esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Models and Analyses
### Analysis of Model Calculation Results
Figure 7 reveals that the number of magnetic particles within the critical distance followed an approximately linear increasing trend with the increasing magnetic particle concentration, being the  
Figure 5: Effects of the magnetic field strength on the critical distance for four different magnetic particle densities: (**a**) \(0.1\times 10^{7}\) 1/L, (**b**) \(1\times 10^{7}\) 1/L, (**c**) \(10\times 10^{7}\) 1/L, and (**d**) \(100\times 10^{7}\) 1/L. Figure 6: Effects of magnetic particle concentrations on the “critical distance” for magnetic particle accumulation. number of the particles equal to the product of the adsorption limit distance and the particle concentration. Although the increase of the magnetic particle concentration caused the critical distance to shrink, its effect was less severe than that of the increasing concentration. The theoretical analysis expresses that the higher the magnetic particle concentration, the larger the number of particles within the critical distance and the brighter the magnetic particle crack images. It should be noted that the theoretical model ignores the kinetic effect and the volume effect; thus, the magnetic particles within the critical distance are stacked after being adsorbed. As a result, the effect of the actual magnetic particle concentration may not be an approximately monotonous increase, as shown in Table 1. Additionally, since with the increase of the concentration of magnetic particles, the brightness of the background of the image will increase, the overall contrast between crack indications and background may be reduced, resulting in difficulties of image recognition. Therefore, it is difficult to conclude from Figure 7 that the increase in the concentration of magnetic particles will improve the recognition ability of the magnetic particle indication image.
## 4 Discussion
### Overexpression of SiTPS Does Not Affect S. indica Colonization Ability  
_SiTPS_ was found to be upregulated at 11 dpi in colonized tomato roots, when its expression was compared to that from a mature fungal colony grown on synthetic medium. However, gene expression data generated from a microarray assay performed on _S. indica_-colonized barley roots showed that _SiTPS_ expression is induced _in planta_ but at different timepoints [57]. In details, when we re-analyzed the data from barley roots, transcripts of _SiTPS_ were found to accumulate specifically at the very early stages of colonization, around 36 to 48 h post inoculation (hpi), both in living and dead colonized roots, when compared to growth on synthetic medium. However, a similar expression pattern was not observed during later stages (5 dpi). This observation could possibly suggest that the gene is more important at the very early stages of the interaction, during the pre-penetration phase, or that differences in _SiTPS_ expression evaded detection by the specific method. Another observation in this study was that when dead and living barley roots were compared, accumulation of _SiTPS_ transcripts was higher in living roots, indicating that _S. indica_ activates _SiTPS_ when association with living tissues takes place.  
Our _SiTPS_ expression data and former literature led us to hypothesize that _SiTPS_ can be involved in colonization of plant roots by _S. indica._ Hence, we established a root-colonization assay, where tomato seedlings were inoculated with either an _SiTPS_-overexpressing mutant or control strains and the ratio between fungal to plant DNA was used to evaluate fungal colonization ability. The _SiTPS_-overexpressing mutant appeared to colonize tomato roots to the same extent as the empty vector-carrying transformant and the wild type _S. indica_, showing that overexpression of the _S. indica_ viridiflorol synthase gene does not influence fungal colonization ability at the specific time-points and in our experimental set-up. In another study, the colonization ability of _Trichoderma virens_, an endophytic fungus able to produce several volatile terpene compounds, was compared with this of deletion mutants and other _Trichoderma_ species that lack the terpene synthase gene responsible for the synthesis of these metabolites [58]. Similar to our findings, the specific study showed that the ability to produce terpen did not correlate with increased fungal colonization ability, since there was no significant difference between colonization ratio of the fungal strains used.  
However, these results do not exclude that the products of _SiTPS_ could play another role _in planta_. For example, fungal sesquiterpenoids could act as signaling or defense molecules against other root-residing microorganisms. In fact, according to a whole transcriptome study performed on colonized roots of barley plants, a putative terpene synthase gene from the orchid mycorrhizal fungus _Serendipita vermifera_ ssp. _bscii_, was upregulated significantly only when another fungus (the pathogen _Bipolaris sorokiniana_) was colonizing the plant roots [59].
