esa-sceva/satcom-chunk-collection huggingface dataset:
# Spot Market Cloud Orchestration Using Task-Based Redundancy and Dynamic Costing
## 3 Task-Based Redundancy in Cloud Systems
### Background to Task-Based Redundancy  
Existing approaches generally assume the need for a particular level of reliability as an axiom, rather than determining it from an analysis of the domain-specific conditions applicable at runtime.  
Previously, we proposed an approach using TBR [6; 31] as a method of modeling and managing the reliability of a complex cloud system by decomposing its subcomponents according to the tasks (or services) a system must be able to reliably accomplish for the user.  
Our approach links the source of the reliability requirement, the predictive model for microservice container failure, and the compound nature of the cloud services under the responsibility of the cloud orchestrator [6].  
The advantage of this approach is that it closely aligns the reliability requirement, and its consequent replication strategy, to the specific service outcomes expected by the user of the cloud system. With modern, enterprise-grade cloud applications generally comprising many microservice components, possibly even hundreds, to create the final solution, each microservice container may be found to be a dependency for several independent tasks [32].  
In the classic approach to reliability, each microservice container is simply replicated to a certain desired level of replication. The level of replication is determined by what the Service-Level Agreement (SLA) defines as an acceptable value for the probability of system failure in a given time interval, such as in \"nine-nines reliability\" (where the probability of failure in the interval is \(1.0\times 10^{-9}\)), which we used as the control for our experiments.  
In contrast, the TBR approach considers the reliability of the system not with respect to redundant microservice containers per se, but with respect to the system's wholisticability to complete a task comprising several microservices. From a software engineering perspective, TBR may be considered an application of use case analysis and microservices-oriented decomposition to reliability analysis [6; 33].  
In the TBR approach, once microservices are grouped according to tasks, the same probabilistic models which are applied to components in general reliability analysis are applied to these tasks, with each microservice making up an in-series or in-parallel component of the task's reliability function according to its configuration [6].
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## Appendix A Compendium of Mappings and Compilations
### Bit-Flip (X) Mixers
#### a.1.2 Max-\(\ell\)-Sat
###### Appendix A.1.6 E3 Lin2  
**Problem:** Given a set of \(m\) three-variable equations \(\mathcal{A}=\{A_{j}\}\), over \(n\) binary variables \(\mathbf{x}\in\{0,1\}^{n}\), where each equation \(A_{j}\) is of the form \(x_{a_{1,j}}+x_{a_{2,j}}+x_{a_{3,j}}=b_{j}\mod 2\) where \(a_{1,j},\ a_{2,j},\ a_{3,j}\in[n]\) and \(b_{j}\in\{0,1\}\), find an assignment \(\mathbf{x}\in\{0,1\}^{n}\) that maximizes the number of satisfied equations.  
**Prior QAOA work:** Considered in Reference [16].  
**Approximability:** No efficient \(1+\epsilon\) classical approximation algorithm unless \(P=NP\)[53]. For the case where each variable appears in at most \(D\) equations, an efficient classical algorithm satisfies a \(1/2+\Omega(D^{-1/2})\) fraction of the equations [23]. Shown in Reference [16] that QAOA with \(p=1\) achieves the same result up to a factor logarithmic in \(D\) for this case.  
**Objective:** Maximize the number of satisfied equations.  
**Phase Separator:\(H_{\mathrm{P}}=\sum_{j=1}^{m}H_{j}\)** where \(H_{j}=(-1)^{b_{j}}Z_{a_{1,j}}Z_{a_{2,j}}Z_{a_{3,j}}\).  
**Resource count for phase separator:** Can be implemented with \(m\) many 3-qubit gates.
## 3 Results and Discussion
### Investigated Systems
The docking groups comprise of thiolate (**System I**), methyl-thiolate (**System II**), pyridine (**System III**), isocyanide (**System IV**) and cyanide (**System V**). System VI is an upright standing biphenyl layer not bearing any docking group. Systems VII and VIII are both bonded via thiolates, but contain different (polar) tail groups (a nitrile in the case of system VII and an amine in the case of system VIII). The molecules are arranged in a herringbone pattern, as can be seen in the top views of the unit cell shown in the Supplementary Materials. Color code: dark yellow: Au, bright yellow: S, grey: C, blue: N, and white: H.
