esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Hybrid Beamforming Technique in MU-MIMO Systems
## 3 Conventional Hybrid Beamforming System  
The hybrid beamforming system based on codebook selects the most suitable analog beam pattern with the channel status of each user in the codebook storing the predesigned analog beam patterns. In addition, the hybrid beamforming system based on codebook designs a digital precoder that eliminates user interference by using ZF scheme based on selected analog beam pattern. The hybrid beamforming system based on codebook is a realistic method and it can be applied to 5G NR. Also, it is important to improve the error performance and the average sum rate. The hybrid beamforming system based on codebook has the advantage of reducing the required channel feedback overhead. The hybrid beamforming system is a method of selecting analog beam pattern within a predesigned codebook to reduce the required channel overhead when beamformers are designed in a multi-user environment.  
Figure 5 shows the linearly divided angles according to the \(B\)-bits at the hybrid beamforming system based on codebook. The range of azimuth angle is \(\left[-\frac{1}{2}\pi,\,\cdots,\,\frac{1}{2}\pi\right]\) and the range of elevation angle is \(\left[0,\,\cdots,\,\pi\right]\). The predesigned precoder is as follows,  
\[V_{RF}=\left\{\mathbf{V}_{RF_{1}},\,\mathbf{V}_{RF_{2}},\,\cdots,\,\mathbf{V}_ {RF_{B^{2}}}\right\}, \tag{8}\]  
where \(\mathbf{V}_{RF_{i}}(i=1,\,\cdots,\,B^{2})\) denotes the each of analog precoders. The hybrid beamforming system based on codebook divides the azimuth and elevation angles linearly according to the number of \(B\)-bits and forms beam patterns through combination of the divided azimuth and elevation angles.  
Although Figure 6 shows the 3 beam patterns to show the example of predesigned beam patterns, the hybrid beamforming system based on codebook forms \(B^{2}\) beam patterns according to the number of \(B\)-bits. The conventional hybrid beamforming system divides the angle linearly without consideration of fading channels and it does not cope with the propagations which have severe frequency selectivity. Therefore, the error performance and average sum rate performance are degraded seriously in the subcarriers which go through deep fading, and it causes errors despite of the forward error correction. The next section describes the advanced hybrid beamforming system that has the same number of beam patterns but has the improved error performance and the average sum rate compared to the conventional hybrid beamforing system.  
Figure 5: The linearly divided angles according to the \(B\)-bits. (**a**) azimuth angles; (**b**) elevation angles.  
Figure 6: The example of predesigned beam patterns at the antenna arrays.
## 4 Experiments
### State-of-the-Art Comparison  
An accurate comparison of the results is challenging since the same training parameters, and validation modalities are used, even if the authors are using the same data, but evidentially not the same partition and metrics for evaluation.  
For Corsican data, we had regrouped in the Table 8 the most outstanding studies and their corresponding announced results. Our model trained with ResNet-50 and Dice loss outperforms the Deep-Fire U-Net [48], CNN residual network [49], Deeplab [36], Custom CNN architecture [50], and the color segmentation approach with fuzzy criteria [51]. Nevertheless, for bee colony algorithm-based color space segmentation [24] and the weakly supervised CNN [52], it is quite difficult to judge following the few represented results.  
Custom CNN residual network [49] outperforms our results, but it seems that no cross-validation was performed, just a single run result that could not be quite accurate. The FLAME dataset is newly released, so the work done is limited to the original dataset. It is clear from Table 8 that the present work model outperforms the UNet approach [11]. Moreover, no work was recorded using Firefront_Gestosa Videos or images, since it is private content. But the results are encouraging regarding the fact that the model is not concepted for this data specifically.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Dataset** & **Global Accuracy** & **Mean Accuracy** & **Mean IoU** & **Weighted IoU** & **Mean BF Score** \\ \hline \multirow{3}{*}{Model\_1} & FLAME & **99.80\%** & **87.81\%** & **84.32\%** & **99.62\%** & **95.66\%** \\ \cline{2-7}  & Corsican & 97.30\% & 89.46\% & 85.76\% & 94.83\% & 91.21\% \\ \cline{2-7}  & Firefront\_Gestosa & **99.98\%** & **89.01\%** & **80.13\%** & **99.96\%** & **98.39\%** \\ \hline \multirow{3}{*}{Model\_5} & FLAME & **99.79\%** & **92.02\%** & **84.74\%** & **99.61\%** & **96.43\%** \\ \cline{2-7}  & Corsican & 97.18\% & 90.49\% & 85.60\% & 94.69\% & 89.81\% \\ \cline{1-1} \cline{2-7}  & Firefront\_Gestosa & **99.97\%** & **92.61\%** & **79.09\%** & **99.95\%** & **98.60\%** \\ \hline \hline \end{tabular}
\end{table}
Table 7: The observed metrics over every set of data for Model_1 and Model_5.  
