esa-sceva/satcom-chunk-collection huggingface dataset:
# JSSTR: A Joint Server Selection and Traffic Routing Algorithm for the Software-Defined Data Center
## 4 System Model and Problem Statement  
### The SDDC Model  
Assume that the SDDC network \(G=(V,E,S)\) contains switches denoted by \(V=\{v_{1},v_{2},...,v_{n}\}\) and servers denoted by \(S=\{s_{1},s_{2},...,s_{m}\}\). Servers and switches are connected by links \(E=\{e_{i,j}=(i,j)|i,j\in\{V,S\}\)_and_\(i\
eq j\}\). The detailed description of notations used in this work is shown in the Abbreviations part of this paper.  
For a request, the total link cost of its routing path \(p\) is defined as \(\sum\limits_{e_{i,j}\in p}c_{i,j}\). Here, \(c_{i,j}\) is the cost of allocating the \(k\)-th request on the link \(e_{i,j}\), and it is calculated by \(c_{i,j}=u_{i,j}*b_{k}\), where \(u_{i,j}\) is the unit cost of link \(e_{i,j}\) and \(b_{k}\) is the bandwidth required by the request. At the same time, the bandwidth utilization ratio of link \(e_{i,j}\) is expressed as \(l_{i,j}\). It is denoted by \(l_{i,j}=ub_{i,j}/tb_{i,j}\), where \(ub_{i,j}\) and \(tb_{i,j}\) represent the occupied bandwidth and total bandwidth of link \(e_{i,j}\), respectively. Thus, for a request, the bandwidth utilization ratio of its routing path \(p\) is defined as \(\sum\limits_{e_{i,j}\in p}l_{i,j}\).  
Likewise, the resource utilization ratio of a server is defined as the percentage between the used resource (CPU, memory and disk) and total resource of the server. Therefore, the resource utilization ratio of server \(s_{i}\) is expressed as \(l_{i}=\delta_{c}*lc_{i}+\delta_{m}*lm_{i}+\delta_{d}*ld_{i}\). The \(lc_{i}\), \(lm_{i}\) and \(ld_{i}\) are the CPU utilization ratio, memory utilization ratio and disk utilization ratio, respectively. For instance, the memory utilization ratio is calculated by \(lm_{i}=um_{i}/tm_{i}\), where \(um_{i}\) and \(tm_{i}\) refer to the used memory and total memory of the server. Meanwhile, \(\delta_{c}\), \(\delta_{m}\) and \(\delta_{d}\) are the coefficients of CPU, memory and disk resource, respectively. In this work, we focus on how to design a meta-heuristic algorithm to solve the joint server selection and traffic routing problem. We did not give much attention to assigning these parameters. Therefore, these parameters are constant, and the \(\delta_{c}\), \(\delta_{m}\) and \(\delta_{d}\) are all set to \(1/3\).
