esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Scheme of Traffic Overflow under Consideration  
This article considers an overflow system in which calls of different traffic classes are redirected to a system of secondary resources when the primary resources are fully occupied. When all secondary resources are also occupied, calls will be lost. Figure 1 shows a general scheme of the traffic overflow system.  
The primary resources of the system shown in Figure 1 are composed of \(r\) components, where each resource \(s\) has a capacity \(V_{s}\), expressed in allocation units (AUs). The secondary resource has the capacity of the server, namely \(V_{0}\) AUs.  
Figure 1: Scheme of the multi-service overflow system.  
This system is offered traffic streams generated by three types of traffic sources considered in traffic theory, that is, Erlang, Engset, and Pascal sources. Each of these traffic types is related to different call streams, described by the flow intensity (call arrival) and the number of demanded AUs. The assumption in the figure is that a primary resource \(s\) is offered \(m_{s}\) traffic classes from the set \(M_{s}\), where \(m_{s}^{\rm Er}\) classes that belong to the set \(M_{s}^{\rm Er}\) are Erlang, \(m_{s}^{\rm En}\) classes that belong to the set \(M_{s}^{\rm En}\) are Engset, and \(m_{s}^{\rm Pa}\) classes that belong to the set \(M_{s}^{\rm Pa}\) are Pascal traffic classes, with \(m_{s}^{\rm Er}+m_{s}^{\rm En}+m_{s}^{\rm Pa}=m_{s}\), \(M_{s}^{\rm Er}\cup M_{s}^{\rm En}\cup M_{s}^{\rm Pa}=M_{s}\) and \(m=\sum_{s=1}^{r}m_{s}\). The parameters of the input traffic are described by the parameter \(A_{c,s}^{\rm X}(n)\), that is, the average traffic intensity of class \(c\) (\(c\in M_{s}\)) type X (\(X={\rm Er}|{\rm En}|{\rm Pa}\)) offered to the primary resource \(s\) that is in the occupancy state \(n\) AUs, and by the parameter \(t_{c}\), that is, the number of AUs demanded by a call of class \(c\) for service.  
Traffic that cannot be serviced by a given resource \(s\) is offered to shared secondary resources with a capacity \(V_{0}\). This traffic is described by the parameter that determines the volume of demanded AUs in the secondary resources (the parameter \(t_{c}\)) and by the parameters that describe the probabilistic properties of these streams, that is, the parameter \(R_{c,s}^{\rm X}\) that determines the average intensity for traffic of class \(c\) (\(c\in M_{s}\)) that overflows from the primary resource \(s\), with the assumption that traffic of class \(c\) offered to the primary resource \(s\) is of type X (\(X={\rm Er}|{\rm En}|{\rm Pa}\)), and the parameter \(\big{[}\sigma_{c,s}^{2}\big{]}^{X}\) that determines the variance of traffic of class \(c\) of type X that overflows from the resource \(s\) of the system of primary resources.
## 4 Conclusions  
In conclusion, we proposed a dual-beam SV-SL SWS and investigated its application in W-band TWT. The SWS was found to have suitable coupling impedance and gentle dispersion characteristics. The simulated transmission characteristics of the 50-pitch structure with the input/output couplers demonstrated good performance in the 87.5-95 GHz frequency range. The transmission loss was substantially lower than the meander-line SWS with dielectric support or substrate. According to the 3-D PIC simulation, when set to a dual beam with 17.2 kV and 0.3 A in total, the maximum output power can reach 200 W at 90 GHz, corresponding to the maximum gain and efficiency of 32.2 dB and 3.63%, respectively. The all-metal SV-SL SWS consists of the following three parts: the metal sheet in the middle is carved by a laser engraving or wire cutting to form a periodic winding groove; the upper and lower metal shells are manufactured by a CNC milling machine or casting; and they are then fixed by resistance welding or brazing. Compared with the microstrip-like structures, SV-SL SWS not only completely solves the charge accumulation problem caused by the dielectric substrate and the loss caused by the seed layer, but it also has good mechanical properties that can account for both thermal properties and large-scale production. As a result, the suggested SV-SL SWS has significant application potential in the W-band TWT.  
