esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Research Methodology
### Performance Measurements
Mathematically, the container request latency (L) can be defined as \[L=T_{response}-T_{start}\] (1) where \(T_{start}\) is the time that the client sends a request for an arrival time and \(T_{response}\) is the time that the response to the request is received. Again, this only captures the container request latency and not the client-side network latency. For multiple requests of \(n\), the average container request latency \(L_{avg}\) can be calculated as \[L_{avg}=\frac{1}{n}\sum_{i=1}^{n}(T_{response,i}-T_{start,i})\] (2)
* _API Response Time (R)_: API response time is defined as the duration between the time that the client sends a request and the moment that the system responds with the intended output on the server-side processing. This focuses on obtaining the distance matrix API's response to the user's request for the arrival time required to travel between two locations based on the distance. A graphical representation of the API response time is shown in Figure 1. The significance of response time in modern computing cannot be overstated, as it directly impacts the user experience. A slow response time can lead to frustration, decreased productivity, and negative perceptions of the system or application. Therefore, it is imperative to measure or optimize response time to enhance user satisfaction. Various techniques such as caching, load balancing, and the optimization of code can be employed to reduce response time. The API response time (R) can be mathematically defined as \[R=T_{response-from-API}-T_{call-to-API}\] (3) where \(T_{call-to-APT}\) is the time when the client request calls the distance matrix API for an arrival time and \(T_{response-from-API}\) is the time that the response to the request returns to the call. For multiple requests of n, the average API response time \(R_{avg}\) can be calculated as \[R_{avg}=\frac{1}{n}\sum_{i=1}^{n}(T_{response-from-API,i}-T_{call-to-API,i})\] (4)
* _CPU Utilization_: When evaluating the performance of the containerized application, it is essential to consider its CPU usage. During the test load, the CPU usage for the container can provide valuable insights into its efficiency and effectiveness. By analyzing the CPU usage patterns, it is possible to identify any bottlenecks or performance issues that may be impacting the container's performance. It is important to note that CPU usage can vary depending on the workload and the hardware configuration of the system. Therefore, it is essential to establish a baseline for CPU usage and compare it against the actual usage during the test load. This comparison can help to identify any abnormal behavior that may be indicative of performance issues.
# The Effects of Augmented Reality Interaction Techniques on Egocentric Distance Estimation Accuracy
## 1 Introduction  
Virtual environment (VE) applications are rapidly increasing. Since the early 1990s, such applications have been developed in diverse domains, such as surgery [1], safety training for cabin safety procedures [2], advanced manufacturing systems integrating robots and humans to execute tasks [3], and usability evaluations [4]. In addition to being applicable in more extensive areas, VEs have recently received attention from computing technology providers such as Microsoft, Google, NVIDIA, HTC, and Samsung--all of which are seeking even more exciting applications. Many recent efforts have focused on applications that allow users to observe and interact in three-dimensional (3D) environments rather than just viewing 3D modeled images. In the development of these applications, immersive virtual environments (IVEs) featuring head-mounted displays (HMDs) are mostly used.  
However, such systems usually generate inconveniences such as musculoskeletal system discomfort, nausea, and difficulty with concentrating because of vertigo caused by simulated sickness [5]. Stereoscopic 3D displays (S3 Ds) have recently gained more popularity. An S3D system needs a relatively lower investment in the hardware, software, and imagery produced than those with HMDs do. VR displays present slightly different images separately to each eye of the viewer. The separation between the two images, which can create slightly different views of a scene for each eye is called parallax. This phenomenon allows the viewer to perceive depth in a scene using different information [6]. Stereoscopic objects may be displayed with zero, negative, or positive parallax, corresponding to at the surface of, in front of, or behind the screen display. One technology that utilizes S3D is the 3D projection display. Unlike HMD-based systems, projection-based displays allow multiple users to view images in VE at the same time. Another advantage of projection displays is the satisfying interaction of augmentation [7], which allows users to interact with virtual images using real objects in a seamless manner. Nevertheless, integrating physical objects may lead to obscuration issues [8].
