esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Substrate Integrated Waveguide Circuits for Beam-Forming Networks Working in X-Band
## 3 Components for the Beam-Forming Network
### Six-Port Directional Coupler  
Another device that can easily be built with a single layer SIW is the six-port directional coupler. Six-port directional couplers are not as common as the four-port counterparts. There are few examples of such devices in the literature [34; 35; 36]. One reason is that the circuit is quite complex to design. Moreover, the coupler can also be realized by combining standard four-port couplers and phase shifters [37; 38; 39; 40; 41; 42]. However, a single block six-port coupler is more convenient because it is usually smaller and it sports a wider band. Moreover, with the proposed design, the three input ports were placed at one side of the device and the three output ports at the other side, which was more useful.  
As opposed to four-port couplers, where the coupling coefficient can be different depending on the application, a six-port coupler is usually designed to divide the input power by three. This means that the scattering matrix of the device is:  
\[S=\frac{1}{\sqrt{3}}\left[\begin{array}{cccccc}0&0&0&e^{i\varphi_{14}}&e^{i \varphi_{15}}&e^{i\varphi_{16}}\\ 0&0&0&e^{i\varphi_{15}}&1&e^{i\varphi_{15}}\\ 0&0&0&e^{i\varphi_{16}}&e^{i\varphi_{15}}&e^{i\varphi_{14}}\\ e^{i\varphi_{14}}&e^{i\varphi_{15}}&e^{i\varphi_{16}}&0&0&0\\ e^{i\varphi_{15}}&1&e^{i\varphi_{15}}&0&0&0\\ e^{i\varphi_{16}}&e^{i\varphi_{15}}&e^{i\varphi_{14}}&0&0&0\\ \end{array}\right], \tag{5}\]  
where the phase \(\phi_{25}\) is taken as reference. The simultaneous application of matching at all ports, reciprocity, no losses, and symmetry planes gives the following phase relations [34; 36]:  
\[\left\{\begin{array}{c}\frac{1}{3}\left(e^{i\varphi_{15}}+e^{i(\varphi_{14} -\varphi_{15})}+e^{i(\varphi_{16}-\varphi_{15})}\right)\\ \varphi_{14}-\varphi_{16}=\pm(2\pi/3)\\ \end{array}\right. \tag{6}\]  
From Equation (6), there are only two phases that can change freely.  
For this kind of device, there was not a known synthesis technique, so it had to be designed only by optimization. The performances obtained for the X-band coupler (Figure 7) were quite good.  
Figure 8a shows the experimental reflection coefficients and the isolation among input ports. Figure 8b shows the experimental transmission coefficients. The experimental phase difference \(\phi_{14}-\phi_{16}\) satisfied Equation (6) with a deviation of \(+5^{\circ}/-8^{\circ}\), as shown in Figure 9. The return loss can be further improved by adding more steps or irises in the waveguides connecting SMA ports to the core of the device. This approach was used, for example, in [34; 35]. However, this increased the final size of the coupler.  
Figure 6: Experimental phase difference between the coupled and direct port of the directional coupler in Figure 4. Figures adapted from [32].
## 3 Parameter Selection for the MSFGWO Algorithm
### Population Size and Iterations
#### 3.1.2 Effects of Number of Iterations  
Next, the population size was set to five, with the number of iterations ranging from 1 to 20 in increments of one. Four satellites were selected, and the Monte Carlo experiment was repeated 50 times to obtain the average value. The influence of the number of  
Combining Figures 7 and 8, when the initial population size is fixed, with an increase in the number of iterations, the average DGDOP value gradually approaches the optimal satellite selection result obtained using the exhaustive search method. The average DGDOP value decreased from 437.9925 to 399.9498, and the average DGDOP error decreased from 40.01% to 27.85%. Meanwhile, the time computation increased from 0.0042 to 0.0422 s. From Figures 7 and 8, it can be clearly seen that although there was a significant decrease in average DGDOP at iteration number 11, the error rate only decreased from 40.01% to 30.96%, while the computation time increased by a factor of approximately 4.57 times; when the number of iterations was set to seven, the error rate decreased from 40.01% to 35.17%, while the computation time increased by a factor of approximately 2.57 times. Therefore, considering the balance between DGDOP values and computational time, the number of iterations should be set as seven, which results in lower computational time while maintaining the DGDOP value close to the optimal satellite selection result.  