Conceptualization, Y.W. and S.W.; Methodology, Y.W. and Y.D.; Software, Y.W. and J.G.; Validation, S.W. and D.X.; Formal Analysis, Y.W. and J.G.; Investigation, Y.W.; Resources, S.W., Y.Z., Z.L. and Z.W.; Data Curation, Y.W. and Y.D.; Writing--Original Draft Preparation, Y.W.; Writing--Review and Editing, S.W; Visualization, Y.W.; Supervision, S.W.; Project Administration, S.W.; Funding Acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Municipal Government of Quzhou under Grant No. 2022D007, the China Postdoctoral Science Foundation (2022M720664), and the National Natural Science Foundation of China (Grant Nos. 61921002, 61988102, 92163204, 62071087 and 62150052).  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
## 4 Results and Analysis
### Stress Response of Foundation  
Figure 14 shows the stress comparison of measuring line 1 (see Figure 7) crossing the ground fissures at three angles in the natural foundation and composite foundation. When the high-speed railway crosses the ground fissure at one angle, the stress jump phenomenon of the composite foundation is weaker than that of the natural foundation. Taking the mean value of the stable transition zone of the stress change curve as the dynamic influence limit, the influence range of stress of the composite foundation is significantly smaller than that of the natural foundation. With the increase in the intersecting angle, the influence range of the ground fissure in both the natural and composite foundations decreases. Therefore, passing through the ground fissure zone at a large angle can effectively reduce the damage impact caused by ground fissure disasters.  
Figure 12: Horizontal variation curve of acceleration amplitude inside the foundation (\(\theta\)=90\({}^{\circ}\)).  
Figure 13: Attenuation curve of foundation acceleration amplitude with depth (\(\theta\) = 90\({}^{\circ}\)).  
Figure 15 is the horizontal variation curve of the stress inside the foundation (measuring lines 4 and 5; see Figure 7). The stress in the composite foundation is symmetrical along the center line of the subgrade, and the stress at the center line of the high-speed railway is the largest. The stress of the footwall is significantly greater than the hanging wall, and the difference in stress response decreases from the center to both sides. Under the train vibration load, the horizontal influence range of stress of the composite foundation at the ground fissure site is 44 m, and the stress is nearly the same at 22 m from both sides of the high-speed railway centerline.  
Figure 14: Stress amplitude change curve of the structural layer along the foundation at different intersection angles (measuring line 1): (**a**) \(\theta=90^{\circ}\); (**b**) \(\theta=60^{\circ}\); (**c**) \(\theta=30^{\circ}\); (**d**) schematic diagram of the location of the survey line and the cross-ground fissures at different angles.  
Figure 15: Horizontal variation curve of stress inside the foundation (\(\theta=90^{\circ}\)).  
Figure 16 shows the stress attenuation curve of natural and composite foundations (measuring lines 2 and line 3) with the buried depth at the hanging wall and footwall. The stress attenuation amplitude of the composite foundation is greater than that of the natural foundation, which is specifically reflected in the stress attenuation of the hanging wall in the composite being 99.1% and that of the footwall being 98.6%, while the stress attenuation of the hanging wall in the natural foundation is 95.2% and that of the footwall is 93.5%. The stress 8 m below the surface of the composite foundation is hardly affected by the ground fissure, and the stress influence depth of the composite foundation is less than that of the natural foundation.
# Solar Filament Detection Based on an Improved Deep Learning Model
## 3 Hybrid CNN-Transformer Model  
### Cnn  
Existing deep learning-based solar filament detection methods are based on CNN. CNN uses convolution operations to extract features.  
Convolutional operations can be seen as linear functions parameterized by the convolution kernel W. There are also structures such as pooling layers and activation functions that add nonlinearity to the model. By stacking these layers, CNN models can be regarded as nonlinear mappings with a large number of parameters. They map the input image to the result image. The values of parameters are determined through gradient descent progress while training. After training, by inputting images into this model, we can obtain the output result images.  
Due to the limitations of convolution operations, CNN tends to focus more on local details such as textures and edges. It is unable to effectively utilize global information like overall structure.
# Augmented Reality: A Systematic Review of Its Benefits and Challenges in E-learning Contexts
## 4 Results  
### Search Results  
As described in the methodology section, articles for this systematic review were retrieved from the Springer, Science Direct, EBSCO, and Google Scholar databases, which are used extensively to retrieve accurate and suitable scholarly data from published qualitative and quantitative studies. These databases were selected because they complied with the protocol requirements and protocol-specific parameters reported in a variety of academic studies. The searches revealed 303 articles from the databases, of which 44 were duplicates, thereby reducing the number of articles to 259. After reviewing their titles and abstracts, 221 articles were excluded because they did not meet the inclusion criteria regarding publication date, language, or focus of the study, and 10 additional studies were excluded after their full-text reviews for eligibility. The findings reported in those studies did not satisfy the objectives of this study in terms of reporting the benefits and challenges of AR in e-learning, leaving 28 articles available for the analysis, see Figure 1. The researcher proceeded to identify the key benefits and challenges of using AR in e-learning contexts using the 28 articles selected for inclusion in this systematic review. The findings are reported in the ensuing individual sections, and recommendations are provided subsequently. The results of the thematic analysis indicated that the application of AR in academic settings had a variety of benefits and challenges, see Table 2. AR was found to promote kinesthetic (tactile) learning, enhance interactivity, facilitate students' engagement and participation, improve satisfaction with learning and outcomes, and encourage collaborative learning, among other benefits. The major challenges, as reported in these studies, may be categorized into pedagogical, learning, and technological issues. Pedagogical issues encompassed resistance from teachers and their lack of skills, training, and experience in the use of AR technologies. Learning issues, based on the findings, included cognitive overload and health issues. Technical issues included connectivity issues and cumbersome devices. Figure 1: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram.
