This study examined the ventral process of C6 in extinct and extant Equus (sister taxa to Equus ferus caballus only) with the purpose of describing normal morphology and identifying anomalous variations relevant to recent studies describing a congenital malformation in E. ferus caballus. Overall, 83 specimens from 9 museums and 3 research/educational facilities were examined – 71 extinct specimens from 12 species and 12 extant specimens from 5 species. The lateral view revealed a large convexity existed in the ventral process between the cranial ventral tubercle (CrVT) and the caudal ventral tubercle (CVT) in the earliest ancestor Hyracotherium grangeri 55mya that receded throughout the millennia to a smaller convexity in E. ferus caballus and sister taxa. The CrVT was visibly shorter and narrower than the CVT with a constricted section directly ventral to the transverse process essentially demarcating the CrVT and CVT. No congenital malformations were evident. As the ventral process of C6 is an integral component for muscle attachment in supporting the head/neck during posture and locomotion, this would indicate the caudal module in the cervical column might be compromised when a partial or complete absence of the CVT is detected via radiographs in modern E. ferus caballus.

With the dramatic increase in the number of mobile users and wireless devices accessing the network, the performance of the fifth generation (5G) wireless communication systems has been severely challenged. Reconfigurable intelligent surfaces (RIS), one of the potential technologies for the sixth generation (6G), has received a lot of attention. Since it is easier to deploy, consumes less power, and is inexpensive. RIS is an electromagnetic metamaterial that serves to reconfigure the wireless environment by adjusting the phase, amplitude and frequency of the wireless signal. To maximize channel transmission efficiency and improve the reliability of communication systems, the acquisition of channel state information (CSI) is essential. Therefore, an effective channel estimation method guarantees the achievement of excellent RIS performance. This paper conducts a comprehensive investigation of the existing channel estimation methods of RIS, analysis and comparison of channel model building and CSI acquisition schemes in different frequency bands, in addition to a comprehensive description of generic channel estimation methods, with a focus on the application of deep learning. Finally, we conclude the paper and provide an outlook in the future development of RIS channel estimation.