## 3 Results and Discussion
### Sample Analysis  
The MIP coating was used to selectively extract IBA from mung bean samples. The solvent extraction process is necessary to transfer the analyte from the sample into a liquid phase. In this study, phosphate buffer with pH 9.0 was used as the extraction solvent. To reduce the matrix effect on the following measurement, petroleum ether was applied to remove some sources of interference. To achieve optimum uptake by the MIP coating, the pH of the aqueous solution was adjusted to 2.5 prior to extraction. The fluorescence intensity of the IBA-MIP-coated chip was measured using a fluorescence spectrophotometer after simply rinsing the chip with the phosphate buffer of pH 2.5. The amount of auxin observed in the mung bean was 0.95 \(\mathrm{\SIUnitSymbolMicro g}\)-1, as calculated using a calibration equation. To further evaluate the extraction performance of the MIP-coated chip, spiked samples were tested. The extraction recoveries were observed to be 91.5% (RSD = 6.1) and 97.5% (RSD = 7.4), with n = 3 for the spiked concentrations of 0.8 \(\mathrm{\SIUnitSymbolMicro g}\)-1 and 2 \(\mathrm{\SIUnitSymbolMicro g}\)-1, respectively. Thus, the present study provides a simple method based on a MIP-coated chip that can be applied for the selective and sensitive determination of auxin in complex samples. To realize automatic analysis and to minimize the sample volume required for measurement, the construction of an on-line detection apparatus using the chip as a flow cell is in progress.
###### Abstract  
Background: We evaluated the performance of an automated algorithm available on a clinical OCT (Canon-HS100) for macular volumetric measurements of eight individual retinal layers. Methods and Analysis: Two consecutive three-dimensional scans were acquired on 29 subjects with healthy retinas. Thickness measurements were obtained from eight individual retinal layers in nine macular sectors based on Early Treatment Diabetic Retinopathy Study (ETDRS) protocol. The repeatability was evaluated using the within-subject standard deviation from which the repeatability limits (Rlimit) and coefficient of variation (CoV) were calculated. Results: The repeatability metrics varied among different layers and sectors. The variation among the sectors was larger in two of the outer layers (plexiform and nuclear layer) and the retinal nerve fiber layer. For the other five layers, the repeatability limit was less than 5\(\upmu\)m and CoV was less than 7.5\(\upmu\)m in all nine ETDRS sectors. Conclusions: The repeatability of the OCT-HS100 to measure eight individual retinal layers is good in general. Nevertheless, the repeatability is not homogeneous among different layers and sectors. This needs to be taken into account while designing clinical measurement protocols.  
layer segmentation; macular layers; optical coherence tomography; repeatability; retinal thickness +
Footnote †: journal: Applied Sciences  
Article Evaluation of Automated Segmentation Algorithm for Macular Volumetric Measurements of Eight Individual Retinal Layer Thickness
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
## 6 Assessment of EMS Integration Options
### Discussion of EMS Integration Option 1c  
Finally, as _Option 1c_ is a combination of _Option 1a_ and _Option 1b_, the sum of the above discussed characteristics have to be considered when integrating an EMS into the SMGW architecture. Additionally, it has to be considered that some SMGWs provide the HAN and CLS functionalities on different Ethernet ports. Then, the EMS has to be connected to the HAN port (which allows access to the _Consumer Interface_) and the CLS port (which allows the communication with the remote EMS) simultaneously.
# Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning
## 4 Problem Solving
### Learning Policy with the Sarsa Method  
In the first part of the new framework, the enemy avoidance problem has been formulated as an MDP. For the second part of the new framework, the agent of the decision making UAV is allowed to learn the policy based on the Sarsa reinforcement learning method.  
There are mainly three classes of methods for solving the reinforcement learning problem [13]; dynamic programming, Monte Carlo methods, and temporal-difference (TD) learning. Dynamic programming methods require a complete and accurate model of the environment (the transition function \(\mathcal{T}\)), while the Monte Carlo methods are not suitable for step-by-step incremental computation. Only the temporal-difference methods require no model and are fully incremental. In the enemy avoidance problem, the environment transition function \(\mathcal{T}\) is unknown, and the policies need to be learned by continuous interaction with the environment. Therefore, the temporal-difference methods are more suitable for the enemy avoidance problem. As one of the temporal-difference reinforcement learning methods, the Sarsa method is chosen to be used in the proposed framework to learn the policy for the enemy avoidance problem.  