## 2 Architectural Models and Research Methods
Considering local meteorological conditions, solar energy can be converted into electricity through solar photovoltaic power generation to supply power to equipment on site, reducing energy loss. The main control building system of the substation has the following issues:  
1. The consideration of renewable energy or heat pump forms to meet the heating load requirements was not considered. 2. The building's ventilation system design is imperfect. 3. There is potential for more energy-efficient heating methods that need to be further optimized. \begin{table}
\begin{tabular}{c c c} \hline
**Name** & **Variable** & \(\boldsymbol{\Gamma_{\phi}}\) & \(\boldsymbol{S_{\phi}}\) \\ \hline Equation of continuity & 1 & 0 \\ Velocity in x direction & \(u\) & \(\mu_{eff}=\mu+\mu_{t}\) & \(-\frac{\partial P}{\partial x}+\frac{\partial}{\partial x}(\mu_{eff}\frac{ \partial u}{\partial x})+\frac{\partial}{\partial y}(\mu_{eff}\frac{ \partial w}{\partial x})+\frac{\partial}{\partial z}(\mu_{eff}\frac{ \partial w}{\partial x})\) \\ Velocity in y direction & \(v\) & \(\mu_{eff}=\mu+\mu_{t}\) & \(-\frac{\partial P}{\partial x}+\frac{\partial}{\partial x}(\mu_{eff}\frac{ \partial w}{\partial y})+\frac{\partial}{\partial y}(\mu_{eff}\frac{ \partial w}{\partial y})+\frac{\partial}{\partial z}(\mu_{eff}\frac{ \partial w}{\partial y})\) \\ Velocity in z direction & \(w\) & \(\mu_{eff}=\mu+\mu_{t}\) & \(-\frac{\partial P}{\partial x}+\frac{\partial}{\partial x}(\mu_{eff}\frac{ \partial w}{\partial z})+\frac{\partial}{\partial y}(\mu_{eff}\frac{ \partial w}{\partial z})+\frac{\partial}{\partial z}(\mu_{eff}\frac{ \partial w}{\partial z})-\rho g\) \\ Turbulent kinetic energy & \(k\) & \(\alpha_{k}h_{eff}\) & \(\text{C}_{k}+\text{C}_{B}-\rho\epsilon\) \\ Turbulent dissipation & \(\epsilon\) & \(\alpha_{k}h_{eff}\) & \(\text{C}_{1\epsilon}\frac{\epsilon}{k}(\text{G}_{k}+\text{C}_{3\epsilon}\text{ G}_{B})-\text{C}_{2\epsilon}\rho\frac{\epsilon^{2}}{k}-R_{\epsilon}\) \\ Temperature & T & \(\frac{\mu}{\text{Pr}}+\frac{\mu_{t}}{\sigma_{t}}\) & \(S_{T}\) \\ \hline \
###### Abstract  
By analyzing the effect of the degree of polarization (DoP) of the partially coherent Gaussian Schell-model (GSM) beam on a heterodyne system of an uplink path and a downlink path, we developed an innovative and noteworthy theory according to which \(\alpha_{sy}\) (signal beam waist radius in the \(y\) direction component) and \(\delta_{Syg}\) (coherence length of the signal beam in the \(yy\) direction component) had a more significant impact on heterodyne efficiency and DoP than the turbulence term on uplink and downlink paths. Namely, the DoP and heterodyne efficiency of an uplink path are higher than that of a downlink path when \(\alpha_{sy}>0.02\) m or \(\delta_{Syg}\geq 0.03\) m. This innovative rule provides an efficient way for increasing the heterodyne efficiency of a signal beam propagating along an uplink or a downlink path channel in satellite-ground communication links in free-space optical heterodyne detection communication.  
heterodyne detection; degree of polarization; heterodyne efficiency; uplink and downlink path +
Footnote †: journal: _Photonics_  
0
Footnote 0: Copyright** © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 Results and Discussion
### Metrological Parameters  
Two major indexes are investigated in this study, which are PU (pad non-uniformity) and PELI (pad effective life time index). PU shows the pad profile and the wear condition during the conditioning process. PELI shows the remaining lifetime of pad by evaluating the change of groove depth. The definition of PU is as Equation (3), where \(T_{max}\)_and_\(T_{min}\) mean the maximum and minimum value of measuring height data. \(T_{pad}\) means the original pad thickness without conditioning. The PU represents the difference of pad profile, whose value varies with the initial setup or within the dressing process. The PELI defines the available lifetime of pad by the remaining pad groove depth. The definition of PELI shows as Equation (4) and \(H_{g}\) and \(H_{g*}\) mean the groove depth before and after conditioning process.  
\[PU=\frac{T_{max}-T_{min}}{2\times T_{pad}}\times 100\% \tag{3}\]  
\[PELI=\frac{H_{g*}}{H_{g}}\times 100\% \tag{4}\]  
To examine the system metrological parameters, 50 measurements were tested for specific rotation speeds. The detailed experimental parameters are listed as Table 5. The results are shown in Figure 8. For the total swing time for each measurement is 50 s, so the length of scanning locus on pad will increase with the faster rotation speed. The result of PU is larger than others when rotation speed is 1 rpm. Lower rotation speed will reduce the scanning area, so the PU value will become unstable. The PELI value is stable because the groove depth is more evenly distributed on the pad surface. The standard deviation of  
Figure 6: Diagram of scanning locus by different pad speed.  
