esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Design
### Performance Testing Scenarios
#### 4.2.2 Scenario 2: Application with Microservices Architecture Using Containers  
In this scenario, it is proposed to transform an application based on a functional monolithic architecture on a KVM into an application with a functional microservices architecture on Containers. Figure 10 represents the structure of the approach applied to the experiment. In this case, the Docker Engine performs Container orchestration. It also provides them with the necessary resources so that each container meets the required conditions and follows the guidelines that this technology requires.  
Unlike the first scenario, the cluster and workers helping conduct most of the system resources are no longer used. In this scenario, Docker handles this issue by using the resources efficiently. As a result of this, ports are exposed for each new microservice. The databases also run inside containers, and each one exposes a port through which it can be accessed from the host. Besides, there is an extra container, namely Sync Volumes, to keep the data and files generated by the databases in the container synchronized with a directory on the host. This occurs to not lose the information generated once the container ends its execution.  
For the interconnection and intercommunication between containers, it is defined in the docker-compose.yml file. As indicated in Figure 4, the containers are connected over a private  
Figure 10: Second Scenario: Application with microservices on containers. An adaptation from [8].  
Figure 9: First Scenario: Monolithic application on a KVM. An adaptation from [8].  
network generated by Docker when the services start. In such a network, ports are exposed that lack access from an external Host, since Docker exposes ports for each container, so that they can be accessed from the host. Hence, there are three NodeJS applications, of which each one is independent and has its libraries and internal and external connection ports. The JMeter Server Agent service is located on the host since Docker is part of the primary system. It exposes its port 4444 to collect the resource performance information when stress tests run.  
Nonetheless, it should be noted that this does not affect the results obtained in the conducted tests since JMeter focuses on the resources' behavior concerning the test script that is executed under its supervision. The NewRelic Server Agent service is also on the host side. In this case, the information sent to the NewRelic Server is affected since it contains global information, which includes processes outside the experiment that are executed and generate resource consumption. Hereby, a filter is used on the server-side of NewRelic to capture the resources' behavior at the exact moment when the stress tests run. This allows a better idea and supports a comparison to be performed concerning the results obtained within the first scenario.  
In terms of resources, it is about maintaining the same characteristics in both scenarios so that the data generated in the tests are as accurate as possible.
## 3 Results and Discussion
### Effect of the Light/Darkness Cycle on the Growth of S. dimorphus  
In these cultures, the light intensity of 60.75 \(\upmu\)mol\(\cdot\)m\({}^{-2}\)\(\cdot\)s\({}^{-1}\) remained constant, and the light/dark cycles of 14/10 (culture C6), 16/8 (culture C7), and 24/0 h (culture C8) on the growth of the microalga were evaluated.  
It was observed that the biomass had a remarkable increment in the culture C7 with a cycle of 16/8 h, since the maximum value was of 4.73 g\(\cdot\)L\({}^{-1}\) (Table 4 and Figure 8) compared to the culture with the light/dark cycle 14/10 h (C6), which had a maximum biomass of 1.85 g\(\cdot\)L\({}^{-1}\) (Table 4). The productivity and the _Fc_ in the culture C7 were 0.44 g cel\(\cdot\)L\({}^{-1}\)\(\cdot\)d\({}^{-1}\) and 0.8 g CO\({}_{2}\)\(\cdot\)L\({}^{-1}\)\(\cdot\)d\({}^{-1}\), respectively. In the culture C8 where the light was kept on all the time (cycle 24/0), the production of biomass was very close to the biomass of the culture C7 (4.79 g\(\cdot\)L\({}^{-1}\), Table 4), but the specific growth rate was lower (0.21 d\({}^{-1}\), Table 4).  