Since the state space in the enemy avoidance problem is continuous, it is impractical to visit each state with each action infinitely often. Therefore, certain function approximations are needed to generalize the state space from relatively sparse interaction samples and with fewer variables than there are states. In this paper, the CMAC (cerebular model arithmetic computer, [19]) technique is also embodied in the proposed framework to approximate the \(Q\)-value function when learning with the Sarsa method.  
The details of how the proposed framework embodies the Sarsa method and the CMAC function approximator to learn the policy for the enemy avoidance problem are elaborated as follows.
# On the Design of a Decision Support System for Robotic Equipment Adoption in Construction Processes
## 3 Development of the Prototype
### Inference Engine
#### 3.3.2 VOI Analysis  
VOI analysis allows one to define which information-gathering activities should be performed by the user. In our case, we calculate the Value of Perfect Information (\(VPI\)), also called value of clairvoyance, on the variables that are uncertain [33]. If exact evidence of variables can be obtained, \(VPI\) can be calculated as follows [32]:  
\[VPI_{e}(E_{j})=\left(\sum_{k}P(E_{j}=e_{jk}\,|\,\mathrm{e})\times EU(\delta_{jk }|\,\mathrm{e},E_{j}=e_{jk})\,\right)-EU(\delta|\,\mathrm{e}) \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & & & & \multicolumn{6}{c}{**Results with Evidence**} \\ \cline{4-13}
**Preferences [\%]** & \multirow{2}{*}{**Result**} & \multicolumn{6}{c}{**BIM**} & \multicolumn{6}{c}{**Project Size**} & \multirow{2}{*}{**VPI**} \\ \cline{4-13}  & & & & \multicolumn{3}{c}{**Not**} & & \multicolumn{2}{c}{**Available**} & \multicolumn{2}{c}{**Small**} & \multicolumn{2}{c}{**Large**} \\ \hline P & q & s & D & MEU & D & MEU & D & MEU & D & MEU & D & MEU & BIM & PS \\
33 & 33 & 33 & R & 93.24 & C & 95.56 & R & 95.28 & C & 95.71 & R & 95.18 & 4.72 & 4.  
where \(EUI(\delta\,|\,\mathbf{e})\) is the Expected Utility (\(EUI\)) of the current best decision \(\delta\), \(EUI(\delta_{jk}\,|\,\mathbf{e}_{j}=e_{jk})\) is the \(EUI\) of the new best decision \(\delta\) with evidence \(E_{j}\) that is averaged over all the possible values \(e_{jk}\) that we may have for \(E_{j}\). As \(VPI\) can be only computed for variables that are non-descendants of decision nodes [43], we can perform this computation for the variables BIM and Project size (PS). The EU of the new best action with evidence can be obtained by adding an influence arc from the uncertain variables BIM and PS to the decision node [33]. In Table 6, we see the result of the VOI computations when we consider different weightings of preferences (p, q, or s). For equal weightings (p = q = s), we see that \(VPI(\text{PS})>VPI\) (BIM). This means that the best information to be acquired is the value of Project Size (PS).
## 4 Sensor Trajectory Design
### Constraints  
As shown in Figure 2, sensor trajectory planning is implemented under the following constraints [17; 29]:  
Figure 2: Sensor motion area and target safety area.  
Target safety area: In order to ensure no collision between the sensor and the target, the distance between \(\mathbf{S}_{k+1}\) and \(\mathbf{\hat{T}}^{k}\) should be greater than or equal to the safe distance \(R\).  
