esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Verification
#### 4.1.3 Image Noise  
In order to evaluate the influence of image noise on the flow transition localization, multiple simulated images similar to Figure 8 are constructed with different noise amplitudes. The relative contrast between the turbulence wedge to the surrounding laminar flow regimes is chosen as \(c_{\text{wed}}=50\%\) and equals the average occurring contrast analyzed in the real measurement image.  
Figure 10 shows the three turbulence wedges with the image processing results. The relative noise amplitudes for the image is (a) 0.1, (b) 1.0, and (c) 1.5 times the measured image noise in a real, reference image. The corresponding CNR are 496, 73, and 33, respectively. With an increasing noise amplitude and reduced CNR, all methods detect local gradient maxima caused by the image noise and not the premature flow transition and falsely interpret them as premature flow transitions. As a consequence, the mean euclidean distance between the true transition line and the evaluated transition line increases.  
Figure 11 shows the result of the mean euclidean distance evaluation for a CNR of approximately 500 to 0 for a simulated image similar to Figure 10. This corresponds to a noise magnitude of a factor of approximately 0.3 to 1.5 the noise magnitude in real measurements. The mean euclidean distance of the state-of-the-art image processing (red) is only shown as reference and should not be focused on, as the method already fails to locate the turbulence wedge for high CNR. The course of the mean euclidean distance for the introduced image processing methods A (blue) and B (green) is increasing with reduced CNR, as the increased noise results in falsely detected premature flow transitions. Both introduced methods prove to be robust towards image noise as the mean euclidean distance does not increase considerably until the noise amplitude reaches a level 1.5 times the reference magnitude in real measurements.  
Figure 9: Mean euclidean distance between the true transition line and the evaluated transition lines of the state-of-the-art image processing (red), method A (blue), and method B (green) over the relative contrast of the turbulence wedge from the maximum (100% = fully turbulent wedge) to the minimum (0% = fully laminar wedge). The introduced image processing methods A and B are able to cope with a much lower contrast (\(\sim\)40%) compared to the state-of-the-art method (\(\sim\)90%) at which the evaluation is still able to detect the turbulence wedge. This contrast limit for the detection can be noted by the sudden increase of the mean euclidean distance for lower \(c_{\text{wed}}\).
## 1 Introduction
### Limitations of Existing Studies  
Overall, both scholars have made numerous achievements in the research on the optimization of project-level bridge maintenance decision making. However, there are still some limitations and shortcomings:  
(1) In the current research on project-level bridge maintenance decision making, the estimation of maintenance measure costs is mostly not specifically aligned with the actual occurrence of defects in bridge elements. The quantification of measure costs is often based on the volume of materials used. However, the cost of maintenance measures goes beyond material expenses, and it is also influenced by factors such as labor, machinery, and regional rates. Therefore, quantifying the cost solely based on material volume may lead to inaccuracies.  
(2) In the current research on bridge maintenance decision making, the definition of maintenance strategies is often not very specific. It is often broadly defined as a category of maintenance activities, such as repairing the superstructure or replacing bridge deck panels. The cost estimation for such general maintenance activities can easily lead to significant deviations and may not fully consider all aspects of the actual engineering requirements.  
In practical applications, if the optimization process could be applied at the level of specific defects, and if maintenance strategies could be refined into concrete measures, it would greatly promote the refinement of bridge maintenance management. This would provide more accurate guidance for actual bridge maintenance decision making.  
The remainder of this study is organized as follows. Section 2 summarizes the framework of bridge project-level maintenance that is established in this paper. Section 3 introduces the model and theory of the sequential decision making framework. In Section 4, the feasibility and validity of the proposed method are verified by the inspection data of a highway loop bridge. Section 5 includes the conclusions and main findings, as well as discusses the limitations of this work and the direction of future research.
# Wastewater Treatment with Bacterial Representatives of the _Thiothrix_ Morphotype
## 3 Methods for Identification of _Thiothrix_ in Wastewater Treatment Plants
### Dentirifying Removal of Phosphorus  
Two methods of phosphorus removal by denitrifying have been described. One is based on the operation of both denitrification and phosphorus accumulation reactions in the same microorganism. Another variant is based on the separate running of these reactions, where denitrification is carried out by one microorganism and phosphorus accumulation by another [124]. Dentirifying organisms that accumulate phosphate can simultaneously remove nitrogen and phosphorus under rotational anaerobic and anoxic/aerobic conditions [125].  
