esa-sceva/satcom-chunk-collection huggingface dataset:
# Germanium Single Crystals for Photonics
## 5 Optical Damage of Germanium at a Wavelength of 10.6 \(\upmu\)m
The studies were conducted on serially produced Ge of optical quality treated using a specially developed chemical-mechanical technology modified for use in optics, and on dislocation-free crystals polished using a chemical-mechanical technology used in microelectronics [83]. It was established that in the range of amplitude values of the radiation power density of 2\(\cdot\)10\({}^{6}\)-4\(\cdot\)10\({}^{8}\) W/cm\({}^{2}\), two main types of damage are realized. At a power density of \(I\) < 4\(\cdot\)10\({}^{7}\) W/cm\({}^{2}\), centers of local microdamages on the surface layer were observed. These local microdamages are the result of microexplosions that form craters on the sample surface [82]. They occur at \(I\) < 4\(\cdot\)10\({}^{7}\) W/cm\({}^{2}\) due to optical breakdown either on absorbing microinhomogeneities of the crystal or on defects in optical processing. Exposure to radiation at \(I\)\(\geq\) 4\(\cdot\)10\({}^{7}\) W/cm\({}^{2}\) leads to melting of a practically continuous surface layer in the irradiated zone, with a depth of 1-3 \(\upmu\)m. This is explained by the avalanche breakdown of nonequilibrium charge carriers of the surface layer of Ge (Figure 7). The molten layer is like a nonlinear absorbing filter, protecting the volume of the optical element from damage by radiation of superthreshold intensity. The nature of the impact, which leads to the destruction of only the surface layer of the optical part, allows for the complete restoration of expensive germanium products through significantly less expensive repolishing. Thus, it was shown that the output semi-transparent mirror made of germanium for high-power pulsed CO\({}_{2}\) lasers is practically \"indestructible\" even in the case of a significant excess of the optical damage threshold. This is an important result, since powerful gas lasers often use a simple semi-confocal resonator, in which the mirrors additionally serve as sealed ends of the laser cell. When pumping the spent working mixture, due to the pressure difference of 1 atm., their surface is subjected to significant mechanical load. In case of a hidden defect in the output mirror (window), it may not withstand. This leads to catastrophic destruction of the germanium window (mirror), which instantly disintegrates into a large number of fragments. Atmospheric pressure catapults them inside the cuvette, at the same time the fragments fly apart and hit the \"blind\" metal mirror, causing catastrophic damage and simultaneously disabling the cuvette's electrode system. Figure 7: X-ray topogram of the surface of a germanium single crystal after exposure to a CO\({}_{2}\) laser pulse. Peak power density at 20 MW/cm\({}^{2}\) (internal stress fields around emerging structural defects are clearly visible). Reprinted with permission from Ref. [5]. 2015, [PERSON].
## 3 Results
### Analysis of Real Samples  
Four real water samples including tap water, sewage water, industrial wastewater, and mineral water were selected to assess the applicability of the proposed method for the determination of the selected OCPs. The real samples were spiked with the mixed standard solutions of the target analytes at two concentration levels (400 and 1000 pg mL\({}^{-1}\)). The unspiked and spiked sample solutions were analyzed with the proposed procedure in Section 2.5. The relative recoveries for the spiked samples were calculated by Equation (3), and the results were given in Table 2.  
\[RR(\%)=\frac{C_{found}-C_{real}}{C_{added}}\times 100 \tag{3}\]  
where \(C_{found}\) is the concentration of analytes after spiking the real sample with a standard solution, \(C_{real}\) is the concentration of analytes in the real sample, and \(C_{added}\) is the concentration of standard solution added to the real sample.  
A comparison study was conducted based on the literature survey for the previously reported works for the determination of OCPs, and the data is shown in Table 3. The results indicated that the extraction time of the proposed method is shorter than that of the other methods. In addition, the recovery, linear dynamic range (LDR), and limit of detection (LOD) of the developed method are better than most of the other methods.
