esa-sceva/satcom-chunk-collection huggingface dataset:
# Theoretical Optimization of Trapped-Bubble-Based Acoustic Metamaterial Performance
## 4 Conclusions  
We have developed a theoretical model to predict the resonant behavior of bubble-based metamaterials. The dispersion relations for the bubble array in bulk and in a vicinity of one and  
Figure 10: Normalized frequency as a function of \(p\) (distance between bubble centers) for metascreen (**a**) w/1 interface and w/reflector, “positive” branch, \(kh\ll 1\), \(\delta\ll 1\); (**b**) w/1 interface and w/reflector, “negative” branch, \(kh\ll 1\), \(\delta\ll 1\); (**c**) w/1 interface and w/reflector, “positive” branch, \(kh\ll 1\); (**d**) w/1 interface and w/reflector, “negative” branch, \(kh\ll 1\). For all plots \(q=1\).  
Figure 9: Normalized frequency as a function of \(q\) (distance between bubble center and interface) for metascreen (**a**) w/2 interfaces and w/o reflector, “positive” branch, \(kh\ll 1\); (**b**) w/2 interfaces and w/o reflector, “negative” branch, \(kh\ll 1\); (**c**) w/2 interfaces and w/reflector (\(r_{0}=0.5\)), “positive” branch, \(kh\ll 1\); (**d**) w/2 interfaces and w/reflector (\(r_{0}=0.5\)), “negative” branch, \(kh\ll 1\).  
two interfaces of acoustic impedance abrupt change were derived. Different degrees of accuracy of the solution were investigated, accounting for dissipation and neglecting it. Multiple bandgaps are predicted for all the solutions. Moreover, for the solutions in bulk and without interface as well as for \"negative\" branches of the solution (1 and 2 interfaces, with and without reflector) the switching between bandgaps is predicted. For \"positive\" branches of the solutions bandgaps are predicted to be stable. The system behavior can be adjusted with both geometrical parameters and reflector properties. In particular, an increase of the distance between bubble centers (decoupling) results in either non-monotonous behavior of system resonant frequency (\"negative\" branch) or its monotonic increase up to saturation/increase up to peak followed by decay to constant value (\"positive\" branch). In contrast, an increase in amplitude reflection coefficient results in a monotonic decrease of the system resonant frequency. These results clearly demonstrate that various regimes of metamaterial operation can be achieved via fine tuning of resonant frequency of the system, adjusting its geometry and reflector properties.  
The following is worth noting. While here we report theoretical developments, the general approach employed to describe resonant behavior of bubble array structures has been experimentally verified in previous studies [13; 22].  
Conceptualization, D.G.; methodology, D.G.; software, D.G.; validation, R.P.; formal analysis, D.G.; investigation, D.G.; resources, R.P.; writing--original draft preparation, D.G.; writing--review and editing, R.P.; funding acquisition, R.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Argonne National Laboratory through grant #ANL 4J-303061-0030A to [PERSON] and titled \"Multiscale modeling of complex flows\".  
The authors declare no conflict of interest.
# Crowdsourcing Framework for QoE-Aware SD-WAN +
## 4 Proposed Framework
### Use of the Proposed Framework for Performance Baseline  
In addition to a real-time response to QoE feedback, this framework can be used for planning and performance analysis of future streaming. In such a scenario, the video stream is saved under different protocols and network conditions to measure QoE via user participation. These saved videos are used to construct a paired comparison stimulus on any chosen crowdsourcing platform in a campaign to be advertised to users asking for participation. Such campaigns include instructions of requirements for participation, consent forms, and terms of any designed stimuli. They include processed videos, and paired comparison stimuli are constructed accordingly. Figure 5 illustrates the crowdsourcing pair comparison model design.  
Paired comparisons can be seen as an alternative for the MOS-based method, due to its simplicity, where the need to decide between five different ratings is eliminated, making it more intuitive judgment. The paired comparison technique makes it easier for users to express their opinions, make decisions, and experience interaction when multiple factors are applied.  