In summary, using the MSFGWO algorithm with a population size of five and an iteration number of seven can ensure accurate satellite selection while achieving fast and efficient selection of optimal visible satellite configurations.
## 6 Experimental Results in the GeoLife Case
### Analysis of Step 1.2  
The output produced by the algorithm consists of \(55,109\) sets of \(K\) elements of frequent trajectories (in the time slot \(\Delta t_{1}\)), for \(K\) from \(1\) to \(14\): these trajectories share candidate corridors of over \(25\) km.  
The advantage is that, compared to the previous step, it improves the execution time. Indeed the scanning of the trajectories to check if they are frequent or not is faster because they have few elements (on average they have a length of about \(16\) cells of \(500\) m each) and also the support threshold has been raised. In fact, if the \(min\_sup\) is larger, then the cardinality of the frequent set \(L_{K}\) and therefore also those of \(C_{K+1}\), are much smaller than in Step 1. Moreover, the number of iterations of the algorithm is also lower (it made 14 iterations instead of 21). Another improvement was that a thinner grid which gives greater precision of the road results.  
The longest corridors of these 14 trajectories are similar to the ones found in the previous case (Step 1). Among these 14 trajectories, 7 are in common with the 15 obtained in the Step 1. The plot of trajectories crossing 51 cells consists of about \(25\) km visible in Figure 5, and like the previous case we verify that this sequence is a candidate corridor of parameter \(M=51\) and \(S=14\).  
Figure 4: A candidate corridor detected with Step 1, with parameters \(M=19\) cells and \(S=15\) trajectories.  
Step 1.2 is a verification of Step 1, also allowed to refine the grid and therefore to have a greater precision (for instance we used a grid with side cells 500 m wide instead of a grid with cells 1300 m per side as in Step 1--see Figure 6).
## 5 Satellite Stabilization Problem
### Example 1. The Joint Estimation and Control Problem
#### 5.1.2 Case B  
The measuring system model is described by the following relationship:  
\[z(t)=(p(t),q(t))^{\mathrm{T}}\]  
The behavior of the trajectories set for different sizes of mini-batch is shown in Figure 2:  
Table 2 shows the results of solving the problem depending on the mini-batch size.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(d\) & \(J[K_{d}]\) \\ \hline
1 & 2970.9487 \\
10 & 315.6251 \\
20 & 256.9266 \\
27 & 167.1622 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Solution results for different mini-batch sizes.  
Figure 2: The behavior of the satellite coordinates \(p,q,r\) (red) and estimations (blue) for different mini-batch sizes: (**a**) \(d=1\); (**b**) \(d=10\); (**c**) \(d=20\); (**d**) \(d=27\).
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author, Year** & **Leads Used** & **Data Used** & **Method** & **Performance** \\ \hline [PERSON] et al. [4] & V2–V4 leads & Normal:320 records & Morphology feature & Acc = NA \\
1992 & & AMI:182 records & ANN & Sen = 79.00\% \\ \hline [PERSON] et al. [5] & V1–V4 leads & Normal: 582 & HMMs with GMMs & Acc = 82.50\% \\
2012 & V1–V4 leads & MI: 547 (heartbeats) & HMMs with GMMs & Sen = 85.71\% \\ \hline [PERSON] et al. [6] & Lead II & 549 records (PTB) & T-wave integral [PERSON] & Acc = 94.74\% \\
2014 & Lead II & (PTB) & [PERSON] & Sen = NA \\ \hline [PERSON] et al. [3] & 12 leads & 2148 instances (a frame of four heartbeats from PTB) & Multiscale Energy & Acc = 96.00\% \\
2015 & & (a frame of four heartbeats from PTB) & SVM & Sen = 93.00\% \\ \hline [PERSON] et al. [7] & 12 leads & Normal: 60 records & Morphological examination & Acc = 96.96\% \\
2018 & & MI: 60 records (selected from PTB) & PCA & Sen = 96.96\% \\  & & (a selected from PTB) & SVM & Spec = 96.96\% \\ \hline [PERSON] et al. [8] & \multirow{2}{*}{Lead II} & Normal: 10546 & Convolutional neural network & Acc = 95.22\% \\
2017 & & MI: 40182 (heartbeats from PTB) & Convolutional neural network & Sen = 95.49\% \\ \hline
[PERSON][PERSON] et al. [9]** & **12 leads** & **12 leads** & **Normal: 79 records** & **multiple instance** & **Acc = NA** \\
**2012** & & **MI: 369 records** & **learning** & **Sen = 92.60\%** \\  & & **(PTB)** & **SVM** & **Spec = 82.40\%** \\ \hline
**Our method** & **12 leads** & **Normal: 79 records** & **Bi-LSTM** & **Acc = 94.77\%** \\  & & **MI: 369 records** & **Hearbeat-attention** & Sen = 95.58\% \\  & & **(PTB)** & **Spec = 90.48\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of selected studies conducted for the detection of myocardial infarction (MI).