# Investigation of the Inverse Magnus Effect on a Rotating Sphere in Hypersonic Rarefied Flow
## 4 Results and Discussion
### Flow Fields  
The flow fields under conditions of \(W=0.00\), \(0.25\), \(0.50\), \(0.75\), and \(1.00\) are displayed in Figure 7a-e, respectively. Under the hypersonic rarefied flow conditions of \(M_{\infty}=10\) and \(Kt_{\infty}=0.51\) (or, equivalently, the Reynolds number \(Re_{\infty}=22\)), the flow features are distinct from their counterparts in the continuum flow regime. For all cases considered here, no flow separation is observed. Each flow field is highly diffusive, in which the shock wave and the boundary layer are substantially thickened and smeared, merging into one smooth zone around the sphere. No discontinuity exists in such a flow field, but the term \"shock wave\" is still used here to denote the front of the compression wave upstream of the sphere. In addition, there are remarkable velocity-slip phenomena on the wall surface, so the gas flow velocities on the sphere differ from the moving wall velocities.  
Figure 7a shows the flow over a stationary sphere, and the flow field is symmetric about the \(x\)-axis. As the spinning rate increases, the asymmetry of the flow grows, and the following phenomena can be noticed. (1) As \(W\) increases, the disturbance from the rotating sphere propagates a longer distance upstream, and thus the disturbed area around the sphere is enlarged. (2) The rotation of the sphere distorts the shape of the shock wave and the shape of the compression zone ahead of the sphere. As \(W\) increases, the distortion becomes more severe. (3) The flow pattern on the sphere varies with \(W\). On the windward side, the gas flow is significantly driven by the moving wall. On the leeward side, the gas streams along the meridional direction and is not affected by the wall motion because of the extremely low density of the gas and the weak interaction between the gas and the wall surface. (4) When \(W\) becomes large, a lateral streamline emerges prominently on the sphere. This lateral streamline emanates from the front stagnation point of the flow field. (5) As \(W\) increases, the front stagnation point shifts downstream (upward and rightward in the current view). Figure 7f shows the velocity profiles along the \(y\)-axis at different rotation rates. The amount of velocity slip at the wall (\(y=100\) mm) becomes larger as \(W\) increases.  
Figure 7: \(Cont.\)
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
## 3 Results
### Wireless Data Relay by Introducing Additional (Relayer) Drone
#### 3.2.2 Extending Relay
The estimated communicable distance by the two-ray ground reflection model between the two drones on water (0.25 m antenna height) was 179 m. During the fourth flight, 30 s of hovering (\(<\)10 m height from the controller) of the relayer drone was conducted, while two complete datasets measured at 10 s intervals were relayed (Figure 18).
# Corrosion Behavior of Stainless Steel in Seawater in the Presence of Sulfide
## 3 Results and Discussion
### Polarization Measurements  
After stabilizing the stainless steels in seawater (at different conditions), the polarization resistance \(R_{\rm p}\) was determined using the linear polarization resistance (LPR) method. Since the potential disturbance in this method is very small (only \(\pm\)20 mV around the \(E_{\rm OC}\)), LPR is essentially a nondestructive method, which is the main advantage over other DC corrosion measurement methods. The results of the LPR measurements for AISI 316L are shown in Figure 5.  