Figure 7: Re—allocating the measurement data into the surface.  
PU in each rotation speed except 1 rpm is less than 1.2%. the overall SD of experiments is 0.48%. SD of groove depth in each rotation speed is under 3 \(\upmu\)m, and the overall SD of rotation speed from 1 rpm to 100 rpm is 5.58 \(\upmu\)m.
## 3 Research Method
### Development of the Research Hypotheses
#### 3.1.6 Relationship between E-Trust and Continuous Usage Intention  
Trust is pivotal in fostering robust customer relationships and driving sustained success because it encourages customer loyalty, CUI, and positive word-of-mouth promotion of products and services [26]. Numerous studies have emphasized its impact on various facets of customer behavior in online environments. [PERSON] et al. [48] explored the positive effect of e-trust on customer loyalty in e-commerce. [PERSON] et al. [54] confirmed the positive relationship between e-trust and CUI in mobile payment systems. [PERSON] et al. [4] and [PERSON] et al. [53] investigated the positive effect of e-trust on CUI in online shopping. Consequently, the current study hypothesizes the following relationship between e-trust and CUI in e-commerce marketplace platforms:  
**Hypothesis 6 (H6).**_E-trust is positively related to CUI._  
1.7. Customer Satisfaction and E-Trust as Mediators of the Relationship between E-Service Quality and Continuous Usage Intention  
Understanding how CS and trust mediate the relationship between SQ and CUI is crucial for understanding consumer behavior dynamics. Previous research has indicated that CS frequently acts as a mediator in various service industries [55]. [PERSON] et al. [56] and [PERSON] and [PERSON] [57] found that SQ influences repurchase intentions through CS in multiple service industries and packaged tour services, respectively. Additionally, [PERSON] et al. [8] and [PERSON] et al. [6] demonstrated that e-SQ affects CUI through CS in the contexts of online services and community group buying platforms, respectively. However, limited research has explored e-trust as a mediator. [PERSON] [58] investigated the mediating role of trust in the relationship between CS and repurchase intention in laptop shops. Moreover, [PERSON] et al. [7] found that e-trust mediates the relationship between e-SQ and customer loyalty in a B2B context. Nevertheless, the mediating effects of CS and e-trust on the relationship between e-SQ and CUI remain unexplored in terms of e-commerce marketplace platforms. Therefore, the current study hypothesizes CS and e-trust as mediators in the relationship between e-SQ and CUI in e-commerce marketplace platforms.  
**Hypothesis 7 (H7).**_CS mediates between e-SQ and CUI._  
**Hypothesis 8 (H8).**_E-trust mediates between e-SQ and CUI._  
**Hypothesis 9 (H9).**_CS and e-trust mediate between e-SQ and CUI._  
This study investigates the relationships between e-SQ, CS, e-trust, and CUI in e-commerce marketplace platforms, focusing on the roles of CS and e-trust as mediating variables. Figure 1 presents the research model and the corresponding hypotheses.
# A Review of PCI Express Protocol-Based Systems in Response to 5G Application Demand
## 1 Introduction
### Organization  
In this paper, we discuss:  
1. Section 1: We review the market demand for 5G and the PCIe-based systems with components, based on Intel(tm), AMD(tm), and NVIDIA(tm) solutions for GPUs and CPUs for system design, as demanded.  
Section 2: We review PC industry capabilities and 5G application market demand for security, the IoT, AI, cloud computing, and storage.
* Section 3: Using a serial bus, we review PCIe base system technology with PCIe, NVMe with storage, and CXL. We review secure solutions with TPM.
