esa-sceva/satcom-chunk-collection huggingface dataset:
# Meta Surface-Based Multiband MIMO Antenna for UAV Communications at mm-Wave and Sub-THz Bands
## 3 Fabrication, Testing, and Comparison of Simulated and Measured Results
### Antenna System Positioning on Drones and Its Assessment for UAV Communications
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Size (mm\({}^{2}\))**} & \multirow{2}{*}{**Ports**} & \multirow{2}{*}{**Operating**} & \multirow{2}{*}{**Max Gain**} & \multirow{2}{*}{**Radiation**} & \multirow{2}{*}{**Coverage**} & \multirow{2}{*}{**Isolation**} & \multirow{2}{*}{**ECC**} \\  & & & **Frequency (GHz)** & & & & \\ \hline
[10] & \(114\times 114\) & 3 & 1.58/2.4/26 & 7.8/3.9/8.1 & - & Upper/lower & \(<\)50 & - \\ \hline
[43] & \(70\times 29\) & 3 & 2.4/5.2/60 & 9.8/7.9/8.4 & \(<\)90 & Half space & \(<\)\(-25\) & - \\ \hline
[44] & \(196\times 15\) & 1 & 0.915 & 4.5 & \(<\)78 & - & - & - \\ \hline
[45] & \(30\times 30\) & 1 & 2.45/5.8/25 & 5.85 & - & - & - & - \\ \hline
[46] & \(30\times 30\) & 4 & 28 & 6.1 & 92 & Half space & \(<\)\(-29\) & \(<\)0.16 \\ \hline
[47] & \(61.4\times 37.2\) & 1 & 1.06/3.6 & \(<\)7.5 & - & Half space & - & - \\ \hline
[48] & \(53\times 53\) & 4 & 2.5, 3.5, 5.5, 7.528 & \(<\)8.5 & \(<\)95 & - & \(<\)20 & - \\ \hline This work & \(15\times 16.5\) & 2 & \
\begin{tabular}{c} 8.1-24.7/ \\ 66–68/74–78/80–140 \\ \end{tabular} & \(<\)14 & \(<\)90 &
\begin{tabular}{c} Upper/lower \\ space \\ \end{tabular} & \(<\)35 & \(<\)0.003 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison between the proposed and recent similar reported antennas. Figure 27: The simulated air-to-ground and drone-to-drone transmission at different distances, azimuth, and elevation profiles (**a**) Co-polarization and (**b**) Cross-polarization.
## 3 The Proposed Energy-Balanced Data Collection (EBDC) Mechanism
### Trajectory Construction Phase
Therefore, the waiting time for each sensor sending its readings to the mobile sink can be reduced significantly. Furthermore, by applying the _RMDC_ scheme, the number of sensors which can directly communicate with the mobile sink is more than that by applying the approach presented in [10]. That is to say, by applying the _RMDC_ scheme, the data-relaying workloads can be reduced, prolonging the network lifetime. As a result, the _RMDC_ scheme also outperforms the existing mobile sink schemes [8, 9, 10, 11]. The considered network environment is a circular monitoring region \(M\) which has been geographically partitioned into \(n\) circular tracks. In the proposed _EBDC_ mechanism, the trajectory \(J_{min}^{t_{n},\tau}=(x_{n}^{\min},x_{n-1}^{\min},...,x_{n}^{\min})\) is scheduled by the proposed three-phase execution. Let \(t_{max}^{t_{max}}\) denote the time duration of each round. For simplicity and without loss of generality, assume that the time duration for mobile sink traversing each track for one sweep repetition is set to \(t\). The value of \(t_{max}^{t_{max}}\) can be evaluated by Equation (27):  
\[t_{round}^{EBDC}=x_{1}^{\min_{1}}t+x_{2}^{\min_{2}}t+...+x_{n-1}^{\min_{1}}t+x_ {n}^{\min_{1}}t=t\times\sum_{i=1}^{n}x_{i}^{\min} \tag{27}\]  
Since the data-relaying workloads of all sensors can be balanced in each round, to simplify the analysis, the following discusses the energy consumption of the sensor \(s_{i}\in S_{n}\), where \(S_{n}\) denotes the set of sensors located in the outmost (boundary) track \(k_{n}\). Let \(e_{n}^{total}\) denote the total energy consumption required for any sensor belonging to set \(S_{n}\) in each round. According to Equation (24), the value of \(e_{n}^{total}\) can be calculated by the Equation (28):  
\[e_{n}^{total}=e_{1\times n}x_{1}^{\min}+e_{2\times n}x_{2}^{\min}+...+e_{(n-1 )\times n}x_{n-1}^{\min}+e_{n=n}x_{n}^{\min} \tag{28}\]  
Let \(e_{max}^{EBDC}\) denote the energy consumption required for sensor \(s_{l}\) in each time unit.
