esa-sceva/satcom-chunk-collection huggingface dataset:
# A Parallel Algorithm for Scheduling a Two-Machine Robotic Cell in Bicycle Frame Welding Process
## 7 Computational Experiments
### Experimental Setup  
Both the new parallel and the sequential algorithms were implemented in C++ and compiled under MSVC++ 14.29. The parallel algorithm was partially written in CUDA C++. To solve APSP in the parallel algorithm, Floyd-Warshall algorithm was used (for implementation details, see in [50]). The MILP model was solved by Gurobi 9.1.2 Optimizer, using a single thread. Experiments were conducted on a PC equiped with Intel Core i7-4930K CPU @3.4 GHz, 32 GB of RAM and GeForce GTX 1080 Ti GPU, with 11 GB GDDR5X memory and 3584 NVIDIA CUDA cores. The test CAP instances were generated with \(m=2\) and a varying number of jobs  
\[n=\{16,32,64,128,256,512,1024,2048,4096,8192\}.\]  
The computational times of both the proposed parallel and the sequential algorithms are invariant to the numerical values of the jobs and setups durations. However, small values are preferred for MILP solver, due to numerical reasons. Therefore, both job and setup times were taken from a discrete uniform distribution \(\mathcal{U}\{0,100\}\). For each instance size, 100 instances were generated for \(n\leq 2048\) and 10 instances for \(n>2048\). Then, for each instance, the following parameters were measured and averaged:CPU--sequential algorithm [12] run time;
* GPU--parallel algorithm run time;
* MILP--sequential MILP solver run time (for smaller instances only).  
Additionally, for the parallel algorithm, a speedup to CPU was also calculated. The speedup is computed to illustrate the feasibility and effectiveness of the GPU implementation. Due to vastly different architectures and hardware parameters, the result cannot be directly used to derive a relation between the number of processors and the speedup.
# Sensor Observation Service API for Providing Gridded Climate Data to Agricultural Applications
## 4 Evaluation of SOS API Performance
### Caching Status  
There are two conditions of caching a request: yes and no.  
The responses of a one-year range of one-element in Table 9 show a similarly good result in any accessing mode and caching status condition. As the challenge of this research is to provide long-term data with multi-meteorological variables, we then tried requesting a 30-year range with four meteorological variables. The responses of requesting data without caching on single- and multi-thread mode are very slow. The slow responses were caused by the structure of AmGSD, which stores each climate variable of each year separately. This means that 120 NetCDF files of AmGSD are read and then returned to NARO Agent. Under multi-processing mode, the response time reduces to 91.882 s, an improvement of 86 percent. The multi-processing mode is therefore a practical option for this structure. From the viewpoint of a service provider, the optimization of the response performance and CPU load can be done by setting a maximum number of processes depending on the number of available CPUs. In our case, we set the maximum processing number to 120 processes as mentioned in the AmGSD accessing mode. The multi-processing is set as the default for providing the service.  
The response with caching of all scenarios shows a similar time, between 0.6 and 1.6 s, which is in the acceptable range for users based on the response time guideline from the information visualizer [30]. Response performance is significantly improved by the combination of caching and the multi-process implementation.
## 2 Materials and Methodology
### Methodology
#### 2.2.6 Evaluation Metrics  
In this study, we utilize several model evaluation metrics to assess our approach. The metrics employed include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (\(R^{2}\)). These metrics provide comprehensive insights into the performance of the model. The model formulas are listed below:  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2} \tag{13}\]The RMSE is as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}}{\left({{{\hat{y}}_{i}}-{{ \mathbf{y}}_{i}}}\right)^{2}}} \tag{14}\]  
The MAE is as follows:  
\[\text{MAE}=\frac{1}{n}{\sum\limits_{i=1}^{n}}{\left|{{{\hat{y}}_{i}}-{{ \mathbf{y}}_{i}}}\right|} \tag{15}\]  
The \(R^{2}\) is as follows:  
\[R^{2}=1-\frac{{\sum\limits_{i=1}^{n}}{\left({{{\hat{y}}_{i}}-{{ \mathbf{y}}_{i}}}\right)^{2}}}{{\sum\limits_{i=1}^{n}}{\left({{{\mathbf{y}}_{i}}- {{\mathbf{y}}}}\right)^{2}}} \tag{16}\]
# Dynamic-RPL: Enhancing RPL-Based IoT Networks with Effective Support of Dynamic Topology Management
## 6 Experimentation
* Throughput: the average of the total number of bits in all the successful transmissions of the data packets per second. * Delay: the average time taken using all the data packets from being transmitted to received. * ETX: the average of the total number of transmissions/retransmissions made to achieve successful delivery of the data packets. * Overhead rate: the average number of local and global control packets being exchanged over the network. * Energy consumption: the energy consumption data provided with the powertrace module indicate the time spent during different more states (CPU, low power mode, transmit, and listen states). Having these multiplied by their corresponding current consumption levels and the power supply voltage (as specified in [33]), the average energy consumption was then calculated in the unit of Joules. * Convergence time: the time needed using all nodes to join the network after having the network originally initiated using the first-running sink node. * Resolution time: the time required for Dynamic-RPL to completely restore the network performance after it started to degrade. This was calculated and then averaged for each simulation run. * Adjustment time: the average time taken using a node running Dynamic-RPL to complete a dynamic topological adjustment during each simulation run.