The role of bacteria of the _Thiothrix_ morphotype in denitrifying phosphorus removal has been suggested in different studies. The presence of these bacteria in the denitrifying phosphorus removal system was mentioned, but their function as denitrifying or dephosphorizing bacteria was not considered [126]. Other studies indicate a dephosphorizing role for the bacteria of the _Thiothrix_ morphotype in such systems. For example, [PERSON] et al. found that bacteria similar in morphotype to _Thiothrix_ played a crucial role in the accumulation of precipitated phosphate in a reactor with simultaneous nitrification-denitrification and phosphorus removal [127]. Moreover, [PERSON] et al. stated that, in a wastewater treatment system where denitrifying phosphorus removal served as the primary pathway of phosphorus removal, the _Thiothrix_ morphotype was associated with phosphorus removal [128].  
In addition, bacteria similar in morphotype to _Thiothrix_ are believed to be capable of denitrifying phosphorus removal [48]. An analysis of metagenomes from a biofilm reactor in which denitrifying phosphorus removal was performed showed that bacteria similar in morphotype to _Thiothrix_ had genes for denitrification and _pit_ genes for the phosphate transporter [49]. Thus, representatives of the _Thiothrix_ morphotype are potentially capable of denitrifying phosphorus removal within a single organism.
## 2 Materials and Methods
### Parameter Selection
#### 2.2.2 Year of Introduction (EIS YEAR)  
The year of introduction (or EIS year) parameter is introduced to the overall analysis in order to provide trends data for the UAV design and market evolution, based on several reasons. First of all, the field of fixed-wing UAVs is a rapidly evolving one, and new designs are being introduced every year. By assessing the year of introduction, it is possible to identify how a design compares to more recent UAV models in terms of performance and capabilities. Moreover, in a similar approach to manned aircraft and, more specifically, to commercial airliners, the EIS classification can be used to identify any trends, e.g., as a result of material technology improvements. Finally, UAV designs can evolve over time, with improvements and modifications being made to address performance and operational issues, sometimes with the introduction of derivative models or \"submodels\". A typical example is the case of the General Atomics MQ-9B Sea Guardian and MQ-9C Avenger, i.e., two versions of the MQ-9 Reaper, which vary considerably in terms of wingspan and payload capacity [34]. The year of introduction for each \"submodel\" can be important for understanding the design's development history and creating relevant trendlines for future ones. However, it should be noted that EIS year is one of the most challenging parameters to accurately obtain, since many UAV manufacturers may avoid presenting maiden flight dates or present market derivative models as completely new designs [7].
# The Modified Void Nucleation and Growth Model (MNAG) for Damage Evolution in BCC [PERSON]
## 3 Results and Discussion
### The MNAG Model
#### 3.2.1 Void Coalescence Term  
Void coalescence dominated at later times when voids that had grown substantially during the growth stage interacted with each other via the local stress fields [62]. The probability of such void coalescence events was directly related to the number density as well as the size of the voids. Here we assumed for simplicity that the change (reduction) in the number density of voids due to void coalescence was linearly dependent on the current void number density as well as the void radius at the growth stage. In addition, we introduced the concept of the characteristic void size, \(R_{t}\), as a measure of the ensemble average void size. At time interval \(\Delta t\), the change in \(R_{t}\), can be expressed as:  
\[\Delta R_{t}=R_{t}\left(\exp\left[\frac{1}{4}\left(\frac{\max(\sigma_{m}- \sigma_{g0},\,0)}{\eta}\right)\Delta t\right]-1\right) \tag{8}\]  
This term is very similar in the analytical form to the void growth term in the original NAG model.  
