure 1: The difference between virtual concept drift and real concept drift (The dashed line represents the decision boundary).
## 4 Results
### Assessment of Discharge Simulations Using Multiple SPPs
#### 4.2.1 Scenario I: Discharge Simulations with Rain-Gauge-Calibrated Parameters
Among the three SPP-driven simulations, the 3B42 RT-forced model run produces the discharge-duration curve that has the best approximation to the observations despite a noticeable overestimation of low and moderate streamflow quantiles (<90%) and a considerable underestimation of high flow quantiles (>90%). However, the 3B42V7- and IMERG-F-driven model runs remarkably underestimate moderate- and high-flow quantiles, which, for instance, systematically underestimate the 95% flow quantile by 43.8% and 46.3%, respectively. The reason for this phenomenon is that all SPPs tend to underestimate the occurrence frequency of heavy rain events at the grid scale (Figure 5) and the frequency of the basin-averaged precipitation at the high-quantile levels (\(\geq\)87.2-95.3%) (Figure 7). Moreover, the simulated flood peak flows and peak timing at the Monywa station in four major historical flood events were analyzed to describe how the GXAJ model captures high flows using the rain-gauge-based precipitation data set and three SPPs. As shown in Table 2, the rain-gauge-forced model run overestimates the flood peak flow of Event 20150908 by 13.4%, gives an underestimation of 2.4%-30.1% in other three flood events, and has considerable flood peak time errors (PTE \(\geq\) 24 h). The 3B42V7-driven simulation overestimates the flood peak flows of Events 20140523 and 20150908 (BIAS\({}_{\text{P}}\) = 36.0% and 12.6%, respectively) and produces an underestimation for Events 20150803 and 20160803 (BIAS\({}_{\text{P}}\) = \(-\)17.1% and \(-\)20.1%, respectively). The 3B42V7-and IMERG-F-driven simulation runs consistently underestimate flood peak flows for all events (BIAS\({}_{\text{P}}\) = \(-\)52.3-- 26.1% and \(-\)52.9-- 28.8%, respectively). All SPP-forced model runs have large errors in capturing the peak timing in certain events. For instance, the peak flow of Event 20160803 is simulated to appear ahead of the observed peak by 66-75 h.  
Figure 9: Performance of 3-hourly discharge simulations from 1 April 2014 to 31 December 2016 at the Monywa hydrological station using the rain-gauge-based gridded precipitation data set, 3B42 RT, 3B42V7, and IMERG-F SPPs under two simulation scenarios. Additionally, the Taylor diagrams were plotted to evaluate the overall hydrological performance of 3B42 RT, 3B42V7, and IMERG-F SPPs based on the observed discharge data at the Monywa station under Scenario I. Figure 11a indicates that the rain-gauge-forced model run provides the best approximation to the observed discharge time series. The performance of the 3B42 RT-driven simulation run is
# Optimizing Energy Efficiency in Cloud Data Centers:
## 5 Proposed Reinforcement Learning-Based VM Placement in Cloud Environment Using RF_SOM Classifier
### VM Allocation with Firefly (FA)  
The Firefly algorithm is a metaheuristic optimization algorithm inspired by the flashing behavior of fireflies. In this algorithm, fireflies are considered agents in the search space, and their blinking brightness is the objective function value, as shown in Algorithm 2. Fireflies are attracted to the brighter ones and move towards them in search of better solutions. This movement is governed by the attraction of other fireflies, which depends on their brightness and distance. As the algorithm progresses, the fireflies converge on the optimal solution, displaying swarm intelligence. The Firefly algorithm has been successfully applied to a variety of optimizing problems due to its simplicity, efficiency, and ability to handle complex search spaces. In our Firefly algorithm, we use the optimization equation [28].  
\[f(\text{mapping})=\sum_{i=1}^{N_{\text{PM}_{i}}}\left|\text{load}_{\text{PM}_{ i}}-\text{load}_{\text{average}}\right| \tag{14}\]where \(N_{\text{PM}_{\text{s}}}\) is the total number of PMs, \(\text{load}_{\text{PM}_{i}}\) represents the load of PM\({}_{i}\), and \(\text{load}_{\text{average}}\) is the average load of all PMs.  