Figure 5: Linear parts of polarization curves for AISI 316L stainless steel in seawater at different (**a**) temperatures and (**b**) S\({}^{2-}\) concentrations (at 298 K). As can be seen, a linear relationship between the applied potential and the current response (linear \(i\)-\(E\) dependence) was obtained in all measurements. Depending on the experimental conditions, i.e., the temperature of the fresh seawater as well as the presence of S\({}^{2-}\) ions in the seawater, the slope of the linear dependencies changes. The \(i\)-\(E\) slope defines the polarization resistance of the tested system, \(R_{\mathrm{P}}\):  
\[R_{\mathrm{P}}=\frac{\Delta E}{\Delta i} \tag{2}\]  
A decrease in the \(i\)-\(E\) slope corresponds to a decrease in the value of \(R_{\mathrm{P}}\) (Table 5). Since this is consistent with the Stern-Geary equation, the corrosion current \(i_{\mathrm{corr}}\) is inversely proportional to the polarization resistance [58]:  
\[i_{\mathrm{corr}}=\frac{b_{\mathrm{a}}b_{\mathrm{c}}}{2.303(b_{\mathrm{a}}+b_ {\mathrm{c}})}\frac{1}{R_{\mathrm{P}}}=\frac{B}{R_{\mathrm{P}}} \tag{3}\]  
A decrease in polarization resistance indicates an acceleration of corrosion rate and vice versa (\(b_{\mathrm{a}}\) and \(b_{\mathrm{c}}\) are the anodic and cathodic Tafel slopes, respectively). An increase in seawater temperature and contaminant concentration adversely affects the corrosion stability of AISI 304L and AISI 316L stainless steel. Similar results were obtained for both steel samples. Table 4 shows that \(R_{\mathrm{P}}\) decreases twofold with increasing temperature and fourfold with increasing sulfide ion concentration. The general corrosion behavior of AISI 304L and AISI 316L steels in seawater at different conditions was investigated by recording the potentiodynamic polarization curves (PD) over a wide potential range. Figure 6 shows the results for AISI 316L steel in seawater at different temperatures and concentrations of S\({}^{2-}\) ions. The corresponding polarization parameters are listed in Table 5. Both stainless steel samples show similar polarization behavior. Since inert gas did not flow through the solution and the cell was not hermetically sealed, the cathodic branch of the polarization curve most likely describes the reduction in water and dissolved oxygen.
## 3 Human-Machine Interfaces for Data Acquisition and User Feedback
### Data Acquisition and Processing  
The acquisition and processing hardware must be adapted to deep-learning techniques chosen to perform the tasks (choice of sensors and camera types; remote or wearable processors). Furthermore, the acquisition step, as with many computer-vision studies or applications, is crucial. In the case of embedded assistive technologies, this data acquisition can be even more difficult, because the user is often a part of the system (the user holds or warms the acquisition devices, while moving) and the conditions of acquisition cannot be easily controlled (day, night, rain, etc.).
###### Abstract  
This study presents a numerical analysis to reduce the overpressure inside protective tunnels for external detonations. A three-dimensional computational fluid dynamics model of a tunnel subjected to detonation for a hemispherical charge with a charge weight of 555 kg and a standoff distance of 7.6 m was established, based on a mesh sensitivity study to obtain an optimal element size, stability analysis of overpressure, and validation study to evaluate the accuracy of the numerical results based on Unified Facilities Criteria (UFC) 3-340-02. A parametric analysis was performed using the validated numerical model to investigate the effects of the entrance shape and blast pockets on the reduction in the maximum overpressure. The maximum overpressures were effectively reduced as the slope angle of the tunnel entrance decreased and the length of the blast pocket divided by the tunnel width decreased. An optimized shape of the tunnel was proposed based on the numerical results, where the peak overpressures were reduced by a maximum of 64.5%. This study aims to protect facilities, personnel, and equipment and further reduce construction costs by lowering the overpressure rating of blast valves in protective tunnels.  
protective tunnel; blast overpressure mitigation; entrance shape; blast pocket +
Footnote †: journal: Applied Sciences
## 3 Improved Algorithm Based on YOLOv5s
### Improvement of SimOTA Based on YOLOv5s with Positive and Negative Sample Matching Strategy  
OTA (Optimal Transport Assignment) is a method of positive and negative sample matching strategy [44,45]; SimOTA is a simplification of it and can be seen as a problem of optimal assignment. SimOTA is considered to filter better positive samples to match the GT box (Ground Truth), thus reducing the cost.  
First, a pre-screening of anchor points is required. In SimOTA, all the anchor points in the range of gt boxes are firstly boxed, and then a box of size 5 \(\times\) 5 is set from the range of gt boxes, which is called the fixed center area, as depicted in Figure 9.  
Calculate the cost function in the candidate region as follows equation.  
\[\cos t_{ij}=L_{ij}^{cls}+\lambda L_{ij}^{reg} \tag{17}\]  
where \(L_{ij}^{cls}\) represents the classification loss, \(L_{ij}^{reg}\) represents the positioning loss, and \(\lambda\) represents the balance coefficient of the positioning loss.  
The classification loss (cls_loss) and position loss (iou_loss) for each anchor point with respect to each gt box are then calculated. First, we determine the ten lowest-cost feature points of each target. Next, we add up the IOUs of the prediction box and the real box  
Figure 8: Improved YOLOv5s network structure.  
