esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Experiment and Results Analysis
### Experimental Analysis and Summary
#### 4.3.4 Experiment on the Effect of Different CCRs on Speedup  
Experiments (1) to (3) show that the HSP-QGA algorithm has obvious advantages in scheduling length, convergence speed, and algorithm performance in both large and small tasks. However, in Experiment (3), speedup was significantly different when CCR = 0.1 and CCR = 4 were performed with the same number of tasks in the same algorithm.  
In this experiment, we set the same number of tasks, to observe the influence of different CCR values on the speedup, and compare and analyze the scheduling performance and applicability of the algorithm. The relevant parameters are uniformly set: the number of processors is 4, the number of tasks is 20, and the number of DAGs is 2. Experimental results are shown in Figure 12. When CCR = 0.1, computing costs are much higher than communication costs, the HSP-QGA has an acceleration ratio of 3.27, 24.3% higher than the PA-CGA, 29.8% higher than the ACOQ, and 40.9% higher than the ACO. When CCR = 6, communication costs are higher than computational costs, and the HSP-QGA has an acceleration ratio of 1.09, which is 13.5%, 11.2%, and 15.9% higher than the other three algorithms, respectively. When CCR = 8, the communication cost is far higher than the computation cost, and the acceleration ratio for all algorithms is less than 1. The value of CCR is usually used to determine the type of task scheduling. Tasks with CCR > 1 are expressed as communication-intensive, and vice versa for computational-intensive. Experimental results show that the HSP-QGA speedup is larger and performs better when CCR = 0.1; when CCR = 6, the performance of the algorithm is equivalent to that of a single machine, but with a slight advantage; when CCR = 8, the algorithm performance is already inferior to that of a single machine. Although the speedup of HSP-QGA is higher than other algorithms under different CCR values, the experimental results show that the algorithm is more suitable for computation-intensive task scheduling.  
Figure 11: Effect of the number of tasks on speedup: (**a**) CCR = 0.1; (**b**) CCR = 4.
## 3 Results and Discussion
### Impacts on Mechanical Properties and Moisture Resistance  
One of the key advantages of employing wood treated with our developed method in the construction sector is the ability of impregnated wood to minimize pore filling with crystallized composites, thereby reducing wood stitching and mitigating internal stresses during the drying process. This attribute proves invaluable in applications involving frame constructions with carpentry joints, as they are no longer susceptible to distortions caused by fluctuations in moisture levels. However, further studies are necessary to confirm these expectations.  
In our tests, impeccably dry impregnated wood (_Pinus_) displayed significantly less warping, averaging just 1.66% of thickness, in contrast to absolutely dry untreated wood, which exhibited standard warping levels, averaging 3.15% of thickness. Additionally, treated wood demonstrated reduced moisture expansion and shrinkage during drying. It is our belief that impregnation, leading to the crystallization of composites within the wood veins, contributes to the preservation of the wood's original geometric shape and enhances its flexural strength. A study by [PERSON] et al. showed that CaCO3 precipitation in poplar wood veins resulted in a 38% increase in flexural strength and a 53% increase in compressive strength compared to the control, as well as in higher wood density [42].  
Furthermore, this process is expected to decrease the occurrence of paint cracks, including fireproofing coatings, and results in a harder wood surface, thereby improving abrasion resistance.  
It is worth noting that the impregnation is long-lasting and does not hinder subsequent wood processing activities, such as cutting, sawing, sanding, or finishing. This flexibility allows the technology to be applied across a wide range of applications, including building materials, wooden structures, and covering materials. However, one limitation is that this process cannot be applied to completed structures at construction sites. Nevertheless, it can be seamlessly integrated into various mechanized and automated manufacturing processes.  
Importantly, our method employs only nonhazardous components for impregnation. As a result, in the end-of-life products, impregnated timber can be repurposed for other uses or managed as nonhazardous waste. This aligns with sustainability goals and offers an environmentally responsible approach to wood treatment and disposal.
