esa-sceva/satcom-chunk-collection huggingface dataset:
# Iterative Interferometric Denoising Filter for Traveltime Picking
## 4 Field Data Example  
### Gulf of Aquba  
A seismic survey recorded near the Gulf of Aquba in Saudi Arabia is also used here to demonstrate the feasibility of the proposed IDF method. A Google map of the area is shown in Figure 7[48]. The recorded 2D profile, indicated by a green line, crosses a normal fault (red line) in Figure 7b. A total of 120 sources and 120 geophones with 2.5 m spacing were used to record these data. A 200 lb accelerated weight drop was used to generate seismic energy. Figure 8a shows the first common shot gather (CSG) as an example of the recorded data. A taper time window with three periods was applied around the first-arrival traveltime, and the resulting CSG is displayed in Figure 8b.  
Figure 6: (**a**) The traveltime difference between picking results on original (clean) data and on interferometric noisy data after applying IDF. (**b**) A histogram of traveltime difference.  
Figure 7: (**a**) A Google Earth satellite image depicts the study area, located on the eastern side of the Gulf of Aquba, north of Saudi Arabia. (**b**) The seismic profile is represented by the green line, and the normal fault is denoted by the red line. The solid segments of the red line represent the visible parts of the fault, while the dashed segment indicates the concealed portion of the fault.  
The proposed IDF was applied to the muted data, and the result is shown in Figure 8c. The IDF-processed seismic data had a greater S/N ratio than the seismograph before the IDF application (Figure 8b). Similar to the synthetic example, we manually picked the first-arrival traveltime information of the original and IDF datasets. The traveltime difference map and histogram are displayed in Figure 9a,b, respectively. The comparison demonstrates that the IDF dataset's first-arrival traveltime is more accurate than the original dataset.  
We utilized the first-arrival traveltime information picked from original and IDF-processed seismograms as the input information for refraction traveltime tomography. Here, we utilized a conjugate gradient to iterate the velocity model and utilized the modified total variation regularization [4] as the regularization term to constrain the tomogram in each iteration. Figure 10a,b are refraction traveltime tomography results using traveltime information picked from original and IDF-processed datasets as the input information. Both of these tomograms indicate the fault located in the middle of the acquisition test line, which is in accordance with the fault represented by the red line shown in Figure 7b, and are concerned with the geology of the site. In this case, the kinematic characters of the IDF-processed dataset could support traveltime tomography used for the static  
Figure 8: The 1 st CSG in (**a**) original, (**b**) muted, and (**c**) muted IDF data recorded at the Gulf of Aqaba.  
Figure 9: (**a**) The picking traveltime difference between original and IDF datasets. (**b**) A histogram of traveltime difference.
## 2 Proposed FFT-DCNN Architecture
### Convolutional Layer  
In this study, the DCNN has two main layers, which are a convolutional layer and a pooling layer, respectively. The convolutional layer of DCNN is mainly utilized to extract features from building structures via convolution operation. In this procedure, convolutional kernels slide on the input sample via using the same size convolution kernel \((h_{C},w_{C})\) where \(h_{C}\), \(w_{C}\), and \(s_{C}\) represent the length, width, and step size of the convolution kernel, respectively. After finishing the convolutional operation, the output matrix can be obtained, and the number of kernels is the same as the output matrix. Output matrix of the convolutional layer is described as:  
\[C_{j}=f\left(X_{i}*W_{j}+B_{j}\right) \tag{2}\]  
\[C~{}=~{}\left[C_{1},C_{2},\cdots,C_{K_{C}}\right] \tag{3}\]  
where \(*\) represents the convolutional operation. \(W_{j}\) and \(B_{j}\) represent the \(j\)-th \((j\in{1,2,\cdots,K_{C}})\) convolution kernel and bias, respectively. \(f\)\((\cdot)\) denotes activation function. \(C_{j}\) and \(C\) are the \(j\)-th output and the entire output, respectively. In addition, the important information  
Figure 1: Architecture of the proposed method.  