## 3 Vehicle: Model Preparation  
To simulate the electromagnetic behavior of an antenna, the biggest challenge is to model the geometry and define the materials of the vehicle and the antenna. The model preparation was performed in CST MWS software and entailed:  
* Simplification of the geometry: removing, altering or joining components;
* Geometry healing: an automatic process that removes CAD incongruuities;
* Changes in spatial orientation and position: moving or rotating the model according to what is necessary;
* Defining the materials for each element: for each component, defining proper and realistic materials.  
Geometry simplification is important, since vehicle models can be extremely complex, especially if they are close replicas of the physical versions. Numerous elements go into making the vehicle model, and a lot of them are too small, too far away from the antenna or composed of materials that do not affect radiation and should not be considered in the simulation. These parts will, however, make the simulation model more complicated if left in. This is a process that requires a thorough analysis of which elements should be removed, and the only way to perform that analysis is through a trial-and-error process, aided by basic radiation knowledge.  
Simplifying the vehicle model can also be dependent on the type of simulation and the position of the antenna. For example, in a scenario where the wavelength in question is smaller than the vehicle's dimensions, an antenna placed near the center of the roof will likely not be affected by other geometric factors, so one may not require more than the roof elements in the simulation; on the other hand, an antenna placed near the edges of the roof will require all the closer geometric factors for more realistic results.  
Defining the materials of the geometry is also a complex process. Different materials will have different effects on radiation and will add to the complexity of the simulation. For example, any reflective material, such as metal, tends to create a simpler simulation scenario; at the same time, a dielectric material might have almost no impact on the results and still raise the simulation's complexity.  
Knowing which materials compose an element is a challenging task. Additionally, even after knowing those materials, it is necessary to know each material's conductivity and tangent of losses for the required frequencies, information which is not always available. Only with this information is it possible to know if the materials have an impact on the antenna's radiation and if it is necessary to include them.
# Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT
## 3 Proposed Augmented Lagrangian-Based Reinforcement Learning
### Proposed AL-SAC Algorithm  
The framework of SAC includes the actor and critic parts, which are for policy evaluation and policy improvement, respectively. A policy is the function that returns a feasible action for a state, denoted by \(\pi\). That is,  
\[\mathbf{a}\sim\pi(\times|\mathbf{s}). \tag{22}\]  
In SAC, the algorithm aims to find the optimal policy which maximizes the average of the entropy of the policy and the expected reward. That is,  
\[\underset{\pi}{\text{argmax}}\ \mathbb{E}\Bigg{[}\sum_{t=0}^{T}\gamma^{t} \bigg{(}r^{(t)}+\mathbf{H}\Big{(}\pi\big{(}\mathbf{a}^{(t)}|\mathbf{s}^{(t)}\big{)} \Big{)}\bigg{)}\Bigg{]}, \tag{23}\]  
where \(\gamma\) (\(0<\gamma\leq 1\)) is the discount factor; \(\mathbf{H}(\pi(\mathbf{a}|\mathbf{s}))\) denotes the Shannon entropy of policy \(\pi\). Considering the hybrid action spaces in our problem, the Shannon entropy for policy \(\pi\) involves the entropy calculation of two parts:  
\[\mathbf{H}^{{}^{\prime}}(\pi(\mathbf{a}|\mathbf{s}))=\beta_{1}\mathbf{H}(\pi(\mathbf{a}_{1 }|\mathbf{s}))+\beta_{2}\mathbf{H}(\mathbf{a}_{2}|\mathbf{s}), \tag{24}\]  
where \(\beta_{1}\) and \(\beta_{2}\) are the entropy temperatures.  