In order to build the performance benchmark, participants are hired to rate videos in a paired comparison stimulus with different streaming protocols to provide feedback on which video has the higher QoE. In any crowdsourcing model design, there must be a method to identify reliable trustworthy participants since experiments are not controlled and lack monitoring that traditional controlled lab environments provide. To verify the reliability of the participant, trap questions must be introduced. Participants are presented with a golden question, where a stimulus is constructed between the original video with  
Figure 5: QoE crowdsourcing pair video comparison model design.  
no applied processing as a reference and an event degradation processed video. If the participant does not rate the original video with a higher QoE, then the participant is considered unreliable, and their results are excluded. A campaign is run over a period of time depending on the number of participants required to conduct the study. Rating scores can be computed at any point during the campaign to show trends and results. Different methods can be used to compute these scores such as the Crowd Bradley-Terry Model [28].
# SAR Image Aircraft Target Recognition Based on Improved YOLOv5
## 4 Experiments and Analysis
### Ablation Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**NO.** & **MFAF** & **SIOU** & **SE-Ghost** & **CA** & \(mAP_{50}\) & \(mAP_{75}\) & \(mAP_{50-95}\) & **PARAM** & **GFLOPs** & **FPS** \\ \hline
1 & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 0.873 & 0.735 & 0.632 & 7.0M & 16.0 & 116 \\ \hline
2 & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 0.912 & 0.813 & 0.733 & 11.5M & 16.0 & 111 \\ \hline
3 & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & 0.881 & 0.747 & 0.651 & 7.0M & 16.0 & 127 \\ \hline
4 & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 0.926 & 0.843 & 0.759 & 11.5M & 16.0 & 118 \\ \hline
5 & \(\times\) & \(\times\) & \(\surd\) & \(\times\) & 0.870 & 0.730 & 0.628 & 4.1M & 12.1 & 118 \\ \hline
6 & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & 0.922 & 0.840 & 0.753 & 8.4M & 12.1 & 119 \\ \hline
7 & \(\times\) & \(\times\) & \(\times\) & \(\surd\) & 0.885 & 0.756 & 0.652 & 7.5M & 17.6 & 116 \\ \hline
8 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 0.931 & 0.856 & 0.772 & 8.8M & 14.2 & 118 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of the ablation experiments.
# GUI for Analysis of Parameters, Accurate Design and Optimization of Microstrip Filters
## 2 Mathematical Preamble to Microstrip Filters
### Scattering Plot  
The scattering parameters (S-parameters) describe how signals interact with a network, and they are essential for characterizing the performance of RF filters, antennas, and other components. The two most important scattering parameters are \(S_{21}\), which measures the amount of power transmitted through the filter from the input port to the output port relative to the incident power, and \(S_{11}\), which measures the amount of power reflected back from the input port of the filter relative to the incident power.  
An approach of the scattering parameters could be obtained using the linear simulation, based on the \(ABCD\) matrices. In this approach, each microstrip line of width \(W_{i}\) and length \(l_{i}\) (see Figure 4a) is represented as a transmission line with characteristic impedance \(Z_{ci}\) and propagation constant \(\beta_{i}(f)\), see Figure 4b,c. A more accurate simulation result could be obtained considering the model of stepped impedance between every two lines [24], however, the linear approach gives good results.  
The \(ABCD\) matrix for the subnetwork line \(i\) is computed with  
\[[ABCD]_{i}(f)=\begin{bmatrix}\cos[\beta_{i}(f)l_{i}]&jZ_{ci}\sin[\beta_{i}(f) l_{i}]\\ jZ_{ci}^{-1}\sin[\beta_{i}(f)l_{i}]&\cos[\beta_{i}(f)l_{i}]\end{bmatrix}, \tag{14}\]  
where \(\beta_{i}(f)\) is expressed in Equation (4), and the frequency \(f\) is the independent variable.  
The \(ABCD\) matrix of the whole filter, at a frequency \(f\) is computed by  
\[[ABCD](f)=\prod_{i=1}^{n}[ABCD]_{i}(f). \tag{15}\]The simulation of a filter is achieved by sweeping the frequency from 0 to any value greater than \(f_{c}\).  