Sensor motion area: Since the motion of the sensor is limited by the maximum speed, the sensor motion area is set to be spherical centering at \(\mathbf{S}_{k}\) with the radius of \(r\) (including the boundary of the sphere). Their boundaries are governed by polynomial equations \(f_{tsb}(x_{\mathbf{s}_{k+1}},y_{\mathbf{s}_{k+1}},z_{\mathbf{s}_{k+1}};\mathbf{ \hat{T}}^{k})=0\) and \(f_{smb}(x_{\mathbf{s}_{k+1}},y_{\mathbf{s}_{k+1}},z_{\mathbf{s}_{k+1}};\mathbf{ S}_{k})=0\), where  
\[f_{tsb}(x_{\mathbf{s}_{k+1}},y_{\mathbf{s}_{k+1}},z_{\mathbf{s}_{k+1}};\mathbf{ \hat{T}}^{k})=||\mathbf{S}_{k+1}-\mathbf{\hat{T}}^{k}||^{2}-R^{2}, \tag{55}\]  
\[f_{smb}(x_{\mathbf{s}_{k+1}},y_{\mathbf{s}_{k+1}},z_{\mathbf{s}_{k+1}};\mathbf{ S}_{k})=||\mathbf{S}_{k+1}-\mathbf{S}_{k}||^{2}-r^{2}. \tag{56}\]
# Gas-Liquid Mixability Study in a Jet-Stirred Tank for Mineral Flotation
## 2 Methodology
### Solver Settings  
Simulations were performed using ANSYS Fluent 18.0. The system of equations is discretized using the finite volume method. Pressure-velocity coupling is achieved using the Phase Coupled SIMPLE algorithm with PRESTOI pressure interpolation. In traditional finite volume methods, velocity components and pressure are often discretized at different grid points. This can lead to inaccuracies in the coupling between the pressure and velocity fields. The PRESTO method reduces such discretization errors by calculating the pressure variable at different grid locations. The equations for momentum, volume fraction, turbulent kinetic energy, and turbulent dissipation rate are solved using the first-order upwind scheme. The second-order upwind scheme generally has an advantage over the first-order scheme in reducing numerical diffusion. However, due to the large grid size and the complexity of the flow characteristics in this study, using the second-order scheme  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Standard** & **RSM** & **RNG** \\ \hline RMSE & 1.84 \(\times\) 10\({}^{-4}\) & 1.08 \(\times\) 10\({}^{-3}\) & 2.27 \(\times\) 10\({}^{-5}\) \\ \hline \end{tabular}
\end{table}
Table 3: Root-mean-square error of the Standard, RSM and RNG turbulence models.  
directly would significantly increase computational costs. Additionally, considering the solution speed and resource limitations in large-scale industrial simulations, the first-order upwind scheme offers a reasonable balance between computational cost and accuracy. Therefore, the first-order upwind scheme was adopted in this study. The time step size is 0.2 s. The convergence criterion for the equations is set to \(10^{-3}\). Table 4 shows the under-relaxation factors.
## 4 Results and Discussion
### Statistical Analysis  
The above testing results were statistically analyzed by a one-way ANOVA (significance level \(\alpha=0.05\)) using MINITAB to examine the significance of modifiers in the rutting, moisture-induced damage, and workability properties, as shown in Table 8. It is clearly evident that the modifier types had significant effects (\(p<0.05\)) for all the parameters related to workability (viscosity, CDI, and TDI values of the Bahia and locking point methods). TDI had the most significant effect because it had the highest F value, followed by the viscosity and CDI values. Similarly, Table 8 indicates that the effects of the modifier types were statistically significant with respect to the rutting properties (%R and Jnr from the multiple stress recovery test; rut depth and dynamic stability values from the wheel tracking test) based on the probability value (\(p<0.05\)). Jnr had the most significant effect because it had the highest F value, followed by the %R, dynamic stability, and rut depth values. Table 8 also evidently indicates that the effects of modifier types were statistically significant with respect to the POTS, ITS, BSR, and TSR values based on the probability value (\(p<0.05\)). POTS had the most significant effect because it had the highest F value, followed by the BSR, ITS, and TSR values.
## 4 Literature Review on DAS+PRS
### Feature Extraction
#### 4.1.1 Time Domain-Based Feature Extraction Methods  
In the published works that use time domain features, some of them did not carry out any feature extraction process [12; 13], and the raw recorded signal was given to the pattern classification algorithm. The authors of [14] also employed the raw recorded signal, but normalized between \(-1\) and \(1\) as an input to the pattern classification algorithm. These works are considered as time domain methods since the original signal actually represents the amplitude of the data along the time. As an alternative, the level crossing rate (LCR) as the single feature in the system is employed in [18].
