esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine
## 5 Related Work  
Since [PERSON] et al. [3] proposed the live migration of virtual machines, there has been considerable work in the development of VM live migration. [PERSON] et al. [9] implemented their system in a VMware platform. It is the first system that supported the migration of unmodified applications on an unmodified mainstream Intel x86-based operating system. Memory compression technology [2; 20] has been widely used to improve migration performance. In these systems, the compression ratio is very important. A small compression ratio will have little effect, while a larger compression ratio may consume too much computing resource.  
[PERSON] et al. [10] presented an adaptive pre-copy algorithm called MPP, which only transmits memory pages when a predefined threshold is met. It significantly reduces the unnecessary migration of memory pages. [PERSON] et al. [11] introduced a method to identify the workload cycles of a VM based on the information to reduce live migration overhead. [PERSON] et al. [12] proposed a novel data filter mechanism to improve the performance of the pre-copy algorithm in terms of the migration time and the amount of migrated data.  
Adaptive pre-paging and dynamic self-ballooning [13] are applied in post-copy algorithm by [PERSON] et al. Adaptive pre-paging technology will forecast the memory access pattern based on the page faults position and the spatial locality principle. The source VM will then adjust the memory pages of the transport order. [PERSON] et al. presented a new mechanism for post-copy algorithm that focuses on recovering the aggregate performance of the VMs being affected. [PERSON] et al. [15] improved the traditional post-copy algorithm by eliminating unnecessary remote page faults.  
Based on the pre-copy algorithm and the post-copy algorithm, [PERSON] et al. [17] designed a hybrid-copy algorithm. All memory pages in the source are copied to the destination first, then the post-copy method kicks. Many other hybrid-copy algorithm [16; 18] are proposed. However, there has been little research done on when to switch from the pre-copy phase to post-copy phase.  
Recently, [PERSON] et al. [5] studied live migration over wide area networks. They proposed a MLDO approach to reduce downtime during live migration over wide area networks. [PERSON] and [PERSON] [21] proposed a geometric programming model and an online multi-VM live migration algorithm based on such model. [PERSON] et al. [22] also studied the live migration for multiple VMs and the parallel migration strategy.
###### Abstract  
Objective. The purpose of this ex vivo study was to compare the Trueness of traditional and digital workflows and to analyze the interfacial fit of CAD/CAM restorations on gypsum and 3D-printed casts (3 DC). Methods: Forty patients underwent indirect posterior adhesive restorations. After tooth preparation, both traditional and chairside procedures were followed. Obtained models were scanned to generate STL files of the intraoral impression (IOS), the conventional cast (RS), and the 3D-printed cast (3 DCS). Superimposition of the casts was performed to evaluate Trueness. Then, for each preparation, two identical CAD/CAM restorations were milled and luteted on RS and 3 DC. Micro-CT scan was performed to evaluate 3D interfacial fit. Results. Surface Trueness analysis showed no significant differences among groups (\(p>\) 0.05), with average Trueness ranging from 11.56 to 17.01 \(\upmu\)m. Micro-CT analysis showed significant differences between gypsum casts (average ranging from 135.78 to 212.31 \(\upmu\)m) and 3 DC (average ranging from 57.63 to 144.55 \(\upmu\)m) for both marginal and internal fit. Conclusions. In adhesive restorations manufacturing, digital and conventional procedures generate casts that are not significantly different. Marginal fit of adhesive restorations is similar to conventional crown design and clinically acceptable. It is assumable that a direct digital workflow could benefit from the usage of 3 DC.  