These results indicate that light of moderate intensity with a long period of time could increase the growth of biomass. [PERSON] et al., 2013, 49 exposed the microalga _S. obliquus_ SA1 to a light intensity of 74.2 \(\upmu\)mol\(\cdot\)m\({}^{-2}\)\(\cdot\)s\({}^{-1}\), with a light/dark cycle of 14/10 h that was fed with 13.8% CO\({}_{2}\). They obtained 4.86 g\(\cdot\)L\({}^{-1}\) of biomass at 34 days of culture. Also, their _Fc_ was of 0.225 g CO\({}_{2}\)\(\cdot\)L\({}^{-1}\)\(\cdot\)d\({}^{-1}\) which was lower than our results (0.8 g CO\({}_{2}\)\(\cdot\)L\({}^{-1}\)\(\cdot\)d\({}^{-1}\)).  
[PERSON] et al., 2016 50, conducted a study in which _Nammocloropsis_ sp. was exposed to several light intensities combined with light/dark cycles and observed that at light intensities of 80 and 120 \(\upmu\)mol\(\cdot\)m\({}^{-2}\)\(\cdot\)s\({}^{-1}\) with light/dark cycle of 16/8 h, the biomass reached its maximum values, but when  
Figure 8: Effect of the light/dark cycles on growth of _S. dimorphus._  
they increased the period to 24 h of continuous light, the biomass did not show any growth. This result was due to the fact that a high intensity of light was coupled with a prolonged period of light, leading to an interruption of growth and subsequent cell death.
## 4 Biaxial Failure Prediction for the PRC with Interfacial Debonding
### Effect of Interfacial Cohesive Strength  
According to the results in Section 3, once the interfacial stress is equal to interfacial cohesive strength, the interface cannot transmit the loadings from the matrix to particles. Therefore, the interfacial strength plays an important role in studying mechanical behaviors. Here, two different interfacial cohesive strengths for the spherical PRC, that is 230 MPa and 330 MPa, are both discussed, as shown in Figure 6. For comparison, the results of the PRC with the interfacial cohesive strength 430 MPa are also shown in the figure. The results by our group [38] indicated that the mechanical properties of composites are decided by matrix properties after completing interfacial failure, and the stress-strain responses of composites with different interfacial cohesive strengths tend to be uniform finally. However, it can be observed from Figure 6 that failure strength is closely dependent on both the interfacial strength and loading conditions. In detail, the failure strength in uniaxial compressive and biaxial compressive loadings is independent of interfacial cohesive strength. The obvious differences can be found in biaxial tensile loadings, where the higher interfacial strength provides the higher failure strength in biaxial and uniaxial tensile loading. This means that the higher stress is needed for the higher interfacial cohesive strength, and more loadings can be loaded on the particles through the matrix materials. For the PRC with interfacial cohesive strengths of 230 MPa and 330 MPa, it is interesting to mention that the effect of the interfacial cohesive strength on the failure envelope is only evident, and to a very limited extent, in tensile-tensile loading.  
Figure 5: The prediction of biaxial failure strength for the particle-reinforced composites (PRC) with and without interfacial debonding.  
Figure 6: The prediction of biaxial failure envelopes for the PRC with different interfacial cohesive strength.
## 4 GA Application in Goods Distribution Systems
### Introductory Considerations  
Typically, in the GA implementations, the objects in the search domain are represented in a binary form. In the case of logistic networks, however, since the search domain occupies a continuous spectrum, it is more appropriate to use a CGA. Second, the choice of the fitness function itself should allow one to assess, with due accuracy, a large number of situations that might occur in the problem area. As part of the evolution, one seeks a propitious balance between elevating customer satisfaction and reducing operational costs. Since in the systems under consideration, the future demand is assumed to be unknown at the moment of taking the stock replenishment decision (as in the practical systems), the fitness value will be determined from a simulation of network behavior for a given demand pattern and repeated many times to reach statistically relevant results.  
The next sections treat the structure of the applied CGA depicted in Figure 2 in detail. The recommended parameter configuration is summarized in Table 6.  
Figure 2: Genetic algorithm (GA) block diagram.