Figure 2: Flowchart of features extraction.  
is lost due to dimensional reduction of input data \(X_{i}\), when the input sample \(X_{i}\) carries out a convolutional operation leading to a change of the feature dimension. To solve the problem, \"same padding\" is utilized in this study, which can keep the same dimension between input and output. After the convolutional operation, the dimensional operation of the output result can be described as:  
\[R\left(C_{j}\right)=\left[\frac{n_{l}-h_{C}+2\times p}{s_{c}}+1\right]\times \left[\frac{m_{l}-w_{C}+2\times p}{s_{c}}+1\right]\times K_{C} \tag{4}\]  
where \(w_{C}\) and \(h_{C}\) are length and width of the frequency information via FFT transformation, respectively. \(K_{C}\) denotes the number of convolutional kernels. \(n_{l}\) and \(m_{l}\) are length and width of convolutional kernels. \(s_{c}\) represents the sliding step.
# Characterization of Narrowband Noise and Channel Capacity for Powerline Communication in France
## 2 Experimental Measurement Setup and Methods  
### Identification of Measurement Sites  
Noise level depends on loads' profiles, density of interconnections, quality of connections and the distance between communication nodes. Noise profile is important in areas with a high connection density, lighting loads and non-linear loads, as well as the types of distribution [27; 33; 35]. In this manuscript, five sites in different locations around the city of Tours in France were chosen to study the noise profiles. Several topologies with various electrical features were also chosen to get a better understanding of the dynamic behavior of the network, such as information about the traffic inside the NB-PLC and especially, at two different points of the network: in the transformer substation, and on the customer side in parallel to the smart electricity meter. In rural networks, the overhead cable length may typically extend up to 1 km, when the number of loads is very low. However, the residential and industrial networks are characterized by a high density of loads connected to a short length of underground cables [33].  
As can be seen in Figure 3, the sites can be classified into five categories: rural, sub rural, urban, urban dense 1, and urban dense 2. For each kind of site, a transformer supplies a variety of installations for several customers. The main characteristics and details of the LV distribution cables, and the number of customers within the same branches of each site, are described below:  
* Rural site: the rated power of the transformer equals 250 kVA can supply 89 customer installations. The transformer and the smart electricity meter are connected using a 200-m long underground cable and a 164 m twisted overhead connection cable. One producer is connected to the network.
* Sub rural site: the rated power of the transformer equals 400 kVA. It supplies 142 customers. The transformer and the smart electricity meter are connected through a 220-m long underground cable. No producer is connected to the network.
* Urban site: it represents a residential site. The rated power of the transformer equals 250 kVA. It supplies 250 customers. The transformer and the smart electricity meter are connected using a 335 m of 150 mm\({}^{2}\) aluminum underground cable, and a 10 m twisted overhead connection cable with a 4 \(\times\) 35 mm\({}^{2}\) section. No producer is connected to the network.
* Urban dense 1 site: the transformer is directly integrated in a building. Its rated power equals 400 kVA. It can supply 65 customers. The transformer and the smart electricity meter are connected through a 115 m aluminum underground cable. No producer is connected to the network.  
* Urban dense 2 site: It does not have the same features as the urban dense 1. The transformer is also implemented in a building, but its rated power equals 650 kVA. It can supply 359 customers. The transformer and the smart electricity meter are connected through a 62-m long aluminum underground cable. No producer is connected to the network.
# Lunar Satellite Constellations in Frozen Low Orbits
## 1 Introduction
Section 3 is devoted to the non-gradient optimization procedure and its implementation details. Then, the constellation performance metrics monitored in this research are summarized in Section 4. The analysis of some candidate frozen constellations and the discussion of their performance, globally and in polar regions, are contained in Section 5. Long-term stability issues for frozen LLOs are also commented there; the cost and optimal periodicity of orbital correction maneuvers are estimated.
# A 5 Gb/s Optoelectronic Receiver IC in 180 nm CMOS for Short-Distance Optical Interconnects
## 2 Circuit Description
### \(f_{T}\) -Doubler  
The \(f_{T}\)-doubler, often implemented by using a differential input stage with cross-coupled feedback or stacked transistors, is designed to extend the bandwidth of the amplifier by effectively doubling the transition frequency \(f_{T}\). However, this improvement in speed and gain-bandwidth product comes with tradeoffs in terms of noise and linearity.  
