esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### The Advantage of DLMVO Components
* The convergence curves presented in Figure 9 illustrate that each improvement helps to improve the convergence speed. Although DLMVO-3 is not the fastest in the initial stage for SDM and SMM, it can converge quickly for DDM. The comparison reveals that the three improved components are conducive to enhancing the performance of the DLMVO. Although the dynamic leader travel strategy contributes more, the absence of any enhanced components will significantly deteriorate the performance of the DLMVO.
# Performance Evaluation of Open-Source Serverless Platforms for Kubernetes
## 2 Materials and Methods  
### Methods Outline  
The overall goal of this study was to determine how well the two serverless platforms, OpenFaaS and Nuclio, would perform when handling HPC workloads. OpenFaaS was chosen because it is currently the most popular open-source serverless platform as measured in GitHub stars ([[https://github.com/openfaas/faas](https://github.com/openfaas/faas)]([https://github.com/openfaas/faas](https://github.com/openfaas/faas)) accessed on 27 May 2022). Nuclio was picked because it presents itself as a data processing-focused platform, and thus was expected to do well in an HPC context.  
Evaluating the performance of the two platforms required designing and setting up benchmarks around representative workloads as well as experimenting with different settings for the platforms in order to find the optimal configuration. Furthermore, using different configurations helps to understand how features of the platforms can help them to excel at the benchmarks. Except for the serverless platforms deployed, the setups should be as identical as possible in order to ensure fairness when comparing the benchmarks.  
To remain in line with the HPC context, the workloads should be implemented in C/C++, as this is a very common language in HPC software and is considered to perform relatively well. Nevertheless, this should not fully exclude alternative implementations if there is reason to suspect they will be able to deliver even better performance. Here, the main configuration variation in the experiments was the number of function instances used. Normally, serverless platforms handle scaling automatically by adding or removing function instances depending on the incoming load for a specific function. In our experiments, the number of function instances was kept constant for each benchmark run in order to maintain a focus on the platform's ability to enable the performance of their function instances rather than the ability of their auto-scaling systems to spawn new instances.  
While the two platforms under study might be close or very different in terms of performance, this may nonetheless be far below anything that would be useful for real-world HPC use cases. Therefore, it was necessary to find a baseline for these new benchmarks as to what performance could be expected. Optimally, this would be done by running the benchmarks on an HPC cluster. However, due to limitations during the execution of our experiments, the baselines for the benchmarks were instead determined by executing them on a single host that had as many CPU cores to spare as were available to each of the serverless platforms. However, in such a comparison, it should be kept in mind that the platforms need to handle networking, run operating systems, run Kubernetes, and run themselves, while the bare-metal systems used for the baseline could carry out all computations locally with enough dedicated CPU cores.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 1 Introduction
The effect of the concrete slab width and distribution angle of the BFRP tendons on prestress loss was discussed using the proposed method. Given various slab width and distribution angles of the prestressed tendon, the prestress loss of BFRP tendons was then compared to that of the steel strands in order to demonstrate the feasibility of the BFRP tendon in cross-tensioned concrete pavement.
# Evaluation of Viscoelastic and Rotational Friction Dampers for Coupled Shear Wall System
###### Abstract  
This research experimentally and numerically evaluates the effectiveness of viscoelastic (VE) and rotational friction (RF) dampers in enhancing the seismic performance of coupled shear wall (CSW) systems. This study consists of two phases: (1) element testing to characterize the hysteretic behavior and energy dissipation capacity of VE and RF dampers, and (2) shake table testing of a large-scale CSW structure equipped with these dampers under the white noise, sinusoidal and Kokuji waves. The experimental results are validated through numerical analysis using STERA 3D (version 11.5), a nonlinear finite element software, to simulate the dynamic response of the damped CSW system. Key performance indicators, including inter-story drift, base shear, and energy dissipation, are compared between experimental and numerical results, demonstrating strong correlation. The findings reveal that VE dampers effectively control high-frequency vibrations, while RF dampers provide stable energy dissipation across varying displacement amplitudes. The validated numerical model facilitates the optimization of damper configurations for performance-based seismic design. This study provides valuable insights into the selection and implementation of supplemental damping systems for CSW structures, contributing to improved seismic resilience in buildings.  
