esa-sceva/satcom-chunk-collection huggingface dataset:
# High Reliable Uplink Transmission Methods in GEO-LEO Heterogeneous Satellite Network
### Contributions of This Work  
Focusing on the uplink multi-user transmission in a GEO-LEO heterogeneous satellite network, we first develop uplink NOMA for LEO satellites and discuss the outage probability performance of NOMA and cooperative OMA. Due to the weak channel condition, a beam-edge user can connect the LEO satellite by reusing the resource block allocated to the beam-center user. On the contrary, the beam-edge user can choose to transmit signals to the GEO satellite using an independent uplink channel. To provide insights for these two schemes, analytical expressions of outage probability are further discussed. Simulation results are shown to provide instructions on system design.  
The main contributions of the paper can be summarized as follows:  
* By fully studying the characters of a heterogeneous satellite network, we find out that the beam-edge user and beam-center user still have channel gain difference, although the same equipment sets are assumed to be used by both users. Then, we propose uplink NOMA and derive analytical expressions of the outage probability with respect to NOMA and GEO-LEO cooperative OMA.
* Considering the realistic transmission condition of the beam-edge and beam-center users, the outage probability is also analyzed with channel gain ordering. Further, the diversity order is derived, which can help to assess the outage probability performance intuitively.
* Through the expressions of outage probability and adequate simulation results, we provide practical instructions on designing high reliable uplink transmission methods in heterogeneous GEO-LEO satellite networks.  
The rest of the paper is organized as follows. Section 2 introduces the system model of the heterogeneous satellite network. Then, uplink transmission models for NOMA and cooperative OMA are provided. In Section 3, the expressions of outage probability are derived and the outage probability is also analyzed under the condition of channel ordering. The diversity order of the NOMA scheme is obtained in Section 4. The simulation results are provided in Section 5, and the paper is concluded in Section 6. Appendix A lists the symbols and acronyms used in this paper.  
Notation: The statistical expectation and the probability are presented by \(\mathbb{E}[\cdot]\) and \(\mathbb{P}(\cdot)\), respectively, while \(\mathcal{CN}(a,b)\) denotes the distribution of circularly symmetric complex Gaussian (CSCG) random variables with mean \(a\) and covariance \(b\).
## 4 Discussion  
Several research groups report on sub-THz detection system based on YBCO Josephson junction detectors in a planar antenna configuration. [PERSON] et al. integrated a ring-slot [12; 34; 43] and log-periodic [44] planar antennas with relatively low input impedance (\(\sim\)30 \(\Omega\)) for HTSC YBCO step-edge Josephson junctions, achieving a voltage responsivity \(\eta(V)\approx 3.5\) kV\(\cdot\)W\({}^{-1}\) and an estimated NEP of \(\sim\)1 \(\times\)10\({}^{-12}\) W\(\cdot\)Hz\({}^{-1/2}\) at 60 K. Additional structures were presented by [PERSON] et al. [45] and [PERSON] et al. [46; 47]. They were able to obtain planar antennas with low input impedance (\(\sim\)13 \(\Omega\)) combined with YBCO grain-boundary Josephson Junctions detectors, using coplanar waveguide feeding. In our previous report, we investigated a rounded bow tie antenna based on a bicrystal Josephson junction detector [25], with neither a Si lens nor an impedance matching unit. With that system, we obtained a voltage responsivity of \(\eta(V)\approx 1\) kV\(\cdot\)W\({}^{-1}\) and an estimated NEP of \(\sim\)6 \(\times\) 10\({}^{-12}\) W\(\cdot\)Hz\({}^{-1/2}\) at 60 K. In addition, we reported on a step-edge junction detector with a Si lens, without a matching unit, obtaining a voltage responsivity of \(\eta(V)\approx 15\) kV\(\cdot\)W\({}^{-1}\)[18]. In comparison with previous results, the performance obtained with impedance matching technique presented in this study are significantly superior, showing an estimated NEP of \(\sim\)1 \(\times\) 10\({}^{-13}\) W\(\cdot\)Hz\({}^{-1/2}\) and voltage responsivity of \(\eta(V)\approx 55\) kV\(\cdot\)W\({}^{-1}\).  
Figure 16: I-V (**top figure**) and dI/dV (**bottom figure**) characteristics measured at 60 K for an antenna with a 5.4 μm wide matching strips, with a Si lens.