## 6 Conclusions and Future Developments  
This paper has investigated the application of OD algorithms to the mitigation of three types of poisoning attacks in the context of the classification of UltraWideBand (UWB) Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) propagation scenarios in two different data sets. The three different OD algorithms were evaluated together with the random forest algorithm for classification. The results show that for this particular problem, the OD algorithm is effective in the following: (1) identifying poisoned samples to a significant degree--more than 70% for selected scenarios and values of the parameters; and (2) increasing the classification accuracy (e.g., surpassing 10% of increase for selected scenarios and values of the parameters) in comparison to the poisoned data. While the SDO algorithm demonstrates a strong detection performance, which is higher than the other two OD algorithms, the improvement in classification accuracy between the sanitized data set and the poisoned data set is relatively balanced across the three different OD algorithms. The approach was evaluated on two data sets (one public data set and another created by the author that is available on request) and using an extensive set of values of the hyper-parameters, which define the impact and severity of the poisoning attacks.  
Future developments will expand this analysis to DL algorithms and poisoning attacks that are specific to that class of algorithm. In particular, poisoned samples may be generated using GANs (Generative Adversarial Networks), while OD algorithms could be applied to different representations of the signals (e.g., spectral domain) rather than using a feature-based approach like the one applied in this paper.  
**Funding:** The author received no additional funding for implementing this study; this work was covered by the institutional budget of the JRC.  
**Data Availability Statement:** This study uses the public eWINE data set [35]. The Radar data set is available on request from the author.  
**Conflicts of Interest:** The author declares no conflicts of interest.  
Figure 16: Improvement of the recall \(Recall_{I}\) for the Radar data set with the SDO algorithm and the random forest classifier for the different attacks within scenario 1 (Office 1) where \(S_{P}=16\) for the FSP and TFP attacks (for the LF attack, \(S_{P}\) does not apply) and different values of \(P_{San}\).
# A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
###### Abstract  
Massive multiple input multiple output (MIMO) technology is one of the promising technologies for fifth generation (5G) cellular communications. In this technology, each cell has a base station (BS) with a large number of antennas, allowing the simultaneous use of the same resources (e.g., frequency and/or time slots) by multiple users of a cell. Therefore, massive MIMO systems can bring very high spectral and power efficiencies. However, this technology faces some important issues that need to be addressed. One of these issues is the performance degradation due to hardware impairments, since low-cost RF chains need to be employed. Another issue is the channel estimation and channel aging effects, especially in fast mobility environments. In this paper we will perform a comprehensive study on these two issues considering two of the most promising candidate waveforms for massive MIMO systems: Orthogonal frequency division multiplexing (OFDM) and single-carrier frequency domain processing (SC-FDP). The studies and the results show that hardware impairments and inaccurate channel knowledge can degrade the performance of massive MIMO systems extensively. However, using suitable low complex estimation and compensation techniques and also selecting a suitable waveform can reduce these effects.  