intraoral scanner; CAD-CAM; model 3D printing; indirect adhesive restorations; Trueness 3D
###### Abstract  
Despite the potential benefits of reducing system costs and improving spectral efficiency, it is challenging to implement cloud radio access network (C-RAN) systems due to the performance degradation caused by finite-capacity fronthaul links and inter-cluster interference signals. This work studies inter-cluster cooperative reception for the uplink of a two-cluster C-RAN system, where two nearby clusters interfere with each other on the uplink access channel. The radio units (RUs) of two clusters forward quantized and compressed version of the uplink received signals to the serving baseband processing units (BBUs) via finite-capacity fronthaul links. The BBUs of the clusters exchange the received fronthaul signals via finite-capacity backhaul links with the purpose of mitigating inter-cluster interference signals. Optimization of conventional cooperation scheme, in which each RU produces a single quantized signal, requires an exhaustive discrete search of exponentially increasing search size with respect to the number of RUs. To resolve this issue, we propose an improved inter-BBU, or inter-cluster, cooperation strategy based on layered compression, where each RU produces two descriptions, of which only one description is forwarded to the neighboring BBU on the backhaul links. We discuss the optimization of the proposed inter-cluster cooperation scheme, and validate the performance gains of the proposed scheme via numerical results.  
C-RAN; inter-cluster cooperation; constrained fronthaul/backhaul; layered compression Article
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 4 Mechanism of Biofilm Formation
### Biofilm Maturation
#### 4.3.3 Extracellular DNA  
eDNA is a component of biofilm matrices and is important for stabilizing biofilm structure. This component is involved in other mechanisms, such as recombination, transformation, and genomic variability. Restriction Fragment Length Polymorphism (RFLP) analysis between intracellular DNA and extracellular DNA concludes that eDNA may not come from the lysis of cells but is purposively secreted to extracellular spaces [63]. eDNA is protected from DNase I and nuclease degradation by other extracellular polymeric substances [64]. Aggregation of OMV, as the early steps for biofilm formation, were also induced by eDNA [63]. The circulating DNA in the biofilm contributes to the higher recombination rate [65; 66].
# Optimization of Water Pressure of a Distribution Network within the Water-Energy Nexus
## Materials and Methods
### Optimization Method  
Water companies are able to alter the pressure of the whole WDN of a city or a specific zone by installing PRV or pumping stations, which reduce or increase the average pressure of the affected zone. Decreasing Have will reduce the operating and maintenance costs of the WDN and will also decrease hav1 for each node, leading to an increase in the operating costs of WPBS. On the other hand, increasing Have will increase the operating and maintenance costs of the WDN and will also increase hav1 for each node, leading to a decrease in the operating costs of WPBS. These conflicting interests between water companies and water consumers call for an integrated water and energy approach to facilitate optimal decisions regarding the WDN average pressure in any zone of the city based on local conditions. To do this, the costs of the water sector and the energy sector related to altering the average pressure must be analyzed.  
According to Section 2.2, the cost of energy consumed by WPBS can be calculated using the following equation:  
\[\text{C}_{\text{E}}=\text{ c}_{\text{E}}\times\text{P} \tag{8}\]  
In this formula, \(\text{C}_{\text{E}}\) is the operating cost of the WPBS and cE is the price of energy. In addition, the operating and maintenance cost of a WDN, which is affected by altering Have, can be calculated using the following equation:  
\[\text{C}_{\text{W}}=(\text{c}_{\text{B}}\times\text{BF}_{1})+(\text{c}_{\text {W}}\times\text{L}_{1}) \tag{9}\]  
In this formula, \(\text{C}_{\text{W}}\) is the operating and maintenance cost of the WDN, cB is the cost of repairing pipe burst, and cw is the price of water. Finally, the optimum pressure point for Have is calculated by the following equation:\[\begin{split}\text{Minimize}:\text{ OF }=\ (\sum\limits_{i=1}^{j}\sum\limits_{k=0}^{23}\text{C}_{\text{E}}(i,k))\ +\ (\sum\limits_{k=0}^{23}\text{C}_{\text{W}}(k))\\ \text{For }1\ \leq\ i\ \leq\ j\ \text{: H}_{\text{min}}<\ (\ h_{\text{avl}})_{i}<\ H_{\text{max}}\end{split} \tag{10}\]  
In this objective function, \(j\) is the number of nodes of the WDN. \(\text{H}_{\text{ave}}\) is the decision variable, which in practice can be changed by installing PRV. The mentioned constraint limits the available pressure of each node that the water company provides. \(\text{H}_{\text{min}}\) and \(\text{H}_{\text{max}}\) denote the minimum and maximum allowable pressure heads at the consumption nodes, respectively. Since the water demand and energy price vary throughout a day, the optimization equation calculates hourly water and energy costs over 24 h.