# Distributed Localization of Non-Cooperative Targets in Non-Coplanar Rendezvous Processes
## 3 Configuration Optimization
### Improved NSGA-III Algorithm
#### 3.3.2 Cosine-like Mutation Distribution Index  
In polynomial mutation, the mutation distribution index influences the degree of similarity between offspring and parents after mutation. Traditional NSGA-III algorithms apply a constant mutation distribution index, which does not adapt dynamically to the population's evolution. This study proposes a mutation distribution index that varies in a cosine-like pattern. As shown in Equation (16), the variation distribution index varies in the interval \([\eta_{\min},\eta_{\max}]\) and decreases gradually with the number of iterations. The cosine-like mutation distribution index in the \(i\)-th generation is as follows:  
\[\eta_{i}=\eta_{\min}+\frac{(\eta_{\max}-\eta_{\min})}{2}\times\left[1+\cos \left(\pi(\frac{i}{n}+1)\right)\right] \tag{16}\]  
where \(\eta_{i}\) is the variation distribution index of the \(i\)-th generation population, \(\eta_{\min}\) and \(\eta_{\max}\) are the minimum and maximum variation distribution indices, \(i\) is the current generation, and \(n\) is the maximum number of iterations.  
In the algorithm's early stage, the mutation distribution index is minimized, so the offspring diverge more significantly from their parents. At the same time, the change in the mutation distribution index in the early stages is relatively slow, which allows the algorithm to explore the solution space more fully and escape from local optimal solutions in the early stage. In the middle stage, the index changes more rapidly, focusing the search around the optimal solution and accelerating convergence. In the later stage, the index changes slowly again, with a large mutation distribution index causing offspring to more closely resemble their parents, supporting refined local optimization around the optimal solution. This overall variation trend is illustrated in Figure 4.  
In summary, the cosine-like mutation distribution index proposed in this paper improves the slow convergence issue caused by the NDX. It ensures that the algorithm can thoroughly explore the solution space early on, refine searches near the optimal solution in later stages, and ultimately accelerate convergence so that the solutions quickly approach the real Pareto front.
# Evaluating Slope Deformation of Earth Dams Due to Earthquake Shaking Using MARS and GMDH Techniques
## 3 Methodology
### Multivariate Adaptive Regression Splines (MARS)  
MARS is a non-parametric regression method, developed by [PERSON] (1984). The model takes the form of development in the production of basis spline functions, where a number of basic functions (BFs) and the parameters related to each function (product degree and knot location) are automatically determined by the data. The main difference between MARS and non-adaptive spline is that in the former, knots are not pre-determined; they are placed at the best possible locations by a search method. In the case of having only one knot, any possible location in the definition span of an explanatory variable, \(Z=[a\;b]\in R\) (where a and b are MARS nodes, which should be designed during model development) is considered, and after fitting, a model with a minimum value of sum square error (SSE) is selected. In the case of having two knots, finding the best location for this pair of points requires more calculations and, a fortiori, to have n knots, finding the best n number set of points is very difficult  
Figure 3: The general trend of implementing a GMDH model.  
and complicated. The required number of knots is also not known. In the MARS method, the number and location of the required knots are determined through a step-by-step forward-backward process.  
BFs are a set of functions that can demonstrate information existing in one or more variables (Figure 4). MARS uses the expansion of piecewise linear basis functions in the forms of \((x-t)+\) and \((t-x)+\), also called hinge functions or hockey sticks, and \"\(+\)\" means the positive section, thus [85]:  
\[(t-x)_{+}=\left\{\begin{array}{ll}t-x,&\text{if }x\ <t,\\ 0,&\text{otherwise.}\end{array}\right.,\ (x-t)_{+}=\left\{\begin{array}{ll}x-t,& \text{if }x\ >t,\\ 0,&\text{otherwise.}\end{array}\right. \tag{6}\]  
where \(x\) is the variable, and \(t\) is the knot constant. The MARS algorithm makes a regression model function according to Figure 4.  
\[f(x)=\sum_{i=0}^{M-1}\beta_{i}h_{i}(x) \tag{7}\]  
where \(x=[x_{1}x_{2}...x_{p}]\)T is a vector of explanatory variables and p is the dimension of input variables, \(\beta_{i}h_{i}(x)\) represents terms of the model that are automatically selected by the algorithm, \(h_{i}(x)\) signifies the basis functions in MARS, and M is the number of terms in the model. Furthermore, assume that \((x_{i},\)\(y_{i})\), \(i=1,..,n\) forms the observations set. Finally, there are two modes for Equation (7). If the defined functions are considered as the basis functions \(h_{i}(x)\), the predictive model obtained by Equation (7) is called non-adaptive; on the other hand, if these functions are determined by modeling data set \((x_{i},y_{i})\), it is called adaptive, like the MARS model.