Figure 9: Example diagram of SimOTA pre-screening.  
corresponding to the ten feature points. The cost matrix and iou matrix are obtained based on the classification loss cls_loss and location loss iou_loss. The cost matrix is used to determine the dynamic_k anchor point with the lowest cost value for each gt box. The loss calculation is performed for the filtered prediction box, and the equation is:  
\[Loss=(L_{cls}+\lambda L_{reg}+L_{obj})/N_{pos} \tag{18}\]  
where \(L_{obj}\) represents the obj loss and \(N_{pos}\) represents the number of Anchor Points that are divided into positive samples.  
In comparison to OTA, using SimOTA speeds up the model calculation reduces training time, and does away with extra optimization parameters. The possible positive sample regions are selected to determine whether they are within the region of the label, and the cost matrix is introduced to make it adaptive to find the real frame to which the current feature points are de-fitted, increasing the number of de-fitted real frames. Meanwhile, SimOTA outperforms SOTA ultralvtics-YOLOv3 [21]. This further demonstrates the high detection accuracy of SimOTA. Applying it to YOLOv5s can improve the detection accuracy and detection speed of the network.
## 3 Results and Discussion
### Analysis of Features
#### 3.3.2 ANOVA Analysis
The post hoc analysis (Table 13) suggests a statistically significant relationship only between CMS1 and CMS4 with a substantial effect size (\(p_{tukey}=0.005\), \(p_{bunferroni}=0.006\), \(d=-0.928\)). This, in turn, suggests that, although more sophisticated condition monitoring sensors affect the MTBF, statistically significant results are reported only for maintenance practices that utilise and rely on a complete set of sensors (e.g., pressure, flow, temperature, contamination, and vibroacoustic). In sum, the influence of maintenance department staff--operators, technicians, engineers, and hydraulic system specialists offer conflicting findings in improving sustainability by the amount of waste oil monthly, presumably due to confounding effects. The performance metrics used to measure overall equipment efficiency through availability, specifically MTTR and MTBF, do not offer statistically significant results. The analysis of maintenance analysis programs, for instance, using advanced maintenance programs \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Comparison**} & \multicolumn{1}{c}{**Mean Diff.**} & \multicolumn{1}{c}{**95\%CI\({}_{\text{L}}\)\({}^{\dagger}\)**} & \multicolumn{1}{c}{**95\%CI\({}_{\text{U}}\)\({}^{\dagger}\)**} & \multicolumn{1}{c}{**SE**} & \multicolumn{1}{c}{**Bias**} & \multicolumn{1}{c}{\(t\)} & \multicolumn{1}{c}{\(d\)\({}^{\ast}\)} & \multicolumn{1}{c}{\(p_{\text{tukey}}\)\({}^{\ast}\)} & \multicolumn{1}{c}{\(p_{\text{tbowf}}\)\({}^{\ast}\)} \\ \hline
1 & 2 & \(-567.035\) & \(-900.745\) & \(-17.953\) & \(213.502\) & \(-5.627\) & \(-2.088\) & \(-0.764\) & \(0.233\) & \(0.392\) \\  & 3 & \(-357.205\) & \(-743.86\) & \(199.218\) & \(223.14\) & \(-5.444\) & \(-1.102\) & \(-0.471\) & \(0.805\) & \(1.000\) \\  & 4 & \(-792.467\) & \(-1235.963\) & \(-200.263\) & \(256.796\) & \(-3.245\) & \(-2.764\) & \(-1.074\) & \(0.052\) & \(0.067\) \\  & 5 & \(-848.589\) & \(-1191.627\) & \(-262.425\) & \(230.391\) & \(-4.888\) & \(-2.823\) & \(-1.142\) & \(0.044\) & \(0.057\) \\
2 & 3 & 219.986 & \(-163.941\) & \(528.936\) & \(173.093\) & \(0.183\) & \(0.955\) & \(0.293\) & \(0.874\) & \(1.000\) \\  & 4 & \(-217.714\) & \(-676.854\) & \(171.782\) & \(213.156\) & \(2.383\) & \(-1.240\) & \(-0.311\) & \(0.728\) & \(1.000\) \\  & 5 & \(-268.598\) & \(-630.812\) & \(61.548\) & \(179.665\) & \(0.739\) & \(-1.379\) & \(-0.378\) & \(0.643\) & \(1.000\) \\
3 & 4 & \(-445.921\) & \(-888.478\) & \(64.831\) & \(232.464\) & \(2.199\) & \(-1.808\) & \(-0.604\) & \(0.374\) & \(0.734\) \\  & 5 & \(-487.654\) & \(-881.565\) & \(-87.631\) & \(197.824\) & \(0.555\) & \(-1.907\) & \(-0.672\) & \(0.320\) & \(0.593\) \\
4 & 5 & \(-46.923\) & \(-488.685\) & \(390.18\) & \(226.334\) & \(-1.644\) & \(-0.223\) & \(-0.068\) & \(0.999\) & \(1.000\) \\ \hline \hline \end{tabular} \({}^{\dagger}\) Bias corrected accelerated (95% lower and upper control limits). \({}^{\ast}\)\(d\) = Cohen’s d statistic; \(p\)-value adjusted using Tukey and Bonferroni correction.  