# Reversible Image Processing for Color Images with Flexible Control
## 3 Proposed Method
### Image Processing
#### 3.1.2 Brightness Increase or Decrease  
The proposed method can increase or decrease the brightness by shifting the integrated histograms of \(Max\) and \(Min\). The integrated histogram is essential for the proposed method so as to ensure that the magnitude relation is invariant before and after the shifting process. In the practical implementation, our method concatenates the \(Max\) and \(Min\) matrices to obtain the integrated histogram while splitting the concatenated matrix into the \(Max^{\prime}\) and \(Min^{\prime}\) ones. We will first describe the brightness increase function:  
1. [label=Step 0: 0]
2. [label=Step 0: 0]
3. Integrate the histograms of \(Max\) and \(Min\) (see Figure 4a).
4. Define the rightmost bin of the histogram as the reference bin. In the case where the number of pixels belonging to the reference bin exceeds 1% of the total number of pixels, the left adjacent bin is alternatively defined as the reference bin.
5. Shift the pixels contained in the bins between the reference and the leftmost ones by \(+1\) (see Figures 4b,c). In the case where the reference bin is not empty, the pixels in the reference and left adjacent bins are superimposed (see Figure 4c).
6. Separate the integrated histogram into the \(Max^{\prime}\) and \(Min^{\prime}\) ones (see Figure 4d).  
In comparison, in the brightness decrease function, the leftmost bin is defined as the reference bin in Step 2, and the pixels belonging to the bins between the reference and rightmost ones are shifted by \(-1\) in Step 3. Here, the threshold of 1% in Step 2 is the optimal parameter determined through our experiments. By introducing this parameter, the proposed method can automatically select a bin with a small number of pixels as a reference bin. The reference bin will be merged with the adjacent bin. This leads to the reduction of the amount of recovery information. We define the numbers of processing times for a brightness increase or decrease as \(I_{VI}\) and \(I_{VD}\), respectively. These parameters enable us to control the degree of effect of these functions. We will give details on the essential information for the recovery process in Section 3.1.3.  
Figure 3: Sharpening and smoothing using \(Max\). (**a**) Region segmentation. (**b**) Target and reference points.
## 5 Conclusions  
This article outlines a simulation for S-parameters and absorbed energy in polymeric nanocomposites containing metallic inserts (iron and aluminum, featuring two particle sizes). The examined frequency range of 0.1-3 GHz is tailored for numerous applications in microwave technologies. The electromagnetic simulation software selected was CST  
\begin{table}
\begin{tabular}{c c c c} \hline
**Insertion Type** & **1 GHz** & **2 GHz** & **3 GHz** \\ \hline Fe—800 nm & 14.8 & 38.7 & 40.5 \\ \hline Al—800 nm & 25.7 & 61.2 & 64.4 \\ \hline Fe—50 nm & 47.3 & 81.8 & 86.6 \\ \hline Al—50 nm & 51.5 & 98.2 & 104.1 \\ \hline \end{tabular}
\end{table}
Table 4: Attenuation [dB] vs. frequency for 1 mm layer, 8% mass ratio, Al and Fe inserts.  
Figure 22: S\({}_{21}\) transmission coefficient comparison for composites with 800 nm inserts.  