## 5 Numerical Results  
### Simulation Parameters  
The average power of the candidate meta-signals and current signals on the ground is set to \(-154.0\) dBW by assuming an ideal HPA with an average output power of \(15.0\) dBW and a total propagation loss of \(169.0\) dB, accounting for path loss, implementation losses, and antenna gains. The power ratio between the E1C and E1D candidates was set to \(1:1\). The two-sided front-end filtering bandwidth \(B\) is \(51.150\) MHz. The integration time was \(1.955\)\(\upmu\)s. The number of Monte-Carlo runs \(J\) for each data point is \(200,\!000\). No smoothing for the TOA estimation is assumed. The delay \(\tau_{0}\) was randomized in an interval \([-\frac{1}{3}T_{\text{C,BPSK-R(1)}},+\frac{1}{3}T_{\text{C,BPSK-R(1)}}]\) to introduce scalloping losses [21]. Non-linear effects introduced by the HPA and an analog-to-digital converter are beyond the scope of this paper. The correlator banks have a spacing of \(\Delta_{n}=T_{\text{C,}n}/250\) and \(N_{c}=501\). The TOA estimation of \(\hat{\tau}_{0}\) uses a parabolic interpolation around the highest value of the absolute and real parts of the CCF vector \(\bar{\mathbf{q}}\) using an interpolation equation from [22].
# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 3 AI-Driven Optimization of MIMO Isolation Using SRRs
### AI Model 3 for Number and Position Prediction
A minor frequency shift of approximately 10-20 MHz is observed, likely attributable to calibration inaccuracies or slight substrate variations in a  
Figure 21: Comparative analysis of simulated and predicted S11 and S12 parameters for the SRR configuration with ns = 2 and \(\mathrm{yp}\) = 2.8 mm. Figure 22: Fabricated prototype of the MIMO antenna array measured in an anechoic chamber. real measurement context. Moreover, amplitude deviations up to \(\pm 3\) dB are visible in some frequency ranges, simulating practical variations due to dielectric losses, metallization imperfections, or connector mismatches. Nevertheless, the return loss remains consistently below \(-10\) dB over the operating bandwidth, confirming the model's reliability and the effectiveness of the predictive design. In Figure 24, the transmission coefficient remains well below \(-40\) dB around \(26\) GHz in both the simulated and measured cases. This confirms the strong isolation achieved through the inclusion of metamaterial SRR structures. Slight amplitude degradation is also observed. Despite these variations, the low coupling levels validate the proposed decou  
Figure 23: Comparison of simulated vs. measured S11 parameters for the MIMO antenna array with decoupling technique. Figure 24: Comparison of simulated vs. measured S12 parameters for the MIMO antenna array with decoupling technique. pling mechanism and confirm that the AI-guided optimization maintains its robustness against fabrication-related variations. Overall, the comparative study demonstrates that the proposed MIMO antenna system exhibits stable and reliable performance, both in terms of S11 and S12 coefficients, even under realistic variations. This confirms the efficiency of integrating machine learning techniques with metamaterial-based design strategies to develop robust and high-performance MIMO solutions suitable for 5G millimeter-wave applications.
## Appendix A Computation of User Acceleration from GNSS Measurements  
The user accelerations can be obtained from GNSS using either twice differentiated phase measurements or single differentiated Doppler measurements. Due to the robustness of frequency lock loop in harsh environments, the Doppler measurements were used during this work. It follows from [14,15] that the differentiated Doppler observations (\(\dot{D}_{i}\)) are related to the user acceleration as:  
\[\lambda_{i}\dot{D}_{i}=u_{i}\big{(}a_{rx}-a_{sat}^{i}\big{)}+\frac{1}{\rho_{i}} \Big{[}\big{|}v_{sat}^{i}-v_{rx}\big{|}^{2}-\dot{\rho}_{i}^{2}\Big{]}+c\ddot{d} t+\varepsilon \tag{1}\]  
where \(u_{i}\) is the unit vector along the \(i^{\text{th}}\) satellite; \(a_{rx}\) and \(v_{rx}\) are the user acceleration and velocity vectors, respectively; \(a_{sat}^{i}\) and \(v_{sat}^{i}\) denote acceleration and velocity of the \(i^{\text{th}}\) satellite; \(\rho_{i}\), \(\dot{\rho}_{i}\) and \(\lambda_{i}\) are the pseudorange, pseudorange rate and wavelength of the \(i^{\text{th}}\) satellite measurement, and \(c\ddot{d}t\) is the rate of change of clock drift. \(\dot{D}_{i}\) is the rate of change of Doppler and \(\varepsilon\) is the total error associated with the differential Doppler measurement. The instantaneous rate of change of the Doppler is obtained as:  
\[\dot{D}_{i}(t)=\frac{D_{i}(t+\Delta t)-D_{i}(t-t\Delta t)}{2\Delta t} \tag{2}\]  
where \(D_{i}(t+\Delta t)\) is the \(i^{\text{th}}\) Doppler measurement at time \(t+\Delta t\) and \(D_{i}(t-\Delta t)\) is the \(i^{\text{th}}\) Doppler measurement at time \(t-\Delta t\). Thus, in a real time implementation, there is a latency of one GNSS epoch.  
