esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Defect Detection Using Adaptive Leaky NLMS Filter in Guided-Wave Testing of Pipelines
## 5 Results
### Cancellation of Backward Leakage  
Figure 11a shows the two sets of signals received from the backward testing direction. As can be seen, these signals are inversely overlapped with little differences in amplitude. This is due to the general propagation routine where the received signals from rings are delayed and inverted depending on the ring spacing.  
After applying the filtering, the phase of the outcome does not change, therefore by switching the inputs to the adaptive filter, two sets of results will be generated in which the signals from the forward direction are overlapped and the ones from the backward direction are inversely overlapped. This is illustrated in Figure 12, where blue and red signals show the two sets of results achieved from adaptive filtering by switching the inputs. This operation not only enhances the defect signals received from the forward direction (Figure 12c) but it also decreases both the backward cancellation (Figure 12a) and flexural noise regions (Figure 12b) significantly.
# Power-Efficient Random Access Design for Machine Type Communication
## 4 Link Budget Analysis and Simulation Results
### Link Budget Analysis  
Before the simulation, the link budget for each coverage class needs to be analyzed so that the operating SNR for each coverage class can be determined.  
The requirement of the receiver sensitivity is \(p-\alpha\) (dBm). Then, the required received SNR is  
\[\Gamma=(p-\alpha)-(w+N_{0}+\zeta)\ (\mathrm{dB}). \tag{21}\]  
where \(w\) (dB-Hz) is the signal bandwidth, \(\zeta\) (dB) is the noise figure, \(N_{0}\) (dBm/Hz) is the noise density.  
For the uplink, all the transmitter power \(P_{\mathrm{UL}}\) can be transmitted on the actual signal operating bandwidth, i.e., \(p=P_{\mathrm{UL}}\). Then, the received SNR becomes  
\[\Gamma_{\mathrm{UL}}=(P_{\mathrm{UL}}-\alpha)-(w+N_{0}+\zeta_{\mathrm{UL}}). \tag{22}\]  
For LTE-\(n\)MTC random access channels, \(w=w^{\mathrm{\dagger\dagger}}(\alpha)\), \(\alpha=155\), \(150\), \(145\), and \(140\) dB.  
For the downlink, the transmitter power is evenly distributed to the whole band. The power on bandwidth \(w\) is \(p=P_{\mathrm{DL}}+w-\Pi\) (dBm), where \(P_{\mathrm{DL}}\) (dBm) is the total transmit power for the entire system bandwidth of \(\Pi\) (dB-Hz). The received SNR becomes  
\[\Gamma_{\mathrm{DL}}=(P_{\mathrm{DL}}-\Pi)-\alpha-N_{0}-\zeta_{\mathrm{DL}}\ ( \mathrm{dB}) \tag{23}\]  
Unlike the uplink, the downlink SNR is not a function of the signal bandwidth \(w\).  
Taking the uplink as an example, assuming \(P_{\mathrm{UL}}=20\) dBm, the required receiver sensitivity is thus \(P_{\mathrm{UL}}-\alpha=20\) dBm \(-\alpha\) dB. The typical noise figure of a base station receiver is \(\zeta_{\mathrm{UL}}=5\) dB, the received SNR according to (22), is  
\[\begin{array}{l}\Gamma(\alpha,w)\\ =(20\ \mathrm{dBm}-\alpha\ \mathrm{dB})-(w\ \mathrm{dB}\cdot\mathrm{Hz}+(-174\ \mathrm{dBm}/\mathrm{Hz})+5\ \mathrm{dB})\\ =189\ \mathrm{dBm}-(\alpha+w)\ \mathrm{dB}\,.\end{array} \tag{24}\]  
For the LTE-nMTC access request signal, \(w=w^{\mathrm{\dagger\dagger}}(\alpha)\), i.e., coverage class dependent. Referring back to Table 2, \(\alpha+w^{\mathrm{\dagger\dagger}}(\alpha)\approx 189\) dB for \(\alpha=\)155, 150, 145, and 140 dB, yielding \(\Gamma=0\) dB for all coverage classes. That is, the operating SNRs for the access request signal are the same for all coverage classes as listed in Table 4.  
The link budget for the LTE-eMTC random access signals can be analyzed similarly, except that unlike in LTE-nMTC where the signal bandwidth is individually optimized for each coverage class, the channel bandwidth \(w\) for LTE-eMTC random access signals are coverage classes independent. The operating SNRs are therefore different for different coverage classes. For the downlink, assuming the transmit power is 46 dBm for 10 MHz bandwidth, and the noise figure is 9 dB. Table 4 shows the corresponding operating SNRs in each random access step.