From a noise perspective, the input-referred noise voltage density \(\overline{\varphi}_{n,in}^{2}\) is primarily determined by the thermal noise contributions from both the input and feedback transistors. Assuming a stacked configuration with transistors \(M_{11}\) and \(M_{12}\), the total input-referred noise can be approximated as  
\[\overline{\varphi}_{n,in}^{2}\approx\frac{8 kT\gamma}{g_{m11}}+\frac{8 kT\gamma }{g_{m12}},\]  
where \(g_{m11}\) and \(g_{m12}\) are the transconductances of the input and feedback transistors, respectively, and \(\gamma\) is the MOSFET excess noise factor. Since both transistors contribute significantly to the noise, careful sizing and biasing are required to minimize \(\overline{\varphi}_{n,in}^{2}\) without compromising bandwidth.  
The input dynamic range of the \(f_{T}\)-doubler is limited by the increased gain and potential instability introduced by positive feedback or stacking. The maximum linear input swing \(V_{in,max}\) can be estimated as  
\[V_{in,max}\approx\frac{V_{ov}}{1-A_{fb}},\]  
where \(V_{ov}\) is the overdrive voltage and \(A_{fb}\) is the effective feedback gain. \(A_{fb}\) approaches unity, the input range narrows, and the circuit may enter a non-linear or unstable regime. Thus, the feedback strength must be optimized to ensure a sufficient linear range while achieving the desired bandwidth.  
An additional benefit of the \(f_{T}\)-doubler lies in its reduced input capacitance. As depicted in Figure 6, the gate-source capacitances \(C_{gs11}\) and \(C_{gs12}\) of the stacked transistors \(M_{11}\) and \(M_{12}\) appear in series from the input perspective. Assuming identical transistor sizes, the effective input capacitance becomes  
\[C_{in,eff}\approx\frac{C_{gs11}C_{gs12}}{C_{gs11}+C_{gs12}}\approx\frac{C_{gs1 1}}{2}\]  
This reduction in input capacitance contributes to an increase in the input pole frequency, further enhancing the overall bandwidth of the system.  
Consequently, the cutoff frequency (\(f_{T}\)) for this \(f_{T}\)-doubler can be given by  
\[\mathrm{f_{t}}\equiv\frac{g_{m11}}{2\pi C_{gs11}}\approx\frac{2g_{m11}}{2\pi C _{gs11}},\]which demonstrates its capability to extend the bandwidth [14].  
In addition, this \(f_{T}\)-doubler functions as an output buffer, providing a sufficient drive capability for subsequent stages while maintaining the symmetry of differential outputs [15].
## 2 Case Study: Closely Spaced mmWave MIMO Systems
### Design 1  
Figure 1 shows the geometry of two identical monopole antennas sharing a common ground plane and dielectric material. The monopoles are individually excited through the probe feeds. In this design configuration, both monopoles are facing the +z-direction. The center-to-center distance between the two monopoles is 6.25 mm (0.535\(\lambda\)), whereas the edge-to-edge distance is 1.25 mm (0.107\(\lambda\)), where \(\lambda\) is the free-spaced wavelength at 25.5 GHz (center frequency). The monopole design is imprinted on Rogers RO4003 (lossy) dielectric material with relative permittivity \(\varepsilon_{\mathrm{r}}\) = 3.38, thickness t = 0.508 mm, and dissipation factor tan\(\delta\) = 0.0027. The common dielectric substrate is also Rogers RO4003 (lossy), but the substrate thickness is 1.524 mm to ensure improved rigidness of the structure. Figure 2 illustrates the reflection and transmission coefficients for Design 1, which indicate the level of mutual coupling between the two monopoles; it ranges from \(-12\) to \(-23\) dB within the frequency range of interest (24 GHz to 27 GHz).  