# Interference Management in Femtocells by the Adaptive Network Sensing Power Control Technique
## 3 System Model
### Path Loss Model  
The path loss models set by the 3 GPP LTE-Advanced for urban deployments in [22], between the macro BS and UE, is given as:  
\[PL_{Macro}(dB)=\left\{\begin{array}{ll}15.3+37.6\log_{10}R&(\text{outdoor UE})\\ 15.3+37.6\log_{10}R+L_{\text{OW}}&(\text{indoor UE})\end{array}\right. \tag{1}\]  
where the distance between the macro BS and UE is \"\(R\)\" and the outdoor wall penetration loss is _Low_.  
The path loss between the femto BS and UE indoor or outdoor in an apartment is calculated by using:  
\[PL_{Femto}(dB)=\left\{\begin{array}{ll}38.46+20\log_{10}(R)&(\text{outdoor UE})\\ \max(15.337.6\log_{10}(R),\ 38.46+20\log_{10}(R))+L_{\text{OW}}&(\text{ indoor UE})\end{array}\right. \tag{2}\]
## 3 Materials and Methods
### Dataset Description  
PPG and ABP data to train the model were obtained from the MIMIC-III Waveform Database [16; 17; 18], which is open access and contains anonymised data from thousands of intensive care unit (ICU) patients. The data in this database is under the Open Data Commons Open Database License [19] and is freely accessible. However, the clinical and demographic data in MIMIC-III is under a different database, which has a strict license and data use agreement, including undergoing specific training [20]. This study aims to investigate the potential of using data from just the waveform database, but the lack of clinical data is a limitation, and the incorporation of such data is discussed as an extension to this study in Section 5.  
Only patient records consisting of both ABP and PPG data were downloaded and processed, as described in Section 3.2. Downloading of data took place as described in the documentation for PhysioNet's databases and makes use of the database's WFDB library [21]. After trawling through 25,000 patients, 4027 patients records, consisting of both ABP and PPG signals, were downloaded. Processing this data, as described in Section 3.2, resulted in a dataset of 1669 patients, representing 890 h of corresponding ABP and PPG waveforms. This is over three times the number of patients used by the next largest study reviewed, [PERSON] et al. [14].  
Below, we consider what the expected standard error is using this sample size. The dataset has a mean SBP and standard deviation of 134.2 mmHg and 18.65 mmHg, respectively, indicating that the normalised standard deviation is \(\sigma=0.1390\). Assuming a normal distribution, the standard error is given by,  
\[\epsilon=Z\frac{\sigma}{\sqrt{n}} \tag{1}\]  
where \(\epsilon\) is the deviation of the mean. Using a 99% confidence interval (\(Z=2.576\)), \(\epsilon=0.88\%\) for the 1669 patients used in this study. The same procedure can be repeated for DBP and MAP to yield similar numbers. In theory, the dataset's mean should be within 1% of the general population. We will use this to justify our results. It is worth mentioning that this confidence holds for the MIMIC-III population. There is, however, an inherent bias in the dataset, in that recordings are taken from ICU patients, and presumably, the ABP and PPG signals have only been measured when there is due cause (such as in patients with cardiovascular diseases). From the mean SBP of 134 mmHg, it is likely that the average patient included is hypertensive. There is no indication of the error introduced when testing on healthy individuals. This is a limitation of this study.