Assuming a critical number density, \(k_{V}\), and critical void radius \(k_{R}*R_{0}\) beyond which void coalescence took place, the reduction in the void number density due to coalescence is:  
\[\Delta N_{Vc}=\max(N_{V}-k_{V},0)\frac{\max(R_{t}-k_{R}*R_{0},\,0)}{R_{c}} \frac{\Delta t}{t_{n}} \tag{9}\]  
where \(N_{V}\) is the current void number density, \(k_{V}\) is the critical void number density above which coalescence occurs, \(R_{t}\) is the current typical void radius, \(k_{R}\) is a unitless scaling constant for the critical void radius, \(R_{0}\) is the void nucleation radius, \(R_{c}\) is a void coalescence reference radius with the same unit as \(R_{t}\), and \(t_{n}\) is a scaling constant for time and assumed to be \(1\) ps.  
As we assumed spherical voids in this paper, the contribution to the void volume, \(\Delta V_{c}\), should be directly related to \(R_{t}3\) and expressed as:  
\[\Delta V_{c}=V_{c0}*[max(R_{t}-k_{R}*R_{0},\ 0)]^{3}\Delta t \tag{10}\]  
where \(V_{c0}\) is the base void coalescence rate (units m\({}^{-3}\) ps\({}^{-1}\)). Hence, the change in the void number density, \(\Delta N_{V}\), due to void nucleation and coalescence in the MNAG model is:  
\[\Delta N_{V}=\Delta N_{Vn}-\Delta N_{Vc} \tag{11}\]  
Additionally, in the MNAG model, we proposed that the change in the total void volume, \(\Delta V\), in a time interval \(\Delta t\), should be:  
\[\Delta V=\Delta V_{n}+\Delta V_{g}+\Delta V_{c} \tag{12}\]  
The damage \(D\) could still be calculated based on Equation (7).  
There were other models like Tepla and Tonks that incorporated the void coalescence in their analytical forms ([PERSON] et al., 1999). Our results from the MNAG model were compared to these models in later sections.
## 1 Introduction
### Porosity Monitoring
#### 1.2.1 Post-Processing Evaluation  
In this section, we define X-ray computed tomography (XCT) and Ultrasonic testing methods of usage pertaining to AM. XCT is defined as a method of establishing 3D representations of objects through obtaining X-ray images centered around an axis of rotation and developing 3D models through mathematical image reconstruction algorithms [49; 50; 51]. Between 2010-2014, XCT became a well-established method for measuring porosity by comparing empty voxels [52]. It has since developed into a common nondestructive testing method [53; 54; 55; 56]. In [56], an ML classifying technique is used for evaluating porosity segmentation and a benchmark is established by examining disparities in porosity segmentation within XCT scans.  
Ultrasonic testing (UT) has been used for assessing damage in metal parts and manufacturing processes [57; 58]. Specifically, DL is used in [59] for ultrasonic testing for porosity evaluation of additively manufactured components. During UT application, ultrasonic waves are emitted into a workpiece [58]. The ultrasonic waves interact with any flaws in the workpiece and detect and evaluate defects to ensure the integrity of the workpiece [58].  
A unique image-based post-processing technique was used in [60], where authors collected a large database of image-based defects. These images were classified into 'good quality', 'crack', 'lack of fusion', and 'gas porosity' to create a set of labels that were then input to a CNN. [PERSON], et al. reported that the CNN was able to classify the types of defects with upwards of 92.1% accuracy with 8.01 milliseconds of computer time recognition.