second rows are the pictures and their corresponding ground truths. The rows from three to seven represent the results given by the four models trained with dice loss. The pixels that should be detected as non-fire, but the model had labeled them as fire pixels (FP), are given in green color. Pink color design pixels that are fire ones, but the model had labeled them as non-fire pixels (FN).  
We note however, that in Table 8 some cells do not present results since the original works do not use the same metrics that have been adopted in this work.
## 2 Materials and Methods
### Stimuli  
#### 2.4.1 First Experiment  
A total of 16 sound sequences have been constructed, based on different combinations of two initial sound sequences, \(\alpha\) and \(\beta\), each with a duration of 90 s, in order to focus on the effect of the sound sequence temporal structure on the sound pleasantness global assessment. The resulting sound sequences have a duration of 3 min, which represents the median duration of a pedestrian trip in the city of Paris [40]. The initial sound sequences \(\alpha\) and \(\beta\) have been recorded with the same binaural technique in the 13 th district of Paris, during April 2015; the sequence \(\alpha\) in a small park (\(L_{50}=55\) dB, \(L_{10}-L_{90}=25\) dB), and the sequence \(\beta\) nearby a large boulevard (approximate flow: 1000 vehicles/hour, \(L_{50}=76\) dB, \(L_{10}-L_{90}=24\) dB). \(\alpha\) and \(\beta\) have been carefully chosen, in orderto avoid particular events, such as too loud two wheelers, dog barks, voices with semantic understanding, or exceptionally strong sound level fluctuations. These events could become very salient markers of the sound environment, and could potentially significantly drive the global and instantaneous sound pleasantness assessment. \(\alpha\) and \(\beta\) have been assessed by the participants before the beginning of the test, on a continuous pleasantness scale from unpleasant (coded 0) to pleasant (coded 10): the average perceived sound pleasantness for \(\alpha\) and \(\beta\) were 8.1 (\(\sigma=2.2\)) and 2.4 (\(\sigma=2.0\)), respectively. Practically, the 16 sound sequences were obtained by combining \(\alpha\) and \(\beta\) with different appearance times. The 16 sound sequences were formed with slow or fast alternations between \(\alpha\) and \(\beta\), evolving from calmness to noisiness, or the inverse. The transitions between each environment lasted at least 30 s (\"Fast\"), which was observed in situ as a minimum walking transition time. \"Slow\" alternation corresponds to a 3 min transition, which is the length of the sound sequence. Each sequence was presented once to the participants.  
This methodology included the repetition of two 1 min initial sound sequences, assessed by the participants at the beginning of the test. Thus, some memory, demand, or transfer effects could have perturbed the experiment. Nevertheless, two points relativize this possible influence: (i) at the end of the test, the participants were orally asked to freely comment on the experiments. If some of them recognized that parts of the sequences came from the same initial sound recordings, as no strong marker of the sound environment (voice, klaxon, etc.) was present, they said something like, \"I think two or three times I heard a part of the same sequence\"; and (ii) all of the sequences were played in a random order to avoid the effect due to the repetition of the initial sequences, always being reported on the same sound sequences.