The parameters of the reflection coefficient, \(S_{11}\), and the transmission coefficient, \(S_{21}\), are obtained from the \(ABCD\) matrix entries with the following expressions:  
\[S_{11} = \frac{A+B/Z_{0}-CZ_{0}-D}{A+B/Z_{0}+CZ_{0}+D}, \tag{16}\] \[S_{21} = \frac{2}{A+B/Z_{0}+CZ_{0}+D}. \tag{17}\]  
The amplitudes of the \(S\) parameters are often defined as:  
\[20\log\mid S_{mn}\mid\ \mathrm{dB},\ \ \ \ m,n=1,2.\]  
One important parameter in filter characterization is the insertion loss between ports defined as  
\[L_{A}=-20\log\mid S_{21}\mid\ \mathrm{dB}. \tag{18}\]
## 2 Materials and Methods
### Adversarial Content-Noise Complementary Learning (ACNCL)
* The pair \(\left\{I_{\text{degraded}}\,,I_{\text{noise}}\right\}\) is used to train the noise predictor \(p_{n}\), which learns to isolate noise patterns. * This training approach enables the network to learn complementary aspects of noise and content synergistically. The process in Figure 1 shows a GAN-based approach integrated with Content and Noise Complementary Learning (ACNCL) for MRI and CT image denoising and tumor detection. The process begins with degraded medical images, which may contain different types of noise. The Noise Identifier (ACNCL-Part 1) first analyzes these images to classify the content and noise types present. Noisy images are then passed through a GAN Generator, which removes noise while reconstructing useful image data. Meanwhile, raw images without noise are also fed into the system for comparison and validation. The GAN Discriminator evaluates the effectiveness of noise removal by distinguishing between the denoised and real clean images. Various CNN architectures like U-Net, DnCNN, DenseNet, DWT, and CA-AGF are then applied to enhance the denoised images, ensuring that important structures are preserved while refining the image quality. After enhancement, the ACNCL-Part 2 predictor is used to detect potential tumor regions in the clean images. The tumor detection results undergo further refinement using PatchGAN, which enforces structural preservation of the tumor features. The processed images are then compared with a medical database to verify and confirm the presence of tumors based on similar cases. This image learning and content comparison phase helps improve detection accuracy. Finally, the model outputs highlighted tumor regions along with an analysis of their severity, providing valuable diagnostic insights. The final results present tumor-marked MRI/CT images, ready for medical evaluation and treatment planning.
# Recent Progress on Graphene-Functionalized Metasurfaces for Tunable Phase and Polarization Control
## 5 Discussion and Conclusions
Although most of the reviewed studies stay in the theoretical designs and numerical validations, we have noticed that active wavefrontshaping, such as anomalous reflection [52] and focusing [53], and electrical tuning of the polarization states [99; 100] have been proven by experiments in the past two years. We believe more and more active devices based on graphene will become feasible with the advances of the fabrication technique in the near future, to enrich the optical components and to address technical challenges for the THz and mid-IR technology. Writing, original draft preparation, J.C.; writing, review and editing, J.C., F.F., and S.C.; supervision, S.C. This research was funded by the National Natural Science Foundation of China No. 61805123 and No. 61831012, the State's Key Project of Research and Development Plan No. 2017 YFA0701000 and No. 2016 YFC0101002, and the Fundamental Research Funds for the Central Universities, Natural Science Foundation of Tianjin No. 18 JCQNJC02200. The authors declare no conflict of interest.
# Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing
## 4 Performance Evaluation
### Experimental Scenarios  
To generate the traffic data, we use the SUMO ([[https://www.eclipse.org/sumo/](https://www.eclipse.org/sumo/)]([https://www.eclipse.org/sumo/](https://www.eclipse.org/sumo/)) (accessed on 5 May 2021)), a well-known microscopic road traffic simulator [15; 34; 35]. As shown in Figure 3, the traffic scenario is a realistic road network at Lotte World, Seoul, South Korea, including 12 signalized intersections. It is obtained and converted from OpenStreetMap ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)) (accessed on 5 May 2021)) at the coordinates of \((37.5112,127.1165)\). A two-phase 90-s regulates the traffic light. Each intersection includes its RSU, and the connected vehicles can communicate and exchange traffic information to the RSU. The proposed method is implemented in the RSU to detect the incident at its coverage area.  