## 4 Conclusions  
Based on the above experiments on the removal ability of coal-based activated carbon under different conditions, this paper mainly draws the following conclusions:  
1. The removal of PM was mainly by the adsorption and filtration of activated carbon. The PM removal efficiency of the AC reactor was above 60% in the entire power range, and the highest PM removal efficiency was 77% at 9 kW. However, the pressure drop of the reactor gradually increased with the running time. In the future, a fan can be installed after the reactor, or the granular AC can be made into a honeycomb structure and the regeneration method of DPF backwashing can be used to prolong the service time.
2. CO can be used as a reducing agent to participate in the denitration reaction under the catalytic action of AC. The removal of CO depends on the adsorption of AC. Since the temperature of the exhaust gas gradually increases with the power, the AC may have a certain degeneration. It is necessary to improve the thermal stability of AC by material and production process for practical purposes.
3. The NO\({}_{\mathrm{x}}\) concentration of the exhaust gas gradually increases with the power. The denitration ability of AC was poor when urea is not sprayed, and the denitration efficiency was 34.5% at 6 kW. When urea was sprayed, NH\({}_{3}\) was generated by urea hydrolysis. With NH\({}_{3}\) as a reducing agent, the NO\({}_{2}\) removal efficiency was close to 100%, and the highest denitration efficiency was 44.8% at 15 kW. Overall, the denitration efficiency of AC was still lower than other denitration catalysts, and it needs to be modified or loaded with other metal oxides to improve its denitration efficiency.  
Figure 8: NO\({}_{\mathrm{x}}\) concentration of AC reactor outlet (with urea).  
**Author Contributions:** Conceptualization, Z.W., J.Z. and Y.J.; Data curation, H.K. and L.C.; Formal analysis, Z.W. and H.K.; Investigation, Z.W., H.K. and L.C.; Resources, Y.J.; Writing-original draft, Z.W.; Writing-review & editing, J.Z. and Y.J.  
**Funding:** This research was supported by the National Natural Science Foundation of China (51876019, 51579026), the Program for Liaoning Innovative talents in University (LR2017048), the Program for High-Level Talent Training in Transportation Industry of China, and the Program of Liaoning Revitalization Talents (XLYC1807117), and the Fundamental Research Funds for the Central Universities of China (3132019331).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Nonlinear Tank-Level Control Using Dahlin Algorithm Design and PID Control
## 2 Materials and Methods
### Mathematical Modeling
#### 2.1.2 Coupled Tank Model  
The CT system consists of two vertical tanks linked by a channel (Figure 2). Each tank has an independent pump for the inflow of liquid. The CT system can be configured as a SISO or MIMO (multiple input-multiple output) system.  
Using the mass balance equation, the mathematical model of the tank system is derived. The nonlinear equations that describe the dynamics of each tank are:  
\[A_{1}\frac{dH_{1}}{dt}=Q_{i1}-Q_{o1}-Q_{o3}, \tag{4}\] \[A_{2}\frac{dH_{2}}{dt}=Q_{i2}-Q_{o2}+Q_{o3}.\]  
Relation (4) can be written as:  
\[A_{1}\frac{dH_{1}}{dt}=Q_{i1}-\alpha_{1}\sqrt{H_{1}}-\alpha_{3}\sqrt{H_{1}-H_ {2}}, \tag{5}\] \[A_{2}\frac{dH_{2}}{dt}=Q_{i2}-\alpha_{2}\sqrt{H_{2}}+\alpha_{3} \sqrt{H_{1}-H_{2}},\]  
where  
\[\alpha_{1}=a_{1}\sqrt{2g},\ \alpha_{2}=a_{2}\sqrt{2g},\ \alpha_{3}=a_{3}\sqrt{2g}. \tag{6}\]
# Smartphone-Based Task Scheduling in UAV Networks for Disaster Relief
## 5 Problem Statement and Solution
### Task-Offloading Optimization
Then, we use the adjusted weighted sum result as the ranking indicator. After the iteration in the evolutionary algorithm part, the ranking metrics for each solution in the resulting solution set are calculated and used to rank the solutions. Finally, the solution set is trimmed according to the group size required by the iteration, and the current best-optimized solutions are kept for the next iteration. Based on the above introduction of the various components of MOMIB, the final algorithm is represented in Algorithm 2. ```
Initialization: The solution group \(g_{0}\) is randomly generated and the objects are calculated according to the set range. 1 Calculate the QoS weight \(\{\eta_{i,1},\eta_{i,2}\}\) according to Algorithm 1
2 repeat
3 Obtain \(g_{1}^{b}\) through bat algorithm part, given \(g_{t}\)
4 Obtain \(g_{t}^{i}\) through immune algorithm part, given \(g_{t}^{b}\)
5 Adjust \(g_{t}^{i}\) by population adjustment evaluation algorithm, given \(g_{t}\)