# Optomechanical Analysis and Design of Polygon Mirror-Based Laser Scanners
## 4 Linearization of the Scanning Function
### The Maximum Number of Reflections (N)  
In the construction in Figure 9a, one can see that, after I\({}_{5}\), another reflection could still occur on mirror M\({}_{2}\), because one has \(\pi/2-\xi_{5}>2\psi\). Similarly, another reflection point could have been placed before I\({}_{1}\) and O. Therefore, to determine the maximum possible \(N\), the construction in Figure 9b must be made, for which, as \(\pi/2-\xi<2\psi\), no reflection previous to I\({}_{1}\) is possible (i.e., no intersection between the ray and M\({}_{2}\)). The number \(N\) corresponds to the last incidence point I\({}_{N}\), where the beam can still encounter a mirror (M\({}_{1}\) or M\({}_{2}\)) or, equivalently (considering the discussion in Section 4.1), the last point where the line OI\({}_{1}\) can still encounter one of the reflections M\({}_{1^{\prime}}\) or M\({}_{2^{\prime}}\). From Figure 9b, one has:  
\[\begin{cases}I_{2}\hat{C}I_{N}+\pi/2-\xi\leq\pi,I_{2}\hat{C}I_{N}=2N\\ 2\pi(N+1)+\pi/2-\xi>\pi\end{cases}=>\ \ \frac{\pi/2+\xi}{2\psi}-1\leq N<\frac{\pi/2- \xi}{2\psi}, \tag{43}\]  
therefore  
\[N=\bigg{[}\frac{\pi/2+\xi}{2\psi}+1\bigg{]}. \tag{44}\]  
In Figure 10a,b, this function is studied with regard to its two parameters. It can be observed how \(N\) increases on a hyperbola when \(\xi\) decreases and how it increases linearly with \(\psi\).
# Efficient Resource Management for Sum Capacity Maximization in 5G NOMA Systems
## 6 Conclusions  
NOMA technology is going to be key candidate to fulfill the high spectral demands of next generation networks. This paper has provided a new resource management scheme for sum capacity maximization under the minimum capacity constraint. First, we have formulated a non-convex resource management problem for sub-channel assignment and power allocation. Second, we have provided two sub-optimal algorithms for sub-channel assignment under fixed transmit power of BS. These algorithms have designed based on user channel condition and user minimum capacity requirement. Third, for any given sub-channel assignment, we have exploited duality theory to calculate the optimal power allocation. Finally, we have provided and compared the results of different proposed NOMA schemes with the benchmark NOMA schemes. It has shown from the results that our NOMA technique is more efficient than the benchmark NOMA scheme.  
Although, the results in this paper consider two users, in the future, our objective is to consider multiuser for evaluation. We also aim to extend our work to multi-cell scenario.  
Figure 4: Capacity of Prop-Sch1 and Sch2 versus user minimum capacity requirement when \(P_{BS}=20\).  
Figure 5: Comparison of Prop-Sch1 and Sch2 when setting the \(C_{1}=1\) bps\(/\)Hz and increasing the Rate requirement of other users  
**Author Contributions:** A.A., A.B., G.M.A., W.U.K., G.A.S.S., contributed to the conception and development of the analytical model of the study. W.U.K., G.A.S.S., and Z.A. contributed to the acquisition of simulation results. All authors read and approved the final manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Theory and Design of Blink Jamming
## 3 Modelling Formation Jamming
### Target Kinematics Model  
The jammer platforms were restricted to constant velocity with a fixed location tracking antenna, in order to isolate the formation jamming effects on the antenna from any other issues. The resulting model is shown in Figure 8, with identical models for the two targets on the top and bottom of the figure. Initial information on the target position and velocity vectors was provided via a MATLAB script, and the resulting target kinematics were updated at every simulation sample time. This data was then converted to spherical coordinates referenced the the antenna position for use in calculating the received power from each target.
###### Abstract  
Graphene has been regarded as one of the most promising two-dimensional nanomaterials. Even so, graphene was still faced with several key issues such as impedance mismatching and narrow bandwidth, which have hindered the practical applications of graphene-based nanocomposites in the field of microwave absorption materials. Herein, a series of Si-modified rGO@Fe\({}_{3}\)O\({}_{4}\) composites were investigated and fabricated by a simple method. On one hand, the degree of defects in graphene carbon could be tuned by different silane coupling reagents, which were beneficial to enhancing the dielectric loss. On the other hand, the spherical Fe\({}_{3}\)O\({}_{4}\) nanoparticles provided the magnetic loss resonance, which contributed to controlling the impedance matching. Subsequently, the electromagnetic absorption (EMA) properties of Si-modified rGO@Fe\({}_{3}\)O\({}_{4}\) composites with poly(vinylidene fluoride-\(co\)-hexafluoropropylene) (PVDF-\(co\)-HFP) were investigated in this work. As a result, the Si(2)-rGO@Fe\({}_{3}\)O\({}_{4}\)/PVDF-\(co\)-HFP composite exhibited the excellent EMA performance in the range of 2-18 GHz. The maximum reflection loss (\(RL_{max}\)) reached \(-\)32.1 dB at 3.68 GHz at the thickness of 7 mm and the effective absorption frequency bandwidth for reflection loss (\(RL\)) below \(-\)10 dB was 4.8 GHz at the thickness of 2 mm. Furthermore, the enhanced absorption mechanism revealed that the high-efficiency absorption performance of Si(2)-rGO@Fe\({}_{3}\)O\({}_{4}\)/PVDF-\(co\)-HFP composite was attributed to the interference absorption (quarter-wave matching model) and the synergistic effects between Si(2)-rGO@Fe\({}_{3}\)O\({}_{4}\) and PVDF-\(co\)-HFP. This work provides a potential strategy for the fabrication of the high-performance EMA materials.  