The vehicles are randomly generated by the randomTrips tool of SUMO with an arrival rate. As default, a binomial distribution is used to randomize the arrival rate, which is the number of vehicles generating per time unit. Three traffic volume scenarios are varying by the arrival rates. The number of vehicles generated is 500, 700, and 1000 vehicles, corresponding to low, medium, and high traffic volume scenarios. To simulate an incident, we stop some vehicles on a lane for a specific duration [35], i.e., 300 s (5 min). The simulation  
Figure 2: Example of the dynamic graph.  
time is 3600 s. The main parameters in the simulator are shown in Table 1. The generated data includes features, such as mean waiting time, mean speed, and the number of vehicles in the form of time series data. An example of generated time series with the anomaly is illustrated in Figure 4. In this generated data, traffic volume is medium, a traffic incident happens at the timestamp of 1000 at intersection _I9_ in the examined road network, and the incident duration is 300 s.  
Figure 4: An example of generated time series with anomaly.  
Figure 3: A realistic road network scenario.
## 4 Discussion
### Sources of Uncertainty  
The keys to the success of data-driven models based on machine learning algorithms when upscaling the relationships between the ancillary variables and GPP from site level to global scale are (1) sufficient sample size for training the model, (2) predictors matching the spatial scale of the target variable at the site scale, and (3) gridded inputs with the same spatial and temporal resolution with global coverage. Although the method we used in this study achieved reasonable performance while employing the fewest explanatory variables possible, some aspects still need further improvements.  
The original spatial resolution of gridded datasets, including VIs, PFT, plant traits, and climate data, is inconsistent, and resampling introduces uncertainties in global GPP estimates. It is widely assumed that the smaller the pixel corresponding to the location of the flux tower, the more reliable the pixel value is, i.e., the more representative of the environmental conditions around the tower. Limited by the spatial resolution of remote sensing and reanalysis data, the predictors cannot match EC towers' GPP estimates well at the ecosystem scale. The spatial distribution of the flux sites is not even and is notably sparse for some areas and vegetation types (e.g., equatorial regions and EBF). In order to better understand these important yet poorly understood ecosystems' physiological responses to global environmental changes, we call for expansion of eddy covariance sites and ecological research stations in these areas. The plant traits utilized take the form of gridded data that were developed based on the TRY database and which are constant in time. This may be altered as a result of climate change [51]. We expect to clarify the mechanisms that lead to plant trait changes and validate or improve our models using time-varying data.
applied sciences Article  
Effect of Different Crestal Sinus Lift Techniques for Implant Placement in the Posterior Maxilla of Deficient Height: A Randomized Clinical Trial  
[PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 2  
Department of Periodontology and Oral Medicine, Faculty of Dental Medicine, Al-Azhar University, Cairo 11651, Egypt [EMAIL_ADDRESS] (M.F.K.); [EMAIL_ADDRESS] (M.M.H.); [EMAIL_ADDRESS] (M.T.E.-D) Dental Health Department, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia; [EMAIL_ADDRESS] Correspondence: [EMAIL_ADDRESS]  
This study evaluated dental implant stability, vertical bone gain, bone density, and crestal bone loss using different crestal sinus lift techniques (osteotomy, Densah burs, and piezosurgery). A total of 21 patients were randomly divided into three groups: Group 1: patients were treated using a Densah drill crestal sinus lift, Group 2: patients were treated using a piezoelectric crestal sinus lift and Group 3: patients were treated using an osteotone crestal sinus lift. The patients in all three groups underwent bone grafting and implant placement. An Ossdell device was used to determine the implant stability by recording the values of the implant stability quotient (ISQ). CBCT was performed before and 6 months after implant placement for radiographic evaluation and comparison among the groups. All dental implants were completely successful, and statistically significant differences from baseline to 6 months were noted in all groups (\(p<0.05\)). The Densah burs technique resulted in the best implant stability of all groups, while the osteetome technique demonstrated better vertical bone augmentation. However, the values for bone density and crestal bone loss showed no significant difference among all treated groups (\(p>0.05\)). All three techniques were successful for crestal sinus lifts with good clinical outcomes at a 6-month follow-up. The Densah group demonstrated better implant stability, shorter surgery time, and fewer complications; however, the vertical bone gain was greater with the osteetome technique.  