massive MIMO; hardware impairments; doubly dispersive channels; channel aging +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Synthesis of Nanoparticles Functionalized with ATRP Inimer (PS-Br NPs)  
In a typical procedure, 28 mmol of styrene (St, 0.99 eq.), 287 umol of divinylbenzene (DVB, 0.01 eq.) and 38 mmol of hexadecane (HD, 0.04 eq.) were added to a 40 mL vial and were mixed with 144 umol of a V-59 (0.005 eq.). After 10 min of mild stirring, 24 mL of 10 mM of aqueous solution of sodium dodecyl sulfate (SDS) was added, and vigorously stirred for 15 min. The mixture was emulsified by using a probe sonicator (Branson 450 Digital Sonifier, Danbury, CT, US) equipped with a titanium solid extender tip, diameter of 1/2\" for 2 min at 0 \({}^{\circ}\)C (20 kHz, 70% amplitude, 10 s on/2 s off). The resulting miniemulsion was transferred to a 50 mL round bottom flask and heated at 80 \({}^{\circ}\)C to initiate polymerization. After 2.5 h of reaction, 2 mL of SDS aqueous solution (250 mM) was added and the reaction mixture was degassed by bubbling with Ar for 10 min. Then, in order to cover PS core NPs with a thin layer of ATRP inimer (Figure 1a), a mixture of St (5.8 mmol, 0.99 eq.), DVB (58 umol, 0.01 eq.), ATRP inimer (59-590 umol, 0.01-0.1 eq.) and V-59 (30 umol) was added dropwise, at a rate of 1 mL h\({}^{-1}\). The mixture reacted overnight at 80 \({}^{\circ}\)C and was then filtered. The PS NPs in the aqueous suspension were purified by precipitation in 200 mL of MeOH and air-dried. To obtain impurity-free PS NPs without HD and SDS, the NPs were redispersed in 40 mL of THF, precipitated in 500 mL of MeOH three times and redispersed in THF and precipitated in hexane. Finally, the NPs were air-dried.  
Figure 1: **(a)** Synthesis of poly(styrene) (PS)-poly(methyl acrylate) (PMA) core-canopy nanoparticles (NPs); **(b)** Solvodynamic radii (\(R_{\text{s}}\)) and **(c)** hydrodynamic radii (\(R_{\text{h}}\)) of PS-PMA NPs in DCM and H\({}_{2}\)O, respectively. For NPs functionalized with PMA chains with a degree of polymerization (\(N\)) ranging from 0 to 639 units with low (0.17 chains nm\({}^{-2}\), \(\square\)), medium (0.8 chains nm\({}^{-2}\), \(\triangle\)) and high (2.5 chains nm\({}^{-2}\), \(\Circinus\)) grafting density, measured by dynamic light scattering at 25 \({}^{\circ}\)C. The inset photo in **(b)** displays the suspensions of PS-PMA\({}_{\text{49k}}\)-\(\sigma_{\text{low}}\), PS-PMA\({}_{\text{51k}}\)-\(\sigma_{\text{medium}}\), and PS-PMA\({}_{\text{55k}}\)-\(\sigma_{\text{high}}\) in DCM at a concentration of 16.67 mg mL\({}^{-1}\).
# Improved Proactive Routing Protocol Considering Node Density Using Game Theory in Dense Networks
## 5 Conclusions  
In this paper, we proposed an algorithm to improve the Optimized Link State Routing protocol in mobile scenarios. This algorithm is based on game theory, where all nodes participate in a simple game to choose an appropriate node willingness depending on its node density. We proposed that all nodes have two choices for its willingness: WILL_ALWAYS or WILL_NEVER.  
We implemented our work in Riverbed Modeler 18.0 and compared it with the original OLSR protocol. Results show that our improved protocol outperformed the original OLSR in all performance metrics except end-to-end delay, which is negligible. The results from our simulations showed that our proposed algorithm could be feasible for real-time applications, especially multimedia applications in multihop networks.  
However, for this paper, we did not consider the battery power of the nodes. It is known that OLSR consumes high power consumption, especially in finding alternative routes and keeping its route tables. Furthermore, another limitation is the choice of the mobility model. Random waypoint (RWP) was chosen as the mobility model. Using another mobility model and different parameters would be another perspective to observe our algorithm.  