# Empowering a Broadband Communications Course with a Unified Module on 5G and Fixed 5G Networks +
## Appendix A Questionnaire Used to Evaluate the Impact of 5G/F5G Module
\\ \(\square\) & The 4K video appeared in the 5 th generation of fixed networks (F5G). \\ \(\square\) & **The smart city appeared in the 5 th generation of fixed networks (F5G). ** \\ \(\square\) & The holograms appeared in the 5 th generation of fixed networks (F5G). \\ \(\square\) & **All the above are correct. ** \\ \hline \hline \end{tabular}
* **Q10. Select the correct answer:** & \(\square\) & Virtual reality appeared in the 5 th generation of fixed networks (F5G). \\ \(\square\) & HD video appeared in the 3 rd generation of fixed networks (F3G). \\ \(\square\) & **All the others are correct. ** & The internet of senses appears in the 6 th generation of fixed networks (F6G). Q11. The 4 th Industrial Revolution is primarily based on the evolution of:  
\(\square\) Energy sector. \(\square\) Electronics. \(\square\) **Robotics. **  
\(\square\) **Mechanization. **  
Q12. Higher Quality of Service (QoS) implies:  
\(\square\) Lower Quality of Experience (QoE). \(\square\) Higher Bit Error Rate (BER). \(\square\) **Lower end-to-end latency. **  
\(\square\) All the above are correct. Q13. Select the correct answer:  
\(\square\) If the QoE is low, there is no problem since the user will always pay for his favorite application, e.g., gaming. \(\square\) **The QoE is much more important to the user than the QoS. **  
\(\square\) The QoE is a direct function of Grade of Service (GoS). \(\square\) The QoE is an objective quantity like QoS. Q14. What bit rates can F5G networks support in the access part of the network? \(\square\) **0-200 Gb/s. **  
\(\square\) **0-100 Gb/s. **  
\(\square\) **0-10 Gb/s. **  
\(\square\) All the above are correct. Q15. What latency can F5G networks support in the access part of the network? \(\square\) Up to 100 ms.  
\(\square\) Up to 50 ms.  
\(\square\) **Up to 10 ms.**  
Q16. What FTTx can F5G networks support in the access part of the network? \(\square\) Fiber to the Building (FTTB). \(\square\) Fiber to the Cabinet (FTTC). \(\square\) Fiber to the Home (FTTH). \(\square\) **Fiber to the Room (FTTR). **  
Q17. What bit rates can F6G networks support in the access part of the network? \(\square\) **0-200 Gb/s. **  
\(\square\) **0-50 Gb/s. **  
\(\square\) **0-10 Gb/s. **  
Q18. What latency can F6G networks support in the access part of the network? \(\square\) Up to 10 ms.  
\(\square\) Up to 5 ms.  
\(\square\) **Up to 1 ms.**  
\(\square\) Up to 10 \(\mu\)s.  
Q19. What FTTx can F6G networks support in the access part of the network? \(\square\) Fiber to the Home (FTTH). \(\square\) Fiber to the Cabinet (FTTC). \(\square\) Fiber to the Building (FTTB). \(\square\) **Fiber to the Everything (FTTE). **
## 1 Introduction
Two different approaches were tested, namely an approach where only the results of the previous iteration are stored in the memory and an approach where the results of all the previous iterations are stored in the memory. * Basic tests were made using the three-layered model, because it is easier to make an FEM model with a three-layer model than with a multi-layer model. Nevertheless, four-, five-, and six-layer models were also tested, which would have been used if significantly better results had been obtained. Multi-layered models are not favorable for FEM modeling. * Because the soil structure is usually unknown, an FEM model of inhomogeneous soil was made. The Wenner method was simulated on the model, and, in this approach, data were obtained that replaced the measurements of the unknown structure of the soil. * The final FEM model was also made, which was obtained on the basis of the presented procedure. [PERSON]'s measurements were also simulated on this and compared with those from the basic model. In [18; 24; 25; 26; 27; 28; 29; 30; 31; 32], the authors deal with similar problems to us in our work. The Genetic Algorithm is used and compared with the results of the standard optimization methods in [25; 26]. Differential Evolution, used in [28], is also proven to be a suitable method. The results shown in [18; 27] confirm that the Genetic Algorithm is also suitable for multi-layered soil models. In our work, the approach of determining soil parameters was based on a larger number of measurements in the observed area, which was not the case in the related works [18; 25; 26; 27; 28; 29; 30; 31; 32]. Five evolutionary methods were also tested and compared in our work, while, in comparable works, one to two methods were usually used [18; 25; 26; 27; 28; 29; 30; 31; 32]. For the dimensioning and optimization of grounding systems, it is essential to determine the parameters of the soil. The procedure or software for determining the parameters can be a module included in the FEM software, or it can be an independent program whose output data are used as input data for any FEM software (e.g., Flux 2018.1.3). The rest of this article is structured as follows. Section 2 provides the theoretical basis of horizontally layered soil models and [PERSON]'s measurements. In Section 3, a new method is shown, with the possibility for consideration of inhomogeneity when also using a horizontally layered soil model. Basic descriptions of the optimization methods used are given in Section 4. Section 5 consists of several subsections, which present the results for all subareas, the convergence of optimization methods, Memory Assistance usage, results using multi-layered soil, and results verification. The final section (Section 6) provides conclusions regarding the presented approach for determining soil parameters.