# Modernising Receiver Operating Characteristic (ROC) Curves +
## Appendix A ROC, Item Response Theory, Control Charts, Rankits and Other Measures
### Ability and Task Difficulty in Control Charts  
Explanations that outline how classification task difficulty and classifier ability vary with a quality characteristic can be sought in earlier theories of control charts from statistical quality control, as noted by [PERSON] and [PERSON] [19]. [PERSON] [20] considered the 'ability of detecting a shift \(\Delta\) in mean \(\mu\) with a control chart' for an explanatory variable, \(x\), measured \(n\) times, with a normally distributed mean:  
\[\overline{x}=N\bigg{(}\mu,\frac{\sigma^{2}}{n}\bigg{)}\]  
Here, the mean of the control chart is shifted:  
\[\mu_{1}=\mu_{0}+\Delta\cdot\frac{\sigma}{\sqrt{n}}\]  
compared with the control limit \(USL_{x}\) which lies at a distance, \(L\), from \(\mu_{0}\):  
\[USL_{x}=\mu_{0}+L\cdot\frac{\sigma}{\sqrt{n}}\]  
The risk, \(\beta\), arising from \(\sigma\) of a false positive decision (FP, 'fall out'), for instance, is equal to the cumulative probability \(\Phi\) above \(USL_{x}\) while \(\overline{x}\) is within specification:  
\[\beta=P\Big{\{}\overline{x}\leq USL_{x}\Big{|}\mu=\mu_{1}\Big{\}}\]  
\[=\Phi(L-\Delta)\]  
\[=\Phi\Bigg{(}\frac{USL_{x}-\mu_{1}}{\frac{\sigma}{\sqrt{n}}}\Bigg{)}\]  
In the modern terminology of the Rasch formula of Equation (1), the decision risk, \(\beta\), will depend, according to [PERSON] and [PERSON] [19], on both (\"agent\") ability, \(\theta=ln(h)=-\frac{L}{\sqrt{2}}\), and (\"object\") difficulty, \(\delta=ln(k)=-\frac{\Delta}{\sqrt{2}}\) in units of \(\frac{\sigma}{\sqrt{n}}\). In turn, this gives:  
\[P(k|h)=1-\Phi\Big{(}-\sqrt{2}\cdot ln(h)+\sqrt{2}\cdot ln(k)\Big{)}=1-\Phi(L-\Delta)\]  
and leads to:  
\[\theta-\delta=-\frac{1}{\sqrt{2}}\Bigg{(}\frac{USL_{x}-\mu_{1}}{\frac{\sigma }{\sqrt{n}}}\Bigg{)}\] (A2)This is the basic expression, with the substitutions \(SL=USL_{x};x=\mu_{1};\textit{and}\ u=\frac{\sqrt{2}\sigma}{\sqrt{n}}\), used in Equation (2) to quantify how classification task difficulty and classifier ability vary with a quality characteristic in the present study.  
The rankit approach has been employed in case studies similar to the present study [7]. In that approach, the observed values are ordered in terms of their relative magnitudes as ranks, \(r=1,\cdots,n\), which can be compared with the Rasch approach to compensating for ordinality as follows:  
\[z_{rank}=\Phi^{-1}\left(\frac{r-\frac{1}{2}}{n}\right)\Rightarrow\]  
\[z_{rank}=\sqrt{2}\cdot(\theta-\delta);\textit{if}\ \Phi^{-1}\left(\frac{r- \frac{1}{2}}{n}\right)=\Phi^{-1}\left[1-P(k|h)\right]\]  
However, ranking data instead of compensating for known counted fractions effects (Appendix A.1) means that valuable, quantitative information is lost. It is unclear how measurement uncertainty, such as might increase by uncorrected nonlinearity, will affect the ranking of raw scores in non-parametric tests. As noted in A1, there seems to be little sense in attempting to correlate merely ranked data with fully quantitative instrument data for other physical properties.
## 1 Introduction
RC columns seldom experience pure axial compression pressures in real-world engineering scenarios. Therefore, it is crucial to enhance the material's ductility to improve the stress performance of the eccentric compressed elements. In 2019, [PERSON] [20] and colleagues performed eccentric compression tests on an engineered cementitious composite (ECC). The tests were performed with a considerable eccentricity of 120 mm. The results indicated that the ECC could substitute certain steel bars. In 2023, [PERSON] [21] and colleagues performed eccentric compression tests on low-eccentric-compression RC columns reinforced with an ECC. The test results showed the superiority of the small ECC-reinforced low-eccentric-compression RC columns compared with plain- mortar-reinforced RC columns. As a result, a variety of cementitious materials for reinforcement are used in a wide range of reinforcement applications, such as micromechanical reinforcement systems for ECC, where fibers play a key role in the establishment of ultra-high tensile ductility and the control of self-generated crack widths [22]. In addition to utilizing carbon nanotubes (CNTs) as reinforcing agents for ECC, nanomaterials have demonstrated the ability to impact porosity, enhance pore structure, expedite cement hydration, and augment the tensile and flexural strength of ECC in order to effectively suppress cracking and expansion. However, certain challenges still remain [23]. This will also serve as a trajectory for the advancement of cementitious materials like ECC and UHPC. Simultaneously, this particular cement-based substance will further advance in the realm of reinforcing. To summarize, applying UHPC-RC greatly enhances bearing capacity and ductility. However, the current research has mostly concentrated on the performance of axial compression. UHPC-reinforced eccentrically compressed columns are important for enhancing the brittle damage characteristics of minor eccentrically compressed aspects and increasing the ultimate bearing capacity of major eccentrically compressed elements of the component. Hence, this study suggests employing UHPC-strengthened RC columns for conducting eccentric compression testing. The experiments examined the impacts of the eccentricity, UHPC reinforcement thickness, and UHPC steel fiber dose on the damage pattern, deformation capacity, and bearing capacity of eccentrically compressed specimens. This study presents the formula for determining the compressive load capacity of UHPC eccentrically compressed specimens in the normal section. This formula serves as an experimental and theoretical basis for designing the cross-sections of UHPC eccentrically compressed specimens. Moreover, the results of the experimental study provide informative value for the application of eccentric specimen reinforcement or UHPC-RC composite structures in existing buildings. Additionally, they enable the provision of design value for the reinforcement application of UHPC and play a crucial role in advancing green buildings in terms of sustainability.
# Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach
## 6 Experiments and Results
### Effectiveness of Normalization Using ANN  
To verify the effectiveness of normalization using an ANN, the prediction accuracy of PPV under three conditions was compared: the proposed method (Nor-ANN), a method in which the normalization value range of parameters is uniformly set to [1; 11] (Nor-1-11), and a case where normalization is not performed (Nor-None). In the experiment, PPV data randomly extracted from the measurement data matrix was replaced with unmeasured values (=0), and PPV prediction was performed using WNMF. This allowed for a comparison between the actually measured PPV and the predicted PPV. The number of  
Figure 9: Memory settings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hidden Neuron** & **MSE** & \(R^{2}\) & **Best Epoch** \\ \hline
2 & 0.0587 & 0.493 & 4 \\
4 & 0.0381 & 0.837 & 8 \\
6 & 0.0245 & 0.874 & 17 \\
8 & 0.0816 & 0.605 & 5 \\
10 & 0.123 & 0.724 & 9 \\
12 & 0.121 & 0.566 & 7 \\
14 & 0.200 & 0.497 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison result of prediction model.  
unmeasured values (hereafter referred to as the number of unmeasured values) was set to 10, and the prediction accuracy of the WNMF for the number of unmeasured values was compared under each condition. When executing WNMF, the rank number was changed from 1 to 8, and the prediction accuracy for each rank was calculated. When comparing each condition, the rank with the highest prediction accuracy was used. The Root Mean Squared Error (RMSE) was used as an evaluation metric for prediction accuracy. RMSE is a metric commonly used to evaluate the prediction accuracy in recommendation systems. The calculation formula for RMSE is shown in Equation (9).  
\[\text{RMSE}=\sqrt{\frac{1}{t}\sum\left(Y-\hat{Y}\right)^{2}} \tag{9}\]  
where \(Y\) represents the actual value of PPV, \(\hat{Y}\) represents the predicted value of PPV, and \(t\) represents the number of missing values. As a result of WNMF, when normalization was performed using ANN, a higher prediction accuracy was shown than when normalization was uniformly performed or when normalization was not performed. Table 4 shows the PPV prediction results using WNMF. RMSE indicates higher prediction accuracy as the value is smaller. As shown in Table 4, Nor-ANN, Nor-1-11, and Nor-None all showed the smallest RMSE at rank 7. As a result of comparing the RMSE of each condition at rank 7, Nor-ANN showed a smaller value than Nor-11 or Nor-None. The results comparing the error ranges (difference between the actual value and the predicted value) for each condition are shown in Table 5. As a result, the error range of Nor-ANN was from \(-0.2281\) to \(0.2024\), and the difference was \(0.4306\). This shows a smaller error range than Nor-1-11 or Nor-None. Through the above results, it became clear that the prediction accuracy of PPV improves more when normalized using ANN than when normalized uniformly or not normalized.
## 4 Image Processing Methods of the Mode Shape  
After converting the vibration vector into the RGB image, the energy concentration position of the mode is determined by viewing the RGB image. As shown in Figure 10, the four modes at 35 kHz are selected. Figure 10a depicts mode 2 with a phase velocity of 1983.80 m/s, and its energy is concentrated at the base of the rail. Figure 10b shows mode 3 with a phase velocity of 2286.06 m/s, and its energy is concentrated at the rail web. Its maximum brightness is at the center of the rail web. Therefore, the crack at the rail web can be detected by this mode. The maximum vibrational positions of the other two modes are at the rail head and the rail base, respectively. Through simple image analysis and observation of the brightness distribution, it is possible to determine the location of the vibration energy concentration of each mode, and provide guidance for installation of the transducer when exciting specific modes. In the following, some simple image processing methods are adopted to analyze the mode image to obtain the vibration characteristics of the guided wave modes.
