esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Low-Dose Computed Tomography (LDCT) has emerged as a valuable imaging modality in modern clinical practice. Historically, the prevailing notion has been guided by \"the deeper, the better\", leading to the development of increasingly complex LDCT networks. However, clinical practice often requires rapid or real-time reconstruction of CT images. This paper proposes a simple structure and computationally efficient CNN model for sparse-view LDCT reconstruction. The proposed model offers versatility by seamlessly integrating the classic FBP module for direct use in sparse-view CT reconstruction. In addition, it can be incorporated into existing dual-domain reconstruction frameworks as a generic sinusoidal domain module, enhancing their computational efficiency. The experimental results show that the proposed network achieves a balance between computational burden and reconstruction quality at several sparse levels. Despite its computational simplicity, it exhibits a competitive reconstruction quality at lower sparse levels (1/2 and 1/4 radiation dose) and even outperforms sophisticated baseline models at higher sparse levels (1/8 and 1/15 radiation dose).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Ours** & **SDS-Net** & **Res-Unet** \\ \hline Number of parameters (k) & 52.12 & 416.64 & 2334.72 \\ \hline FLOPs (G) & 10.931 & 87.595 & 99.655 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computational efficiency of the three methods.  
**Author Contributions:** Conceptualization, B.Y.; methodology, F.G.; software, F.G.; validation, F.G. and H.F.; formal analysis, F.G. and Z.Y.; investigation, F.G.; resources, C.L.; data curation, L.Y.; writing--original draft preparation, F.G.; writing--review and editing, B.Y.; visualization, C.L.; supervision, B.Y.; project administration, B.Y.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
This document is the result of a research project funded by the Sichuan Science and Technology Support Program (2021 YEQ0003, 2023 YFSY0026, 20223 YFH0004).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. These data can be found here: [[https://github.com/GFP-1011/efficientLDCT](https://github.com/GFP-1011/efficientLDCT)]([https://github.com/GFP-1011/efficientLDCT](https://github.com/GFP-1011/efficientLDCT)).  
**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 5 Applications in Human-Robot Interaction
### Teleoperation  
VR and AR systems are suitable solutions for enabling remote control of robots in scenarios that are inaccessible or dangerous for humans. Relevant considerations in the design of effective teleoperation systems highlighted by the reviewed articles include intuitiveness, low latency, and situational awareness [47,52]. In this context, ref. [47] propose an intuitive VR interface for enabling multiple users simultaneously immersed in a synchronized VR scene to define a robot's positions and actions. A solution to increase the human operators' situational awareness in teleoperation tasks is proposed in [52]. For this, the authors of [52] present the Tele robotic Operation based on an Auto-reconstructed Remote Scene (TOARS) algorithm, which uses deep learning methods to detect objects and their physical properties from point cloud information generated by a Kinect v2. This information is subsequently rendered as virtual objects with physical properties (e.g., friction and weight) in the Unity-based simulation to provide a more immersive interaction. In addition, the authors provide an alternative for reducing communication latency produced by rosbridge_ suite. For this, they reduce the amount of transferred data over the network generated by Kinect v2. Moreover, a TCP/IP connection between Unity and ROS is implemented to improve the communication performance in comparison with ROS#. An AR solution using gamepads for the teleoperation of industrial robots is proposed and evaluated in [79]. Results from usability testing performed in [79] suggest that gamepads can provide higher usability for teleoperation tasks than conventional teaching pendant devices, regardless of the user's previous experience in robotics and/or AR technology. Unlike most of the reviewed articles, which use Unity as the main game engine platform, ref. [80] proposes a VR solution for robot teleoperation developed with Unreal Engine version 4 for high-fidelity visualization, denoted as Vicarios. The Vicarios framework aims to improve situational awareness and user performance in remote robot manipulation tasks by implementing a method denoted as viewpoint-independent motion mapping. In this approach, operators can choose between different viewpoints without worrying about remapping their motions to each viewpoint. Vicarios uses the robcogiai [66] tools to import 3D models of robots using the URDF, then connects the Unreal Engine to ROS. Two MR-based hybrid 3D/2D visualization paradigms, denoted as MR-3 DSV (integrating 3D stereoscopic vision and monocular RGB cameras) and MR-3 DPC (integrating 3D point cloud and monocular RGB cameras), are proposed in [81]. These paradigms aim to provide more immersive and intuitive teleoperation for non-skilled operators. The results from [81] indicate that using hybrid 3D/2D vision paradigms can reduce the training effort, completion time, and cognitive workload compared with
## 3 Results and Discussion
### Analysis of CIM's Potential for the Calculation of the Indicators
#### 3.2.5 The Indicator to Measure the Used Density and Flexibility  
This indicator encourages land-use efficiency, diversity of uses, and increase density through the building height. It has been promoted by SBToolPT Urban, SNtool, and LEED ND, while addressed by ISO 37120, and SDGs. Therefore, this indicator is recognized as a prioritized environmental concern. The SBToolPT Urban Guide indicates two calculation parameters for this indicator: the Percentage of Land Use Efficiency and the Percentage of Areas with Flexibility of Use. The first one is calculated by the ratio of the number of inhabitants and the building footprint area. The second is calculated by the ratio of the areas with flexibility of use and the project area. The study found out both parameters can be calculated using the digital model, once the criteria can be assessed by the model, and applying a Dynamo routine to perform the calculations.