## 3 Joint Extraction from Tunnel Face Based on Traditional Image Processing Methods
### Morphological Processing  
Due to the presence of filling materials in the joints and the effects of lighting, pixels that originally belonged to the same joint may appear as disconnected points, as shown in Figure 11(a), making it look like multiple joints.  
To address the missing pixels shown in Figure 11(a), morphological processing is performed, as illustrated in Figure 13. The principle involves the following:  
1. Dilation operation: applying a dilation operation to the two joints in Figure 12(a) results in the connected joint image shown in Figure 12(b).
2. Erosion operation: performing an erosion operation removes the dilated pixels from non-breakpoint areas while retaining the breakpoint pixels, resulting in Figure 12(c).
3. Merging pixels: finally, merging the new breakpoint pixels with the original joint pixels forms the new joint shown in Figure 12(d).  
Figure 11: Binarized image.  
Figure 12: Comparison of joints Before and After Morphological Processing.  
After applying morphological processing to the joints with breakpoints shown in Figure 11(a), the result is as shown in Figure 11(b). It can be seen that morphological processing connects pixels in joints by applying dilation and erosion operations, effectively addressing disconnected points due to lighting or filling materials.
## 4 Quantum and Classical Image-Processing Algorithms
### Quantum Tomography and Quantum Fisher Information  
A further quantum approach to image analysis and detection schemes will be employed to achieve super-resolution (or, eventually, for maintaining the desired resolution and speeding up acquisition and elaboration of the quantum plenoptic images) and to compare correlation plenoptic detection scheme to the ultimate quantum limits. The basic concept underpinning the Fisher Information super-resolution imaging is the formal mathematical analogy between the classical wave optics and quantum theory, which makes it possible to apply the advanced tools of quantum detection and estimation theory to classical imaging and metrology [29; 55]. The advantage of this approach lies in the ability to quantify the performance of the imaging setup based on rigorous statistical quantities. Inspired by the quantum theory of detection and estimation, quantum Fisher information, a quantity connected with the ultimate limits allowed by nature, is computed for simple 3D imaging scenarios like localization and resolution of two points in the object 3D space [30; 56; 57; 58]. For example, one might be interested in measuring the separation of two point-like sources and seeking the optimal detection scheme, extracting the maximum amount of information about this parameter. We shall thus employ quantum Fisher information to design optimal measurement protocols within the quantum plenoptic devices, able to extract specific relevant information, for enhanced resolution, with a minimal number of acquisitions. The question of the minimal number of detections over the correlation planes which achieves acceptable reconstruction quality is of relevance because of the direct connection to the amount of processed data and can lead to reduced time for data processing.
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
## 3 Ethernet for Automotive Communications
#### 1.3.1 IEEE Audio Video Bridging  
The IEEE AVB family of standards was developed by the IEEE 802.1 Working Group to provide improved synchronization, bounded latency, and reliability over IEEE 802.1Q networks. The main AVB standards are the following:  
* IEEE 802.1 AS-2011: Timing and Synchronization for Time-Sensitive Applications
* IEEE 802.1 Qav-2009: Forwarding and Queuing for Time-Sensitive Streams (FQTSS)
* IEEE 802.1 Qat-2010: Stream Reservation Protocol (SRP).  
The IEEE 802.1 AS-2011 [33] achieves a common notion of time in the network through a suite of protocols that enable the end stations and switches to synchronize their local clocks with each other. In particular, first, a Best Master Clock algorithm is run to choose the node with the best local clock quality. Such a node, called the grandmaster, acts as the time reference source in the network. Second, the Generalized Precision Time Protocol (gPTP) is used to send the grandmaster clock value to the other network nodes along a spanning-tree path.  
The IEEE 802.1 Qat [34] defines the SRP, which makes it possible to register and reserve bandwidth for some real-time traffic classes, called the Stream Reservation (SR) classes. This is achieved by reserving resources within the switches along the complete path between the talker (i.e., the source node) and the listener (i.e., the destination node).  