6\(t=t+1\) until Coverage to a prescribed accuracy or \(t>Max\_Iteration\)
```  
**Algorithm 2**Multi-Objective Mutation-Immune Bat (MOMIB) algorithm
The same five soil types have been taken into account. The results of this study reveal that the soil type has a significant effect on the response of buildings in the case of bounding as well as in the no-pounding case. However, the soil type effect is more significant in the case of bounding than the no-pounding case. Furthermore, [PERSON] et al. [72; 73; 74] investigated the accuracy of five different formulas (ABS, SRSS, DDC, Australian Code, and Naderpour et al. formula; see Equations (1)-(6)) in evaluating the seismic gap upon the consideration of different soil conditions. The same five soil types have been taken into account. It was found that the seismic gap has a significant influence on the response of colliding buildings. For all soil types, larger gaps do not necessarily lead to lower responses unless it is large enough to eliminate collisions at all (this finding was also emphasized by other works reported in the literature (see [28; 33; 71; 75], for example)). The results of this study also show that all five formulas provide poor estimates when considering different soil types. The ABS and the Naderpour et al. formulas were found to be always conservative, but they overestimated the minimum gap. Moreover, the DDC and Australian Code formulas provided overestimate, accurate, and underestimate results, and the SRSS formula provided both accurate and overestimated results. Similar findings were reported concerning the Australian Code formula (Equation (5)) as it was found that it provides accurate results only in the case of the far-field earthquakes considering the in-between gap equal to 1% of the height of the taller building and not related to the height of the shorter building (see [76] for details). In the case of near-field earthquakes, or in the case when the gap was considered as equal to 1% of the height of the shorter building, the Australian Code formula (Equation (5) provides inaccurate results and underestimates the gap [76]. The aforementioned literature review illustrates that the soil types may have a significant effect on the response of colliding buildings under seismic excitation. Indeed, the currently used formulas for the evaluation of the seismic gap show the discrepancy between providing accurate, underestimate, and overestimate results upon the consideration of different types of soil. Thus, it is necessary to develop new accurate equations for the separation gap that are capable of eliminating collisions as well as taking the soil type into account. Therefore, the aim of this study is to propose new effective equations for the evaluation of the optimum seismic gap by introducing the modification of the correlation factor (see Equation (4)) for different soil types defined in the ASCE 7-10 code [69], i.e., for hard rock, rock, very dense soil and soft rock, stiff soil, and soft clay soil. By designing the buildings and providing separation between them based on the proposed equations, no collisions will occur between them, which means that providing spacing based on the proposed equations will provide more safety to the vibrating buildings during earthquakes.
# On-Orbit Experimental Result of a Non-Deployable 430-MHz-Band Antenna Using a 1U CubeSat Structure
## 4 HNT Mission of the BIRDS-4 Project  
The HNT mission is one of the BIRDS-4 missions. HNT stands for Hentenna. The mission objective is to demonstrate a Loop Hentenna. To achieve that, the CubeSat carried a Loop Hentenna and a dedicated CW (Continuous Wave) transmitter. Figure 3 shows the overall block diagram of BIRDS-4 satellite. The HNT mission works in coordination with the HNT mission block, MISSION BOSS, MAIN PIC, COM PIC, and UHF TRANCEIVER in the block diagram. BIRDS-4 has a complex internal connection due to its numerous missions. In this paper, only the operation of the parts related to the HNT mission will be briefly described.  
Figure 3: Overall block diagram of BIRDS-4.  
Figure 2: Flight models of BIRDS-4 satellites. (Left: Maya-2, Center: Tsuru, Right: GuaraniSat-1).  
The bus system always transmits a Morse beacon intermittently on CW using \"UHF dipole ANT\", as in Figure 3. During the HNT mission, the same kind of signal is transmitted from the Loop Hentenna, as shown as \"Satellite Body\" in Figure 3, during the off time of the beacon, as shown in Figure 4. By comparing the reception strength of these signals on the ground, we can estimate how much gain is achieved in space. The bus section uses a half-wavelength dipole antenna.  
