# Electrification of Smart Cities
## 2 Short Summary of the Papers
According to the findings of our recommended program, it outperforms a single SDN controller framework by a factor of more than ten times the throughput. Multi-view subspace clustering has drawn significant attention in the pattern recognition and machine learning research communities. However, most of the existing multi-view subspace clustering methods are still limited in two aspects. (1) The subspace representation yielded by the self-expression reconstruction model ignores the local structural information of the data. (2) The development of subspace representation and clustering are used as two individual procedures, thereby failing to account for their interactions. To address these problems, [PERSON] et al. [5] proposed a novel multi-view subspace-clustering method fusing local and global information for one-step multi-view clustering. The city of Tampere in Finland aims to be carbon-neutral by 2030 and seeks to determine how the electrification of public transport would help achieve this climate goal. Thus far, research has covered topics related to electric buses, ranging from battery technologies to lifecycle assessment and cost analysis. However, less is known about electric city buses' performance in cold climatic zones. [PERSON] et al. [6] collected and analyzed weather and electric-city-bus-related data to ascertain the effects of temperature and weather conditions on the electric buses' efficiency. Data were collected from four battery-electric buses and one hybrid bus as a reference. The buses were fast-charged at a market and slow-charged at a depot. The test route ran downtown. Water distribution infrastructure (WDI) has been well established and significantly improves living quality. Nonetheless, aging WDI poses a challenging worldwide problem entailing the wasting of natural resources, leading to direct and indirect economic losses. The total losses due to leaks are valued at USD 7 billion per year. However, [PERSON] et al. [7] developed a multi-classification multi-leak identification (MC-MLI) scheme to combat this problem. In this MC-MLI scheme, a novel adaptive kernel (AK) program is developed to adapt to different WDI scenarios. The AK program improves overall identification capacity by customizing a weighting vector and transforming into the extracted feature vector. Afterwards, a multi-classification (MC) scheme is designed to facilitate efficient adaptation to potentially hostile inhomogeneous WDI scenarios. The MC scheme comprises multiple classifiers for customizing the network to different pipelines. Solar forecasting plays a crucial role in the renewable energy transition. Major challenges related to load balancing and grid stability emerge when a high percentage of energy is provided by renewables. These can be tackled by new energy management strategies guided by power forecasts. [PERSON] et al. [8] presented a data-driven and contextual optimization-forecasting (DCF) algorithm for solar irradiance that was comprehensively validated using short- and long-term predictions in three US cities: Denver, Boston, and Seattle. Moreover, step-by-step implementation guidelines with which to follow and reproduce the results were proposed. Fault-cause identification plays a significant role in transmission line maintenance and fault disposal. With the increasing types of monitoring data, i.e., micrometeorology and geographic information, multiview learning can be used to realize the fusion of information for better fault-cause identification. To reduce the amount of redundant information in different types of monitoring data, [PERSON] et al. [9] proposed a hierarchical multiview feature selection (HMVFS) method to address the challenge of combining waveform and contextual fault features. To enhance the discriminant ability of the model, an \(\varepsilon\)-dragging technique is introduced to enlarge the boundary between different classes.