absorbing materials; Si-modified rGO@Fe\({}_{3}\)O\({}_{4}\)/PVDF-\(co\)-HFP composites; dielectric loss; magnetic loss; electromagnetic properties +
Footnote †: journal: materials  
Article
# Assessing the Probability of Winning a Point in Men's Padel: A Comprehensive Analysis
## 3 Results
### Match Analysis

Table 4: Match analysis of points with first serve and according to rally length, strike zone, the finish zone, winner, and point ending. A total of 13.8% of the points ended with a short rally and a final hit from zone 3. The values were very similar to those observed in zone 2 for the serving pair (85.8% won by winners) but different in the defending pair (68.8% winners). In both cases, winners with crossed trajectories predominated. Although the situation where the point ended with a medium rally and a hit from zone 1 only occurred on 4.7% of occasions, it is interesting to note that the serving pair managed to win the point in 10% of the cases by a winner while the defending pair did so in 25% of the cases. Unforced errors were more common from hitting the net. When the final shot was executed from zone 2 and ended in a medium rally, it was observed that the serving pair achieved 53% of points through winners (42.5% RT), with a predominance of cross-court shots as the final trajectory in both teams. This situation occurred 11.5% of the time. It is interesting to note that the percentage of service winners decreased significantly compared to the short rally in the same zone but increased for the defending pair. A total of 8.7% of the points ended after a medium rally and a final hit from zone 3. In this situation, up to 76.6% of the points won on serve were won by a winner (66.7% RT), again with a predominance of crossed shots in both teams. A decrease in the percentage of points won by winners was observed for the serving pair compared to the points won from this area in the case of short rallies, but no such decrease was observed for the defending pair. When the point ended after a long rally and a stroke executed from zone 1, although this only occurred in 4% of the cases, the same tendency observed in the case of a medium rally was repeated. In this situation, there were higher percentages of winners for the pair receiving the service (16.7% and 40%, respectively). A total of 12.2% of the points ended after a long rally and a final shot executed from zone 2. In this case, the serving pair won 55.2% of the points through winners (66.1% RT), predominantly with cross-court shots in both cases. In both pairs, the points won by unforced errors were mostly due to shots directed to the net. Finally, points ending after a long rally and a final hit from zone 3 accounted for 9.5% of the points analyzed. In this situation, winners clearly predominated as a way of finishing the point, with 92.8% in the serving pair and 70.6% in the defending pair. Again, there was a high proportion of winners with cross-court shots although there was also a considerable percentage of winners with parallel shots.
## 4 LLC Converter Design Steps
### Resonant Component Selection  
According to [66], once that the output voltage \(V_{o}\), the output current \(I_{o}\), the input voltage range \(V_{in\_min}\), \(V_{in\_max}\) and the resonant frequency \(f_{r}\) are defined, it is possible to obtain the circuit parameters.  
Firstly, the maximum and minimum DC gain \(M_{max}\), \(M_{min}\), the transformer turn ratio \(n\), and the maximum switching frequency \(f_{max}\) are calculated. Then, the equivalent AC resistance of load, \(R_{ac}\), which refers to the transformer primary side is calculated as follows:  
\[R_{ac}=\frac{8n^{2}}{\pi^{2}}\frac{V_{o}}{I_{o}}=\frac{8n^{2}}{\pi^{2}}R_{load} \tag{6}\]  
where \(R_{load}\) is the converter load resistance.  
Also the ratio \(L_{n}\) of the magnetizing inductance of the transformer and the resonant inductor can be derived. The maximum value for \(Q\) is calculated as the minimum value between the \(Q\) needed to work in the ZVS operating region at minimum input voltage and full load condition and the \(Q\) needed to work in the ZVS region at no-load condition and maximum input voltage.  