Figure 5 shows the main internal structure. Part of the main structure has the aluminum used as a conductor replaced by PEEK (Poly Ether Ether Ketone), which is an insulator. This constitutes part of the Loop Hentenna structure, other than the horizontal element around the feed point. The feed point and the horizontal element are placed on the -X outer panel. Figure 6 shows a picture of the -X outer panel. It is a printed circuit board. The copper foil patterns connect the feed points to the rails of the CubeSat on either side. They are screwed to the ribs of the rails to connect electrically. In other words, the aluminum main frame and the -X panel act as a Loop Hentenna. In addition, an L-type matching circuit is built at the feed point to match the impedance of the antenna to 50 \(\Omega\).  
Figure 4: Order of signal transmission during the HNT mission.  
Figure 5: Loop Hentenna structure and BIRDS-4 main structure.
## 4 Results and Discussion
### Stress-strain Characteristics of Amended Stabilized Soil  
From the data given in Table 3, it can be seen that the strength of 3% lime-stabilized soil increases from 517.69 kPa to 563.08 kPa on the addition of 0.25% PG. This amounts to an increase of 8.77% at 7 days of curing. When the lime content increases to 5.5%, the optimum PG content increases to 0.5%. At this dosage, the strength increases from 981.32 kPa to 1119.23 kPa, accounting for a 14.05% strength gain. At 7% lime dosage, 1% PG developed a maximum strength of 1345.34 kPa from 1181.26 kPa, a gain of 13.89%. When the curing period increases to 28 days, the percentage strength gains of 3%, 5.5% and 7% lime amended with 0.25%, 0.5% and 1% PG, respectively, amounted to 9.90%, 18.95% and 19.65%, respectively. Stress-strain curves and soil stiffness play an important role in settlement calculations. The stress-strain characteristics of the lime-stabilized soil are shown in Figure 1. The solid lines represent the stress-strain relationships of pure lime-stabilized soils, whereas the dashed lines represent the lime-stabilized soil specimens modified with their respective optimum doses of PG. The very first inference that can be obtained from Figure 1 is that the addition of lime to soil results in a change in stress-strain characteristics of the soil with an increase in lime content. The stress-strain behavior of the soil changes from plastic failure to brittle failure with an increase in the lime content, as is evident from the stress-strain curves for the three lime contents. There is also an increase in the stiffness of the soil with the strain corresponding to peak stress reducing with an increase in the lime content of the soil. For L1, the strain corresponding to peak stress is 0.021 which significantly reduces to 0.013 for L2 and L3. The second major inference that can be obtained from Figure 1 is that the addition of PG to the lime-soil system further increases the stiffness of the composite. This is true for lime contents up to 5.5%. Beyond 5.5% lime content the addition of PG does not contribute much to any increase in the stiffness of the system. The strain corresponding to peak stress for combinations LPI, LP2 and LP3 were 0.011, 0.011 and 0.013, respectively. To more accurately understand the stiffness behavior of the stabilized soil, the modulus of elasticity of the soil was determined and has been discussed in the next section.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Combination** & **7 Days (kPa)** & **28 Days (kPa)** \\ \hline L1 & 517.69 & 547.48 \\ L2 & 981.32 & 1398.77 \\ L3 & 1181.26 & 1881.45 \\ LP1 & 563.08 & 601.66 \\ LP2 & 1119.23 & 1663.8 \\ LP3 & 1345.34 & 2251.07 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Strength of Lime and Optimum PG Combinations.  
Figure 1: Stress-strain behavior of lime-stabilized soil with and without optimum PG content.
# Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions
## 3 The Theory of Shop Scheduling Problems
### Shop Layouts
#### 3.2.4 Flow Shop System  
At the opposite end of flexibility, _flow shop_ (FS) is another well-known manufacturing system. FS features a number of machines; each machine performs a single process on jobs that flow through in a unidirectional fashion [63].  
Note that FS do possess a degree of flexibility as it can process different jobs simultaneously (although machines can only perform one task on one job at a time) and jobs are not required to be processed by each machine in the sequence [63]. Additionally, jobs may be allowed to have different completion times, instead of strictly being uniform [63].  
The _flow shop scheduling problem_ (FSSP) often looks to minimize makespan, total flowtime, tardiness, idle time, and other performance measures to ideally find an optimal schedule [64]. While not NP-hard by default, FSSP can become NP hard with a sufficient number of constraints, batch size, and number of machines [65].  
The simplest and the most common version of FS is when the order of machines for all jobs are predetermined and fixed, and the modifiable element is the schedule of jobs on machines [66]. The _flow line_ (also known as _assembly line_) is a subcategory of FS with less flexibility and more effectiveness and speed (due to its simplicity). Unlike FS, flow line requires all jobs to be identical [67]. Added complexity to a flow line scheduling problem is often variation in processing time of machines. Understanding the structure of the flow lines is particularly important since some scheduling problems in literature are defined as hybrid layouts featuring sections of flow lines. For example, an assembly-type FS is a hybrid production system featuring production areas arranged as a JS to produce component parts. These components or subassemblies are fed into a flow line arranged as a FS for final assembly operations [68, 69].  