## 4 Primary and Secondary Paths  
Active control is employed to reduce noise in this research, hence knowledge of the dynamic properties of primary and secondary paths in the frequency range of interest for active control will facilitate the control system development and implementation [12]. Their analysis is provided in this section.  
Primary and secondary paths' models in the form of Finite Impulse Response (FIR) filters of length \(N_{m}=128\) were obtained experimentally. During the real system identification experiment, each path was excited with a optimized multi-tonal signal of 4096 samples. The signal was reproduced and the response was recorded eight times for each path. Afterwards, the correlation method was used to estimate the average impulse response of the considered path. The identified models were used both in the control system, e.g., for the Filtered-x part of the FxLMS algorithm, and for the paths' analysis presented in this section. The amplitude function of the identified impulse response estimate is referred to as amplitude response.  
Figure 2: The schematic representation of the laboratory setup (all dimensions are given in mm).
## 3 The Channel Statistics of the HST Environment
### The Stationary Interval  
The averaged power delay profiles (APDPs) defined in [21] can be used to compute the stationary interval.  
\[\overline{B_{h}}(t_{k},\tau)=\frac{1}{N_{PDP}}\sum_{k}^{k+N_{PDP-1}}\left|h_{pq} (t_{k},\tau)\right|^{2} \tag{18}\]  
\[h_{pq}(t_{k},\tau)=\sum_{i=1}^{I}h_{i,pq}(t_{k})\delta(\tau-\tau_{i}) \tag{19}\]  
where \(t_{k}\) is the time of the \(k\)th snapshot. \(N_{PDP}\) represents the averaged number of power delay profiles.  
By using Equation (11), the coefficient of correlation between two averaged number of power delay profiles is obtained using the following equation:  
\[c(t_{k},\Delta t)=\frac{\int\overline{B_{h}}(t_{k},\tau)\overline{B_{h}}(t_{k }+\Delta t,\tau)d\tau}{max\int\overline{B_{h}}(t_{k},\tau)^{2}d\tau\overline{B_ {h}}(t_{k}+\Delta t,\tau)^{2}d\tau} \tag{20}\]  
After that, the stationary interval is determined using:  
\[T_{s}(t_{s})=max\Delta t|c(t_{k},\tau)\geqslant c_{thrh}; \tag{21}\]  
where \(c_{threshold}\) is the correlation coefficient threshold. Similarly, the stationary distance can be computed as  
\[S_{d}(t_{k})=max\Delta_{d}|c(t_{k},\tau)|\geqslant c_{thrh}; \tag{22}\]  
where \(\Delta_{d}=v_{R}\times\Delta t\).
## 4 Discussion
### Optimization Study on EHTC Calculation Model  
Considering that the occurrence of rainfall and evaporation heat transfer do not correspond exactly in real time, we calculated EHTC with some simplifications and hypotheses. It must be pointed out that our study on EHTC is still in the preliminary exploration stage for the presentation of the evaporative concept, discussing the degree of influence of EHTC on THTC and indicating that EHTC is non-negligible in the tropical island region. To date, the daily average EHTC can be obtained by the simplified calculation model, and the optimization study on the calculation model will appear in our follow-up work.
# Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities
## 3 The Compression Framework
### Preservation of the Derived Quantities
#### 3.2.2 Quantization of the Lagrange Parameters  
The constraint satisfaction step produces four Lagrange multipliers per \(39\times 39\) histogram, and therefore we need to store four additional floating-point numbers per node. Since the parameters \(\mathrm{V}^{1},\dots,\mathrm{V}^{4}\) can be obtained from the metadata to which all users have access and represent the common geometry across all timesteps in the XGC application, we do not consider those parameters as additional costs. To improve storage efficiency, we apply PQ to quantize the Lagrange multipliers. The number of sub-quantizers is set as equal to the number of Lagrange multipliers. To quantize the Lagrange parameters with \(B_{L}\) bits, each sub-quantizer of PQ can use \(2^{B_{L}}\) centroids to represent each Lagrange parameter, and thus the number of entries in the entire PQ dictionary is \(4\times 2^{B_{L}}\). We store both the quantized Lagrange parameter indices and the PQ dictionary. The \(B_{L}\) parameter can be set to achieve a suitable trade-off between the overall compression ratio and the QoI error.  
Importantly, the change of PD error after the constraint satisfaction step needs to be considered. Suppose the maximum quantization error of Lagrange multipliers is \(\epsilon_{L}\). From Equation (23), \(\frac{f_{ij}}{f_{ij}^{0}}\) is bounded:  
\[\frac{f_{ij}}{f_{ij}^{0}}\leq\exp\Big{(}-K_{ij}+\Big{|}\epsilon_{L}\Bigl{(} \mathrm{V}_{ij}^{1}+\mathrm{V}_{ij}^{2}+\mathrm{V}_{ij}^{3}+\mathrm{V}_{ij}^{4 }\Bigr{)}\Big{|}\Big{)}. \tag{27}\]  
Before QoI optimization, we have a reconstructed histogram \(\mathbf{f}^{0}\) that has a PD error bound \(\epsilon_{f_{0}}\). Therefore, each entry of the histogram is also bounded:  
\[\Big{|}f_{ij}^{0}-f_{ij}^{p}\Big{|}\leq\epsilon_{f_{0}}, \tag{28}\]  
where \(f_{ij}^{p}\) indicates the original PD. Let \(E_{ij}\) be \(-K_{ij}+|\epsilon_{L}(\mathrm{V}_{ij}^{1}+\mathrm{V}_{ij}^{2}\ +\mathrm{V}_{ij}^{3}+\mathrm{V}_{ij}^{4 })|\). From Equations (27) and (28), the PD error bound is \[\left|f_{ij}-f_{ij}^{p}\right|\leq\left|f_{ij}^{0}\exp E_{ij}-f_{ij}^{p} \right|=\left|\left(f_{ij}^{0}-f_{ij}^{p}\right)\exp E_{ij}+f_{ij}^{p}\exp E_{ ij}-f_{ij}^{p}\right|\] \[\leq\epsilon_{f_{0}}\exp E_{ij}+\left|f_{ij}^{p}\big{(}\exp E_{ij} -1\big{)}\right|. \tag{29}\]  
Therefore, users can anticipate the increase in the error bound due to the constraint satisfaction step and choose the number of bits for Lagrange parameter quantization, with this step simultaneously impacting the PD and QoI errors.
## 3 Results and Discussions
### Effect of Operating Variables
#### 3.2.2 Effect of Initial Manganese Concentration in Solution on Mn(II) Sorption  
To investigate the applicable characteristics of DX adsorbent, the effect of initial concentration in the solution on Mn(II) sorption was studied (Figure 3). With the increase in the initial concentration of Mn(II) in the solution, the sorption of DX increased, but the removal of Mn(II) decreased significantly. When the initial DX dosage was 12 g\(\cdot\)L\({}^{-1}\), the sorption amount of DX on Mn(II) was 4.70 mg\(\cdot\)g\({}^{-1}\) in the solution with the initial concentration of Mn(II) of 1 mM\(\cdot\)L\({}^{-1}\), and the removal rate was as high as 95.37%. The sorption of DX was enhanced by 15-30% at the initial concentration of 1.5 mM\(\cdot\)L\({}^{-1}\) versus 2.0 mM\(\cdot\)L\({}^{-1}\), but its removal rate of Mn(II) was reduced by 12-32%. The initial metal ion concentration provides the necessary driving force to overcome the metal mass transfer resistance between the water-solid phase and is an important factor affecting the heavy metal sorption efficiency. Although low metal ion concentrations provide lower driving forces, the number of ions competing for available active sites in the biomass is also low. Sufficient surface area exists to absorb the available ions in the liquid phase, so the sorption capacity of DX is limited, while the removal of Mn(II) is at a high level [35]. As the initial metal concentration increases, the driving force of the active biosorption sites increases, resulting in an enhanced equilibrium sorption capacity of the DX adsorbent [36]. However, the saturation of available biosorption sites on the biosorbent surface prevented further binding of metal ions,  
Figure 2: Sorption effect of manganese on three different soil samples.  
which led to significant inhibition of manganese ion removal. Therefore, the availability and sorption capacity of active biosorption sites on the surface of biosorbents are key factors affecting effective metal uptake efficiency. In practical applications, suitable adsorbents should be selected according to the heavy metal concentration of the wastewater.