* Section 4: We review PCI Express, CXL, USB, and a security technical roadmap. We review Intel(tm), AMD(tm), and NVIDIA(tm) product roadmaps for AI and deep learning.
* Section 5: Based on the product domain, we review the requirements for technical solutions.
* Section 6: We summarize system design solutions for AI, IoT, cloud computing, and storage.
* Sections 7 and 8: We describe the way in which system design using serial bus can simplify solutions for the problem of market demand.
## 4 Implementation and Results
### Cloud Computing Operation  
Based on the data collected from fog layers, the objective of maximization of total social welfare mentioned in Section 4.1 is implemented at the cloud computing layer. The detailed parameters of various stakeholders are listed in Table 1.  
When real-time optimization is performed at the cloud layer, the \(\omega\) at each time slot is taken as \(\varpi\) (average value) in Figure 8. The intelligent GA tool is used to optimize the decision-making objectives globally at cloud layer. The parameters of GA are selected as follows: population size is chosen as 40, maximum genetic algebra 30, mutation rate 0.05, and cross-inheritance is 0.6. Before calculating the whole time (1-24 h), we take the input data of 13 h to verify the validity of the algorithm. The convergence trajectory of the GA algorithm is shown in Figure 9.  
It can be seen that the results begin to converge in the 23 rd generation. The result of real-time purchasing load is shown in Figure 10.  
The real-time retail price of DN used to calculate the real-time revenues of stakeholders is shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Normal Customers** & **Prosumers** & **DSO** \\ \hline \(\alpha_{0}=0.3\) & \(\alpha_{1}=0.95\), \(\beta_{1}=0\) & \(a=0.78,b=1\) \\ \(\omega\in(0,1]\) & \(\alpha_{2}=1\), \(\beta_{2}=1.2\) & \(P_{\text{traff}}=25\text{Cent}/\text{kWh}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of various stakeholders in DN. DN: distribution network, DSO: distribution system operator.  
Figure 10: The amount of electricity purchased over 24 h in the wholesale market.  
Figure 9: Optimization convergence trace of genetic algorithm (GA).  
After the implementation at the fog/cloud layers, the DSO can create a purchasing strategy from the wholesale market. The wholesale market price is set as 0.353 cents/kWh. Finally, the cost and revenue of each stakeholder in a day (24 h) are listed in Table 2  
The above results show that different operational information in the DN can be organized and managed real-time under the proposed energy management architecture. The system provides an automated energy management and hour-by-hour decision-making process for the next-generation DN.
## 4 Discussion
Future studies using high-resolution industrial CT or coordinate measuring systems as a reference could overcome this limitation. Additionally, since no physical replica of the crown-preparation geometry was available for direct measurement, potential distortions during virtual segmentation and STL generation--especially from CBCT--may have contributed to observed inaccuracies. Since all datasets represent reconstructed geometries derived from different scanning modalities, the findings are inherently limited to relative comparisons. While the inclusion of a true reference model would have enabled a more definitive evaluation of dimensional accuracy, our comparative approach still provides valuable insights into the performance differences between CBCT-based and optical scanning methods under clinically relevant conditions. Another of the technical limitations of this study relates to the use of the STL file format for 3D model generation and analysis. While STL is widely supported by most dental CAD/CAM systems and adequate for surface-based geometric evaluation, it only encodes external surface geometry without preserving color, texture, or internal structural data. This may limit the fidelity of complex anatomical surfaces and potentially affect the precision of modeling workflows in more advanced applications. Future studies employing alternative file formats such as Polygon File Format (PLY) or Non-Uniform Rational B-Splines (NURBS), which retain richer geometric and surface detail, may offer improved accuracy for in silico modeling and digital dentistry simulations [38, 39].
