esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials Characterization as a Basis of Structural Integrity Assessment
In case no additional damage is to be caused to the material, only non-invasive methods, hence those related to non-destructive testing (NDT), come into play. NDT methods are principally based on mechanical and electromagnetic waves that follow the fundamental physical principles of reflection and transmission, possibly added by some mode conversion. Mechanical waves include the range from classical vibrations up to ultrasound while electromagnetic waves range from radar to gamma rays. The fundamental question now arises as to which of the waves react to the different damage mechanisms (dislocation, slip bands, martensitic transformation, cracks, others) accordingly? This question has to be answered for every case of structural integrity assessment as well as material, such that an optimum solution of structural health monitoring can be developed. This underlines the importance of a microstructural analysis and description of a material's degradation as well as its link to the physical answers provided by the different NDT techniques. Today this fundamental interaction between materials science with respect to degradation and NDT, or even broader as the physical monitoring options versus structural integrity assessment, can be still seen to be quite at its infancy. The state-of-the-art principle applied, that loads acting on a structure do lead to cracks and hence degradation, which can be visually inspected (being an NDT technique when professionalized) and where the information monitored can be used to characterize the degree of degradation, which can be used to prognose a component's residual life until critical failure, is principally correct. However, this approach is affected by a large number of uncertainties, which include, among others:  
* the precision of the loads having been monitored,
* the appropriateness of numerically describing a structure's behavior i.e., the local loading and hence the distribution of stresses, strains and possibly other parameters relevant to degradation,
* the understanding of the damage mechanisms to occur regarding the material's degradation process,
* the precision to get different damage mechanisms numerically described also in the sense of prognostics,
* the understanding of potential interactions NDT techniques may have with the damage mechanisms to be observed,
* the ability to numerically describe the interactions between NDT techniques and
* the damage mechanisms and any further uncertainties that may arise due to human error once a monitoring concept may have been established. The MibaLeb project described here is an approach to enhance prognostics of aging infrastructure and to reduce the number of uncertainties referenced above.
## 3 Sensor Scheduling
### Single Sensor Scheduling  
The simplest case is to model one sensor that observes a group of sites at discrete time points. Some physical systems require virtually zero time for changing a site being observed. For example, the time of a camera refocusing, which is installed on a satellite, is negligibly small. This assumption leads to the model proposed by [PERSON] and [PERSON] in [26].  
Assume that we need to observe \(n\) sites during \(T\) time periods. During every period a sensor is allowed to watch only at one of \(n\) sites. The scheduling decision can be modeled using binary variables \(x_{i,t}\)  
\[x_{i,t}=\left\{\begin{array}{ll}1,&\mbox{if $i$-th site is observed at time $t$},\\ 0,&\mbox{otherwise},\end{array}\right. \tag{2}\]  
\(t\) is a discrete variable and \(t=1,2,...,T\). If a site \(i\) is not observed for some period of time, it leads to the penalty that is proportional to the time of not observing this site. This penalty can be modeled using another group of decision variables. Let \(y_{i,t}\) denotes the time of last visiting site \(i\) before time moment \(t\). Let us note that variables \(x_{i,t}\) completely determine values of \(y_{i,t}\). Fixed penalty \(a_{i}\) and variable penalty \(b_{it}\) are associated with site \(i\) at time moment \(t\). Thus, the penalty at time \(t\) associated with site \(i\) is  
\[a_{i}(1-x_{i,t})+b_{i,t}(t-y_{i,t}). \tag{3}\]  
[26] suggests minimizing maximum loss over all sites and time intervals. Thus the objective function is defined as \(\max_{i,t}\{a_{i}+b_{i,t}(t-y_{i,t})\}\). This objective function can be linearized and consequently the problem looks as following  
\[\min C\] (4) s.t. \[C\geq a_{i}(1-x_{i,t})+b_{i,t}(t-y_{i,t}),\;\forall i=1,\ldots,n,\;\forall t=1,\ldots,T, \tag{5}\] \[\sum_{i=1}^{n}x_{i,t}\leq 1,\;\forall t=1,\ldots,T,\] (6) \[0\leq y_{i,t}-y_{i,t-1}\leq tx_{i,t},\;\forall i=1,\ldots,n,\; \forall t=1,\ldots,T,\] (7) \[tx_{i,t}\leq y_{i,t}\leq t,\;\forall i=1,\ldots,n,\;\forall t=1,\ldots,T,\] (8) \[y_{i,0}=0,\;\forall i=1,\ldots,n,\] (9) \[x_{i,t}\in\{0,1\},\;\forall i=1,\ldots,n,\;\forall t=1,\ldots,T,\] (10) \[y_{i,t}\in I\!\!R,\forall i=1,\ldots,n,\;\forall t=0,\ldots,T. \tag{11}\]  
Constraints (6) ensure that the sensor visits only one site at a time. Constraints (7) -(8 ) set the dependence \(y_{i,t}\) on \(x_{i,t}\). That is \(y_{i,t}\) is set to \(t\) if and only if the sensor is observing site \(i\) at time \(t\) otherwise \(y_{i,t}=y_{i,t-1}\).  