## 2 PMSM Mathematical Model  
The voltage equations of the PMSM in the \(abc\) reference system are the following:  
\[\begin{bmatrix}V_{a}(t)\\ V_{b}(t)\\ V_{c}(t)\end{bmatrix}=\begin{bmatrix}R_{s}&0&0\\ 0&R_{s}&0\\ 0&0&R_{s}\end{bmatrix}\cdot\begin{bmatrix}I_{a}(t)\\ I_{b}(t)\\ I_{c}(t)\end{bmatrix}+\begin{bmatrix}\frac{d\phi_{a}(t)}{dt_{c}^{2}}\\ \frac{d\phi_{b}(t)}{dt_{c}^{2}}\\ \frac{d\phi_{c}(t)}{dt}\end{bmatrix} \tag{1}\]  
where \(R_{s}\) is the stator's winding resistance, \(I_{a},~{}I_{b},~{}I_{c}\) are the stator's phase currents, and \(\psi_{a},~{}\psi_{b},~{}\psi_{c}\) are the stator's flux linkages, which are given by the following equations:  
\[\begin{bmatrix}\psi_{a}(t)\\ \psi_{b}(t)\\ \psi_{c}(t)\end{bmatrix}=\begin{bmatrix}L_{aa}&L_{ab}&L_{ac}\\ L_{ba}&L_{bb}&L_{bc}\\ L_{ca}&L_{cb}&L_{cc}\end{bmatrix}\cdot\begin{bmatrix}I_{a}(t)\\ I_{b}(t)\\ I_{c}(t)\end{bmatrix}+\begin{bmatrix}\psi_{apm}(t)\\ \psi_{bpm}(t)\\ \psi_{cpm}(t)\end{bmatrix} \tag{2}\]  
where \(L_{xx}\) is the self-inductance of the x phase, \(L_{xy}\) is the mutual inductance of the \(x\) and \(y\) phases, and \(\psi_{apm},\,\psi_{bpm},\,\psi_{cpm}\) are the flux linkages associated to the windings abc and the permanent magnets. A schematic representation of the PMSM model is shown in Figure 1.  
To simplify the above equations, the Park transform is used, where the Park matrix is obtained as:  
\[T^{\theta}=\frac{2}{3}\begin{bmatrix}sin(\theta)&sin(\theta-\frac{2\pi}{3})&sin( \theta+\frac{2\pi}{3})\\ cos(\theta)&cos(\theta-\frac{2\pi}{3})&cos(\theta+\frac{2\pi}{3})\\ 0.5&0.5&0.5\end{bmatrix} \tag{3}\]  
with respect to an arbitrary reference frame with reference angle \(\theta\) and \(q\)-axis alignment. By selecting \(\theta=\theta_{e}\), where \(\theta_{e}\) is the rotational reference angle, the simplified voltage equations are the following:  
\[\begin{split} V_{q}&=R_{s}I_{q}+L_{q}\frac{dI_{q}}{dt}+p \omega\big{(}I_{d}L_{d}+\psi_{pm}\big{)}\\ V_{d}&=R_{s}I_{d}+L_{d}\frac{dI_{q}}{dt}-p\omega I_{q}I_{q}\end{split} \tag{4}\]  
where \(p\) is the number of pole pairs, \(L_{q}\), \(L_{d}\) are the d-q reference frame inductances, \(\psi_{pm}\) is the flux linkage associated with permanent magnets. The zero sequence _Vo_ is neglected here.
# My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination
## 3 State of the Art
### Destination-Less Forwarding Protocols
#### 3.1.2 Spray and Wait Routing  
Spray and Wait routing [9] uses the same concepts for message exchange as Epidemic, i.e., SVs and re-sync period. It has been designed for destination-oriented applications, but can be transformed also for destination-less ones, since it is flooding-based. In contrast to the use of hop counts in Epidemic, the flooding is limited by the number of replications of each message. Any source node generating a message produces \(L\) copies initially and exchanges \(L\)/2 copies to a neighbour, keeping half of the copies to itself. This is the spray phase where nodes spray half the copies of messages they have, until only one copy exists at the source as well as all the forwarders of the message. The final copy at the source and forwarders is held (in their respective caches) until the destination node is encountered and delivered directly. This is the wait phase. The variations of spray and wait routing are based on the distribution of replicas to encountered nodes as explained in [10]. When used as a destination-less protocol, Spray and Wait will deliver the message to exactly as many nodes in the network as the number of original copies at the source. For example, if the number of replicas is 10, the first 10 encountered nodes will receive the message. This is obviously not desired. If we extend the number of replicas to the number of all nodes in the network, Spray and Wait becomes Epidemicagain. Thus, we do not consider Spray and Wait for our comparative study, but Epidemic.