By knowing \(L_{n}\), \(Q\), \(R_{ac}\), and \(f_{r}\), the value of the resonant tank capacitor \(C_{r}\) can be obtained:  
\[C_{r}=\frac{1}{2\pi f_{r}QR_{ac}} \tag{7}\]  
Thus, the resonant inductor and magnetizing inductance can be found using the following equation:  
\[L_{r}=\frac{1}{(2\pi f_{r})^{2}C_{r}} \tag{8}\]  
\[L_{m}=L_{n}L_{r} \tag{9}\]  
The deadtime, \(DT\), can be calculated as follows [6]:  
\[DT\geq 8L_{m}f_{s}C_{t} \tag{10}\]  
where \(f_{s}\) is the switching frequency, \(L_{m}\) is the transformer magnetizing inductance, and \(C_{t}\) is the total parasitic capacitance seen in the primary side [6; 68; 69; 70].
## 5 Data Analysis and Method Implementation
### Fourth Stage: Hyper-Parameter Optimization and Model Fitting  
After selecting ET as the most suitable classifier and visualizing its performance with all datasets, the final stage is to tune its relevant hyperparameters and check the model performance with each dataset to compare with the results before and after tuning the hyperparameters.  
As mentioned in the previous section, a hyperparameter is a factor whose value dominates the learning process. These hyperparameters are to be set before executing a machine learning algorithm, unlike model parameters that are not fixed before execution; instead, they are optimized during the algorithm's training. These factors or parameters are tunable and can openly affect how well model trains. Hyperparameters may impact directly on the training of ML algorithms. Thus, to achieve maximal performance, it is essential to understand how to select and optimize them [83]. The study includes optimizing three  
Figure 14: Performance of ET on Pohang.  
important ET hyperparameters (listed below), and each classifier is configured based on them.
* Number of Trees: One of the critical hyperparameters for the ET classifier is the number of trees that the ensemble holds, represented by the argument \"n_estimator\" ExtraTreeClassifier. In general, the number of trees increases until the performance of the model stabilizes. A high number of trees may lead to overfitting and a slowing down of the learning process, but the unlikely ET algorithms approach appears to be immune to overfitting the training dataset given that the learning algorithm is stochastic.
* Feature Selection: The number of randomly sampled features for each split point is perhaps an essential factor to tune for ET, but it is not sensitive to the use of any specific value. Argument \"max_features\" in the classifier is used to select the number of features. While selecting the features randomly, the generalization error can get influenced in two ways; first, when many features are selected, the strength of individual tree increases, second when very few features are selected, the correlation among the trees decreases, and overall the whole forest gets strengthened.
* Minimum Number of Samples per Split: The last hyperparameter for optimizing is the number of samples in a tree node before any split. The tree adds a new split that occurs when the number of samples divides equally or if the value exceeds. The argument \"min_samples_split\" represents the minimum number of samples required to split an internal node, and the default value is two samples. Smaller numbers of samples result in more splits and a more rooted, more specialized tree. In turn, this can mean a lower correlation between the predictions made by trees in the ensemble and potentially lift performance.
###### Abstract  
Large dynamic Doppler caused by high-speed flight of hypersonic vehicles brings a great challenge to acquisition, tracking and correct demodulation of telemetry and communication signals. Different from receiving schemes of existing telemetry and communications, a novel communication method based on Doppler diversity reception for hypersonic vehicles was proposed in this study. Firstly, the proposed method offers a coarse acquisition of Doppler frequency offset and its rate-of-change by fast-coarse acquisition method, and then decreases the large Doppler frequency offset into a certain range through compensation. Finally, signals with residual Doppler frequency offset are processed directly by Doppler diversity reception method without a complicated tracing method. Different from the traditional receiving scheme with acquisition and tracking module, the proposed scheme lowers requirements on acquisition accuracy and neglects the accurate tracking module, thus shortening telemetry and communication time significantly for hypersonic vehicles. Simulation results demonstrate that the proposed method can achieve satisfactory performance when the residual Doppler is no higher than 1 kHz with normalized Doppler frequency offset and when the normalized artificial frequency shift is 0.01 and 0.4 respectively.  
large dynamic Doppler; Doppler diversity; coarse acquisition; telemetry +
Footnote †: journal:
# Underwater Sensor Network Redeployment Algorithm Based on Wolf Search
## 6 Conclusions  
An underwater sensor network redeployment algorithm based on wolf search was developed to solve the problem of node deployment optimization with existing obstacles. In the process of node redeployment, the algorithm always exhibits implicit parallelism, and each node searches for the monitoring target independently without exchanging information with the other nodes. A number of areas of the monitoring space are searched. The algorithm has good coverage and low energy consumption. When congestion control is incorporated in the algorithm, it makes the node distribution tending to event distribution. When nodes encounter obstacles, they escape automatically. Hence, it prevents node failure caused by external force. Meanwhile, the algorithm jumps out of the local optimal solution. For our future work, because our obstacles are limited, we plan to establish different types of obstacles and distribution models that combine specific environmental factors, such as water flow. Experiments will then be conducted in a specific water environment.  