## 4 Performance Evaluation
### Simulation Results  
The considered simulation results are (i) the received data rate and (ii) the TP change rate with different number of users, i.e., 1, 10, 20,..., 90, 100. In order to focus on the channel condition and to avoid selecting TP considering the system bandwidth, we choose the SINR as the TPS criterion. We average the results of 10 performances in the same network environment.
## Nomenclatures  
Symbol  
\(A\)  
Area  
Number of test runs  
\(a_{0}\)  
Coefficient of constant term  
\(a_{1},\ a_{2},a_{3},a_{4},a_{5},a_{6}\)  
Coefficients of each multi-order term  
\(b\)  
Factor level  
\(C_{P}\)  
Specific heat of water  
\(c\)  
Factor number  
\(D\)  
Heat exchanger diameter  
\(D_{m}\)  
Mass diffusion coefficient  
\(D_{t}\)  
Thermal diffusion coefficient  
\(d\)  
Nozzle diameter  
E  
Expectation  
Energy  
\(e\)  
Enthalpy  
\(e_{r}\)  
Relative error  
\(F\)  
Correlation matrix  
\begin{tabular}{l l} \(F\) & Spatial correlation function \\ \(f\) & Correlation vector \\ \(\overrightarrow{g}\) & Gravity acceleration \\ \(H\) & Distance of nozzle-to-plate \\ \(H_{\rm C}\) & Height of the premixed cone \\ \(h\) & Local convective heat transfer coefficient \\ \(k\) & Thermal conductivity \\ \(\overrightarrow{l}\) & Diffusion flux \\ \(L\) & Latin square \\ \(M\) & Molar mass \\ \(\ddot{m}\) & Mass flow rate \\ \(N\) & \(N\)th species \\ \(N_{r}\) & Total mesh points along the radial direction \\ \(N_{z}\) & Total mesh points along the axial direction \\ \(N\mu\) & Local Nusselt number \\ \(\overline{Nu}\) & Average Nusselt number \\ \(n\) & Number of variables \\ \(n_{s}\) & Number of sample points \\ \(n_{r}\) & Mesh points in the central flame region along the radial direction \\ \(n_{z}\) & Mesh points in the central flame region along the axial direction \\ \(P\) & Static pressure \\ \(PV\) & Simulated value of CFD \\ \(Q\) & Volume flow rate \\ \(\tilde{Q}_{T}\) & Total heat transfer rate \\ \(q\) & Local heat flux \\ \(R\) & Nozzle radius \\ \(W\) & Net productivity \\ \(Re\) & Reynolds number \\ \(r\) & Radial direction \\ \(\Delta r\) & Radial mesh size \\ \(S_{h}\) & Heat source \\ \(SV\) & Predicted value of the KM \\ \(\tilde{g}^{2}\) & Mean square error \\ \(T\) & Temperature \\ \(\overrightarrow{V}\) & Velocity vector \\ \(V_{e}\) & Velocity of nozzle exit \\ \(V_{r}\) & Radial velocity component \\ \(V_{z}\) & Axial velocity component \\ \(W\) & Net productivity \\ \(X\) & Molar fraction \\ \(x\) & Variable or parameter \\ \(Y\) & Mass fraction \\ \(y\) & Unknown objective function of \(x\) \\ \(Z\) & Local deviation \\ \(z\) & Axial direction \\ \(\Delta z\) & Axial mesh size \\ \(1\) & Unit vector of \(n_{s}\times 1\) \\ Greek Symbols \\ \(\beta\) & Constant mean \\ \(\theta_{k}\) & Unknown correlation parameter \\ \(\mu\) & Dynamic viscosity \\ \(\rho_{2}\) & Density \\ \(\sigma^{2}\) & Process variance of the spatial correlation function scalar \\ \(\overrightarrow{\tau}\) & Stress tensor \\ \(\phi\) & Equivalence ratio \\ Subscripts \\ \(act\) & Actual \\ \(ad\) & Adiabatic flame \\ \(atm\) & Atmospheric pressure \\ \end{tabular}
## 3 Results and Discussion
### Droplet Size Distribution  
A log-normal distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. If the water droplet diameter (\(d\)) is log-normally distributed, then \(\ln(d)\) has a normal distribution with a log mean \(\mu\) and standard deviation \(\sigma\), as shown in Figures 4 and 5.  