# Multivariate Time Series Data Prediction Based on ATT-LSTM Network
## 4 Experiment
### Experimental Results and Analysis
#### 4.3.2 Model Prediction  
The experiment was divided into three parts. Firstly, we trained the ATT-LSTM model on the training dataset. Then, we further trained and adjusted the parameters on the validation set, made a preliminary evaluation of the model, and finally tested on the test set. Evaluate the model's predictive ability according to the two evaluation indicators of MAE and RMSE. After training and testing, we got the final ATT-LSTM model. We took a part of the continuous time nodes in the test set of the two data sets, used the trained ATT-LSTM model. The model's predictive ability is shown in Figure 7.  
Figure 7: (**a**) NASDAQ100 dataset model comparison chart. (**b**) PM2.5 dataset model comparison chart.  
model to predict this part of the data, and output the prediction result. Then, we compared the actual data with the predicted results. The two data sets' prediction comparison results are shown in Figure 8a,b.  
Figure 8 clearly and directly shows the prediction effect of the ATT-LSTM model. It can be seen from Figure 8 that the overall model still has an error between plus and minus 0.5-2. Figure 8a is the prediction result of some continuous data on the Nasdaq 100 dataset and the comparison of actual data. This part of the data has two small fluctuations in the early period, and the latter is stable data, and the overall data value has not changed much. The prediction result of the model on the data conforms to the trend of data fluctuation. In the later stage of stable data, the prediction result of the model has an error of about plus or minus one. Figure 8b shows the prediction results of some continuous data on the Beijing PM2.5 dataset and comparing actual data. The data of this part fluctuate significantly as a whole, with both significant mutations and multiple small mutations. The prediction results of the model on this part of the data are in line with the trend of data fluctuations as a whole, indicating that the model can successfully capture the mutation information in the data set and learn.  
Figure 8: (a) Nasdaq100 dataset prediction results. (b) Beijing PM2.5 dataset prediction results.
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Code** & **Sitename** & **Lat** & **Lon** & \begin{tabular}{c} **Elevation** \\ **(masl)** \\ \end{tabular} & **IGBP Code** & **Metabolism** & **Homogeneity** \\ \hline CZ-Stn & Stitna & 49.036 & 17.970 & 580 & DBF & C3 & Homo \\ \hline CZ-wet & Trebon (CZECHWET) & 49.025 & 14.770 & 426 & WET & C3 & Hetero \\ \hline DE-Akm & Anklam & 53.866 & 13.683 & - & WET & C3 & Hetero \\ \hline DE-Geb & Gebesee & 51.100 & 10.915 & 161 & CRO & C3 & Homo \\ \hline DE-Gri & Grillenburg & 50.950 & 13.513 & 377 & GRA & C3 & Hetero \\ \hline DE-Hai & Hainich & 51.079 & 10.453 & 467 & DBF & C3 & Homo \\ \hline DE-HoH & Hohes Holz & 52.085 & 11.219 & 220 & DBF & C3 & Hetero \\ \hline DE-Hte & Huetelmoor & 54.210 & 12.176 & 2 & WET & C3 & Homo \\ \hline DE-Hzd & Hetzdorf & 50.964 & 13.490 & 385 & DBF & C3 & Hetero \\ \hline DE-Kli & Klingenberg & 50.893 & 13.522 & 481 & CRO & \begin{tabular}{c} Rotation: C3 \\ 2018, C4 2019 \\ \end{tabular} & Homo \\ \hline DE-Obe & Oberänerburg & 50.787 & 13.721 & 755 & ENF & C3 & Homo \\ \hline DE-RuR & Rollesbroich & 50.622 & 6.304 & 515 & GRA & C3 & Hetero \\ \hline DE-RuS & Selhausen Juelich & 50.866 & 6.447 & 103 & CRO & C3 & Homo \\ \hline DE-RuW & Wustebach & 50.505 & 6.331 & 624 & ENF & C3 & Homo \\ \hline DE-Tha & Tharandt & 50.963 & 13.565 & 403 & ENF & C3 & Homo \\ \hline DK-Sor & Soree & 55.486 & 11.645 & 52 & DBF & C3 & Hetero \\ \hline ES-Abr & Albuera & 38.702 & \(-\)6.786 & 280 & SAV & C3 & Homo \\ \hline ES-Agu & Aguamarga & 36.940 & \(-\)2.033 & 203 & OSH & C3 & Hetero \\ \hline ES-LM1 & \begin{tabular}{c} Majadas del \\ Tietar North \\ \end{tabular} & 39.943 & \(-\)5.779 & 264 & SAV & C3 & Homo \\ \hline ES-LM2 & \begin{tabular}{c} Majadas del \\ Tietar South \\ \end{tabular} & 39.935 & \(-\)5.776 & 269 & SAV & C3 & Homo \\ \hline FI-Hyy & Hyytiala & 61.847 & 24.295 & 190 & ENF & C3 & Homo \\ \hline FI-Let & Lettosuo & 60.642 & 23.960 & 124 & ENF & C3 & Homo \\ \hline FI-Sii & Sikaneva & 61.833 & 24.193 & 166 & WET & C3 & Hetero \\ \hline FI-Var & Variro & 67.755 & 29.610 & 395 & ENF & C3 & Homo \\ \hline FR-Aur & Aurade & 43.550 & 1.106 & 244 & CRO & C3 & Homo \\ \hline FR-Bil & Bilos & 44.494 & \(-\)0.956 & 39 & ENF & C3 & Hetero \\ \hline FR-EM2 & Estrees-Mons A28 & 49.872 & 3.021 & 85 & CRO & C3 & Homo \\ \hline FR-FBn & Font-Blanche & 43.241 & 5.679 & 434 & MF & C3 & Homo \\ \hline FR-Fon & \
\begin{tabular}{c} Fontainebleau- \\ Barbeau \\ \end{tabular} & 48.476 & 2.780 & 112 & DBF & C3 & Homo \\ \hline FR-Gri & Grignon & 48.844 & 1.952 & 123 & CRO & C4 & Homo \\ \hline FR-Hes & Hesse & 48.674 & 7.065 & 323 & DBF & C3 & Homo \\ \hline FR-Lam & Lamasquere & 43.496 & 1.238 & 179 & CRO &
\begin{tabular}{c} Rotation: C3 \\ 2018, C4 2019 \\ \end{tabular} & Hetero \\ \hline FR-LGt & La Guette & 47.323 & 2.284 & 157 & WET & C3 & Hetero \\ \hline FR-Mej & Mejusseaume & 48.118 & \(-\)1.796 & 39 & GRA & C4 & Homo \\ \hline IT-BFt & Bosco Fontana & 45.198 & 10.742 & 42 & DBF & C3 & Homo \\ \hline IT-BCi & Borgo Cioffi & 40.524 & 14.957 & 9 & CRO & C4 & Hetero \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont.
# Candidate Waveforms for ARoF in Beyond 5G
## 2 Requirements to Waveforms for Beyond 5G  
In this section, different key requirements associated with waveform formats for mmWave transport over ARoF systems are reviewed. Three main scenarios are distinguished: (i) general wireless communications, (ii) mmWave wireless communications, and (iii) ARoF systems. As shown in Figure 2, any requirement considered through this paper is connected with one or several 5G requirements, and thus they jointly allow a good comparison of candidate waveforms for 5G and beyond.
### SEVIRI Errors of Omission and Commission
#### 5.1.2 SEVIRI's Background Thermal Characterization  
In combination, the absence of a SEVIRI background temperature measurement and/or the failure of a potential SEVIRI fire pixel to breach the contextual background thresholds have a moderate impact on SEVIRI's active fire detection performance. Regardless of the ROI, less than 2% of the MODIS active fire pixels that SEVIRI failed to detect were located adjacent to a SEVIRI pixel without a defined background temperature, accounting for no more than 10% of SEVIRI's omission rate (Table 2). If SEVIRI did measure the background temperature, however, then between 5% and 17% of the MODIS active fire pixels missed by SEVIRI were located adjacent to a potential SEVIRI fire pixel that could not be confirmed as a true fire pixel since its thermal signal was not sufficiently above the background. In combination, the absence of, and/or vagaries in, the background thermal characterization procedure of the SEVIRI FTA algorithm may have accounted for between 22% and 33% of SEVIRI's omission rate with respect to MODIS in any ROI.