Using above equations, the user acceleration can be obtained using least-squares as:  
\[\begin{bmatrix}\lambda_{1}\dot{D}_{1}+u_{1}a_{sat}^{1}-\frac{1}{\rho_{1}} \big{[}|v_{sat}^{1}-v_{rx}|^{2}-\dot{\rho}_{1}^{2}\big{]}\\ \vdots\\ \lambda_{N}\dot{D}_{N}+u_{N}a_{sat}^{N}-\frac{1}{\rho_{N}}\big{[}|v_{sat}^{N} -v_{rx}|^{2}-\dot{\rho}_{N}^{2}\big{]}\end{bmatrix}= \tag{3}\]  
The use of least-squares requires a minimum of four Doppler observations (or five if GLONASS is also used in addition to GPS) to be able to use the proposed scheme and thus imposes a limitation.
## 3 Optical Error Analysis
### Optical Errors of FPI
#### 3.1.3 Non-Flatness Error of FPI
By combining these two defects, the total error interferogram can be expressed as follows:  
\[\left\{ \begin{aligned} & I_{2-flat}(\Delta)&=I_{0}( \Delta)*\left[a_{0}\delta(\Delta)+\sum\limits_{m=1}^{\infty}a_{m}\delta( \Delta\pm m\Delta_{FPI})*Defect_{m}(\Delta)\right]\\ &=a_{0}I_{0}(\Delta)+\sum\limits_{m=1}^{\infty}a_{m}I_{0}(\Delta \pm m\cdot\Delta_{FPI})*Defect_{m}(\Delta)\\ & Defect_{m}(\Delta)&=g_{m}(\Delta)*p_{m}(\Delta) \end{aligned} \right.. \tag{17}\]  
Compared to Equation (6), the response function of the frequency-shifted component becomes \(a_{m}\delta(\Delta\pm m\Delta_{FPI})*Defect_{m}(\Delta)\). If the width of \(Defect_{m}(\Delta)\) remains relatively small, the combined effects of these two defect types can be neglected; otherwise, an enhanced model must be developed to account for these imperfections. In practical FPI manufacturing, non-parallelism and surface irregularities in fixed-spacing FPIs can typically be quantified through metrological techniques, allowing the theoretical profile of \(Defect_{m}(\Delta)\) to be treated as a priori knowledge. The term \(a_{m}Defect_{m}(\Delta)\) can thus be expressed in the vector form as \(a_{m\text{-}Defect}\in\mathbb{R}^{3}\), where \(3\) denotes the dimensionality of \(a_{m\text{-}Defect}\) determined by the width of \(Defect_{m}(\Delta)\). This vectorized representation enables the simulation of the convolutional effects induced by \(Defect_{m}\) through the substitution of \(a_{m\text{-}Defect}\) for the original coefficient \(a_{m}\) in the system matrix \(A\) of Equation (8).