As illustrated in Figure 3, the architecture of the proposed AL-SAC algorithm incorporates a constraint part in addition to the original actor, critic parts, and replay buffer. Specifically,  
* **Actor part**: it deploys a policy network denoted by \(\pi\), which generates the policy of device association and bandwidth allocation;
* **Critic part**: it deploys a value network and a Q-value network, denoted by \(\mathcal{V}\) and \(\mathcal{Q}\), estimating the value of state and state-action, respectively;
* **Constraint part**: it deploys Lagrangian multiplier networks and cost networks, denoted by \(\mathcal{L}\) and \(\mathcal{C}\), estimates the cost value of constraints and adjusting the Lagrangian multipliers accordingly.
* **Replay buffer**: it is used in DRL to store the tuples, i.e., \(\{s^{(t)},a^{(t)},r^{(t)},s^{(t+1)},G_{m}^{(t)}\}\), from which the sampled tuples are used in neural network training.  
In the following, we describe the updating process of each neural network component in the three parts network.
## 8 Future Research Trends  
This paper is mostly focused on the optimization of time and energy constraints for MR navigation. However, there are other collections of objectives, including motion smoothness, sensor noise, multi-robot systems, and many more, that must be considered for further research. MR mobility in the real world frequently suffers from uncertainties and sensor noise. Also, development can involve looking at ways to modify a strategy when working across multiple robot systems, landscapes, or energy resources while preserving or enhancing functionality. The optimization approach can be expanded in the near future to address multi-robot systems and resolve issues with coordination, avoidance of collisions, and distribution of tasks for large-scale operations [52; 53]. The inclusion of complex frameworks and noisy sensors within the optimization paradigm might become a topic for prospective exploration. To guarantee that the robot's strategy for navigation maintains efficiency despite an environment of uncertainty, that might utilize approaches includingBayesian optimization, stochastic simulation, or robust optimization processes [54]. The planning and re-planning routes described in this paper can be used to gather relevant physical characteristics, including variations in temperature, wind speed, etc., that can further be implemented for the surveillance associated with firefighting and border surveillance.  
An analytical approach and simulation-based findings are presented in this study. Further research might focus on applying the intelligent PSO method to live robots along with testing its accuracy in real-world situations. This might include taking responsibility for the limitations associated with actual equipment, coping with constraints on time, and solving everyday challenges with a sensor interface and communications. Subsequent research might examine how the strategy for optimization can be applied to various additional robot types, limitations, and environments. For instance, to enable collaboration in making decisions between the MR and its human user, additional studies will investigate ways to include human behavior and restrictions within the optimization paradigm [55]. By addressing these problems and factors that develop throughout real-world environments and extending limitations for MR navigation, we can extend both the scope and application of the optimization approach for future research.
# Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction
## Appendix A  
The dataset obtained in the measurement sites described in the paper and adopted in this study is reported in the table below. It can be used for scientific purposes, citing the source of the data and referring to this paper.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Progressive** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Traffic** \\ **Volume (Q)** \\ **[veh]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Percentage of** \\ **Heavy Vehicles** \\ **(P) [\%]** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Total Honking** \\ **Occurrences** \\ **(H) [Counts]** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Continuous** \\ **Equivalent** \\ **Sound Level** \\ **(L\({}_{eq}\)) [dBA]** \\ \end{tabular} } \\ \hline Site 1 & 1 & 570 & 3.3 & 110 & 74.0 \\  & 2 & 582 & 2.2 & 135 & 74.6 \\  & 3 & 574 & 3.7 & 112 & 74.5 \\  & 4 & 576 & 2.8 & 118 & 74.7 \\  & 5 & 585 & 1.7 & 267 & 74.4 \\  & 6 & 589 & 2.5 & 249 & 74.9 \\  & 7 & 572 & 3.4 & 128 & 75.1 \\  & 8 & 584 & 1.3 & 175 & 74.5 \\  & 9 & 593 & 2.4 & 191 & 74.3 \\  & 10 & 597 & 1.5 & 231 & 75.0 \\ \hline Site 2 & 11 & 216 & 0.0 & 126 & 69.9 \\  & 12 & 241 & 0.0 & 102 & 70.0 \\  & 13 & 238 & 0.0 & 110 & 69.7 \\  & 14 & 239 & 0.0 & 102 & 69.5 \\  & 15 & 220 & 0.0 & 106 & 68.0 \\  & 16 & 230 & 0.0 & 115 & 69.3 \\  & 17 & 226 & 0.0 & 100 & 69.5 \\  & 18 & 244 & 0.0 & 123 & 70.2 \\  & 19 & 219 & 0.0 & 93 & 68.6 \\  & 20 & 246 & 0.0 & 90 & 69.8 \\ \hline Site 3 & 21 & 513 & 0.0 & 363 & 76.5 \\  & 22 & 423 & 0.6 & 293 & 73.0 \\  & 23 & 419 & 0.7 & 316 & 75.0 \\  & 24 & 505 & 0.4 & 322 & 75.2 \\  & 25 & 464 & 0.6 & 330 & 76.0 \\  & 26 & 463 & 0.2 & 278 & 72.2 \\  & 27 & 450 & 0.5 & 267 & 72.8 \\  & 28 & 422 & 0.1 & 161 & 72.3 \\  & 29 & 433 & 0.2 & 210 & 72.5 \\  & 30 & 369 & 0.8 & 135 & 72.0 \\ \hline Site 4 & 31 & 1092 & 3.8 & 204 & 78.7 \\  & 32 & 1315 & 3.7 & 209 & 79.7 \\  & 33 & 1143 & 3.4 & 205 & 78.9 \\  & 34 & 1141 & 3.6 & 210 & 78.5 \\  & 35 & 1350 & 3.3 & 232 & 79.5 \\  & 36 & 1169 & 3.5 & 257 & 78.8 \\  & 37 & 1344 & 3.2 & 229 & 79.1 \\  & 38 & 1090 & 3.2 & 268 & 78.5 \\  & 39 & 1279 & 3.7 & 237 & 79.3 \\  & 40 & 1377 & 3.1 & 279 & 79.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Full dataset containing traffic volumes (Q), percentage of heavy vehicles (P), total honking occurrences in 15 min (H) and continuous equivalent sound level (\(L_{eq}\)) for all the measurements, in all the sites.
# Dimensional Stability of SBR-Modified Cementitious Mixtures for Use in 3D Additive Construction
###### Abstract  
This study experimentally investigated the dimensional stability of SBR (styrene butadiene rubber)-modified cementitious mixtures in order to determine whether their properties are sustainable as a 3D additive construction material. Dimensional stability refers to resistance to material deformation caused by changes in internal relative humidity and temperature. Hence, drying and thermal shrinkage, which are the primary factors affecting dimensional stability, were tested. The mixing ratio of SBR-modified cementitious mixtures was determined based on a predetermined optimal flow of \(70\%\pm 1\%\) applicable for 3D additive construction applications. The results of this study showed that the elastic modulus, and drying shrinkage strain, excluding the coefficient of thermal expansion, all significantly improved as the SBR/cement ratio increased. In particular, drying shrinkage can be a disadvantage in 3D additive construction because drying in the printed mixtures is rapid due to the large specific exposure area of moldless construction. Consequently, mitigating drying shrinkage is very important. The elastic modulus, drying shrinkage, and coefficient of thermal expansion were all found to be associated with the dimensional stability obtained in this study. It was concluded that using SBR-modified cementitious mixtures was advantageous in terms of dimensional stability.  