## 3 Energy Efficient Joint Power Control and User Association Optimization Algorithm
### Joint Optimization Algorithm
#### 3.2.1 The Lower Level Problem: Power Control  
Problem P3 is a non-convex optimization problem. In this paper, we approximate the objective function into a convex function in each iterative step. Then, Problem P3 can be transformed into a convex optimization problem. The transformation process is shown as follows.  
**Lemma 2**.: _There exists \(\log_{2}\left(1+\gamma\right)\geq\alpha\log_{2}\left(\gamma\right)+\beta\), where \(\alpha=\gamma^{*}/1+\gamma^{*},\beta=\log_{2}\left(1+\gamma^{*}\right)-\left( \gamma^{*}/1+\gamma^{*}\right)\log_{2}\left(\gamma^{*}\right)\). If only if \(\gamma=\gamma^{*}\), then the inequality can take the equal sign. The proof can be seen in Reference [38]._  
According to Lemma 2, the equation \(f\left(p\right)\geq\sum\limits_{j}\sum\limits_{k}x_{jk}\ln\left(\overline{a}_ {jk}\right)-\omega P_{tot}=\overline{f}\left(p\right)\) is obviously available, where \(\overline{a}_{jk}\left(q\right)=\alpha_{jk}\log_{2}\left(\gamma_{jk}\left(q \right)\right)+\beta_{jk}\).  
Since the _log-sum-exp_ function in the objective function of Problem P5 is proven to be a concave function [39], the non-convex optimization Problem P3 can be transformed into Problem P5. Note that Problem P5 is a convex optimization and can be solved by the interior point method.  
\[P5: \max\limits_{q}\overline{f}\left(q\right) \tag{19}\] \[s.t. 2^{q_{j}}\leq p_{j,\max} \tag{20}\]  
Here, \(q_{j}=\log_{2}p_{j}\). To sum up, the power control algorithm based on approximately iterative method (Algorithm 3) can be designed as follows in this paper. In Algorithm 3, the objective function increases monotonously with the iteration number increasing and finally converges to a fixed value, which can be seen in the simulation later. The gap between the solution of the proposed algorithm and the optimal solution are described in [40; 41].  
```
Initialize: iteration number \(n=0\), the maximum tolerance error \(\sigma_{2}\), the flag of convergence \(\xi^{n}\), each BS's transmit power \(p_{j}^{0}=p_{j,\max},\forall j\) while\(\xi^{n}>\sigma_{2}\)do Update \(n=n+1\);  According to \(\gamma_{jk}^{n-1}\), calculate \(\alpha_{jk}^{n-1}\) and \(\beta_{jk}^{n-1}\);  Calculate the optimal solution \(q^{n}\) of Problem P5 by the interior point method;  According to \(q_{j}=\log_{2}p_{j}\), calculate \(p^{n}\) and \(\gamma_{jk}^{n}\);  Calculate \(\Delta\gamma_{jk}^{n}=\left|\gamma_{jk}^{n}-\gamma_{jk}^{n-1}\right|\)/ \(\gamma_{jk}^{n-1},\forall j,k\); Update \(\xi^{n}=\max\limits_{j,k}\Delta\gamma_{jk}^{n}\); endwhile
```  
**Algorithm 3** Power control algorithm based on approximately iterative method
###### Abstract  
The construction industry has witnessed a substantial increase in the demand for eco-friendly and sustainable materials. Eco-friendly concrete containing Ground Granulated Blast Furnace Slag (GGBFS) and Recycled Coarse Aggregate (RCA) is such a material, which can contribute to a reduction in waste and promote environmental sustainability. Compressive strength is a crucial parameter in evaluating the performance of concrete. However, predicting the compressive strength of concrete containing GGBFS and RCA can be challenging. This study presents a novel XGBoost (eXtreme Gradient Boosting) prediction model for the compressive strength of eco-friendly concrete containing GGBFS and RCA, optimized using Bayesian optimization (BO). The model was trained on a comprehensive dataset consisting of several mix design parameters. The performance of the optimized XGBoost model was assessed using multiple evaluation metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R\({}^{2}\)). These metrics were calculated for both training and testing datasets to evaluate the model's accuracy and generalization capabilities. The results demonstrated that the optimized XGBoost model outperformed other state-of-the-art machine learning models, such as Support Vector Regression (SVR), and K-nearest neighbors algorithm (KNN), in predicting the compressive strength of eco-friendly concrete containing GGBFS and RCA. An analysis using Partial Dependence Plots (PDP) was carried out to discern the influence of distinct input features on the compressive strength prediction. This PDP analysis highlighted the water-to-binder ratio, the age of the concrete, and the percentage of GGBFS used, as significant factors impacting the compressive strength of the eco-friendly concrete.  