## 3 Results and Discussion
### The Dynamic Response of the Masses
[25] considers influences of measuring devices and of fixtures exceeding their mass, decreasing the deviation further. Especially the deviation at the low frequency can be decreased by this method by a factor of five. The determination of \(HI_{pp,fit}\) over several masses has the advantage that it is determined over a larger range of loads. Thus, nonlinear effects, especially in the lower load range, are not extrapolated to results in the higher load range. Furthermore, the measurement noise relative to the measured force has less influence on the determination of \(HI_{pp}\). The deviation can be more than halved for both test benches. The resulting deviation is 0.0433 kg for the low frequency test bench and 0.0237 kg for the high frequency test bench. Since the values are derived from the related test results themselves, these only give an indication of the possibilities of the method. In the following subsection, the use of the specific correlation is applied to two compliant elements. \begin{table}
\begin{tabular}{l c c} \hline
**Average Deviation \(e_{AM,abs}\) (\(e_{AM,rel}\))** & **Low Freq. Test Bench** & **High Freq. Test Bench** \\ \hline static calibration & 0.6252 kg (12.0 \%) & 1.1583 kg (252.6 \%) \\ \hline mass cancellation by [PERSON] [26] & 0.4567 kg (8.50 \%) & 0.1125 kg (18.23 \%) \\ \hline \(HI_{pp,fit}(m_{0})\) by [PERSON] et al. [25] & 0.0982 kg (1.68 \%) & 0.0628 kg (10.46 \%) \\ \hline \(HI_{pp,fit}(m_{i})\) over added masses & 0.0433 kg (0.74 \%) & 0.0237 kg (4.21 \%) \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of average deviation \(e_{AM}\) by Equation (22) for static calibration, mass cancellation by [PERSON] [26], \(HI_{pp,fit}(m_{0})\) by [PERSON] et al. [25], \(HI_{pp,fit}(m_{i})\) over different added masses.
## 4 The Role of Metallurgy Slags in Sustainable Construction
### Innovative Uses in Bricks and Ceramics
The CO\({}_{2}\)-curing process at 15 bar, 50 \({}^{\circ}\)C, and 6 h resulted in compressive strengths of 24 to 49 MPa, surpassing those of traditional sand-time bricks. The lifecycle assessment revealed a CO\({}_{2}\)-saving potential of 337 kg CO\({}_{2}\) per cubic meter of brick compared to conventional methods, highlighting the significant environmental benefits and industrial feasibility of CO\({}_{2}\)-curing slag-based bricks. [PERSON] et al. [272] analyzed the corrosion behavior of CaO-Al\({}_{2}\)O\({}_{3}\)-SiO\({}_{2}\)-MgO-Cr\({}_{2}\)O\({}_{3}\) slag on MgO refractory bricks used in chromite smelting. Their study investigated the reaction mechanisms between the slag and the refractory, revealing that the main slag phases were pyroxene, chromium spinel, and magnesium olivine. An SEM-EDS analysis showed that, at the contact surface, calcium magnesium olivine and magnesium olivine were predominant, with calcium magnesium olivine increasing as slag basicity rose. Thermodynamic calculations indicated that higher slag basicity lowered the melting temperature and viscosity, allowing molten slag to infiltrate the brick pores, accelerating corrosion. [PERSON] et al. [273] assessed the mechanical performance and contaminant leaching behavior of cement-based and fired bricks containing ferrochrome slag. The study compared these slag-containing bricks with conventional ones, revealing that bricks with 20-30% ferrochrome slag had higher compressive strength and lower water absorption. Contaminant leaching of trace metals and chloride was similar or lower than controls, except for higher manganese and chromium leaching in slag-containing debris due to their elevated concentrations in the slag. Cement-based bricks provided the highest strength, while fired bricks showed the least contaminant leaching. However, not all studies have yielded positive results. [PERSON] et al. [274] examined the effect of slag composition on the corrosion resistance of high-chromia refractory bricks used in industrial entrained-flow gasifiers. Their study found that slag types influenced corrosion severity, with high-Ca/Na slag causing the most damage, followed by high-Fe slag, and high-Si/Al slag being the least corrosive. High-Ca/Na slag reacted with the refractory, forming low melting point phases that disrupted the brick's matrix. High-Fe slag led to iron phase precipitation and spinel layer formation at the interface. The interaction of ZrO\({}_{2}\) in the refractory with CaO from the slag formed calcium zirconate, which weakened the refractory's structure and increased corrosion. After this analysis, it is observed that, although most studies highlight the benefits of using slag in bricks and ceramics manufacturing, such as improved mechanical properties and environmental advantages, it is essential to carefully assess the slag composition to mitigate potential negative effects, such as increased corrosion or contaminant leaching.