Studio Suite, analyzing the power loss at different frequencies, serving as a necessary step in advanced bonding technology within the microwave range. The real constraints in the electromagnetic simulation are detailed, including a related application of the Nicolson-Ross-Weir method in infinite media, which unveils new perspectives in modeling the behavior of nanocomposites.  
It was shown that, because of their greater conductivity, Al particles can capture more energy than Fe particles in composite materials, at both insertion scales. At elevated frequencies, the power loss density increases for both metals. Overall, based on the data, the reflection phenomena are minimal. It was demonstrated that adding nanometric inserts will decrease transmission across the material. It is evident that the transmission drops considerably as the frequency increases in the GHz spectrum, although a saturation effect was noted after 2-2.5 GHz. In general, a value over 80 dB is considered sufficient for a 1 mm thick hot melt layer; nonetheless, it is observed that the highest values are reached for Al insertions and, correspondingly, for both metallic particles sized at 50 nm, reinforcing the findings from the power loss simulations.  
The substantial decrease in transmission, associated with a slight reflection, confirms that composites containing metallic powder absorb microwave energy effectively, especially at frequencies exceeding 1.5 GHz, validating their possible use as hot melts for innovative reversible bonding applications.  
Conceptualization, R.F.D. and R.C.C.; methodology, R.F.D. and R.C.C.; validation, R.C.C.; formal analysis, R.F.D. and R.C.C.; investigation, R.F.D. and R.C.C.; data curation, R.F.D. and R.C.C.; writing--original draft preparation, R.F.D. and R.C.C.; writing--review and editing, R.C.C.; visualization, R.F.D. and R.C.C.; supervision, R.C.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data are presented in this study.  
The authors declare no conflicts of interest.
# Influence of Structural Parameters on Deformation Behavior of Metal Rhombic Chain Mesh
## 2 Materials and Methods
### Model Test Validation  
To validate the effectiveness of the numerical simulation method considering pretension effects, a testing apparatus and methodology incorporating pretension effects were designed. The test netting measured 770 mm \(\times\) 570 mm, with a wire diameter of 3.2 mm and a mesh size of 53 mm for the metal chain netting. The netting material had an elastic modulus of 193 GPa and a density of 6300 kg/m\({}^{3}\). To ensure uniform force transmission, angle steel of 40 mm \(\times\) 40 mm \(\times\) 4 mm was selected for the test frame. Pretension was applied unidirectionally with four tension points on each side, with tension forces measured through force sensors. The chain netting was tested under five initial pretension (FT) conditions: 200 N, 250 N, 300 N, 350 N, and 400 N; four concentrated load (FN) conditions: 80 N, 90 N, 100 N, and 110 N; and four distributed load (FD) conditions: 8 N, 9 N, 11 N, and 12 N. Both concentrated and distributed loads were applied statically by hanging mass blocks at chain netting nodes. Wire displacement at measurement points was measured using a WXY80 wire-pull current pulse displacement sensor, as shown in Figure 5.  
The metal netting was configured with 1 concentrated load point and 59 distributed load points, as shown in Figure 6, and 23 vertical displacement measurement points, as  
Figure 4: Integral and local finite element model of mesh coat.  
Figure 5: Mesh coat arrangement diagram.  
shown in Figure 7. All loads were applied perpendicular to the netting plane, with load application points and displacement measurement points illustrated in Figure 8.  
Figure 9 presents the validation of the numerical simulation against the experimental results for vertical displacements at 23 measurement points under concentrated and distributed loads. During testing, the initial chain netting pretension (FT) was 200 N, with concentrated loads (FN) of 80 N, 90 N, and 100 N, and distributed loads (FD) of 8 N, 10 N, and 12 N. The vertical displacement refers to the change in vertical direction measured by wire-pull displacement sensors after load stabilization.  
Figure 6: Arrangement of measurement points.  