# Design and Testing of a LUT Airfoil for Straight-Baded Vertical Axis Wind Turbines
## Nomenclature  
H-VAWT H-type Vertical Axis Wind Turbines  
VAWT Vertical Axis Wind Turbines  
HAWT Horizontal Axis Wind Turbines  
LUT The name of the newly designed airfoil (Lanzhou University of Technology)  
DTU Technical University of Denmark  
_SST_ Shear Stress Transport  
\(R_{e}\) Reynolds number  
\(\alpha\) angle of attack, \({}^{\circ}\)  
\(\theta\) azimuthal angle, \({}^{\circ}\)  
\(\omega\) rotational speed of the wind turbine, rad/s  
\(\mathrm{V_{in}}\) freestream velocity, m/s  
R rotor radius, m  
C airfoil chord length, mm  
SQP Sequential Quadratic Programming  
\(\sigma\) penalty factor  
\(\mathrm{C_{L}/C_{d}}\) lift-drag ratios  
\(\mathrm{C_{L}}\) lift coefficient  
\(\mathrm{C_{d}}\) drag coefficient  
\(\mathrm{r_{0}}\) leading-edge radius  
\(\mathrm{C_{P}}\) Pressure coefficient  
PIV Particle Image Velocimetry  
FS Full Scale
## 3 UAV Power Supply and Charging Mechanisms
### Hybrid Powered UAVs
#### 3.5.2 Solar Cells plus Battery  
Another method for extending the mission length of UAVs is the installation of PV panels to operate alongside the existing batteries. When solar irradiation is available, PV cells are often employed to power a UAV or refill its battery. The battery, in turn, is utilized for functioning at night or during hours when solar radiation is limited [67]. The use of solar power as an energy source allows small size UAVs to carry larger payloads and can increase flight periods to more than 24 h, allowing for multi-day flying [68]. The authors in [69] studied the design and validation of a solar power management system (SPMS) for their solar-cell and battery-powered experimental UAV. Their results reveal that when the angle of incidence of sunlight varies from 0 to 45 degrees, the power consumed from the solar cells fluctuates depending on the load situations and may be reduced by up to 30%. This means that changes in aircraft attitude will have a direct impact on the power generated by the solar system.
## 5 Results
### Effects of Pre-Processing  
From the above discussion it is apparent that the two main concerns are modelling high frequency components such as acceleration \"ringing\" caused by sudden forceful impacts and exact matching of the signal magnitude. This suggests the use of low pass filtering and scaling as obvious pre-processing approaches. Both are parts of our pipeline (Section 3.1). Note that we are talking about standard scaling when **learning the regression**; that is, with the mean and standard deviation computed on our dataset of generic motions being performed by other users. As the mean may be different in this dataset, we simply undo the standard scaling, which is fully reversible, when obtaining simulated signals.  
Other pre-processing parameters within our pipeline are the sliding window sizes for training the regression. We experimented with window sizes of 50 and 16 values, representing 1 s and 0.32 s, respectively. In our early experiments training the regression models, it became clear that the smaller value was better, providing a smaller regression loss and better overall classification. Thus, for brevity, we report all results with the smaller window size. Regarding window step, we keep it at 1 to increase the amount of training data.  
Figure 10: Real signals (red) versus simulated (blue) in the regression training set (generic motions) for acceleration norm, linear and not. All cases trained without neither filtering nor standard scaling.  
Figure 9: Real signals (red) versus simulated (blue) in the regression training set (generic motions) for acceleration and gyro norm. All cases trained without neither filtering nor standard scaling.
## 2 Debt Compensation: An Example  
Debt cancelation algorithms have as data input the debts (mainly invoices) among companies; that is, companies must provide, clearly and precisely, information on how much is owed and to which companies. Thus, the algorithm is able to construct a graph representing the debts among these companies, allowing an evaluation of various debt cancelation cycles. cancelation is applied for the minimum amount common across the components of such a cycle. Next, we show a graph highlighting the relationships among different companies and a small example of a debt cancelation cycle. Obviously, in the real economy, such transactions can include many more companies, making the problem nontrivial.  
Suppose there are three companies, as shown in Figure 1. In this case, company A has a debt (e.g., an invoice) of 1500 EUR with company B, which in the graph is represented by an arrow directed from company A to company B, where the weight is the amount owed (1500 EUR). Likewise, company B has a debt with company C of 4500 EUR and a debt of 500 EUR with company A. As can be seen, this creates a debt cycle where multiple companies owe each other; therefore, a cancelation worth 500 EUR can be established. As mentioned earlier, cancelation cycles are always applied for the lower amount. After compensation, we obtain the new graph shown in Figure 2.  
Therefore, we started with an example where the initial total debt was 6500 EUR, i.e., the sum of all the debts of the participants. After the cancelation, we can clear 1500 EUR; therefore, the new total debt is 5000 EUR.  