crestal sinus lift; dental implant; osteetome; piezosurgery; ossedensification Article
## 1 Introduction
Finally, the VM placement method is implemented to map an appropriate destination host for each VM to be migrated. VM placement problems often have specific optimization goals, such as using the smallest number of hosts to mount a given number of VMs. After implementing VM placement, underloaded hosts need to be addressed. By migrating all VMs on the underloaded host to other suitable hosts as much as possible, and shutting down or switching these underloaded hosts to an energy-saving state, the host energy cost of the CDC can be further reduced. The operating cost of the CDC involves many aspects, including host energy consumption, VM migration cost in server consolidation, SLAV penalty, cooling, security, storage, and network bandwidth overhead. The parts directly related to the computing resources used by users, including CPU, memory, disk, and network, are host energy consumption, VM migration overhead, and SLAV penalty. Therefore, in this paper, we comprehensively consider the operating cost of a CDC based on multiple computing resources, establish a relevant cost model, and propose a corresponding server consolidation solution. Our contributions are as follows:  
1. We formally identify the multi-resource-based cost model for server consolidation, which involves host energy consumption, VM migration, and SLAV. Based on the cost model, the optimization problem is given. 2. A convolutional auto-encoder-based filter is leveraged to denoise the VM workload trace. Then, we propose an attention-based RNN method to predict the future workloads of the VMs. Based on the prediction results, a host workload detection policy is proposed. 3. To minimize the total cost of server consolidation, we propose a VM selection policy and a VM placement algorithm which consider the multi-resource demands of VMs in the present and future. 4. We conduct simulations to evaluate the performance of our proposed solution ARP-TSCP. The simulations' results indicate that ARP-TSCP can reduce host energy con sumption by 18.5-30.3%, SLAV cost by 38-52%, and total cost by 26.1-39.3% as compared to the baseline methods. The remainder of the paper is organized as follows. In Section 2, we review the related work. In Section 3, we formally propose the cost model and then define the optimization problem to be solved. In Section 4, we propose the heuristic algorithms to solve this problem. In Section 5, we evaluate the performance of the proposed method by real VM workload, trace-driven simulations. In Section 6, we conclude the paper and discuss future works.
# Low-Cost _Ka_-Band Satellite Receiver Data Preprocessing for Tropospheric Propagation Studies
## 2 Materials and Methods
### Experimental Setup
#### 2.1.1 The RF Outdoor Unit  
The outdoor unit comprises a 65 cm dual-reflector parabolic dish manufactured by ALCOMA (Model UNI2-18) [28]. Although this antenna is designed for terrestrial point-to-point links, small modifications allowed us to use it for any satellite link with linear polarization with any orientation, not only vertical or horizontal, as in the terrestrial case. The second element is an LNB from Norsat (Model BDC-9000 XBN) [4] that has its own, fairly stable local oscillator. The LNB output connector (N-type 50 \(\Omega\)) is hooked up to the indoor unit by means of a 3 m low-loss coaxial cable (0.5 dB/m). The Intermediate-Frequency (IF) cable provides 15 V DC power to the LNB.  
The antenna provides a gain of 39 dBi at mid-band (11.70-19.70 GHz), and its input port is a rectangular waveguide (WR42). The LNB covers the frequency range 19.20-20.20 GHz, providing low-noise amplification and block conversion to the standard 0.950-1.950 GHz satellite receiver IF. This unit has an overall gain of 55 dB and a noise figure of 1.3 dB. We believe that this piece of equipment, being a commercial, \"mass\"-market one, is the main source of diurnal variation observed in the template and is due to its exposure to changes in ambient temperature (Section 3).