## 2 The WCLL COB Segment  
The EU DEMO WCLL BB, in accordance with the DEMO baseline 2017 [8], is subdivided into 16 identical toroidal sectors of 22.5\({}^{\circ}\), each of them composed of an Outboard and an Inboard region. The Outboard one consists of three segments, Left, Central, and Right (LOB, COB, and ROB, respectively); instead, in the Inboard region, two symmetric segments, Left and Right (LIB and RIB), are foreseen. The Segments are based on the Single Module Segmentation architecture, consisting of a single elementary cell repeated about 100 times for the entire poloidal length of the Segment, which is closed at the extremities by two plates, the Top and Bottom Cap.  
The study reported therein is more specifically focused on the Bottom Cap region of the WCLL COB segment, reported in Figure 1. Internally, the COB structure is reinforced by a grid of Stiffening Plates (SPs) located along the poloidal-radial and toroidal-radial planes, respectively named horizontal and vertical SPs (SPh and SPv), with a thickness of 10 and 12 mm, respectively. The external steel structure, named Segment Box (SB), enclosing the SPs grid, is composed of a First Wall (FW), directly placed in front of the plasma and covered with 2 mm of Tungsten (coloured in orange in Figure 1), and laterally closed by Side Walls (SWs). Immediately behind the SPs, the Manifolds region (for water and PbLi) is located, divided by a Back Plate (BP). Finally, at the rear is the Back Supporting Structure (BSS) aimed at connecting the SB with the Vacuum Vessel (VV) by means of the attachment system (in grey in Figure 1). The liquid metal eutectic alloy (acting as breeder, neutron multiplies and tritium carrier), the Lithium-Lead, flows across the SPs in that region called Breeder Zone (BZ), guided in its poloidal path to a series of Baffle Plates. The BZ is actively cooled by subcooled pressurised water (15.5 MPa and 285/328 \({}^{\circ}\)C of inlet/outlet temperature) flowing inside the bundle of Double Walled Tubes (DWTs), whose geometric layout varies along the poloidal length of the Segment due to the different thermal loads each region is subjected to [9]. Instead, square cooling channels are located in the FW-SWs region, crossed by water in the same condition aforementioned, devoted to cooling that region. As for the DWTs, the number of cooling channels per elementary cell depends on its poloidal location within the segment. An overview of the cooling channels and DWTs layout for each poloidal region of the COB Segment is reported in [9]. As to the BC region, the reference geometric layout foresaw a Cap plate of 25 mm of thickness and any dedicated cooling system is foreseen.
## 5 Conclusions  
In this paper, we propose a method to generate the data for the video inpainting and SR task, making it possible to train a neural network in a supervised way. We design a deep model and introduce non-local blocks to our work to combine the non-local features, further proposing a generalized model that efficiently deals with the inpainting and super-resolution reconstruction tasks, which is also the first generalized model. The proposed model is able to successfully inpaint an incomplete video and generate SR frames. The comprehensive benchmark evaluations demonstrate that our model performs superiorly over representative methods, revealing the powerful versatility of our method in video inpainting and SR tasks.  
Although our model may not perform as well as a model that is dedicated to handling a single task, we may compromise some single-task performance due to our pursuit of universality. However, the recent models have limited improvement in PSNR and SSIM index and little improvement in visual effect. The results of our model can already meet the application requirements of most scenarios, while pioneering the field of research on model universality, which is of great significance. In the future, our work will primarily concentrate on two elements. Firstly, we will modify our proposed model to enhance performance on a single task. Secondly, we will continue to explore more efficient universal models for video restoration.  
Conceptualization, H.J.; methodology, Y.Z.; software, Y.Z.; validation, Y.Y.; formal analysis, Y.Z.; investigation, Y.Y.; resources, H.J.; data curation, Y.Z.; writing--original draft preparation, Y.Z. and Y.Y.; writing--review and editing, Y.Z. and Y.Y.; visualization, Y.Y.; supervision, Y.Y.; project administration, Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript. Funding through National Natural Science Foundation of China Enterprise Innovation and Development Joint Fund Key Project (Grant No. U19B2004). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All data included in this study are available upon request by contact with the corresponding author. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
Figure 9: The comparison between the output of decoder and the whole network.