# A General Calibration Method for Dual-PTZ Cameras Based on Feedback Parameters
## 4 The Estimation of Intrinsic Parameters  
Variations in a PTZ camera's intrinsic parameters are mainly caused by changes in the zoom value. The intrinsic parameters of the camera include focal length, distortion parameters, the principal point, etc. We mainly researched the calibration of the focal length and radial distortion parameters, which can affect the actual use of dual-PTZ cameras. The principal point is idealized as the zoom center [6], regardless of changes in the variable focal length. Using the previous calibration technique under different zoom values, we can obtain several groups of discrete, camera-intrinsic parameters using this method; hence, the functional relationship between the zoom value and focal length and the radial distortion is established. With this function, we can estimate the intrinsic parameters according to the zoom value fed back by the camera in real time.
# KeyNet: An Asymmetric Key-Style Framework for Watermarking Deep Learning Models
## 4 The _KeyNet_ Framework
### Watermark Extraction and Verification  
The verification process of ownership involves a would-be owner in the role of prover and the authority in the role of verifier. The would-be owner claims that a remote model \(h^{\prime}\) is part of his/her IP. The authority is given the WM carrier set \(D_{wm}\), the would-be owner's _signature_, the signing function \(sign\), and remote access to \(h^{\prime}\). The authority sets an accuracy threshold \(T\) and a number of required verification rounds \(r\) to decide whether \(h^{\prime}\) is the IP of the would-be owner. In each round, the authority randomly selects a sample \(c\) from \(D_{wm}\), signs it using _signature_ in a random position \(p_{k}\), and sends the signed sample \(c^{p_{k}}\) to the remote model \(h^{\prime}\). The predictions \(h^{\prime}(c^{p_{k}})\) (which contain the encoded WM information) are forwarded to the would-be owner. The latter passes them to her private model \(f\) to obtain \(l_{k}=f(h^{\prime}(c^{p_{k}})\). As the relationship between positions and labels is one-to-one, the would-be owner can use \(l_{k}\) to tell the authority the position \(p_{k}\) of her signature in \(c\). After \(r\) rounds, the accuracy _acc_ of the would-be owner at detecting the positions is the number of correct answers divided by \(r\). If \(acc\geq T\), then authority certifies that \(h^{\prime}\) is owned by the would-be owner.  
Note that the authority can also send the samples without signing them or sign them using fake signatures different from _signature_. In this case, the would-be owner should tell the authority that this sample does not contain her signature. That is possible because in these cases the private model gives them the label \(0\). Algorithm 3 formalizes the verification process.
```
0: Remote access to \(h^{{}^{\prime}}\), threshold \(T\), number of rounds \(r\)
0: Boolean decision \(d\) (True or False) on \(h^{{}^{\prime}}\)'s ownership.
1:\(correct=0\)
2:\(d=False\) //Decision on the ownership of \(h^{{}^{\prime}}\).
3:for each round \(i=1,2,\ldots r\)do
4:\(c\gets randomSample(D_{wm})\)
5:\(p_{k}\leftarrow\) randomPosition(), \(\text{k}\in 1,2,\ldots z\)
6:\(c^{p_{k}}\gets sign(signature,c,p^{k})\)
7:\(predictions\gets h^{{}^{\prime}}(c^{p_{k}})\)
8:\(l_{k}\gets f(predictions)\)
9:\(answer\leftarrow\) Position corresponding to \(l_{k}\)
10:if\(answer=p_{k}\)then
11:\(correct\gets correct+1\)
12:endif
13:endfor
14:
15:\(acc=correct/r\)
16:if\(acc\geq T\)then
17:\(d\leftarrow\) True
18:endifreturn\(d\)
```  
