Abstract
Aims. HD206893 is a nearby debris disk star that hosts a previously identified brown dwarf companion with an orbital separation of similar to 10 au. Long-term precise radial velocity (RV) monitoring, as well as anomalies in the system proper motion, has suggested the presence of an additional, inner companion in the system. Methods. Using information from ongoing precision RV measurements with the HARPS spectrograph, as well as Gaia host star astrometry, we have undertaken a multi-epoch search for the purported additional planet using the VLTI/GRAVITY instrument. Results. We report a high-significance detection over three epochs of the companion HD206893c, which shows clear evidence for Keplerian orbital motion. Our astrometry with similar to 50-100 mu arcsec precision a fforded by GRAVITY allows us to derive a dynamical mass of 12.7(+1:2) (-1:0) M-Jup and an orbital separation of 3.53(+0:08) (-0:06) au for HD206893c. Our fits to the orbits of both companions in the system use both Gaia astrometry and RVs to also provide a precise dynamical estimate of the previously uncertain mass of the B component, and therefore allow us to derive an age of 155 +/- 15 Myr for the system. We find that theoretical atmospheric and evolutionary models that incorporate deuterium burning for HD206893c, parameterized by cloudy atmosphere models as well as a '' hybrid sequence '' (encompassing a transition from cloudy to cloud-free), provide a good simultaneous fit to the luminosity of both HD206893B and c. Thus, accounting for both deuterium burning and clouds is crucial to understanding the luminosity evolution of HD206893c. Conclusions. In addition to using long-term RV information, this e ffort is an early example of a direct imaging discovery of a bona fide exoplanet that was guided in part by Gaia astrometry. Utilizing Gaia astrometry is expected to be one of the primary techniques going forward for identifying and characterizing additional directly imaged planets. In addition, HD206893c is an example of an object narrowly straddling the deuteriumburning limit but unambiguously undergoing deuterium burning. Additional discoveries like this may therefore help clarify the discrimination between a brown dwarf and an extrasolar planet. Lastly, this discovery is another example of the power of optical interferometry to directly detect and characterize extrasolar planets where they form, at ice-line orbital separations of 2 4 au.

Abstract
The increasing complexity of recent Wi-Fi amendments is making optimal Rate Adaptation (RA) a challenge. The use of classic algorithms or heuristic models to address RA is becoming unfeasible due to the large combination of configuration parameters along with the variability of the wireless channel. We propose a simple Deep Reinforcement Learning approach for the automatic RA in Wi-Fi networks, named Data-driven Algorithm for Rate Adaptation (DARA). DARA is standard-compliant. It dynamically adjusts the Wi-Fi Modulation and Coding Scheme (MCS) solely based on the observation of the Signal-to-Noise Ratio (SNR) of the received frames at the transmitter. Our simulation results show that DARA achieves higher throughput when compared with Minstrel High Throughput (HT)

Abstract
A low-cost single-layer substrate-integrated waveguide (SIW) cavity-backed slot antenna is proposed for millimeter-wave applications. The structure is designed to operate at the W-band. The T-shaped slot antenna is placed on the back-side of the SIW and fed by a grounded coplanar waveguide (GCPW) transmission line. A transition between the (GCPW) and the SIW is also designed. The simulated results provide that the antenna has a stable gain over the frequency range (98.79-100.56) GHz with a maximum value of around 6 dBi also high radiation efficiency.

Abstract
This paper proposes the use of a Gaussian window on the array factor as an interference mitigation method, aiming to avoid the computational complexity of the MVDR algorithm at the cost of a slight performance reduction. We show that by optimizing the parameters of the Gaussian window, it is possible to effectively mitigate the interfering signal if it is received within a certain angular range from the desired signal, while being still effective beyond that range. Finally, we show that the effectiveness of this approach is maintained across the full frequency reception range of the Ka-band, and confirm its validity using 8 × 8 and 16 × 16 array sizes. © 2022 IEEE.

Abstract
Applying machine learning (ML), and especially deep learning, to understand visual content is becoming common practice in many application areas. However, little attention has been given to its use within the multimedia creative domain. It is true that ML is already popular for content creation, but the progress achieved so far addresses essentially textual content or the identification and selection of specific types of content. A wealth of possibilities are yet to be explored by bringing the use of ML into the multimedia creative process, allowing the knowledge inferred by the former to influence automatically how new multimedia content is created. The work presented in this article provides contributions in three distinct ways towards this goal: firstly, it proposes a methodology to re-train popular neural network models in identifying new thematic concepts in static visual content and attaching meaningful annotations to the detected regions of interest; secondly, it presents varied visual digital effects and corresponding tools that can be automatically called upon to apply such effects in a previously analyzed photo; thirdly, it defines a complete automated creative workflow, from the acquisition of a photograph and corresponding contextual data, through the ML region-based annotation, to the automatic application of digital effects and generation of a semantically aware multimedia story driven by the previously derived situational and visual contextual data. Additionally, it presents a variant of this automated workflow by offering to the user the possibility of manipulating the automatic annotations in an assisted manner. The final aim is to transform a static digital photo into a short video clip, taking into account the information acquired. The final result strongly contrasts with current standard approaches of creating random movements, by implementing an intelligent content- and context-aware video.

Abstract
The subjectiveness of multimedia content description has a strong negative impact on tag-based information retrieval. In our work, we propose enhancing available descriptions by adding semantically related tags. To cope with this objective, we use a word embedding technique based on the Word2Vec neural network parameterized and trained using a new dataset built from online newspapers. A large number of news stories was scraped and pre-processed to build a new dataset. Our target language is Portuguese, one of the most spoken languages worldwide. The results achieved significantly outperform similar existing solutions developed in the scope of different languages, including Portuguese. Contributions include also an online application and API available for external use. Although the presented work has been designed to enhance multimedia content annotation, it can be used in several other application areas.