```
1:Input: vmList, hostList
2:Output: Allocation of VMs
3:averageLoad\(\leftarrow\)calculateAverageLoad(hostList)
4:for each vm in vmList do
5: Initialize bestHost\(\leftarrow\)null and minLoadDiff\(\leftarrow\)Double.MAX\(\_\)VALUE
6:if vm is sensitive then\(\triangleright\) Test with (RF + SOM) Model
7:for each host in hostList do
8: current Load\(\leftarrow\)host.getLoad()
9: loadDiff\(\leftarrow\)| currentLoad-averageLoad\(\mid\)
10:if loadDiff < minLoadDiff then
11: minLoadDiff\(\leftarrow\)loadDiff
12: bestHost\(\leftarrow\)host
13:endif
14:endfor
15:else
16: vmList.add(vm)
17:continue
18:endif
19:endfor
```  
**Algorithm 2**VM Allocation with Firefly using RF_SOM model
# Recent Advances in Wearable Devices for Non-Invasive Sensing
## 2 Materials and Structural Design
### Materials
#### 2.1.4 Liquid Metal Alloys and Ionic Liquids  
Liquid metal alloy has good conductivity, fluidity, and flexibility [56]. It occupies the geometry of its container in the liquid phase at room temperature, which prevents mechanical mismatch between the material and the human skin [57; 58]. In addition, the liquid metal alloy has exciting functionalities, such as printability, patterning ability, and self-healing [59]. It also can form a 3D interconnect due to the oxide layer that forms spontaneously on its surface [60]. The mechanical and electrical properties of liquid metal can be enhanced significantly by forming a composite with other materials such as metal nanoparticles and CNTs [39]. Eutectic gallium-indium alloy (EGaIn) or a gallium-indium-tin alloy (Galinsta) are two of the most popular liquid metal alloys for wearable devices due to their low viscosity, low toxicity, high conductivity, and high surface tension [40; 61]. [PERSON] et al. developed a stretchable wearable EGaIn-based electrocardiogram (ECG) sensor. The EGaIn provided the sensor with high conductivity and stretchability. The electrical connections were kept stable, even when they were stretched over 100% [62]. The ionic liquid is another type of liquid conductor. Unlike liquid metals, ionic liquids can be spread easily onto many elastomers, either forming a uniform coating [63] or becoming trapped between the textile fibers and forming liquid bridges. For example, short-chain ionic liquid species ([C\({}_{2}\)mim][BF\({}_{4}\)]) formed liquid bridges in small fiber-gaps [64]. The ionic liquid bridge between fibers was used as stretchable conductors and they could respond to the external mechanical deformation. Such liquid alloy conductive fibers can be used in many fields, including flexible electronic products, smart clothing, interconnects, and antennas.
## 5 Bioavailability of PAHs
### Surfactant  
As mentioned in the previous sections, some of PAHs are characterized by their high hydrophobicity as well as low solubility, as they have the ability to be strongly adhere to soil particles and be slowly released into the water phase [135]. Among the different alternatives to overcome the problems of low bioavailability during bioremediation of PAHs is the application of surfactants. The functionality of these additives basically depends on reducing interfacial surface tension, and thus increasing their solubility [30; 33; 34; 136; 137]. The efficiency of these surfactants is influenced by many factors, including surfactant type and concentration, PAH hydrophobicity, temperature, pH, salinity, dissolved organic matter, and microbial community. An imperative and crucial element for effective PAH remediation is the selection of the optimum ratio of mixed surfactants to avoid the inhibition of microbial activities [33; 34; 137]. Nowadays, various groups of surfactants are available, and each one is being used under certain conditions to be compatible with the available environment. Furthermore, biosurfactants that are produced by microorganisms are receiving more favor, as they considered more environmentally friendly [33; 138]. Interestingly, Both Tween-80 and rhamnoid were found to improve the bioremediation fluoranthene [139]; however, it should be considered that the application of surfactants may not always lead to enhanced PAH biodegradation or removal. In fact, if the surfactant is preferentially used as an easier carbon substrate than PAHs for soil microorganisms, it may actually inhibit PAH biodegradation. Selection of surfactant types is therefore crucial for the effectiveness of surfactant-aided bioremediation of PAH-contaminated soils [140].