Single Sensor Scheduling problem is NP-hard [27] therefore various greedy heuristics have been proposed. The idea behind greedy algorithm is simple. At time \(t=1\) we find the site with the smallest penalty then at next time period we find another site with the smallest penalty. This sequence is repeated for all \(T\) time intervals. Thus the complexity of suggested approach is \(O(nT)\). [PERSON] and [PERSON] has also suggested the \"look ahead\" modification of greedy heuristic which takes more computational time but computational experiment demonstrate that the solution is improved compared to the initial simple greedy optimization.
## 4 Evaluation Metrics
### Efficiency  
Transmission efficiency of the CCES scheme is defined as the amount of error-free bits transmitted in unit time, measured by bits per second (\(BPS\)), as shown in (16).  
\[BPS=\frac{N(\mu)}{\mu}, \tag{16}\]  
where \(N(\mu)\) represents the number of error-free bits transmitted in \(\mu\) seconds.  
We also introduce another novel representation for BPS. Since \(m_{k}^{\prime}\) is converted into \(e\) end spreading sequences and supposing the length of \(m_{k}^{\prime}\) is \(b\), the bits carried by each legal piece is \(\frac{b}{e}\). Thus,  
\[BPS=r\frac{b}{e}, \tag{17}\]  
where r means the number of legal pieces demodulated correctly by \(P_{r}\) per second. Obviously, \(e\) is inversely related to \(BPS\).
# A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection
## 3 Deep-Learning Scheme Based on the MIMO-NOMA System  
In wireless communication, signal detection can be considered to be a classification process of recovering a discrete sequence from an impaired signal. The DL technique has good ability to address this problem. Therefore, in this section, we consider a novel detector that adopts a DNN in a MIMO-NOMA system. In contrast to the traditional SIC block, which divides the process of detection into separate blocks, including channel estimation, MMSE detection, demodulation, channel decoding and signal decision, the deep learning method can perform all these procedures as a single process. The optimal parameters can be acquired by continuous iteration to determine the rules relating the output and the label.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture
### Power System State Estimation (PSSE)
#### 4.2.3 Static State Estimation (SSE)
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Cite** & **Contributions** & **Input** & **Algorithm** & **Evaluation** \\ \hline
[61] & Performance of WLS according to different combinations of measurement features. & Voltage magnitudes, active and reactive power flows and injections. & WLS & Estimate voltage magnitude and voltage angle in 3-bus testbed and IEEE 14-bus testbed. \\ \hline
[62] & Use Linear WLS to estimate PMU measurements; use Non-linear WLS to estimate unregulated mixed measurements. & Voltage magnitudes, voltage angle shift, active and reactive power flows, and injections. & Non-linear WLS + Linear WLS in 1,2,5 buses of IEEE 14-bus testbed. \\ \hline
[63] & Factorise non-linear WLS model into two stages: linear filter stage and non-linear estimate stage. & Squared voltage magnitudes, power flow, injection. & Linear WLS + Nonlinear WLS & Estimate in IEEE 118-, 298-bus testbed, it has a lower computational cost and provides higher accuracy, but the intermediate vectors between two stages can be verified. \\ \hline
[64] & Assign weights to PMU measurements in WLS to reduce measurement uncertainty. & Voltage magnitudes, phase angle. & Propagation of Uncertainty + WLS & Estimate upper and lower limits of voltage magnitude and voltage angle to reduce uncertainty in WLS in IEEE 14/30/57/188-bus testbed. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Static state estimation studies regarding WLS algorithm.