The above-mentioned log-normal distribution uses the following mean and standard deviation (SD) formulas [40] for the droplet diameter (\(d\)):  
\[\text{Mean }=e^{\left(\mu+\frac{\sigma^{2}}{2}\right)} \tag{12}\]  
\[SD=\sqrt{e^{2\left(\mu+\sigma^{2}\right)}-e^{\left(2\mu+\sigma^{2}\right)}} \tag{13}\]  
As illustrated in Figure 4, the measured droplet diameter right after injection moderately fits the log-normal distribution, with an \(R^{2}\) value \(\geq\) 0.9.  
A z-score, often referred to as a standard score, provides valuable insight into the deviation of a data point from the population mean. To be more precise, it quantifies the number of standard deviations by which a droplet diameter falls below or exceeds the population mean.  
\[\ln(d)=\sigma z+\mu \tag{14}\]  
\[z=\frac{d-\text{Mean }}{SD} \tag{15}\]  
For the normal distribution of droplets, the following equation was used:  
\[d=\sigma z+\mu \tag{16}\]  
where \(d\) represents the droplet diameter (\(\mu\)m), \(\mu\) represents the mean (\(\mu\)m), and \(\sigma\) represents the standard deviation (\(\mu\)m) for normally distributed data. A normal distribution is defined  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Exp. Run Case** & **Arithmetic Mean, \(\mu\)m** & **Standard Deviation, \(\mu\)m** & **Kurtosis** & **Skewness** \\ \hline N1H0 & 342 & 90 & 1.37 & 0.91 \\ N1H1 & 394 & 121 & \(-\)0.56 & 0.24 \\ N1H2 & 462 & 169 & \(-\)0.79 & 0.18 \\ N2H0 & 369 & 95 & 7.0 & 2.21 \\ N2H1 & 470 & 164 & 1.3 & 1.0 \\ N2H2 & 445 & 180 & 0.27 & 0.77 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimentally measured SMD, standard deviation, kurtosis, and skewness of droplet size distribution.  
as a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence compared to data far from the mean, which is also known as a Gaussian distribution.  
From Figures 4a and 5b, it is clear that the quantile-quantile plots exhibit fairly good fitting with \(R^{2}\) values larger than 0.9. Therefore, we can conclude that the model fits well with the data, and we used this log-normal distribution as an input parameter (water droplet distribution) in the respective CFD simulations.
# Estimation of Resonance Frequency for Systems with Contact Using Linear Dynamics Methods
## 2 Approach to Estimation Method  
Classical modal analysis in usage for cases where contact exists leads to one of two approaches: neglect the contact [20; 21] or fixed edge of the contact like it was all the time closed [22; 23; 24; 25]. For the specimen considered in this paper, it can be observed, that the actual resonance frequency occurs between values of both approaches.  
The closer look at the response of the vibration in the resonance verified by non-linear dynamics shows that a full vibration cycle is built out of the lower half-cycle part (while the contact is closed) and the upper half-cycle part (while the contact is open). The lower half-cycle frequency is close to the frequency calculated by modal analysis with a fixed edge of the contact, while the upper half-cycle resonance frequency is equal to the one calculated by modal analysis with neglected contact.  
Based on that knowledge, the idea appeared to create an expression that will use both approaches of modal analysis to calculate, based on the resonance frequency values gained in both analyses, the resonance frequency that will appear in reality. The illustration of one period of non-linear response with both half-cycles' conception is shown in Figure 1.  
The method that allows for the estimation of the resonance frequency of the system with usage of only linear modal analysis would be a very useful tool for engineers in industrial applications because of the simplicity, low resource demands compared to non  
Figure 1: Illustration of two half-cycles on example of a nonlinear response vibration period. Opened contact at left and closed contact at right side.  
linear dynamics, and easily accessible by well-known FEA codes, in contrast to the methods listed in the first paragraph.  
The main rule for the formula is to use both of the analysis half-cycle period times, collected by linear modal analysis, and to introduce a parameter related to the contact length to compensate for the error. To omit the contact length as a direct parameter--which is influenced by geometry--the parameter that represents the stiffening of the system by contact is introduced as the ratio of the resonance frequency of the system extracted with modal analysis with a fixed contact edge (f\({}_{\text{e}}\)) to the resonance frequency of the system extracted with modal analysis with neglected contact (f\({}_{0}\)).