machine learning eco-friendly concrete; compressive strength; XGBoost; Bayesian optimization 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 4 Signal Processing from RSL to Rainfall Map
### Path-Average Rain Rate Estimation  
After wet/dry classification, baseline determination, and WAA compensation, the rain-induced attenuation in the rainy period is derived, and then the path-average rain rate can be calculated according to Equation (7). The longer the CML link, the greater theattenuation caused by rain on the path, which alleviates the difficulty of low rain rate detection. In addition to the ITU _k-R_ model in Equation (1), rain rate can also be estimated by DSD or data-driven algorithms. [PERSON] et al. (2020) calculated the specific attenuation and rainfall intensity based on DSD, and then the relationship between specific attenuation and rainfall intensity was obtained by using nonlinear fitting. [PERSON] et al. (2020) also confirmed that the coefficients \(a\) and \(b\) in _k-R_ relationship of stratiform and convective rain can be estimated based on local DSD measurement, and compared to the coefficients in the ITU-R P.838 document, the derived coefficients achieved an improved rain rate estimation. DL has outstanding advantages in solving the problem of complex dependent data association mining. It can learn the relationship between the input parameters of multi-attribute features and the real-time output rainfall intensity, so as to directly establish the relationship model between CML total attenuation and rainfall intensity. By taking the advance of DL, [PERSON] et al. (2019) designed a network structure based on gated recurrent unit (GRU) in recurrent neural network (RNN) to evaluate the rainfall retrieved by CMLs. As compared to the traditional ITU model in Equation (1), the GRU-RNN based model has better performance in terms of lower RMSE and bias, but higher complexity and poorer robustness. A trade-off between robustness and performance can be optimized by introducing a time normalization (TN) layer into the GRU-RNN model. The ability of data-driven methods based on GRU to relate attenuation and rain rate, which can overcome the uncertainties in short links, has been proven by ([PERSON] et al., 2020).
# DAB Signal Preprocessing for Passive Coherent Location
## 2 Materials and Methods
### DAB Signal Correlation Properties  
The spectrum of the acquired DAB signal and its amplitude waveform have been depicted in Figures 2 and 3, respectively. The autocorrelation function computed for this signal is shown in Figure 5, with the power levels normalized to the central correlation peak.  
Figure 4: DAB frame organization.  
Figure 5: Autocorrelation of DAB signal.  
Figure 3: Amplitude plot of the DAB signal (one complete frame).  
Besides the peak at \(\tau=0\), several side peaks at multiples of \(T_{\rm F}\) occur. Such ambiguity peaks of the illuminating signal are generally undesirable in radar applications [17; 26; 29]. As shown in [31], these side peaks come from the deterministic parts of the DAB signal, i.e., the PR and FIC symbols in the frame. Therefore, the removal or substitution of these symbols improves the signal's autocorrelation function, as will be demonstrated in the following subsection.
# A Low-Overhead Countermeasure against Differential Power Analysis for AES Block Cipher
## Appendix A Retrieval and Prevention of a 128-Bit Secret Key  
The identification and prevention of an entire 128-bit secret key of AES is shown in Figures 11 and 12, respectively. There are 16 snippets in these figures (Figures 11 and 12). Each snippet determines one byte of a 128-bit key. The original selected values (also shown in a decimal representation in column two of Table 2) for the secret key are 161, 120, 91, 119, 45, 205, 212, 31, 158, 85, 163, 69, 124, 139, 38 and 236. Figure 11 reveals that the key byte values (shown in top on each snippet) are identical to the values provided in column two of Table 2. This means that we successfully obtain the 128-bit secret key of the selected AES algorithm. For the case of implemented countermeasure, the achieved values (given on top of each snippet in Figure 12) in decimal are 246, 129, 91, 3, 175, 113, 70, 137, 153, 71, 105, 103, 45, 223, 38 and 244. It can be seen that these values are distinct from the values reported in column two of Table 2. Subsequently, our proposed countermeasure provides resistance against DPA attacks (which was the persistence of this work).