coupled shear wall; viscoelastic damper; rotational friction damper; shake table test; numerical validation
## 3 Beamformer Design
### The Optimality of Rank One Beamformer  
In Reference [21], the optimal condition for the two user MISO NOMA transmission system is presented as  
\[\frac{P_{1}|\mathbf{h}_{1}\mathbf{v}_{1}|^{2}}{P_{2}|\mathbf{h}_{1}\mathbf{v }_{2}|^{2}+\sigma_{1}^{2}}=\frac{P_{1}|\mathbf{h}_{2}\mathbf{v}_{1}|^{2}}{P_{ 2}|\mathbf{h}_{2}\mathbf{v}_{2}|^{2}+\sigma_{2}^{2}}, \tag{5}\]where \(\mathbf{v}_{i},\ i=1,2\) are the \(M\times 1\) pre-coders for \(x_{j}(n)\) and \(\sigma_{i}^{2},\ i=1,2\) are the additive noise powers at the UTs. The channel vectors \(\mathbf{h}_{j},\ j=1,2\) are \(1\times M\) vectors toward the UTs from the AP. It is easy to see that the condition in (5) is met with \(|\mathbf{h}_{1}\mathbf{v}_{1}|=|\mathbf{h}_{2}\mathbf{v}_{1}|\) and \(|\mathbf{h}_{1}\mathbf{v}_{2}|=|\mathbf{h}_{2}\mathbf{v}_{2}|\). Note that the condition in (5) comes from the minimum operator taken for the SINRs of two NOMA UTs, which enforces those SINRs to be equalized at the optimum points.  
Extending the same reasoning to the cases of \(J\ (J>2)\) users, the optimal points should satisfy the conditions \(|\mathbf{h}_{1}\mathbf{v}_{k}|=|\mathbf{h}_{2}\mathbf{v}_{k}|=\cdots=|\mathbf{ h}_{l}\mathbf{v}_{k}|\) for \(k=1,\ldots,J\) due to the minimum operator and the following equalization enforcement. From these conditions, it is obvious that rank one pre-coder (\(\mathbf{v}_{1}=\cdots=\mathbf{v}_{J}\)) is sufficient to achieve these conditions regardless of \(J\).
## 2 Materials and Methods
### Volumes and Mean Absorbed Dose Comparisons and Statistical Analysis  
The volumes were separately obtained according to pre- and post-treatment images and the adopted modality for VOI generation (i.e., manual contouring on pre-treatment images, termed as V\({}_{\text{T,m}}\), V\({}_{\text{L,m}}\) and V\({}_{\text{NTL,m}}\) for tumor, liver and NTL, respectively, and rigid or deformable registration algorithms on post-treatment images, named V\({}_{\text{T,r}}\), V\({}_{\text{L,r}}\), V\({}_{\text{NTL,r}}\) and V\({}_{\text{T,d}}\), V\({}_{\text{L,d}}\), V\({}_{\text{NTL,d}}\), respectively). Mean absorbed doses by the tumor, the liver and the NTL were determined from the pre-treatment and the post-treatment absorbed dose distributions accordingly. In particular, on the same post-treatment absorbed dose distribution, mean absorbed doses were separately extracted for the rigid and deformable recontouring workflow. These will be referred to as D\({}_{\text{T,m}}\), D\({}_{\text{T,r}}\) and D\({}_{\text{T,d}}\) for tumor, D\({}_{\text{NTL,m}}\), D\({}_{\text{NTL,r}}\) and D\({}_{\text{NTL,d}}\) for NTL and D\({}_{\text{L,m}}\), D\({}_{\text{L,r}}\) and D\({}_{\text{L,d}}\) for liver.  
For all volumes and absorbed mean doses, the comparisons between the pre-treatment and post-treatment data (separately for rigid and deformable recontouring approach) and between rigid and deformable recontouring method on the same post-treatment absorbed dose distribution were performed following a Bland-Altman analysis. In this analysis, the average difference between the two measurements represented the bias, while the 95% limits of agreement (LOA), obtained by the bias \(\pm\) the standard deviation multiplied by 1.96, represented the precision. Agreement was also assessed by the ICC, the Pearson's correlation coefficient and the concordance correlation coefficient (CCC); a value \(\geq\) 0.8 indicated excellent correlation. Statistical significance was defined by a \(p\)-value < 0.05.
## 3 Results and Discussion  
### Characterization of Nanocontainers  
#### 3.1.1 Transmission Electron Microscopy and Energy Dispersive X-ray Spectroscopy  
The morphology of the nanocontainers is illustrated in Figure 1. Taking into account the TEM images, it is observed that they are hollow spheres with a diameter of \(180\pm 40\) nm. The presence of the ATT compound loaded into the nanocontainers is also depicted. From the corresponding EDS analysis, it is clearly denoted that the fabricated nanocontainers consisted of cerium, titanium and oxygen elements (Table 2). The existence of sulfur and carbon is assigned to the ATT.