3D additive construction; SBR-modified cementitious mixtures; dimensional stability; modulus of elasticity; drying shrinkage; coefficient of thermal expansion Article
# Named Entity Correction in Neural Machine Translation Using the Attention Alignment Map
## 2 Machine Translation
### Conventional Named Entity Recognition  
There have been many studies for named entity recognition (NER) based on recurrent neural networks (RNNs) [37; 38]. Similar to neural machine translation, the input is a sequence of tokens, \(\mathbf{\hat{X}}=[\mathbf{\hat{x}_{1}}\ \mathbf{\hat{x}_{2}}\ \cdots\ \mathbf{\hat{x}_{n}}]\in\mathbb{R}^{p_{x}\times n}\), and the output is a sequence of binary labels indicating which tokens are named entities, so the length of the output is the same as that of the input sequence: \(\mathbf{t}=[t_{1}\ t_{2}\ \cdots t_{n}]\in\mathbb{B}^{n}\). The example target encoding is shown in Table 1. Each word in the \"Truth\" and \"NMT output\" is underlined if it is a named entity. In those cases, the target labels are assigned one. The objective of named entity recognition is finding a sequence that maximizes the posterior probability of \(\mathbf{t}\) given the input,  
\[\mathbf{t}^{*}=\arg\max_{\mathbf{t}}p(\mathbf{t}|\mathbf{X})\,, \tag{7}\]  
where \(\mathbf{t}^{*}\) is an optimal NER result. Recently, a novel model for NER based only on attention mechanisms and feedforward networks achieved state-of-the-art performance on the CoNLL-2003 NER task [39].
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
## 6 Experimental Simulation Results
### Transmitted Power Spectral Density Sweep and Cable Cross-Section Sweep  
Recommendation ITU-T G.9964 [20] deals with PSD. In the power line case, in the 25 MHz band the transmit PSD is limited to \(-\)55 dBm/Hz. This value has been selected as a reference and is gradually reduced. Table 9 describes the input parameters including used cable types. The NAYY cables are designed for underground installations. Figure 8 displays the results' maximal transmitting PSD values.  
For the highest value \(-\)55 dBm/Hz, the application rate has begun to decrease at approximately 300 m according to Figure 8a. With the reduction of transmitting power, the range of communication decreases, where the lowest value was simulated at 600 m. The parallel of transmit PSD reduction can be an increase of the background noise power which is constant in time and frequency domain.  
Figure 8b displays the results of the cross-section sweep simulation. Due to a smaller cross-section, the ranges of communication and the application rate are reduced. Because of the change in the cross section, the primary and secondary parameters of the line change. The rate difference of NAYY 4 \(\times\) 50 SE and NAYY 4 \(\times\) 240 SE at 500 m is 28 Mbps.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **(a)** \\ \hline \hline
**Parameter** & **Value** \\ \hline \hline
**Cable type (LV)** & **NAYY 4 \(\times\) 150 SE** \\ \hline
**Bandwidth** & **25 MHz** \\ \hline
**Modulation** & **QAM1024** \\ \hline
**Transmit PSD** & \(-\)55; \(-\)65; \(-\)60; \(-\)70 dBm/Hz** \\ \hline
**(b)** & \\ \hline
**Parameter** & **Value** \\ \hline \hline
**Cable cross-section (LV)** & **NAYY 4 \(\times\) **M SE** \\ \hline
**Bandwidth** & **25 MHz** \\ \hline
**Modulation** & **QAM1024** \\ \hline
**Transmit PSD** & \(-\)55 dBm/Hz** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Input channel parameters of **(a)** transmit PSD sweep; **(b)** cable cross-section sweep.  
Figure 7: **(a) Comparison of throughput with modulation sweep; **(b)** Comparison of application and physical rate—QAM4096.