## 4 Experiments and Results
### Experimental Setup
#### 4.1.3 Implementation Details  
We implemented the proposed model using Python 3.8 in the PyTorch framework on an NVIDIA RTX 3090 Ti GPU from Beijing Yuangui Interactive Technology Co., Ltd., Beijing, China. We utilized TorchAudio 2.1.0 to extract the Mel-spectrogram with 128 Mel bins using a 2048-size FFT (with a Hanning window) and a hop size of 1024 at a sampling rate of 22,050 Hz. Moreover, we employed the Adam optimizer for training with a learning rate of \(1\times 10^{-3}\), a weight decay of \(1\times 10^{-4}\), 200 training epochs, and a batch size of 16. We used cross-entropy loss as the training loss function.
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
## 2 Two-Way Ranging (TWR) Overview
### Time-of-Flight Estimation  
Equations (1) and (2) form a system of 2 equations with 2 unknowns \(T\) and \(\tau\). If the known transmit timestamps and estimated receive timestamps are shared between users, we have enough information to trivially solve the system. In reality, however, the clocks driving these devices can drift and the platforms may be in motion, so \(T\) and \(\tau\) may change between frames, thereby creating a system of two equations with four unknowns. For reasonable frame rates (\(\geq\)10 Hz) and modern RF hardware, both \(T\) and \(\tau\) are well approximated by simple first- or second-order Markov models. By injecting these models and collecting data over multiple frames, we can construct closed-form estimators [47] or even Kalman tracking filters [5] to estimate \(T\) and \(\tau\). Various design implications may inform your choice of algorithm, but fundamentally, they all estimate \(\tau\), from which we can infer the distance between platforms \(d\). For multi-antenna systems, we can estimate the distance \(d_{ij}\) between each transmit antenna \(i\) and receive antenna \(j\), creating spatial diversity that enables relative position and orientation estimation. This configuration is depicted in Figure 3.
# Multiple Attribute Decision-Making Method Using Linguistic Cubic Hesitant Variables
## 6 Conclusions  
To express the hybrid form of both interval/uncertain linguistic information and hesitant linguistic information, this study originally proposed the LCHV concept, along with the internal LCHV and external LCHV concepts, and the operational relations of LCHVs. Then, the linguistic score function of the LCHV was presented to rank LCHVs. Furthermore, the WAA and WGA operators of LCHVs based on the LCMN extension method were proposed to aggregate LCHV information, and then their properties were indicated. Next, a MADM approach was established based on the WAA and WGA operators of LCHVs to solve MADM problems with LCHV information. Finally, an illustrative example was presented for the application of the developed approach in the LCHN setting. The proposed MADM approach can solve MADM problems with interval/uncertain linguistic and hesitant linguistic arguments in the LCHV setting.  
However, the main highlights of this study are summarized below:  
1. The LCHV concept extends the existing LCV concept to express the interval/uncertain linguistic and hesitant linguistic arguments simultaneously for the first time.
2. This paper proposed two weighted aggregation operators of LCHVs based on the LCMN extension method and the linguistic score function of LCHV for the first time.  