Figure 7: Arrangement of loading points. \(\square\)\(\square\)\(\square\) indicates the location of the loading point for centralized loading, and \({}^{\prime}\triangle^{\prime}\) indicates the location of the loading point for distributed loading.  
metal netting wires offers the advantages of a lower computational cost while maintaining a higher calculation accuracy, effectively meeting the computational requirements for metal netting deformation.
## 3 Numerical Simulation Study of Sub-Millimeter Liquid Droplets
### Computational Analysis
\[\frac{\partial}{\partial t}(\alpha_{g}\rho_{g}u_{g,i})+\frac{\partial}{\partial x_ {j}}\big{(}\alpha_{g}\rho_{g}u_{g,i}u_{g,j}\big{)}=-\alpha_{g}\frac{\partial p} {\partial x_{j}}+\frac{\partial\tau_{g,ij}}{\partial x_{j}}+\frac{\partial\tau _{gg,ij}}{\partial x_{j}}+\frac{\alpha_{g}\rho_{g}}{\tau_{r}}(u_{g,i}-u_{l,j} )+\alpha_{g}\rho_{g}g_{j}-F_{g,sj}, \tag{9}\]  
\[\rho_{l}\left[\frac{\partial}{\partial t}(\alpha_{l}u_{l,j})+\frac{\partial}{ \partial x_{j}}\Big{(}\alpha_{l}u_{l,j}\Big{)}\right]=-\alpha_{l}\frac{ \partial p}{\partial x_{j}}+\frac{\partial\tau_{l,ij}}{\partial x_{j}}+\frac{ \partial\tau_{l,sj}}{\tau_{r}}+\frac{\alpha_{l}\rho_{l}}{\tau_{r}}(u_{g,i}-u_ {l,j})+\alpha_{l}\rho_{l}g_{j}-F_{l,sj}. \tag{10}\]  
In the momentum equation, \(\tau_{m,ij}\) is the molecular stress tensor and \(\tau_{s,ij}\) is the Reynolds stress tensor whose components have the following form:  
\[\tau_{m,ij}=\mu_{m}\left(\frac{\partial u_{m,i}}{\partial x_{j}}+\frac{ \partial u_{m,j}}{\partial x_{i}}-\frac{2}{3}g_{ij}\frac{\partial u_{m,l}}{ \partial x_{l}}\right)(m=g,l) \tag{11}\]  
\[\tau_{ms,ij}=\mu_{t}\left(\frac{\partial u_{m,j}}{\partial x_{j}}+\frac{ \partial u_{m,j}}{\partial x_{i}}\right)-\frac{2}{3}\mu_{t}g_{ij}\frac{ \partial u_{m,l}}{\partial x_{l}}-\frac{2}{3}\rho_{m}k\delta_{ij}(m=g,l), \tag{12}\]  
where \(\tau_{r}\) is the velocity relaxation time, \(F_{sj}\) is the surface tension, \(\mu\) is the molecular dynamic viscosity, \(\mu_{t}\) is the turbulent viscosity coefficient, \(m=g\) denotes the gas phase, and \(m=l\) denotes the liquid phase. \[\frac{\partial}{\partial t}(\rho E)+\frac{\partial}{\partial x_{j}}\big{[}u_ {j}(\rho E+p)\big{]}=\frac{\partial}{\partial x_{j}}\Bigg{(}k_{eff}\frac{ \partial T}{\partial x_{j}}\Bigg{)}+\frac{\partial}{\partial x_{j}}\Big{(} \tau_{eff,ij}\cdot u_{i}\Big{)}. \tag{13}\]  
In the above energy equation, \(E\) is the total energy, \(k_{eff}\) is the effective thermal transmittance, and the effective viscous stress tensor, \(\tau_{eff,ij}\), is given as follows:  
\[\tau_{eff,ij}=\sum_{m=g,l}\alpha_{m}\rho_{m}\big{(}\tau_{m,ij}+\tau_{ms,ij} \big{)} \tag{14}\]  
In numerical simulations, where the gas phase is considered an ideal gas, it is imperative to augment the ideal gas state equation to complete the control equations mentioned above as follows:  
\[p=\rho_{g}RT \tag{15}\]  
where \(p\) represents the gas pressure, R is the gas constant, and \(T\) stands for the temperature.