This paper was supported by the National Key Research and Development Program of China (2016 YFC0201400), the National Natural Science Foundation of China (NSFC61273072), the Provincial Key Research and Development Program of Science and Technology Department of Zhejiang Province (2016C03G2040707), and the National Natural Science Foundation of China and Zhejiang Joint Fund for Integrating of Information and Industrialization (U1509217).  
[PERSON] and [PERSON] conceived and designed the research; [PERSON] and [PERSON] performed the research; and [PERSON], [PERSON], and [PERSON] wrote the paper. The authors declare no conflict of interest.
## 5 Routing Algorithm
### Our Proposed Routing Algorithm
The Q-value fluctuates in the initial couple of rounds, but it converges after a certain number of rounds. Figure 7 shows that link disconnection is lower at learning rate of \(\alpha=1.0\). It is due to higher network density because more nodes in the network increase the number of hops. Figure 6: Fluctuation of the Q-value and coverage with respect to the number of rounds with a learning rate (\(\alpha=0.9\)). ```
1 Require: Information of nodes
2 Ensure:\(Q\)-table
3\(\%\) Initialization;
4 Zeros-matrix \(\longrightarrow\)\(Q\)-table;
5 Distance between nodes \(\longrightarrow\)\(d_{t}\);
6 Maximum communication distance \(\longrightarrow\)\(C_{max}\);
7 Energy remaining of nodes \(\longrightarrow\)\(E_{pr}\);
8\(\%\) Run clustering algorithm;
9 whileCHs sets are not emptydo
10 foreverySN\({}_{i}\)do
11\(\%\) computation of next hopping node;
12 Its coordinate \(\longrightarrow\)\(y_{t}(i)\);
13 foreverySN\({}_{i}\)do
14 Its coordinate \(\longrightarrow\)\(y_{t+1}(j)\);
15 if\(d_{t}(i,j)<C_{max}\)and\(y_{t}(i)<y_{t+1}(j)\)then
16 The available next hopping node (\(SN_{i}\longrightarrow\)\(SN_{j}\));
17
18
19 end if
20
21 while\(SN_{i}\) ensure the available next hopping nodedo
22\(\%\) calculate the rewards and \(Q\)-table;
23 compute\(E_{hop}\) and \(D_{hop}\) using Equations (19) and (20);
24\(Q_{t}(s_{t},v_{t})=f(E_{hop})\cdot(1-\beta)\);
25\(R_{t+1}=f(D_{hop})\cdot\beta\);
26
27 end if
28
29\(\%\)\(Q\)-table update accordingly ;
30 foreveryround ofnumberdo
31 select a state \(s_{t}\);
32 if\(SN_{i}\)=0 then
33\(Q_{t+1}(s_{t},v_{t})=(1-\alpha)\cdot Q_{t}(s_{t},v_{t})+\alpha\cdot[\gamma \cdot maxQ_{t}(s_{t+1},v_{t})+R_{t+1}]\)
34
35 end if
36\(s_{t}\gets s_{t+1}\);
37
38 end for
```  
**Algorithm 2**QL-Algorithm  
Figure 7: Link disconnection percentage increase with the increase of network density. The learning rate \(\alpha=1.0\) shows the low number of link disconnection. ```
1 Require: Information of node
2 Ensure: Best path selection
3% Initialization
4 All nodes update BS about their location and remaining energy. 5 On the basis of algorithm 1 Base station form clusters in the network and calculate the Q-table. 6% Data Sending
7 forevery source nodedo
8% Select route for data forwarding
9 Chose state \(s_{t}\) as current node;
10 whileif the data not reached to Base stationdo
11 ifNext hopping nodes are availablethen
12 Chose next state \(s_{t+1}\) with highest Q-value;
13 else
14 Chose the nearest node having the highest energy remaining as next state \(s_{t+1}\);
15
16 end
17 Forward node states from \(s_{t}\) to \(s_{t+1}\) along with collected information. 18
19\(s_{t}\gets s_{t+1}\);
20% calculate energy consumption
21 By using Equation (6) update the remaining energy;
22
23 end
```  
**Algorithm 3**Best Path Selection Scheme