## 4 Experimental Results
### Inputs to Build VKGs: Ontology, Mappings, Queries, Databases, and Ontop
#### 4.4.3 Databases  
Ontop supports standard relational databases engines via the Java Database Connectivity (JDBC) API. These databases include all major commercial relational databases including DB2 ([[https://www.ibm.com/db2](https://www.ibm.com/db2)]([https://www.ibm.com/db2](https://www.ibm.com/db2))), Oracle ([[https://www.oracle.com/](https://www.oracle.com/)]([https://www.oracle.com/](https://www.oracle.com/))), MS SQL Server ([[https://www.microsoft.com/en-us/sql-server/sql-server-downloads](https://www.microsoft.com/en-us/sql-server/sql-server-downloads)]([https://www.microsoft.com/en-us/sql-server/sql-server-downloads](https://www.microsoft.com/en-us/sql-server/sql-server-downloads))), and most popular open-source databases such as PostgreSQL ([[https://www.postgresql.org/](https://www.postgresql.org/)]([https://www.postgresql.org/](https://www.postgresql.org/))), MySQL, H2 ([[https://www.h2](https://www.h2) database.com/html/main.html]([https://www.h2](https://www.h2) database.com/html/main.html) (accessed on 21 May 2025)), and HSL ([[https://www.hsl.fi/en/hsl/open-data](https://www.hsl.fi/en/hsl/open-data)]([https://www.hsl.fi/en/hsl/open-data](https://www.hsl.fi/en/hsl/open-data))). In addition, Ontop can be used with federated databases such as Teiid and Exareme ([[http://madgik.github.io/exareme/](http://madgik.github.io/exareme/)]([http://madgik.github.io/exareme/](http://madgik.github.io/exareme/)) (accessed on 21 May 2025)), formally called ADP [71], to support multiple data sources including relational databases, XML, CSV, and web services [48].
## 3 Results and Discussion
### Mechanical Properties
#### 3.3.1 Stress-Strain Curve
In comparison to Figure 19a, the increased confining pressure significantly alters the stress-strain curve of the grouting reinforcement body and effectively limits the slippage of the rock block along the fracture surface. Figure 20 presents the stress-strain relationship curves of the grouting reinforcement body with a 60\({}^{\circ}\) crack dip angle under varying confining pressures and freeze-thaw cycles. As depicted in the figure, regardless of whether the confining pressure is 3 MPa or 9 MPa, the stress-strain curve of the grouting reinforcement body does not exhibit a plastic deformation stage. Instead, the curves reveal that after reaching the peak, axial stress drops instantaneously. This behavior occurs because the stress reaches the shear strength of the slurry-rock interface layer, leading to shear slip failure along the fracture surface. Additionally, the large crack dip angle, combined with the smooth fracture surface, promotes slip shear failure along the extended fracture surface, even under high confining pressure. In summary, the stress-strain curve of the grouting reinforcement body varies with changes in crack dip angle, confining pressure, and the number of freeze-thaw cycles. When the crack dip angle is 0\({}^{\circ}\), 30\({}^{\circ}\), or 45\({}^{\circ}\), the grouted reinforcement exhibits clear plastic deformation and ductility following peak strength. At a crack dip angle of 60\({}^{\circ}\), the stress-strain curve shows no plastic deformation stage, regardless of the confining pressure, and the axial stress drops instantaneously upon reaching its peak. The primary cause of this behavior is the stress surpassing the shear strength of the slurry-rock interface layer, leading to shear slip failure along the fracture planes of the rock block. Moreover, the number of freeze-thaw cycles has a profound influence on the stress-strain behavior of the grouting reinforcement body. Under consistent crack dip angles and confining pressures, an increase in freeze-thaw cycles extends the compaction stage of the grouting reinforcement body, marginally enhances the curve's concavity, shortens the elastic deformation stage, flattens the slope, and reduces peak strength. This phenomenon is predominantly attributed to the freeze-thaw damage sustained by the grouting reinforcement body following repeated freeze-thaw cycles. With temperature changes, the pore water in the sample continuously undergoes water-ice phase transitions. Under the influence of frost heaving forces, internal pores progressively expand and interconnect, leading to a gradual increase in their volume and size within the sample. During the thawing process, the deformation of internal pores in the rock becomes irreversible, and mineral components precipitate due to water erosion, weakening the cohesive forces between internal particles. Consequently, the internal structure degrades after multiple freeze-thaw cycles.