The IEEE 802.1 Qav [35] provides prioritization for the flows belonging to the SR classes and applies traffic shaping to the SR frames to prevent traffic bursts. In particular, for each SR traffic class, the standard provides formulas to calculate the maximum frame size and the maximum number of frames belonging to the class that can be sent in a given time interval. To enforce such a limit, a Credit-Based Shaper is applied at the output ports of both the switches and end nodes. This way, it is possible to guarantee bounded latency to the frames belonging to the SR classes.  
With the AVB standards, Ethernet became able to provide bounded latency to real-time flows, i.e., 2 ms for SR Class A and 50 ms for SR Class B over 7 hops in the network.  
In automotive communications, AVB can effectively support the transmission of audio/video streams in the multimedia domain, but it is not suitable for time-critical and safety-critical control traffic. For this reason, starting from 2012, the IEEE TSN family of standards was defined, which broadened the scope from audio/video to multiple types of flows, with diverse QoS and generally more stringent requirements [36]. The TSN standards that are relevant to automotive communications are described in the next section.
## 5 Empirical Results
In this case, we used \(N=5000\) observations for training, a look back period of \(L=30\) days, and a forecast period of \(F=10\) days, as this was the best case result for the deep learning net. Rather than rerun all cases, we only examined the OS nonstationary case (NS) as this is the one that is relevant for a practitioner trying to make money from predicting market direction. Here, \(OA=0.517\) (SD = 0.15), i.e., logistic regression has an accuracy level much worse than that of the neural net (OA = 0.582), though this is not statistically significant. In addition to the logistic regression, we also ran six other prediction models using the same feature set and labels. These six models are: decision trees, random forest, linear discriminant analysis, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Forecast Period \(F\) in days**} \\ \hline
**Metric** & **5** & **10** & **15** & **20** & **25** & **30** \\ \hline OA & 0.605 & 0.582 & 0.567 & 0.565 & 0.556 & 0.552 \\ FPAA & 0.648 & 0.587 & 0.702 & 0.641 & 0.697 & 0.667 \\ Precision & 0.594 & 0.573 & 0.557 & 0.558 & 0.549 & 0.546 \\ Recall & 0.986 & 0.981 & 0.987 & 0.986 & 0.989 & 0.992 \\ AUC & 0.546 & 0.526 & 0.517 & 0.517 & 0.515 & 0.508 \\ F1 & 0.710 & 0.705 & 0.702 & 0.703 & 0.699 & 0.699 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Forecast metrics as the forecast period varies in the set \(F=\{5,10,15,20,25,30\}\) days. These results are for the OS nonstationary case. Metrics correspond to those reported in Table 1.  
k-nearest neighbors, naive Bayes, and support vector machines. These were all for the case of practical interest, i.e., the rolling OS test. Table 3 reports the results for the deep learning net and all the other models for comparison. While deep learning does better than the other models, in no case is it possible to beat the market in a statistically significant manner.
## 4 Simulation and Evaluation
### Experimental Setup  
We use two virtual machines (VMs) running Ubuntu V20.04 LTS to demonstrate the reliability of our method. In one VM, the Mini-net V2.2.2 was loaded and used to simulate a software-defined network. At the edge, aggregation, and core levels of our architecture, there are eight, four, and two OpenFlow switches, respectively. There is a client linked to each access switch. The sub-flows of the module are supported by redundant links incorporated into the network at each level. Instead of employing a virtual machine on the same PC or machine to manage the internet, we increased the ODL controller operating in the same network. Because of the ODL controller's efficacy as described in [35], we employed it in our experimental analysis.  
In the following paragraphs we explain the parameters we have evaluated for comparison of our proposed methodology with NDN mobility handover schemes proposed in [25] and [26]. We evaluate the delay, throughput, CPU utilization, packet loss ratio, and the jitter for comparison of our proposed strategy with the previous schemes suggested in the literature [25; 26].