Figure 1: _Cont._
# Multiport Single Element Mimo Antenna Systems: A Review
###### Abstract
In all these designs, more than one antenna elements are integrated or etched on single substrate to apprehend a compact MIMO antenna. This occupies more space and hence presents a big challenge in the industrial design process. The shared radiator with multiport arrangement is a good alternative to miniaturizing the overall size of MIMO antennas. However, due to the difficulties in getting high isolation among connected ports, only a countable number of antenna designs having shared radiators with more than one port are available in literature. Compared to the array designs and separated ports, there is much less focus on common radiator designs with multiple feeding ports. To regulate mutual coupling, these coupled ports with the shared radiator require extra effort. Thus, designing an antenna becomes critical while considering the required parameters suitable for portable devices. Currently, we could not find any related review paper accessible in the literature which can provide us in detail about the shared radiator multiport MIMO antenna systems and their performance improvement techniques. So, in this paper, we have endeavoured to explore all the multiport MIMO antennas with a shared radiator, presented in recent years in a holistic manner considering their performance-enhancing techniques and aims to address the design issues for the further advancement in the MIMO antenna design as per their application. Our main aim in this paper is to provide a comprehensive literature review of multiport MIMO antenna systems having one shared radiator and a comparative study on different multiport-shared radiator antenna designs and the isolation improvement techniques used in these designs. The primary antenna features of shared radiator multiport MIMO antennas such as gain, frequency, mutual coupling, ECC and their applications are reviewed and compared in this paper. In addition, a brief overview of MIMO mathematical concepts and approaches used in these antenna designs for increasing the isolation are also discussed. The rest of this paper is structured as follows. Section 2 discusses the mathematical concepts of MIMO, whereas a detailed literature review of diverse types of isolation techniques is elaborated in Section 3. The findings of single-element multiport MIMO antenna classification are summarized in Section 4. Section 5 then gives a detailed summary and discussion of the important findings of the paper.
## 3 The Proposed NSPSO Sampling Algorithm
### Dynamic Particle Repulsion Strategy  
When solving RMOPs such as finding LSF, it is particularly important to ensure that the optimal solution set of the algorithm can be uniformly distributed on LSF under a certain density. To this end, a dynamic repulsion strategy is proposed to control the density  
Figure 4: Vibration problem caused by normal search.  
of solutions. When the distance between two particles is smaller than a predefined repel distance \(d_{rep}\), this strategy will give both particles opposite repulsive velocity increment to maintain certain distance. This strategy can improve the diversity and distribution uniformity of the particles. The repel velocity increment for each particle is shown in Equations (11) and (12).  
\[\mathbf{r}_{i}(t)=\left\{\begin{array}{ll}\mathbf{0},&if\ d_{nbst_{1}}^{i}(t)\geq d _{rep}\\ \mathbf{\beta}_{nbst_{1},i}^{o}(t)\cdot(d_{rep}-d_{nbst_{1}}^{i}(t)),&if\ d_{nbst_{1}}^{i}(t)<d _{rep}\end{array}\right., \tag{11}\]  
where  
\[\mathbf{\beta}_{nbst_{1},i}^{o}(t)=\frac{\mathbf{x}_{i}(t)-\mathbf{x}_{nbst_{1}}^{i}(t)}{ \left\|\mathbf{x}_{i}(t)-\mathbf{x}_{nbst_{1}}^{i}(t)\right\|} \tag{12}\]  
Here, \(r_{i}(t)\) is the vector of repulsion for particle \(i\) at iteration \(t\); \(\mathbf{x}_{nbst_{1}}^{i}(t)\) records the position of the closest neighbor of particle \(i\) at iteration \(t\); \(d_{nbst_{1}}^{i}(t)\) is the distance from \(\mathbf{x}_{nbst_{1}}^{i}(t)\) to \(x_{i}(t)\) at iteration \(t\); \(d_{rep}\) is repulsion distance that was used to keep a certain distance between particles; \(\beta_{nbst_{1},i}^{o}(t)\) is the unit vector from \(\mathbf{x}_{nbst_{1}}^{i}(t)\) to \(x_{i}(t)\); and \(\parallel\) is the L2 norm operator.  
Dynamic repulsion also has a side effect in the same way as normal search. Supposing the optimal region of a RMOP is a curve inside the variable plane as shown in Figure 5, dynamic repulsion strategy may let the marginal particles repelled from the optimal region unexpectedly, causing a convergency problem. In Figure 5, the figure on the left is expected result, while the right is the wrong result caused by this problem.  