## 4 Results
### Configuration of the Simulation Environment
#### 4.2.5 Configuration of RSUs  
Although the number of vehicular nodes may fluctuate based on traffic, the number of RSUs in a particular area remains constant unless a malfunction occurs or new ones are installed. For the purposes of a 600 s simulation run, we maintained a fixed number of RSUs unless otherwise specified. As IZI communication frequently occurs over a wired network, the RSU network was built as a wired network [85]. We created a grid on the map of the traffic network and placed RSUs at predefined vertical as well as horizontal intervals. While the vertical interval (\(\delta y\)) was dependent on the map's size and the number of installed RSUs, the horizontal interval (\(\delta x=130\) m) stayed constant. Through point-to-point connectivity, the RSU network connected nearby RSUs to form a mesh network. RSUs employed Carrier Sense Multiple Access-Collision Detection (CSMA-CD) via a shared bus to connect with the SDN controller. The wired backbone (point-to-point connections and CSMA-CD bus) complied with Gigabit Ethernet requirements, operating at a data rate of 1000 Mbps with a propagation delay of 10 \(\upmu\)s. A DSRC interface for I2V and V2I communication was included on every RSU; they are modeled as stationary nodes using the NS3 \"Constant Velocity Mobility Model\" with zero x, y, and z velocities.
# Performance Analysis of Cooperative Non-Orthogonal Multiple Access in Visible Light Communication
## 3 BER Analytical Expression
### Cooperative PD-NOMA
#### 3.2.2 Multiple Relays  
We generalize our model to consider a cooperative system with \(M-k+1\) branches \(b_{0}\), \(b_{1}\),..., \(b_{M-k+1}\) where \(\text{b}_{0}\) represents the direct transmission for \(1\leq k<(M-1)\) and branch \(b_{g}\) consists of \(N_{g}\) relays. The probability of error of the user \(k\) is expressed as:  
\[\begin{array}{c}P_{\varepsilon_{k}}=\sum\limits_{\varepsilon=0}^{M-k}\sum \limits_{j=1}^{\varepsilon}\left(\begin{array}{c}M-k\\ \sum\limits_{i=1}^{\varepsilon}P_{\varepsilon_{i}^{j}N_{g_{i}^{j}}}(SNR_{ \varepsilon_{i}^{j}N_{g_{i}^{j}}})\prod\limits_{l=1}^{M-k-\varepsilon}\left[ 1-P_{\varepsilon_{i}^{j}N_{g_{i}^{j}}}\left(SNR_{\varepsilon_{i}^{j}N_{g_{i}^{ j}}}\right)\right]\\ \times Q\left(\frac{\sqrt{2}(SNR_{LED-k}+\sum\limits_{j=1}^{M-k-\varepsilon} \text{SNR}_{\varepsilon_{i}^{j}})-\sum\limits_{i=1}^{\varepsilon}SNR_{ \varepsilon_{i}^{j}})}{\sqrt{SNR_{LED-k}+\sum\limits_{g=1}^{M-k}\frac{SNR_{ \varepsilon_{i}^{2}}}{SNR_{g_{i}^{j}}}}}\right)\end{array}\right) \tag{33}\]  
where \(\varepsilon\) is used to index the users forwarding erroneously detected message to \(U_{k}\), \(E^{j}\) and \(C^{j}\) are set of \(\varepsilon\) and \((M-k-\varepsilon)\) distinct elements from the set \(\left\{1,2,\ldots,M-k\right\}\), respectively. \(E_{i}^{j}\) and \(C_{l}^{j}\) are the \(i\)-th and \(l\)-th elements of \(E^{j}\) and \(C^{j}\), respectively. It is noted here that \(\varepsilon=0\) when all users are transmitting the message intended to \(U_{k}\) correctly. In [20], the authors describe a system that consists of source, relays and destination with a probability of error always calculated at the destination. Therefore, we modify Equation (28) [20] into Equation (33) to be in consistence with the presented cooperative PD-NOMA system in Figure 3 where the users with good channel conditions are relays. For any number of relays, the probability of error is calculated at any user, not only at the destination, which is \(U_{1}\) in the presented model. Without loss of generality, we calculate the equivalent SNR of each branch and the direct transmission SNR using the same concept as Equations (30) and (31), respectively.