## 3 Simulation Modeling and Verification
### Creation of a Surface-Fitting Contact Model  
A simulation analysis of the contact between the SG flexible tool and the marble surface was carried out using Abaqus, and the contact situation between the SG flexible tool and the marble surface during polishing is shown in Figure 5a,b. In the Abaqus software, a model of the SG flexible tool with identical dimensions was created, assigned the same experimental material properties, and divided into tetrahedral mesh elements. During mesh refinement, the mesh in the contact area was set to 1 mm. Five simulation models were created in the software, each representing a different curvature (k) of the marble surface: 0.004, 0.008, 0.012, 0.016, and 0.02. The free mesh division technique was used with a mesh refinement size of 1 mm in the contact area. The material properties of the marble surface were set based on the properties of Hanbaiyu marble, with a density of 2800 kg/m\({}^{3}\), [PERSON]'s modulus of 85 GPa, and Poisson's ratio of 0.28 [33]. In each simulation model, when the SG flexible tool contacted the marble surface, the marble surface was set as the master surface, and the SG flexible tool was identified as the slave surface. The friction coefficient in the contact area between the SG flexible tool and the marble surface was set to 0.
## 3 Results
### Chemical Characterization
While PC-1 was capable of separating CTRL, mainly in the 1 st quadrant, from the treatment's clusters, PC-2 allowed for the separation of the VAC and MAP treatments mainly in the 3 rd and 4 th quadrants, respectively. Regarding the UL samples (Figure 7b), the PCA was not able to discriminate between the MAP and VAC treatments, both being located close to the origin and mostly on the positive axis of the PC-1, while the CTRL samples were located on the negative axis of the PC-1--except for CTRL on day 0, which was positively associated with the underlying patterns captured by the PC-1. Figure 6: Functional group categories and number of VOCs identified by GC–TOF-MS for each: (**a**) _P. umbilicalis_ and (**b**) _U. lactuca_. As shown by Figure 8a, the compounds \"8-heptadecene (isomer 2)\" (code 2579-04-6) and \"heptadecane\" (code 629-78-7) in the 3 rd quadrant and \"pentadecanal\" (code 2765-11-9)\" in the 4 th quadrant seemed to be the most important variables to explain the changes in PU samples throughout storage days. Examining Figure 8b-d, it can be seen that the compounds \"2,4-decadienal, (_E_,_E_)-\" (code 25152-84-5) in the 1 st quadrant, \"2,4-decadienal (isomer)\" (code 2363-88-4) in the 2 nd quadrant, \"1-hexanol\" (code 111-27-3) in the 3 rd quadrant, and \"penaldehyde\" (code 100-52-7) in the 4 th quadrant of PC-1 vs. PC-2 seemed to be the most relevant variables that accounted for the variance in UL samples. Analyzing PC-2 vs. PC-3, \"8-heptadecene (isomer 1)\" (code 16369-12-3) in the 3 rd quadrant was distinguished as another relevant variable. In the case of PU seaweed (Table 1), the concentration of 8-heptadecene (isomer 2) appeared to increase over the storage period for both the CTRL and MAP treatments. By day 15, the MAP treatment exhibited a concentration approximately twice that of CTRL, with values of 27.679% and 14.502%, respectively. For the VAC treatment, the concentration on day 15 showed the lowest recorded level of 5.527%. Heptadecane displayed a similar behavior; by day 15, on one side, the concentration of heptadecane in MAP was approximately five times that of CTRL (with values of 10.283% and 2.037%, respectively), while, on the other side, the lowest level of 1.143% was recorded for the VAC treatment. Regarding pentadecanal, there was a significant increase in concentration for both CTRL and MAP on day 3, with values of 17.544% and 8.747%, respectively, compared to the initial concentration of 1.826%. On day 6, pentadecanal decreased in CTRL while increasing in MAP, showing a little oscillation on day 9 and, on days 12 and 15, the concentrations  
Figure 7: Principal component analysis (PCA) of the seaweed treatments and storage periods: Score plot graphics of PC-1 vs. PC-2 for (**a**) _Porphyna umbilicalis_ and (**b**) _Ulva lactuca_. were very close. For the VAC treatment, the concentration initially decreased but remained relatively stable during storage, averaging around 1.755%.