As can be seen in the example, circular debt compensation can reduce the liquidity needs of companies, help to solve the problem of non-payment, and improve the balance sheet by facilitating access to credit for companies. In the example, an economic activity  
Figure 1: Debt relation among three companies.  
of 6500 EUR is resolved with only 5000 EUR of liquidity. The remainder can be resolved through a multilateral agreement for the exchange of services and/or goods. Note that, for a higher minimum debt between two companies, cancelation becomes more beneficial; therefore, the system contains lower liquidity requirements.
## 3 Candidate Universal Decoders for URLLC  
A number of channel codes offering a certain reliability order have been developed for short block lengths, ranging in computational complexity. To clarify, while a code might be appropriate for a particular application, it might result in significant performance degradation in other cases. For code-specific decoders, every code has a unique decoder that is incompatible with other codes. For instance, a low-complexity BP (belief propagation) decoder is advantageous for LDPC or PBRL-LDPC codes, but it is not optimal for polar codes. Similarly, the decoding algorithm of successive cancellation (SC) is generic to polar codes only [23]. Consequently, it becomes reasonable to choose a universal (code-agnostic) channel decoder rather than a code-specific one to achieve maximum likelihood (ML) performance. This section discusses the universal decoding algorithms and also AED and bit-flip decoders in the context of a URLLC scenario.  
Figure 2: System model for universal decoder.
## 5 Simulation Results and Discussions
### Performance Analysis of Users  
In this part, the user's performance analysis is mainly investigated. As is mentioned in Algorithm 1, when users were influenced by co-channel interference, the proposed interference-aware cooperative anti-jamming distributed channel selection algorithm (ICADCSA) was based on multi-agent Q-learning. When users are not influenced by co-channel interference, the proposed ICADCSA algorithm was based on single Q-learning. For better clarification, we use cumulative normalized utility \(U_{cum}\) to show the effective of ICADCSA approach, which is defined as follows:  
\[U_{cum}=\sum_{i=1}^{PN}\left(1-f\left(a_{n}\left(t\right),a_{j}\left(t\right) \right)\right)\frac{1}{I_{n}\left(c_{n},t\right)}-w_{s}\delta_{s}\left(a_{n} \left(t\right),a_{n}\left(t-1\right)\right)-w_{c}\delta_{c}\left(I_{n}\left(c _{n},t\right)\right), \tag{19}\]  
where \(PN\) is the number of packet in every update and \(PN\) is set to be 20 in the simulation, which means the cumulative normalized utility updates per 20 slots, while the time of each update is 23.6 ms.  
Figure 7: The time-frequency diagram after LCADCSA converging in the first stage.  
Furthermore, as can be seen from Figures 8-10, both single-Q learning and multi-agent Q-learning can realize high cumulative utilities within 50 update numbers (about 1.18 s). For the purpose of evaluating the effectiveness of the ICADCSA algorithm, it was compared to the sensing based algorithm and multi-user single Q-learning. In the sensing-based algorithm, users selected channels that were not jammed by the jammer after sensing the current channel states, and in multi-user single Q-learning, each user adopted single Q-learning independently to avoid the jamming channel, while ignoring the existence of mutual interference. Simulation results show that users can achieve higher cumulative normalized utilities \(U_{cum}\) using the ICADCSA algorithm when there exists mutual interference between users. The reason is that, in the proposed algorithm, users can learn the actions of jammer and can also adjust their channel selection strategies jointly according to their sensed interference level and shared information. Thus, the users can avoid malicious jamming and co-channel interference simultaneously.
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 3 System Model
### Network Update Process
#### 3.3.2 Positioning Update  
The location monitor block will keep asking for new data from the HEO satellite every \(t\) minutes.  
The parameter \(t\) is not concrete which will change depending on the characteristics (e.g., speed or altitude) of the MS.  
Since the whole process and decisions are made by the MS, there may be some mistakes due to the message latency. For example, when MS performs slice selection analysis, because the network has not had time to update the latest network situation for MS, the slice selected by MS may be assigned to other users at this time, but MS does not know this situation, resulting in allocation (access) failure.  
Therefore, the proposed procedure contains a feedback loop which is designed to solve the possible volatility of the network, as you can see in Figure 4. Every failure record of allocation will be transmitted backward to the first step to lower the fail rate of the next allocation decision.