\end{table} 
Table 11: Post hoc ANOVA test of MAP.
# Selection of a Photovoltaic Panel Cooling Technique Using Multi-Criteria Decision Analysis
## 2 Methods and Criteria
### Environmental Aspects  
Air cooling and water cooling in large PV installations have both direct short-term impacts as well as indirect long-term effects on the environment. Absorption and liberation of the heat accumulated on the panels disturb the thermal profile and can harm the flora and fauna of the location. Water cooling consumes considerable water taken from nearby reservoirs or from underground [41]. Usually, the treatment of water becomes necessary in order to protect panels from chemical effects such as corrosion and deposits. Passive cooling methods have a lesser environmental impact which is restricted to the impact of material usage and thermal interaction with surroundings. In the case of active cooling methods, the use of conventional power is avoided, and a fraction of the energy produced by the panel is used to drive fans or pumps used in the panel cooling mechanisms. Hence, emissions due to this electrical usage are not accounted for. However, the use of material for the piping and ducting for cooling are associated with emissions during their manufacturing process. About 0.00185 kg of CO\({}_{2}\) is produced for every kilogram of steel produced [42] and 5.42 kg of CO\({}_{2}\) for every kilogram of PVC. The other emissions occurring due to the installation of cooling systems are negligible and hence not taken into account. Phase change cooling involves the use of organic compounds or salts for latent heat absorption. Paraffin or similar organic chemicals which are used in PCM-based PV cooling are not bio-degradable and hence are harmful to the environment. In the case of heat pie cooling, low boiling point organic chemicals such as acetone are used which are also harmful to the environment. Hence, qualitative ranking is done in the case of environmental effects due to the different cooling mechanisms based on an extensive study of the impact of each method.
## 4 Experiment Results and Discussion
### Experiments on the CIFAR-10 Dataset
#### 4.2.2 Conversion to SNN and Performance Evaluation  
An SNN model was organized with the same architecture as the VGG-16 model. The weight values for the convolution kernels and fully connected layers of the trained VGG-10 model were transferred to the corresponding weights of the SNN. The threshold values of the spike neurons in the SNN were determined by the proposed threshold balancing technique. Figure 11 shows the accuracies of the compared methods for a range of different latencies.  
As observed in Figure 11, the SNN models converted by the proposed method achieved higher accuracy than those of other methods [25; 26] in the range of latencies from 64 time steps to 512 time steps. Furthermore, our converted SNN model showed a reasonable accuracy of roughly 90% (89.97%) with a latency of 256 time steps, which is higher than those of other methods. With a latency of 512 time steps, all the compared methods achieved an accuracy higher than 90%. With a latency of 2048 time steps, our method built a model with an accuracy of 92.47%, while the SNN trained in [PERSON] et al.'s work [26] achieved an accuracy of 93.63%. This difference in the accuracies might stem from two factors. First, their trained CNN model achieved higher accuracy than our models. The accuracy of the trained CNN models strongly affects that of the converted SNN model. Second, their input was encoded using the Poisson distribution. There is a chance that their method obtained better performance due to probabilistic characteristics of the Poisson encoding. Despite that, as shown in Figure 11, the proposed method produced SNN models with stable and higher accuracies for short latency situations.