## 4 Discussion  
### Spatial Distribution of the IWUE of Forest Ecosystems  
The IWUE of forest ecosystems exhibited an obvious latitudinal effect, with an initial increase followed by a decrease with increasing latitude. This can be explained by the decrease in solar radiation with increasing latitude, resulting in decreased evapotranspiration and, thus, an improved IWUE. However, when the latitude increased to 35\({}^{\circ}\) N, the carbon sequestration ability of the forest ecosystems decreased due to low temperatures, leading to a gradual decline in WUE. This indicates that temperature is the main factor affecting the IWUE with changes in latitude. Similarly, [PERSON] et al. demonstrated that the annual net carbon flux varies linearly with latitude, most likely because latitude mainly affects temperature and radiation [26]. [PERSON] et al. also found that temperature and solar radiation were the main factors controlling the annual WUE of forest ecosystems [27]. This also confirms that the lower IWUE of ENF since half of ENF was distributed in high latitudes with lower annual temperatures. At the same latitude, the IWUE significantly differed among forest ecosystems; in particular, the IWUE of EBF was the highest. This may be because EBF were mainly distributed at low latitudes, and plants have a greater carbon sequestration potential at appropriate temperatures. In addition, the high canopy density formed by EBF results in higher humidity, inhibiting evapotranspiration and, therefore, maintaining a higher IWUE. Besides, the thicker litter layer in EBF limits the evaporation of soil moisture [28]. [PERSON] et al. have reported similar results [24], whereas [PERSON] observed that the WUE of needle-leaved tree species was higher than that of broadleaved tree species since needle-leaved trees have a higher carbon sequestration capacity [29]. This discrepancy may be due to the difference in research scales; whereas [PERSON] used the stand scale, in the present study, the ecosystem scale was used. The IWUE of DBF was significantly lower than that of other ecosystems, most likely because of the short growing season of DBF, with about 6 months for carbon fixation and all 12 months for evapotranspiration, resulting in a low IWUE. [PERSON] et al. also showed that although broadleaf forests usually store more water than needle-leaved forests, the WUE of needle-leaved forests is lower because of excessive transpiration [30].
## 3 Performance Characterization and Reliability Assessment
### SEM Analysis  
Figure 11 presents crucial cross-sectional Scanning Electron Microscope (SEM) images that reveal the bonding interface characteristics for each of the three resonator width configurations. The images, captured using a HITACHI S-4700 microscope (Hitachi High-Technologies Corporation, Tokyo, Japan), provide detailed visualization of the metal-to-metal bonding regions between the cap wafer and device substrate. The L1 configuration (left image) shows the edge bonding area with a width of approximately 10 \(\upmu\)m, where bonding occurs only at the periphery since this design lacks resonators in the cap wafer. The L2 configuration (middle image) displays the partial width matching with the cap resonator width of 85 \(\upmu\)m compared to the device substrate resonator width of 220 \(\upmu\)m, creating an asymmetrical bonding interface. The L3 configuration (right image) exhibits the most uniform bonding with matching resonator widths between cap wafer and device substrate, featuring a bonding overlap of approximately 10.3 \(\upmu\)m even with slight misalignment. All three configurations show minute voids of approximately 180 nm at the bonding interface, which the researchers note is a common challenge in Au-Au thermocompression bonding processes. Despite these microscopic voids, the SEM analysis confirms that the L3 configuration achieves the most optimal bonding quality while demonstrating that misalignment within \(\pm\)10 \(\upmu\)m does not significantly impact the RF performance or mechanical integrity of the devices. These microscopic insights directly correlate with the superior mechanical strength demonstrated by L3 in subsequent reliability tests and explain the enhanced electrical performance observed in RF measurements.