## 3 Experiment Results
### The Effect of Pixel Threshold on Measurement Accuracy and Stability
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Pixel**} & \multicolumn{4}{c}{**Five Groups of Actual Measured value (mm)**} \\ \cline{2-6}  & **First** & **Second** & **Third** & **Fourth** & **Fifth** \\ \hline
0 & 10 & 9.96 & 10.06 & 10.01 & 10.06 \\
50 & 9.97 & 10 & 9.95 & 9.97 & 9.94 \\
100 & 9.96 & 10.06 & 10.04 & 9.96 & 10.05 \\
150 & 10 & 9.99 & 9.95 & 9.95 & 9.93 \\
200 & 9.91 & 9.9 & 9.83 & 9.83 & 9.88 \\
250 & 9.74 & 9.78 & 9.81 & 9.85 & 9.77 \\
300 & 9.84 & 9.74 & 9.76 & 9.82 & 9.88 \\
350 & 9.77 & 9.72 & 9.68 & 9.86 & 9.81 \\
400 & 9.79 & 9.76 & 9.69 & 9.75 & 9.7 \\
450 & 9.65 & 9.74 & 9.69 & 9.73 & 9.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The effect of pixel threshold on measurement value: actual measured value. Figure 13: The measurement data under different pixel thresholds.
## 3 Results and Discussion
### Comparison of Results  
In Figure 14, we present the results from this investigation, i.e., paper (using three methods from Sections 2.2.1-2.2.3), which are either simulated, calculated, approximated or the data obtained from literature [16; 37; 40; 41; 42], for electron density \(N_{e}\) at reference height as a function of X-ray flux. The area of importance is between the two red lines. It can be noticed that the results differ from each other in some cases by an order of magnitude or more. Although the data are scattered, especially for stronger flares (right side of the graph), they still tend to increase with the intensity of the X-ray radiation. As noted, our results were acquired on the basis of analysis of SID events on NAA/24.00 kHz signal and using methods described in the text. Electron density at reference height \(h\) = 74 km changed from \(N_{e}\) = 2.16 \(\times\) 10\({}^{8}\) m\({}^{-3}\) to \(N_{e}\) 10\({}^{11}\) m\({}^{-3}\). Additionally, we compared data with the \(N_{e}\) data obtained using approximate Equation (5) (plus sign in Figure 14) and simulations (black star) using the flared method. The results are in fair agreement with previous studies. We can conclude that measurements over a long path give slightly higher slope, i.e., lower values of electron density than expected, but are quite satisfactory. This is important because most results concerning investigation in the D-region are scattered and vary in order of magnitude and we need to take into account all the data and correct the existing ones. During SIDs the time profile of electron density follows the solar X-ray flux variation.  
Figure 13: A scatter plot of a pair of D-region parameters, i.e., \(H^{\prime}\) versus \(\beta\), is shown for SID cases analysed in this study using LWPC and two-component exponential model together with ones obtained using flared method from Github project [29].
## 1 Introduction
In previous studies, we have reported that BO inhibited KOA progression in a rat model [15] and further demonstrated its analgesic effect on KOA [16]. Although many studies have reported the effect of various natural ingredients on the inhibition of KOA progression, no report has suggested the inhibition of KOA progression by a clinically-used drug. Therefore, we decided to conduct further research on BO. In the early stage of KOA, multinucleated osteoclasts proliferate in the subchondoral bone, and abnormal bone metabolism is observed before the cartilage tissue begins to degenerate [17]. With KOA progression, subchondoral bone shows bone cyst formation, abnormal osteoclastosis, and osteophyte formation [18]. It has been reported that chondrocytes also release the receptor activator of the nuclear factor kappa-B ligand, which promotes osteoclast differentiation and proliferation in the subchondoral bone [19]. Based on these reports, it is suggested that suppressing osteoclast differentiation and proliferation in the subchondoral bone in the early stages of KOA plays a very crucial role in inhibiting subsequent KOA progression. Some of the BO ingredients could inhibit osteoclast differentiation and proliferation. Magnoflorine in _Sinomenium_ stem has been reported to inhibit osteoclast differentiation [20]. Furthermore, isoflavone in _Astragalus_ root has a female hormone-like effect and has the potential to inhibit osteoclast proliferation [21]. These findings indicate that BO suppresses osteoclast differentiation and proliferation in the subchondoral bone when administered to KOA-induced rats. To prove the inhibitory effect of BO on the KOA progression, it is necessary to examine the effect of inhibiting osteoclast differentiation and proliferation in the subchondoral bone, in addition to inhibiting cartilage tissue degeneration. Thus, this study aimed to investigate the inhibitory effects of BO on osteoclast proliferation in the subchondoral bone in a KOA rat model.