Within FS, jobs often follow similar routes or are identical, as in flow line. When jobs are processed in the same order on every machine, meaning their routes are identical, a permutation schedule is developed [70, 71]. A FS in which this type of schedule occurs is known as _permutation flow shop_. The processing times on a machine may vary from job to job.  
The _flexible flow shop_ (FFS) consists of multiple stages. One or more parallel machines are available in each stage, but at least one of these stages includes multiple machines [45]. This system allows jobs to \"skip\" stages if they are not necessary [72, 73].
## 3 Results
### Radiative Effect of Aerosols, tropospheric NO\({}_{2}\) and Clouds
#### 3.2.1 Annual
The highest RE\({}_{\rm ic}\) values appear over Ioannina (\(-26\) W/m\({}^{2}\)), Olympus (\(-25\) W/m\({}^{2}\)), and Xanthi (\(-23\) W/m\({}^{2}\)), while the lowest values appear over Athens (\(-15\) W/m\({}^{2}\)) and HCMR (\(-13\) W/m\({}^{2}\)) (Table 2). The annual RE\({}_{\rm ic}\) and RE\({}_{\rm ic}\) for all the 16 locations of interest can be seen in Figure 4c,d, respectively. In general, RE\({}_{\rm ic}\) and RE\({}_{\rm ic}\) exhibit a latitudinal south-to-north increasing gradient in accordance to previous results  
Figure 4: Annual average radiative effect of (**a**) aerosols, (**b**) tropospheric NO\({}_{2}\), (**c**) liquid clouds and (**d**) ice clouds over 16 locations in Greece for the period 2005\(-\)2019. Mind that a different scale is used for the radiative effect of each parameter and that the radiative effect of tropospheric NO\({}_{2}\) is smaller by two orders of magnitude. in the area [22; 94]. [PERSON] et al. [94], using a radiative transfer model and satellite-based aerosol and cloud observations at a coarse spatial resolution, calculated an average net surface shortwave cloud radiative effect of \(-44.5\) W/m\({}^{2}\) over the Eastern Mediterranean. This is close to the combined (liquid and ice) cloud radiative effect (\(-55\) W/m\({}^{2}\)) we calculated here, taking into account that in their analysis they included oceanic areas where cloud coverage is quite limited compared to the land areas, and hence the cloud radiative effect is smaller (Figure 5). It is obvious that clouds dominate, and hence RE\({}_{\rm{aer}}\) and RE\({}_{\rm{NO2}}\) are expected to be different under clear and cloudy skies. In Figure 6, the radiative effect of aerosols and tropospheric NO\({}_{2}\) over the 16 locations studied here is shown for clear, liquid cloud-covered, and ice cloud-covered skies. We see that the average RE\({}_{\rm{aer}}\) for all the locations under clear skies (\(-18\) W/m\({}^{2}\)) is about 3-4 times larger than for liquid (\(-5\) W/m\({}^{2}\)) and ice (\(-6\) W/m\({}^{2}\)) cloud-covered skies; for individual locations, it can be up to 4-5 times larger (e.g., Olympus). For tropospheric NO\({}_{2}\), the average RE\({}_{\rm{NO2}}\) for all the locations under clear skies (\(-0.095\) W/m\({}^{2}\)) is double that for liquid (\(-0.045\) W/m\({}^{2}\)) and ice (\(-0.051\) W/m\({}^{2}\)) cloud-covered skies. RE\({}_{\rm{aer}}\) and RE\({}_{\rm{NO2}}\) are smaller under cloudy conditions, as part of the solar radiation is already reflected by clouds before reaching the aerosol and tropospheric NO\({}_{2}\) layers, which, on a climatological basis, are primarily residing below the clouds.
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 3 Swarm Robotics Design and Simulation
### Simulated Hardware Design for Robots
#### 3.1.3 Power and Communications Systems  
The software regulates power consumption for a range of robot functions such as movement, lifting, and communication using a simulated lithium-ion battery pack. For extended mining operations, this power-management system ensures that each robot can operate continuously for up to 8 h. The software controls a simulated 2.4 GHz wireless mesh network that the robots use to communicate. By facilitating reliable data exchange between the robots, the network facilitates coordinated operations such as ore transport and collection. Top-mounted LEDs and optical beacons (with a range of 1.8 m) improve the communication between the robots. For swarm coordinated operations, the software ensures that these communication channels are managed effectively.