## 4 Experimental Results
### Performance Evaluation  
#### 4.2.1 Coverage Area Comparison  
The coverage area is a key performance indicator of a drone deployment optimization method. The hybrid method achieved the largest coverage area, which was 26.57 km\({}^{2}\), thus outperforming the particle swarm optimization at 25.01 km\({}^{2}\) and the genetic algorithm at 24.62 km\({}^{2}\), as indicated in Figure 3. This improvement in coverage efficiency is due to the reinforcement learning aspect, which adapts drone deployment dynamically to real-time changes in the environment. The results demonstrate that the integration of Q-learning in GAs ensures the optimum utilization of drone features, and, thus, a greater area is covered.
## 5 Simulation Results and Analysis
Specifically, the improvement in the transmission rate from \(P_{s}=0.5\) W to \(P_{s}=2.5\) W with four relays for the proposed algorithm is approximately 1.23 bit/s/Hz. Meanwhile, the simulation result in Figure 5 verifies the conclusion that the system with more relays has a larger transmission rate under the same source's transmitted power. In addition, it is noteworthy that the improvement in the transmission rate diminishes when the number of relays is large enough. This is because the performance of the system is mainly restricted by the source's transmitted power. Figure 4: The transmission rate versus the source transmit power \(P_{s}\) with different number of relays. Figure 5: The transmission rate versus the number of relays with different source transmit power \(P_{s}\). In Figure 6, we exhibit the transmission rate versus the energy conversion efficiency \(\bar{\xi}\) with five relays. Considering the different situations, the energy conversion efficiency of the relay will also be different. Therefore, it is necessary to explore the effects of different energy conversion efficiencies on our algorithm, which can prove the reliability of the algorithm. It is clear that as \(\xi\) is gradually adjusted from 0.2 to 1, the information transmission rate is increasing. In particular, when \(\xi\) is varied from 0.2 to 1, the transmission rate is improved about 17.2%, 16.4%, 15.8% for \(P_{S}=0.5\) W, \(P_{S}=1.5\) W and \(P_{S}=2.5\) W, respectively. Figure 7 shows the subcarrier allocation for energy harvesting and information decoding in the first timeslot. It is clear that more subcarriers are utilized for energy harvesting, which goes toward meeting the energy requirements for relays to forward information in the second timeslot. Figure 8 shows the subcarrier allocation for signal forwarding in the second timeslot. We can find that that due to the difference in subcarrier channels, the subcarriers allocated for relays to forward signals are different. For instance, the 5 th relay is assigned only one subcarrier to transfer information while the 8 th relay is allocated seven subcarriers. Therefore, it can be expected that if a subpar channel is found to exist, the system will no longer allocate the subcarrier to this channel. Moreover, by comparing Figures 7 and 8, it can be observed that the more subcarriers are assigned by the relay for information decoding, the larger proportion of subcarriers are allocated to forward re-encoded information.
## 5 The Proposed Approach
### Setup Phase
```
1 pragmasolidity>=0.6.12<0.9.0;
2
3 contractRoot_IPFSAddress_Mapping{
4//Addressofthecontractowner,i.e.,\"thebroker
5 addresspublicowner;
6
7//Arraytostoretheaddressesofpublishers/subscribers
8 address[]publicclients;
9
10//MappingtostoretherootsandthecorrespondingIPFSaddress
11 mapping(bytes32=>bytes32)privaterootToIPFSAddress;
12
13//Eventomitedwhentnloekupmadebyapublisher/subscribersfails
14 eventClientClient(addressindexedrequester,bytes32 root);
15
16//Modifiertorestrictaccesstothecontractowner
17 modifieronlyDwner(){
18 require(msg.sender==over,\"Notauthorized\");
19-;
20}
21
22//Constructortosettheownerofthecontract
23 constructor(){
24 owner=msg.sender;
25}
26
27//Functiontoaddasnewroot-IPFSAddressmapping(onlycallablebytheowner)
28 functionaddRootIPFSAddress(bytes32 root,bytes32 IPFSAddress)publiconlyDwner{
29 rootToIPFSAddress[root]=IPFSAddress;
30}
31
32//Functiontoaddasnewpublisher/subscriberaddress(onlycallablebytheowner)
33 functionaddClient(addressclient)publiconlyDwner{
34 clients.push(client);
35}
36
37//ViewfunctiontoretrievetheIPFSAddressassociatedwithagivenroot
38 functiongetIPFSAddress(bytes32 root)publicviewreturns(bytes32){
39 bytes32 IPFSAddress=rootToIPFSAddress[root];
40 returnIPFSAddress;
41}
42
43//Functiontoentaneventsignalingtheintegrityviolation
44 functionpullTheAlarm(bytes32 root)public{
45 if(isClientAddress(msg.sender)){
46 emitClientAlert(msg.sender,root);
47}
48}
49
50//Internalfunctiontocheckifamaddressisintheclientsarray
51 functionisClientAddress(addressaddr)internalviewreturns(bool){
52 for(uint=0;i<clients.length;i++){
53 if(clients[i]==addr){
54 returntrue;
55}
56}
57 returnfalse;