A compensation term was therefore added to the dynamic particle repulsion strategy.  
\[\mathbf{re}_{i}(t)=\underset{j=1,2,\cdots,d}{\text{min}}\bigg{\{}e^{-(f(\mathbf{x}_{ nbst_{j}}^{i}(t))-f(\mathbf{p}_{g}(t)))}\bigg{\}}\cdot(1-e^{-(f(\mathbf{x}_{i}(t))-f(\mathbf{p}_{ g}(t)))})\cdot(P_{c}(t)-P_{i}(t)) \tag{13}\]  
where \(\mathbf{x}_{nbst_{j}}^{i}(t)\) are the neighbors that forms the hyperplane for the particle \(i\); \(P_{c}(t)\) is the arithmetic average of the coordinates of \(\mathbf{x}_{nbst_{j}}^{i}(t)\).  
Apparently, this term is active only when the current particle is not optimal, and its best neighbors are optimal. Therefore, a compensation velocity increment will be added to a particle when it is repelled from the optimal region.
## 1 Introduction
To the best of our knowledge, this is the first secure communication suite that has been implemented and tested in the field. Second, by means of experiments and real data, the paper proves that the resulting security suite is indeed suitable for an underwater acoustic networking environment. We believe that reporting design choices and field performance is valuable in itself because it may allow the research community to orient towards either refinements of the proposed approach or different approaches altogether, however with the long-term objective of achieving operational implementations of secure underwater acoustic networks. The paper is organized as follows. Section 2 discusses related work. Section 3 and Section 4 describe the system and threat model, respectively. Section 5 describes the cryptographic suite whereas Section 6 describes the secure routing. Section 7 presents performance evaluation carried out by means of experiments in the field and simulations with real data. Finally, Section 8 reports our final considerations.
## 7 Performance Evaluation
### Discussion  
The experimental results present a comprehensive evaluation of two distinct simulations: binary ant colony algorithm with simulated annealing (BACA-SA) and binary ant colony algorithm with hill climbing (BACA-HC). Our primary objective was to assess the performance of these algorithms in solving the sensor placement problem. To ensure statistical significance, we conducted 100 repetitions of the experiments for each algorithm.  
Initially, we established a set of common parameters for the BACA, SA, and hill climbing algorithms, with 100 iterations and 100 ants being consistent across all tests. We maintained a sensing range of four units for the sensors in all experiments. However, we introduced variability in other parameters to accommodate different scenarios, which led to three distinct experiments conducted in sensing fields of varying sizes: 300, 600, and 1000 units.  
Surprisingly, the results revealed a remarkable trend that contradicted our initial assumption. The BACA-HC approach consistently outperformed the BACA-SA in achieving better field coverage across all three experiments.  
The consistent outperformance of BACA-HC over BACA-SA, as showcased in our experimental results, has significant implications for the efficacy of these algorithms in solving the sensor placement problem. The incorporation of hill climbing within the binary ant colony optimization process appears to enhance the algorithm's capability to explore the search space and discover better sensor configurations, leading to superior coverage results.  
While the initial assumption posited BACA-SA as the preferred approach, the experimental evidence convincingly supports the advantage of BACA-HC in terms of achieving better field coverage. This unexpected outcome warrants a reevaluation of our initial assumptions and provides valuable insights for future research in the field of sensor placement optimization. Further investigations into the specific factors contributing to the performance disparities between the two algorithms can pave the way for optimizing both BACA-HC and BACA-SA in different applications and scenarios.
## 4 Results  
### The Synthetic NDVI  
We assessed the accuracy of the synthetic NDVI in the four test regions for each year, using the independent Landsat images. The statistics shown in Figure 3 demonstrate that the synthetic NDVI has a close correspondence with the NDVI derived from the original Landsat images, with R\({}^{2}\) ranging from 0.82 (\(p<0.001\)) in 2000 to 0.93 (\(p<0.001\)) in 2011, and a root mean square error (RMSE) ranging from 0.02 in 2012 to 0.09 in 2004.