## 3 Formation Cooperative Search and Transformation Strategy  
### Formation and Environment Initialization  
The commonly used robot formation is shown in Figure 2a-e, and in order to achieve a cooperative exploration at different depths and reduce unnecessary energy consumption when moving in AUV formation [32; 33], this paper chose a formation structure based on the geese type to complete the formation initialization (as shown in Figure 2f), in which the bottom robot extends the exploration range in two directions at 90\({}^{\circ}\), and the interval between neighboring robots is the distance threshold \(D_{S}\), which is used to ensure the communication quality. The system randomly selects a robot as the initial virtual leader during the collaborative search process, and when any robot in the formation detects an obstacle, it switches to the new virtual leader to guide the other robots in their travels, resulting in a high formation flexibility and fault tolerance.  
When the formation encounters a complex obstacle environment in the form of submarine gullies and bulges due to crustal movements, robots must be able to change formation  
Figure 1: Overall block diagram of AUV formation capture in unknown environment.  
Figure 2: Schematic diagram of different formations.  
to traverse the obstacle domain, guarantee the communication quality between AUVs and take advantage of cooperative operations. To facilitate an explanation of the proposed scheme, an underwater virtual environment containing a large array of obstacles is created in this paper (Figure 3). The yellow cube represents the target, the cubes distributed around it represent the small obstacles, the four dark columns represent the large obstacle areas, and the six cyan nodes represent the formation of six AUVs.
## 3 SIF Retrieval Methods
### Adapting an Airborne Atmospheric Correction Scheme for Proximal Sensing Data  
In Section 3.2, the standard FLD and SFM formulations were modified to include a first order compensation of the O\({}_{2}\) absorption effects acting on the atmospheric path between the target and the sensor. However, as pointed out in Section 2.3, the multiplication or division of spectral functions that were previously convolved according to the ISRF will result in errors, particularly in the absorption regions. In consequence, in order to compensate for the ISRF convolution effects, it is necessary to use the atmospheric functions at a finer spectral resolution than the instrument's spectral resolution. Since this is hardly the case in practice, here we describe the adaptation of a typical airborne atmospheric correction strategy to simultaneously deal with: (1) the O\({}_{2}\) absorption effects; and (2) the particular instrument ISRF, when measuring both solar irradiance and the up-welling canopy radiance for proximal sensing (Figure 4).  
In essence, the idea is based on using the solar irradiance signal acquired at the same tower-mounted height (e.g., top of the tower), either using a reference panel or with an upward looking cosine corrected sensor, to fit an atmospheric Radiative Transfer Model (RTM) (steps  
Figure 4: Adaptation of a standard airborne atmospheric correction scheme when applying the SFM SIF retrieval approach for proximal sensing data. Red bold angle brackets in step 4 indicate the signal to be convolved according to the ISRF.  
and 2 from Figure 4). This way, the atmospheric state is fully characterized and the RTM can provide all the atmospheric functions required to apply the SFM with the O\({}_{2}\) compensation, i.e., \(\overline{E}\) and \(t^{\uparrow}\) according to Equation (12) (step 3 from Figure 4). Note that the use of a finer spectral resolution sensor for acquiring the solar irradiance at TOC, \(\overline{E}\), will not entirely solve the problem since the upward transmittance, \(t^{\uparrow}\), is still required at a high spectral resolution. Once the atmospheric functions \(\overline{E}\) and \(t^{\uparrow}\) are characterized, the SFM can be consistently applied at sensor level by convolving \(\langle(\overline{E}/\pi\cdot\rho_{MOD}+F_{MOD})t^{\uparrow}\rangle\) at each iteration of the minimization process in which \(\overrightarrow{x_{\rho}}\) and \(\overrightarrow{x_{F}}\) change (step 4) (see Equation (12)).  
\[arg_{\overrightarrow{x_{\rho}},\overrightarrow{x_{F}}}^{\rightarrow}min \underbrace{\left(\langle L\rangle-\langle\left(\overline{E}/\pi\cdot\rho_{ MOD}+F_{MOD}\right)t^{\uparrow}\rangle\right)}_{Sensor} \tag{12}\]