# Adaptation of Parallel SaaS to Heterogeneous Co-Located Cloud Resources
###### Abstract  
Cloud computing has received increasing attention due to its promise of delivering on-demand, scalable, and virtually unlimited resources. However, heterogeneity or co-location of virtual cloud resources can cause severe degradation of the efficiency of parallel computations because of a priori unknown application-specific performance metrics, load imbalance, and limitations of memory bandwidth. This paper presents the runtime adaptation of parallel discrete element method (DEM) Software as a Service (SaaS) to heterogeneous or co-located resources of the OpenStack cloud. The computational workload is adapted by using weighted repartitioning and runtime measured performance of parallel computations on Docker containers. The high improvement in performance up to 48.7% of the execution time is achieved, applying the runtime adapted repartitioning when the load imbalance is high enough. The low load imbalance leads to the close values of computational load, when small variations in the system load and performance can cause oscillations in subsets of particles. Memory stress tests cause heterogeneity of non-isolated containers, which reduces the performance of memory bandwidth bound DEM SaaS on the co-located resources. The runtime adapted repartitioning handles the constant and periodically variable performance of non-isolated containers and decreases the total execution time of DEM SaaS.  
heterogeneous co-located cloud resources; weighted partitioning; runtime measured performance; Docker containers; discrete element method; memory bandwidth bound applications Article
## 3 Experiments and Result Analysis
### Materials and Methods
#### 3.1.3 Experimental Configuration and Experimental Procedures  
In this study, each model was conducted with the same computer configuration, the same dataset, and the same evaluation metrics, which was to ensure that the experiments were comparable and fair and that the differences in the performance of the different models were attributed to the models themselves rather than to variations in the experimental setup. In this experiment, the computer setups used in this investigation were as follows: GPU: NVIDIA GeForce GTX 1650 Ti, CPU: AMD Ryzen 7 4800H, RAM: 16 GB. Python 3.6 was the experimental environment. The models involved in the experiment all used the same dataset, including a dataset size of 20,000 data points. Dataset attributes were temporal position data (longitude, latitude, and depth) and sailing attitude data (pitch, yaw, and roll), and there was a data ratio of 8:2 between the training set and the test set.  
The experimental steps of each model can be divided into four processes: data preprocessing, model construction, model training, and model prediction. The overall experimental procedure was to first preprocess the data, including removing redundant values and filling null values. In the second step, BiLSTM, DBiLSTM, and AWOA-DBiLSTM models were constructed. The BiLSTM model consists of an input layer, an output layer, a dropout layer, and a Fully Connected (FC) layer, in which the data features are not differentiated, and all data are input to the model in the form of a sliding window. In contrast to the BiLSTM model, DBiLSTM is composed of two BiLSTMs and adds a filter gate and a reset gate between the two layers; the filter gate filters the features that are not related to the position information, and the reset gate resets the filtered physical pose features. The model differentiates the data features; the differentiated physical pose data are input to the first BiLSTM layer for feature extraction, and the position data are input to the second layer, after the physical pose data are feature-extracted, they are input to the second BiLSTM layer through the filter gate and the reset gate to obtain fusion with the position features. AWOA-DBiLSTM uses AWOA to optimize the neural network based on DBiLSTM. The third step used the divided training set input to the model for repeated training until the loss function of the model was not decreasing, as a way to optimize the optimization of the weights and biases of the model. The fourth step was to use the constructed model to make predictions through the test set and visualize the predicted trajectories.
# On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies
## 5 Performance Evaluation
### Power Consumption and Lifetime  
The SES mechanism provides an efficient energy-saving mechanism because the devices are sleeping most of the time, turning on their transceivers during short periods of time for transmitting or receiving video packets. In addition, according to the type of device in an IEEE 802.15.5 network (_i.e.,_ whether the node acts as _mesh coordinator_, _mesh or end device_), it can be intuitively deduced which of them will have a higher power consumption during the _inactive period_ of the _SES reservation-based_ method:  
* _Mesh coordinator_: Most of its power consumption is due to the continuous performance of synchronization tasks [28], which are out of the scope of this paper. Regarding data transmission power consumption, which is the focus of this section, the mesh coordinator will not have to perform data transmission and the only significant consumption is from packet reception during the allocated slots (it changes the radio transceiver state from _sleep_ to _listening_).