## 5 Results  
We evaluated the proposed mechanisms through simulation by using the NDNsim module [38] of the NS-3 simulator. NDNsim implements the default NDN architecture. We modified this module to add support for wireless communications and our proposed mechanisms. In the following sections, we present the simulation parameters used in our evaluation. First, we conducted experiments to investigate the performance of NDN when compared to the TCP/IP stack running the OLSR protocol on a WMN-based scenario. We also conducted experiments to parametrize our mechanisms and then we compared the performances of PIF, ReCIF, and ReCIF+PIF with both the default NDN forwarding mechanism and LFBL.
## 6 Objective QoE Evaluation of Cloud Gaming
### FR Objective QoE Metrics  
This study employed three well-known FR metrics: Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Video Multi-method Assessment Fusion (VMAF) (VMAF). SSIM analyses the similarity between the original and distorted images by comparing them. It employs contrast, luminance, and structure to identify the similarities. SSIM is scored between 0 and 1, with 0 indicating no similarity and 1 indicating complete similarity between the images. PSNR uses the ratio of the signal to the distorted power of the noise between two images (one original, one distorted) and gives a rating measured in dB. According to the authors in [41] PSNR above 37 is regarded as good quality. However, the authors in [42] claimed that having PSNR of more than 20 was regarded as good for online video streaming videos. Finally, VMAF is an open source metric developed by Netflix [43] that utilises a combination of various metrics and machine learning algorithm that has been pre-trained on other videos. It assigns a score between 0 and 100, with 0 being the lowest quality and 100 representing the highest.
## 4 Experiments
### Metrics
#### 4.1.1 Intrinsic Metrics  
When ground truth labels are unavailable, only a few metrics are available to evaluate the performance of a clustering technique [71]. Silhouette is a method that provides a concise measure of how similar an object is to its cluster, compared to other clusters through the usage of distance metrics. Any metric can be used; typically, Euclidian is used to calculate the silhouette coefficient, and the results range from \(-1\) to \(1\), where a high value means the clusters are well separated, minimizing the distance intra-cluster and maximizing the distance inter-cluster [72].
# More on Pipelined Dynamic Scheduling of Big Data Streams
## 2 Related Work and Discussion
\begin{table}
\begin{tabular}{c p{142.3 pt} p{142.3 pt}} \hline \hline
**Paper** & **Properties** & **Description** \\ \hline
[18; 19] & 4 Properties: Gracefulness, Fluidness, Non-intrusiveness, Fluentness & A reconfiguration message is transmitted to the splitter with a new routing table. Then, the splitter transmits to the proper replicas the set of migration messages. \\ \hline
[22] & Maintain in-flight messages and their internal tasks states & DCR policy: executes in-flight messages until their completion CCR policy: directly broadcasts checkpoint events and processes only one event at a time. \\ \hline
[23] & Minimizes synchronization barriers & Workers update their local task assignment and restart execution upon notification by the master node. \\ \hline
[24] & thread-level migration & A key/value store is placed on each worker node so the workers become aware of the scheduling changes and manage the executors during runtime \\ \hline
[25] & linear programming modeling & Use a repository that stores the operators code so the replicas save and restore their state during reconfigurations. \\ \hline
[26] & A migration cost model and a balance model are used as metrics & Particle swarm optimization (PSO), with two improvement measures, the loop context and particle grouping \\ \hline
[27] & Maintains persistence by storing operator states & Replication-based state management that keeps state backups on different node to facilitate recovery. \\ \hline
[28] & QoS and Workload driven migrations & Computation Migrations: Changing the parent of a given child in the network hierarchy due to QoS or load-balancing considerations State migration: The data that is generated by an application component, must be made available during migration of this component. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Migration policies in dynamic stream scheduling schemes. migrated. Thus, synchronization is required. Usually, most of the strategies do not efficiently address this buffering issue. The scheme presented in this work deals with these issues in an efficient manner. Specifically, synchronization is achieved by pipelining the various operations involved in processing, transferring as well as migrating of data and tasks. The buffering issue is resolved by applying a stepwise procedure, where each task receives data in its input queue, only from one task at a time, thus buffering needs are minimized.