**Algorithm 3** Watermark Verification
## 2 Methods
### Setup of the Model Tests  
A total of 12 tests were performed (Table 2). To study the effects of wave height and frequency on the erosion ratio and retreat, we set up a variety of different waves for comparisons and parametrical study. Among them, a test is selected as the Baseline Case for a detailed explanation of various phenomena. The selection of the Baseline Case is based on the completeness of the test result data and the coverage of multiple different wave scenarios. The wave frequencies of the 12 tests ranged from 0.33 Hz to 1 Hz and the significant wave heights ranged from 1.72 to 5.42 cm. Table 2 lists the significant wave height H\({}_{8}\), wave frequency, erosion ratio, retreat, and Arias Intensity for each test.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Initial Water Level** & **Signifi. Wave Height** & **Wave Frequency** & **Erosion Ratio** & **Retreat** & **Arias Intensity** \\  & **H\({}_{\rm I}\)** & **H\({}_{\rm S}\)** & **f** & **E** & **R\({}_{\rm e}\)** & **I\({}_{\rm A}\)** \\  & **[cm]** & **[cm]** & **[Hz]** & **[\%]** & **[cm]** & **[m/s]** \\ \hline \multirow{3}{*}  
For comparison, we conducted three tests with the same setup (Baseline Case, Case 1, and Case 2). Varying wave frequencies of 0.33 Hz, 0.416 Hz, and 0.5 Hz were set in the Baseline Case and Cases 1 and 2, which are relatively lower frequencies compared to Cases 3 to 11, which used fixed wave frequencies such as 0.833 Hz, 0.916 Hz, and 1 Hz. To study the effects of different wave heights on the volume of slope erosion and retreat, this study generated waves of three different heights at 0.833 Hz, 0.916 Hz, and 1 Hz.  
This study assumed the [PERSON] model law scaling ratio, \(\lambda\), as 1/80. According to the [PERSON] model law, wave height of prototype = wave height of model/\(\lambda\), and wave period of prototype = wave period of model/\(\sqrt{\lambda}\). For the Baseline Case, the significant wave height and frequency/period of model scale, corresponding to prototype scales, are listed in Table 3. The significant wave height of the prototype in this study is about 1-4 m, and the wave period of the prototype corresponds to around 17-28 s. Waves with heights between 1 and 4 m are typically generated under moderate/rough sea states or slightly stronger storm conditions. The depth of the water in the flume is set to 30 cm, which corresponds to a 24 m depth of the prototype. The height of the slope model was built to 50 cm, which corresponds to a 40 m height of the prototype slope. For reference, in the 4 th column of Table 3, we also listed the Douglas Sea Scale and description of wave state according to World Meteorological Organization ([PERSON] et al. [35]).
## 1 Introduction  
During the outbreak of African Swine Fever (ASF) in South Korea in 2019, the media predicted that the pork supply would decrease and that pork prices would rise by the end of September of that year. At the end of October, after the ASF outbreak was controlled, however, pork prices collapsed due to a sharp fall in consumption [1]. This situation is thought to have stemmed from the reduced will of consumers to purchase pork, as news about the ASF outbreak was reported and the information was delivered to consumersthrough various types of media, including SNS. When livestock epidemics such as ASF or Avian Influenza (AI) occur, they can have a negative effect on demand and cause volatile changes to livestock prices because the supply of livestock products will be reduced because of the pre-emptive slaughter of animals at risk and the fall in consumer demand [2; 3]. Previous outbreaks such as Bovine Spongiform Encephalopathy and Foot and Mouth Disease demonstrated the effect on livestock products [2; 4; 5; 6; 7]. News about livestock diseases was considered to be an advertisement against livestock consumption [2; 5; 6].  
Recently, the outbreak of infectious diseases like ASF, AI, Middle East respiratory syndrome (MERS), and COVID-19 have affected the whole world. Many data-based approaches and algorithms have been proposed to predict the spread of diseases and to use social media data for disaster management [8; 9; 10; 11]. In this study, we wanted to test if there were threshold effects of infectious disease-related news on retail livestock prices and to determine the threshold values using a threshold autoregressive (TAR) model when there were outbreaks of major infectious diseases. We also aimed to determine if the response periods of livestock retail prices varied depending on the threshold effects after the price was initially affected. Finally, we compared the prediction accuracies of the TAR and autoregressive (AR) models of livestock retail prices to show the benefit of threshold models.