# A Survey of Vehicular VLC Methodologies
## 5 Vehicular VLC Applications  
Despite VLC vehicles having a limited communication range for transmitting waves, this technology has had comprehensive and advantageous implementation, especially in applications that require long distances to communicate with peers, such as in a platoon setting or within emergency implementations that need light electronic brakes from nearby peers, in addition to some intersection assistance implementations within a small area. On the other hand, vehicles can communicate out of range via VLC over the entire network or along a route; however, such cases require a longer latency so that messages are redirected through a multi-hop connection. As a result, vehicular VLC is not feasible if the application being considered must meet real-time requirements. Heterogeneous technologies with greater communication ranges (such as radio frequency (RF)) can be used to maintain contact for some implementations, especially outside the local area. The scaling needs for an application are implicitly impacted by the scope requirement (the number of vehicles required to connect). Therefore, VLC for vehicles can be used when there are an unlimited number of cars or there is the need to communicate with between vehicles within the line of sight. Regarding the necessity for the group structure, VLC vehicles can deal with other applications using either a short-range discontinuous or the long-range continuous method to complete the communication between cars. In theory, the virtual local coordination of vehicles can provide areas within the needs of limited real-time behavior. Therefore, a high delivery reliability is necessary to meet delivery time limitations.  
However, in actual practice, it will become possible to connect with neighbors directly, especially if the optical channel has cases of interest to the user. Therefore, vehicular VLC should be explored as a supplementary communication channel for applications with strict reliability requirements to improve the system's reliability and resilience. On the other hand, vehicular VLC is the only means of connection for implementations that can withstand network-related delays and accept best-effort connectivity applications for vehicular networks, which can be provided through vehicular VLC links alone or in a system of other heterogeneous networks. It is now possible to select these applications to be realized for employment within the VLC vehicle connection. The following are essential applications of vehicular VLC.
## 3 System Architecture
### Intermediate Fog Layer  
The fog layer functions as a high computing layer to perform various tasks at the edge of the SAVP system, which mainly include: keeping a record of the field devices (e.g., IoT-enabled RFID readers and tags), along with their unique IDs and hashes of IDs; the LIBC module to authentic the AVs at each stage of parking, from the parking entrance to the parking slot to the parking exit, and to continuously perform checks to record information on the parking activities; ensuring coded RFID-tag-data integrity, processing information back to the cloud for further analytics if required; long-term storage; data validation, etc. In short, in the SAVP system, each fog node acts as an intermediate node between the cloud and the edge nodes, or as a local administrator in the local area network (LAN), to manage the overall operations of the centric cloud closer to the edge nodes. Fog nodes are connected to the cloud through the gateways in a wide area network (WAN) via the global system for mobile communications (GSM) or long-term evolution (LTE).
## 3 Shortest Queue Length-Cluster-Based Routing Protocol
### Initial Cluster Creation Process  
This paper designs a satellite self-organizing network clustering process based on the high-rate wireless self-organizing network clustering method from reference [18]. A ground control node is introduced, and satellites send network access requests and their information (mainly position information) to the ground node. The ground node completes the division of cluster structures and the assignment of cluster heads and then sends the cluster affiliation information of each node back respectively.  
The details of the clustering process executed by the ground control node are as follows:  
1. The ground control node receives a network access request from a node;
2. The ground node determines whether there is a cluster head that is close enough to the node. If there is, the node is assigned to the nearest cluster head; if not, the node becomes a new cluster head;
3. After the assignment is completed, the ground node updates the local cluster structure information and ends the node's cluster allocation process.
## 2 Materials and Methods
### Loop-Closure Detection  
Loop-closure detection aims to determine whether the robot has returned to a previously visited localization. Without absolute localization, loop closure cannot be simply judged based on position, as odometry errors accumulate over time. In visual SLAM, loop-closure detection primarily involves three key components: (1) similarity calculation, where the similarity between the current and previous keyframes is computed; (2) candidate frame selection, where keyframes that are likely to have loop closures are selected based on similarity results; and (3) loop verification, which is critical as incorrect loop closures can lead to severe localization errors, typically involving inliers and co-visible frames. We focus on the first two aspects.  
We employ a multi-layer detection approach to ensure both detection speed and accuracy.