58}
59}
```  
**Listing 3**Solidity Smart Contract for handling the mapping between the root of the MHT and the IPFS address.
## 3 Case Study
### Calculation of Diesel Engine with Shock Absorbers in Normal Condition
#### 3.2.2 Crankshaft Stress of Diesel Engine with Shock Absorbers in Normal Conditions  
As shown in Figure 10, it is the peak crankshaft stress curve in normal conditions within 0 to 0.9 s. The stress value in any time in Figure 10 represents the maximum value of the entire crankshaft at that moment, and the peak crankshaft stress curve is used to reflect the trend of the crankshaft stress over time. In Figure 10, it can be noticed that between 0 and 0.05 s, there are two peak crankshaft stresses, 432 MPa and 442 MPa, which are significantly higher than the peak crankshaft stress within 0.05-0.9 s. Under normal operating conditions, the stress state of the crankshaft is periodic in each working cycle. In 0-0.9 s, nearly eight working cycles are calculated. The two higher crankshaft stresses within 0-0.05 s only appeared at the beginning of the calculation, did not appear again in the subsequent calculation results. Furthermore, EXCITE calculation is an iterative calculation, which will be affected by the initial value, and there is a process in which the simulation iteration gradually converges. Considering that these two higher crankshaft stresses in 0-0.05 s may be abnormal results caused by insufficient convergence in the initial stage of simulation, they are not used in the analysis of crankshaft stress results.  
Within 0.05-0.9 s, the maximum crankshaft stress in impact conditions is 364 MPa at 0.177937 s at the eighth crank pin, as shown in Figure 11. That does not exceed the yield limit of the crankshaft material (650 MPa) with a large safety margin.  
Figure 10: Peak crankshaft stress curve under normal condition within 0 to 0.9 s. The x-axis represents the time in seconds, and the y-axis represents the peak crankshaft stress in MPa.  
In short, in 0-0.9 s, the crankshaft stress in impact conditions does not exceed the yield limit of the crankshaft material with a large safety margin, so the crankshaft of the diesel engine with a shock absorber is safe under normal conditions.  
Through the calculation, it can be found that the main bearing can maintain the normal lubrication state, and the crankshaft stress does not exceed the yield limit, so the diesel engine with shock absorbers can work normally in the normal condition.
## 2 Proposed Methods
### Adaptive Focus Window
#### 2.1.2 Selection of SL Region  
OTSU threshold segmentation [33] is performed on the processed image to further select and eliminate the SL region. The algorithm divides the entire image into target and background regions, then finds the optimal solution that maximizes their interclass variance as the threshold. Although the grayscale difference between the SL and target FOV is significant (Figure 2b), the pixel ratio imbalance can easily lead to OTSU calculation failure. Hence, a grid screening mechanism was designed to weaken this effect: (i) the image is divided into adjacent grids with the same size; (ii) the SL's central highlight pixels are located using a preset saturation threshold, then used as a benchmark to screen all grids; (iii) the grayscale of the grids without highlight pixels is set to 0 and called \"turning off\"; otherwise, it is called \"light up\" without operation (Figure 2b); (iv) the \(Mask_{SL}\) is obtained by performing the OTSU algorithm only on \"light up\" grids.  
Since the SL region may be extremely large or small due to subtle variations in imaging conditions, \(Mask_{SL}\) is then modified based on statistical knowledge to improve generalization ability. The probability of a pixel \(X\) in the FOV existing in area \(\alpha\) is defined as follows:  
\[p(X\in\alpha)=N_{\alpha}/N_{Mask_{FOV}} \tag{1}\]  
where \(N_{\alpha}\) represents the number of pixels contained in \(\alpha\). If \(p(X\in Mask_{FOV}\cap\overline{Mask_{S}})<p(X\in Mask_{FOV}\cap Mask_{S})\), the SL regions become dominant in the image contents, and this case is excluded from the sharpness evaluation. If \(p(X\geq th)<p(X\in Mask_{FOV}\cap a_{grid})/2\), the pixel ratio imbalance still potentially exists, and the highlighted area of the SL region is selected as \(Mask_{SL}\).