* _End device_: This type of device corresponds to the source node that sends packets continuously through the network. Therefore, its power is consumed during the transmission in one slot per SES time structure, that is, one slot for each _wakeup interval_.
* _Mesh device (router)_: Unlike the other two types of nodes, mesh devices require two consecutive slots of the _inactive period_, one for receiving from the previous node and another for transmission to the next node. Consequently, they are the devices that have the highest power consumption.  
On the other hand, all nodes have additional power consumption during the whole _active duration_ since their transceivers are either in the _listening_ state or in the _transmit_ state. Consequently, the _active duration_ affects to a greater extent the power consumption metric than the _inactive duration_, where nodes remain asleep for most of the time (their state is switched only to perform the scheduled transmissions/receptions in the reserved slots).  
Figure 14: Power Consumption metric for the different scenarios.  
It can be observed in all the evaluated scenarios--Figures 14 and 15--how the AO has a considerable impact on power consumption (regardless of the WO, nodes configured with a low AO value consume less energy). It should be noted that both power consumption and lifetime metrics were calculated for node 1, which has a high power consumption as it is a mesh device and it is located close to the destination node where all the paths generated by the TDLS algorithm converge.
## 2 Materials and Methods
### Build a Portable Device  
Solidworks 2016 software (Dassault Systemes, Paris, France) was employed to design the integral structure of the device. The structure schematic and physical diagram of the portable nondestructive detecting device for tea polyphenols in fresh tea leaves is shown in Figure 2, where the spectrometer, plant probe, and Y-type optical fiber are the crucial components of the device. Spectrometer 1 (FieldSpec HandHeld 2, Analytical Spectral Devices Inc., Boulder, CO, USA) had a wavelength range of 325-1075 nm and a spectral resolution of 3 nm. Spectrometer 2 (SW2520, OtO Photonics Inc., Hsinchu City, Taiwan, China) had a wavelength range of 900-1700 nm and a spectral resolution of 15 nm. The plant probe (Analytical Spectral Devices Inc., Boulder, CO, USA) comes with a standard reference panel and tungsten-quartz halogen lamp. The Y-type fiber (SIH400 Y6+1 type fiber, Shenzhen Xinrui Photonics Technology Co., Ltd., Shenzhen, China) had a fiber core diameter of 400 um and a length of 1 m, allowing the transmission of a spectral range of 400 to 2200 nm. To prevent damage to the lithium battery from over-discharge and to enable dual-mode switching of alternating current (AC) and direct current (DC) power supplies, polymer lithium batteries (Genaier 12 V/13000 MAH, Shenzhen Sentech Power Technology Co., Ltd., Shenzhen, China) with a DC uninterruptible power supply (UPS 12 V/5 A, Shenzhen Xin Yuan Jia Industry Co., Ltd., Shenzhen, China) were employed.  
Figure 1: Fresh tea leaves of Jin Guanyin (**a**) and Fuyun 6 (**b**).
# Operationalizing Dyadic Urban Traffic Interaction Studies: From Theory to Practice
## 5 Discussion
### Interaction Studies vs. Experimental Psychology Studies
#### 5.1.2 Statistical and Analytical Challenges  
Given the complexity and interconnected nature of the data collected in traffic interaction studies, simple analytical techniques like ANOVA (Analysis of Variance) commonly used in psychology-style studies may not be sufficient to describe the key insights from an interaction study. These traditional methods often assume independence among variables, an assumption which is frequently violated in interaction studies where multiple factors are inherently interdependent.
# A Virtualization Infrastructure Cost Model for 5G Network Slice Provisioning in a Smart Factory
## 2 Background
### Virtualization Infrastructure
virtualization infrastructure can be centralized in an edge/central cloud, distributed to different locations, or deployed locally inside each factory. The decision to centralize or distribute the virtualization infrastructure affects the cost allocation between respective business actors and is dependent on the use-case requirements and the business model for network slice provisioning. Typically, the data centers involve top-of-rack or access switches, aggregation switches, and core switches. Various deployment architectures such as the two-tier tree, three-tier tree, and fat tree can be suitable for designing the topology of the switches in the data center as well as for different data center sizes [24]. However, in this paper, the scope in terms of switches is limited to the top-of-rack/access switches, and different switch topologies are not evaluated. The top-of-rack switches are assumed to connect the servers in the racks to the rest of the network, and a redundancy of two switches per rack is typically assumed. The servers are the main virtualized resource that host the VMs running the VNFs, thus providing the network, computation, and storage resources required per slice [10. The VNFs can be deployed in the central cloud in the factory, or the UP and CP can be distributed between the factory and the central cloud [22, 23]. The major cost components for the virtualization infrastructure are discussed as cost input for the cost model later in the paper.