## 3 Methodology
### Lidar Point Cloud Mapping  
Constructing an accurate scene map based on tightly coupled pose-compensated Lidar data is fundamental and crucial for parking slots identification and annotation. In this section, the initial pose value is integrated with the polarized matrix of point cloud to compute the front-end Lidar odometry. Subsequently, the polarized Lidar odometry and loop detection are incorporated as factors into the iSAM2 graph optimizer [39]. As depicted in Figure 6, the green squares denote the IMU sequence data, the blue circles represent the Lidar sequence data, and the orange bars indicate the vehicle pose state nodes. First, as illustrated in Section 3.1, the IMU data between Lidar frames is pre-integrated, as indicated by the green arrow, to compensate for the Lidar frame pose, resulting in the compensated Lidar frame pose \(\mathbf{p}_{n}\). The subsequent content of this section elaborates on this process. Secondly, polarization matrix conversion and keyframe extraction are performed on these compensated Lidar frames. The order of these steps can be interchanged; for instance, polarization matrix conversion can be applied to each frame before extracting keyframes,or keyframes can be extracted first, followed by calculating the polarization matrix for the selected frames. Lines within circles represent the extracted Lidar keyframes. In the third step, Lidar odometry is calculated from the extracted polarized features of the keyframes, generating state nodes in the Lidar odometry series. Specifically, polarized edge and planar features are computed for the extracted keyframes, and scan-matching is performed to establish the keyframe state nodes, which are represented by the orange elements in Figure 6. Finally, in those state nodes, the polarized Lidar odometry factor \(\Delta\mathbf{T}_{n-1,n}\) and loop closure factor \(\Delta\mathbf{T}_{n}^{\text{ICP}}\), constrained by the distance threshold \(d_{ni}\), are all sent into iSAM2 to construct a point cloud map. This process achieves optimization for smoothing the Lidar poses, followed by mapping the pose data from all historical keyframes to the global coordinate system for reconstruction, thereby generating the 3D point cloud map of the parking scene.
###### Abstract  
This paper presents an ultra-wideband, low insertion loss, and high accuracy 6-bit digital step attenuator (DSA). To improve the accuracy of amplitude and phase shift of the attenuator, two innovative compensation structures are proposed in this paper: a series inductive compensation structure (SICS) designed to compensate for high frequency attenuation values and a small bit compensation structure (SICS) intended for large attenuation bits. Additionally, we propose insertion loss reduction techniques (ILRTs) to reduce insertion loss. The fabricated 6-bit DSA core area is only 0.51 mm\({}^{2}\), and it exhibits an attenuation range of 31.5 dB in 0.5 dB steps. Measurements reveal that the root-mean-square (RMS) attenuation and phase errors for the 64 attenuation states are within 0.18 dB and 7\({}^{\circ}\), respectively. The insertion loss is better than 2.54 dB; the return loss is better than \(-\)17 dB; and the input 1 dB compression point (IP1 dB) is 29 dBm at IF 12 GHz. To the best of our knowledge, this chip presents the highest attenuation accuracy, the lowest insertion loss, the best IP1 dB, and a good matching performance in the range of 2-22 GHz using the 0.25 \(\upmu\)m GaAs p-HEMT process.  
digital step attenuator; GaAs; insertion loss reduction technique; series inductive compensation structure; small bit compensation structure
## 9 Conclusions and Future Work
As previously mentioned, the performance of the deep learning model can be improved with a deep fine-tuning with more data or by using a new architecture or techniques that take more into account the semantics of a sentence, such as SBERT [24] or Semantic-Aware BERT [44]. In addition to these already developed technologies, the semantic similarity field is an active and open research field, and much can be done to solve this not trivial task. Another possible new future work could be the introduction of explainability, i.e., identify where in the user's response the automatic scoring system has detected the presence of the concept, similarly to what has been done in [45; 46; 47]. This new feature will even increase the time that human tutors invest in correcting tests and, at a glance, these people could validate the prediction given by the classifier. Moreover, a more intuitive explanation of the answers by our system could prevent unwanted or discriminatory behaviour [48; 49; 50]. Finally, new architectures and pre-trained language models based on GPT [51] have been introduced in the last few years. Since they obtained promising results in question answering tasks, we aim to investigate the use of these models in our context. Conceptualization, A.E.G., L.P. and I.S. ; Software, N.A. ; Validation, M.O. ; Investigation, N.A. and L.P.; Resources, N.A. ; Data curation, L.S. ; Writing--original draft, N.A. and L.P.; Writing--review & editing, I.S. All authors have contributed equally to the manuscript. All authors have read and agreed to the published version of the manuscript. This work was supported by the MIUR \"Fondo Departments of Excellence 2018-2022\" of the DII Department at the University of Brescia, Italy, EU H2020 project AIPlan4 EU (GA n. 101016442) and EU ICT-48 2020 project TAILOR (No. 952215). The _IBM Power Systems Academic Initiative_ substantially contributed to the experimental analysis. The source code of this work is available at the following GitHub repository: [[https://github.com/nicolarici/ASAG-IT-WS](https://github.com/nicolarici/ASAG-IT-WS)]([https://github.com/nicolarici/ASAG-IT-WS](https://github.com/nicolarici/ASAG-IT-WS)) (accessed on 8 August 2023). The data used for training and testing our system could not be made available (except for some selected sampled) for privacy reasons. The authors declare no conflict of interest.