The advantages of the WAA and WGA operators of LCHVs are that: (a) The two aggregation operators based on the LCMN show the objective extension operations without the subjective extension forms depending on decision makers' preferences; (b) all the linguistic values in the aggregated LCHVs still belong to the predefined TTS; and (c) the WAA and WGA operators are the simplest and most common weighted aggregation operations used for MADM problems.
* The developed MADM approach with LCHVs extends the existing MADM approaches with LCVs so as to carry out the MADM problems with the hybrid information of both interval linguistic arguments and hesitant linguistic arguments, which the existing ones cannot handle.  
Based on this study, new aggregation operators of LCHVs and ranking methods of LCHVs will be further presented and applied in the medical diagnoses of kidney cancer, prostatic cancer, and gastric cancer and the selection problems of suppliers and manufacturing schemes as future work in the LCHV setting.  
[PERSON] originally proposed LCHVs, their operations, and the MADM method of LCHVs; [PERSON] provided the calculation and comparative analysis of an illustrative example. The authors wrote the paper together.  
This paper was supported by the National Natural Science Foundation of China (No. 61703280).  
The authors declare no conflicts of interest.
# A Novel Channel Coding Scheme for RFID Generation-2 Systems
## 4 Simulation Results
This can be better appreciated by looking at Table 2, where both Viterbi decoders shall compute \(2\cdot S\cdot N\) distances, with \(N\) equal to 32 at least, while the proposed scheme is just an analog demodulator working on four parallel waveforms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & **Codeword Length \(N\)** & **Number of Operations** & **Number of Comparisons** \\ \hline Soft decision Viterbi & 32 & \(2\cdot S\cdot N\) Euclidean distances & \(S\cdot N\) \\ Hard decision Viterbi & 32 & \(2\cdot S\cdot N\) Hamming distances & \(S\cdot N\) \\ Proposed scheme & 4 & - & \(N\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Computational Complexity Analysis: \(S=4\) states of the two Viterbi decoders.
# Building a Realistic Virtual Simulator for Unmanned Aerial Vehicle Teleoperation
## 2 Building a Realistic UAV Simulator
### Quadrotor Study Case  
The quadrotor is a UAV with rotary wings that can take off and land vertically. This vehicle can perform rotational and translational movements with the help of four rotors attached to a rigid body, which are equidistant from its mass center.  
Figure 1 shows a schematic diagram of the quadrotor navigation. Based on this figure, the position and orientation of the quadrotor can be described for any time instant through six generalized coordinates. In this figure can be distinguished two coordinate systems: the fixed or universal coordinate system \(\{I\}\), from which these generalized coordinates are measured; and the mobile quadrotor coordinate system \(\{B\}\), attached to the frame of this vehicle from the mass center, which is used to analyze points of interest on the quadrotor body. In this way, the coordinates in \(\boldsymbol{\xi}=[x,y,z]^{T}\) measure the position of the mass center concerning the fixed coordinate system \(\{I\}\). Regarding the variables in \(\boldsymbol{\eta}=[\phi,\theta,\psi]^{T}\), they describe the orientation of the quadrotor in terms of a consecutive sequence of [PERSON]'s angle rotations. Of course, there are several alternatives for the above sequence. Still, this framework utilizes the convention \(ZXY\), also called Pitch-Roll-Yaw (PRY), since humans can intuitively interpret it, and it is helpful to avoid particular singular points [42]. In the \(ZXY\) convention, the initial frame rotates first around the axis \(y_{B}\) (this is also the axis \(y_{I}\)) by angle \(\theta\) (pitch); then, the resulting frame \(\{B^{\prime}\}\) rotates around the axis \(x_{B^{\prime}}\) by angle \(\phi\) (roll); finally, the resulting frame \(\{B^{\prime\prime}\}\) rotates around the axis \(z_{B^{\prime\prime}}\) by angle \(\psi\) (yaw); the resulting frame \(\{B^{\prime\prime\prime}\}\) denotes the final orientation of the quadrotor. Figure 2 illustrates how the previous successive rotations are carried out.  
Figure 1: The quadrotor navigation scheme.  
Figure 2: _Cont._Based on the above, it is necessary to derive a model of the vehicle that can simulate its behavior in a way close to reality. Therefore, the kinematic and dynamic studies are developed next for the quadrotor