# Small Satellite Tools for High-Resolution Infrared Fire Monitoring
## 2 FireBIRD Primary Mission Requirements  
The primary objectives of the FireBIRD mission are infrared remote sensing of high-temperature events (HTE). These objectives are briefly described in the FireBIRD Mission Requirement Document [3] by the following items:  
* Establishment of a pre-operational fire monitoring constellation consisting of TET-1 and BIROS;
* Installation and testing of different operational modes for continuous monitoring of selected regions;
* Testing of different overflight formations and maneuvers to demonstrate the agility of the satellites;
* Survey of vegetation fires, peat fires, and coal seam fires and derivation of important climate-related variables of these fires, in particular the three parameters of the Essential Climate Variable (ECV) \"Fire Disturbance\":
* precise geographic localization of \"Active Fires\" fires
* estimation of the upwelling radiant power of the fire, i.e., estimation of the \"Fire Radiative Power\" (FRP); and
* fire-area mapping parameters, e.g., the \"Burned Area\"--for daytime data takes, as well as deriving other fire attributes, such as the strength and length of forest and bush fire fronts (\"fire line strength and length\"), and further:
* Observation and assessment of volcanic and geo-thermal activities, especially for:
* monitoring of selected active volcanoes;
* detection of thermal precursors of eruptions;
* monitoring and thermal mapping of volcanic eruptions; and
* mapping and detection of thermal changes in geo-thermal active areas.
* Validation, i.e., critical evaluation of accuracy:
* of fire attributes, such as the FRP, effective fire area, effective fire temperature, and others in the spatial mapping of the detected HTE; and
* by cross validation of fire emission data, especially those from spatially coarser resolution satellite sensors, which detect large fires at shorter time intervals than FireBIRD.  
The recommended Noise Equivalent Difference Temperature (NEAT) of the mid-infrared (MIR) band and the thermal infrared (TIR) band of a space-borne sensor for high-resolution IR fire monitoring is 0.3-0.5 K and 0.2 K, respectively, for a 300 K blackbody source [4]. The BIRD mission demonstrated that these NEDT numbers can be achieved by IR sensors equipped with cooled photo detectors in the MIR and TIR bands. The NEAT represents the radiometric measurement accuracy.
# Corrosion Monitoring Techniques in Subcritical and Supercritical Water Environments
## 2 Non-Invasive Monitoring Techniques
### Acoustic Emission
The AE signals generated during the transgranular SCC process include sudden and continuous types, with corresponding AE sources being crack propagation and plastic deformation [78; 83]. However, only continuous AE signals are generated during the intergranular SCC process, corresponding to plastic deformation [79]. Although some studies suggest that AE is highly sensitive to the very early stages of crack development in high-temperature water, there are few related studies [84]. Various other non-invasive corrosion monitoring technologies are also widely used in engineering practice. Multi-frequency electromagnetic sensors, consisting of a transmitter and receiver coil, can be used for the corrosion and integrity detection of oil pipelines. Due to the skin effect, low-frequency electromagnetic scanning can calculate the thickness of pipeline metal, while high-frequency electromagnetic scanning can detect the characteristics of the inner wall [44]. Optical fiber sensors (OFS) are also small, flexible, lightweight, susceptible, and compatible with fiber optic data communication networks, which can measure corrosion in several areas within a single optical channel. Passive radio-frequency identification (RFID) sensors, especially chipless passive RFID sensors, are a comprehensive wireless sensor family for pipeline corrosion, with low cost, compact size, lightweight, and remote sensing [8]. These non-destructive monitoring techniques are primarily based on physical methods, which monitor the contour, depth, and other characteristic information of the corrosion layer online and, to some extent, provide information on alloy corrosion behavior. They have broad application prospects in the corrosion monitoring of pipes and equipment exposed to sub-/supercritical water systems. Please see the literature for more detailed information on non-destructive monitoring techniques for alloy corrosion [8; 85].