## 2 Related Studies
### Our Previous M-PVMS  
Our M-PVMS for small farm owners manages information from heterogeneous inverters, such as the power amount, temperature, and tilt sensors. The M-PVMS consists of the local monitoring server and the integrated monitoring server, as shown in Figure 3. The local monitoring server sends the integrated monitoring server the monitoring data generated in junction boxes and inverters using RS232 communication. The problem is that different inverter manufacturers use different data protocol formats. To solve this problem, the local monitoring servers convert data generated in heterogeneous inverters into integrated data with the metamodel-based model transformation. The integrated monitoring server collects the data received from the local monitoring servers using TCP/IP communication to manage all data, which are stored in a database and analyzed through big data systems. They exist for services such as target load prediction, real-time prediction of new renewable energy, integrated control, optimal control status, and operational integration monitoring.  
Figure 1: Structure of metamodel-based model transformation engine.  
Figure 2: Structure of common solar energy monitoring system.  
Figure 3: Structural diagram of proposed metamodel-based photovoltaic monitoring system (M-PVMS).  
This software platform consists of the Java framework, RS232 communication middleware, Netty [15], Hadoop [16], and visualization module-based TCP/IP communication middleware. All collected data are stored in a server database and analyzed in Hadoop to support various services. The visualization module uses graphs to display all data to end users and managers.  
Figure 4 shows the structure of the metamodel used to convert data generated in heterogeneous inverters into integrated data. Each local monitoring server automatically performs the function to convert heterogeneous inverter data formats in accordance with metamodel structures, which is composed of a root element called the SolarEnergyModel. It contains the system ID and time of the transmitted protocol where there exist subnodes, such as PlantDisplay, Inverters, Sensors, and Junction Boxes. The PlantDisplay node exhibits total monitoring results, such as current power, daytime power, yesterday's power, monthly power, last month's power, and total power. Inverter nodes combine the information collected by each inverter node, which comprises data such as current power, day power, yesterday's power, total power, output current, output voltage, input power, input current, input voltage, frequency, and warning data. The sensors node consists of some data, such as horizontal solar radiation, gradient solar radiation, module temperature, ambient temperature, CO2 density, slope, etc. The JunctionBox node shows the voltage and current values of the PV modules connected to the real junction box.  
Our M-PVMS monitoring system is shown in Figure 5. The current and past amounts of power generations can be collected from the real-time monitoring on the webpage.  
Figure 4: Integrated metamodel for heterogeneous data transmissions.
# NewApproach to Predict the Motion Characteristics of Single Bubbles in Still Water
## 1 Introduction  
### Background  
Gas-liquid two-phase flow widely exists in nature. It plays an important role in engineering applications, especially while dealing with the problems related to sea-air exchange, cavitation of buildings due to the high-speed dam water, natural gas transportation, etc. Under the effect of gravitational force, buoyancy force, and surface tension, the bubbles may experience a change in shape, breakage, as well as merging. These scenarios will lead to the occurrences of high-speed jets and strong turbulence in the water bodies nearby, which affect the mechanism of sea-air exchange and pollutant movement. These interactions are closely related to the states and nature of single bubbles. Therefore,it is essential to have an understanding of the motion characteristics of single bubbles in order to study its effect on sea-air flux exchange, stress exerted on the building structures, and provide a fundamental theoretical basis for the development of marine engineering.  
In most of the natural phenomena, as well as real-life practical applications, bubbles in water bodies play a significant role. Based on the previous analyses, the phenomena such as underwater sound wave propagation, atmospheric-marine gas exchange, ship navigation, laser transmission underwater, nutrient supply for marine life, etc., had shown a close relationship with the motion characteristics of single bubbles in the water [1].  
Bubbles can be formed through different processes such as wave rolling, ocean biological activities, ship navigation, etc. The existing scientific evidence had proved that the gases entrained by breaking wave contained 30% of human-generated CO\({}_{2}\), which may help to alleviate the global warming issue. In addition, the splash and entrainment due to the breaking wave can enhance the exchange of heat, water vapour, gas, and energy in the sea.  
On the other hand, the acoustic characteristic of the bubbles on the ocean surface layer is one of the most eye-catching issues. A large number of bubbles resulting from the breaking wave will cause a great impact on the optical properties of seawater. It will scatter the light and affect the remote sensing information of the sea surface [2]. Hence, studying the motion characteristics of the single bubbles has an important theoretical and scientific significance for the mechanisms of two-phase flow.