## 6 Results and Discussions  
### Verification of DG Absorption Strategy Based on FMSS  
Under the operating condition of the distribution network system mentioned above, according to the above algorithm, the consumption of DG is adjusted and controlled in real time through the control strategy of FMSS. I added a sequential optimization step in MATLAB program to change the output of photovoltaic power generation (PV) and wind power output throughout the period, means that the load changes the output every half hour for a time node, and results in sequential optimization. The diurnal variation curves of PV, wind power generation and the load are obtained when FMSS is not connected and the rated capacity of different FMSS is connected, as shown in Figure 7. The maximum output of wind power is set as 1 (P.U.) and the maximum output of PV is 1.2 (P.U.). The \(Y\)-axis represents the output of DG/load capacity, and the \(X\)-axis is the time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **Node Number** & **DG Type** & **The Largest Output** \\ \hline
1 & 9 & Wind power & 1.0 MVA \\
1 & 14 & PV & 1.2 MVA \\
1 & 17 & PV & 1.2 MVA \\ \hline
2 & 10 & Wind power & 1.0 MVA \\
2 & 13 & PV & 1.2 MVA \\
2 & 16 & PV & 1.2 MVA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Access location and rated power of DG.  
Figure 6: A scenario of the distribution network with three-terminal flexible multi-state switches.  
Figure 7a shows the condition where the distributed power supply is not connected, and Figure 7b-e shows the rated capacity of the FMSS that is connected is 1 MVA,3 MVA,5 MVA and 10 MVA, respectively.  
By comparing the output of DG without access to FMSS as shown in Figure 7a and the access to FMSS as shown in Figure 7b-d, it can be seen that under the same load change and after FMSS is connected, the output of PV and wind power are significantly improved. In other words, FMSS in the optimization model mentioned above can effectively improve the consumption capacity of the distributed power supply in the distribution network.  
As can be seen from Figure 7b-d, with the increase of the rated capacity of FMSS, the output of photovoltaic and wind distributed power supply also increases. This indicates that, within a certain range, appropriately increasing the rated capacity of FMSS can increase its capacity to consume DG. Comparing Figure 7d,e, it can be seen that the rated capacity of FMSS has been doubled, but the output of DG has hardly changed. This is because when the FMSS of 5 MVA is connected, the output of DG has been optimized near its rated capacity in accordance with the optimization strategy in this paper. Therefore, it is of little significance to increase the capacity of FMSS in this case.  
Figure 7: Cont.  
Figure 7: Optimal output of DG in network 2 with different FMSS capacity.
## 4 Nonlinear Time History Analyses in OpenSees  
In this section, we present the results of several Nonlinear Time History Analyses (NLTHAs) carried out on a seismically base-isolated structure, subjected to bidirectional earthquake excitation, in order to investigate the accuracy of the novel OpenSees uniaxial material object described in Section 3.2.  
In particular, such a model is employed to reproduce the decoupled biaxial hysteretic behavior of each elastomeric bearing adopted in the base isolation system of the analyzed structure. Due to its uniaxial nature, this model is not able to take into account the bidirectional interaction typically displayed by the restoring force components of each bearing along two generic orthogonal transverse directions. The implementation of a biaxial version of such a model, already formulated by [PERSON] et al. [32], will be the objective of future works.  
In order to demonstrate the accuracy properties of the proposed uniaxial material, the NLTHAs results are compared with those obtained by employing the OpenSees BoucWhen material object. Such a model, originally formulated by [PERSON] and [PERSON] [15; 16], has been subsequently proposed by [PERSON] et al. [14] to simulate the complex response of elastomeric bearings.  
In this work, the Newmark's constant average acceleration method is adopted to numerically solve the nonlinear equilibrium equations of the analyzed structure since such a numerical time integration method is both accurate and unconditionally stable [33; 34].  
Figure 4: Types of hysteresis loop shapes that can be simulated with the OpenSees uniaxial HystereticPoly material model.