In our future work, it is necessary to extend this code to include more parameters like battery power. Most IoT devices are battery powered; thus power conservation is of utmost importance. It is also necessary to extend this code to consider Quality of Service (QoS) parameters, particularly for a high packet delivery ratio for multimedia application. Furthermore, we plan to implement our improved protocol on a testbed to test its efficiency.  
Conceptualization; O.O. and K.G.; Model design/experiment/analysis: O.O.; supervision: K.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Impacts of Land-Use Change from Primary Forest to Farmland on the Storage of Soil Organic Carbon
## 3 Results  
### Changes in SOC Storage after Primary Forest Converted to Farmland  
The results (Figure 3a,b) showed that the SOC storage decreased by 8.69 Mg/ha, corresponding to a decrease of 26.3% (\(p<0.05\)). According to the climate zone, the SOC storage in the tropical region decreased by 6.70 Mg/ha, and the ratio decreased by 23.6% (\(p<0.05\)). The SOC storage in temperate regions decreased by 12.09 Mg/ha, and the ratio decreased by 30.6% (\(p<0.05\)).  
The depth analysis of different soil profiles (Figure 4a,b) shows that the SOC storage of the 0-20 cm soil profile was the most affected after the primary forest changed to farmland. The absolute amount decreased by 10.45 Mg/ha, and the ratio also decreased by 31.6% (\(p<0.05\)). In the 0-30 cm soil profile, SOC storage decreased by 8.97 Mg/ha, and the ratio decreased by 28.2% (\(p<0.05\)). For the soil depth above 30 cm, SOC storage changed the  
Figure 3: (**a**) Changes of absolute SOC storage in different climatic zones. (**b**) Percentage of SOC storage changes in different climate zones.  
Figure 2: Symmetry test of funnel plot by [PERSON]’s publication bias method.  
least; the absolute amount decreased by 7.22 Mg/ha, and the ratio decreased by 10.1% (\(p<0.1\)).
## 5 Numerical Results
### Accuracy Assessment  
To identify the application range of the developed closed-form approximation, we start assessing the accuracy of the model by comparing its results to those obtained using the computer simulations. To this end, Figure 4 shows the UAV LoS blockage probability obtained using the proposed model and computer simulations for various UAV altitudes and BS heights \(h_{U}\) and \(h_{A}\), respectively, street width \(l=20\) m, mean building height and standard deviation \(E[H_{B}]=30\) m and \(\sigma[H_{B}]=10\) m, block width and length of  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline mmWave BS height, \(h_{T}\) & 5 m \\ UAV height, \(h_{R}\) & 150 m \\ Carrier frequency, \(F_{C}\) & 28 GHz \\ Emitted power, \(P_{T}\) & 0.02 W \\ mmWave BS and UAV antenna gains, \(G_{T}\), \(G_{R}\) & 15 dB, 5 dB \\ Number of vertical and horizontal streets, \(M_{H}\), \(M_{V}\) & 10, 10 \\ Length and width of building blocks, \(b_{I}\), \(b_{w}\) & 100 m, 100 m \\ Street width, \(l\) & 20 m \\ SNR threshold, \(S_{T}\) & 0 dB \\ Thermal noise, \(N_{0}\) & \(-\)174 dBm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of notation and parameters.  
\(b_{w}=b_{l}=100\) m. The considered region of interest is formed by considering 10 horizontal and vertical building blocks interchanged with streets.  
By analyzing the results shown in Figure 4, one may deduce that the proposed model allows for the approximation of the results obtained via computer simulations quite closely. Similar observations have been made for rooftop-mounted mmWave BSs. Notably, the developed model slightly overestimates the actual value of the probability. This is explained by the inherent structure of the model that assumes that all the LoS visibility regions are completely independent. This observation allows us to identify the applicability regions of the model. First of all, observe that due to the abovementioned property the model always provides the upper bound on the UAV LoS blockage probability. Secondly, the results become more accurate as of the area of the zone and/or the density of the mmWave BSs increase. Based on these results, when discussing the response of the UAV blockage probability to system parameters and assessing the effect of rooftop-mounted mmWave BSs, we thus utilize the developed model.