## 1 Introduction  
The importance of documentation and metric knowledge in the field of cultural heritage takes on critical relevance when considering the alarming degradation affecting much of the built historical legacy [1], coupled with the necessary interventions for its restoration and conservation. In this context, the meticulous documentation of these valuable structures not only enhances understanding but also provides architects and engineers with the necessary tools to optimize the processes of restoration, safeguarding, and improvement of these cultural treasures [2]. However, a challenge has arisen due to the funding accessibility of these investigations [3]. This situation has catalyzed the creativity and commitment of stakeholders and researchers, who have focused their efforts on creating low-cost geomatic tools and methodologies, without compromising metric accuracy [4]. Among these solutions, multi-image photogrammetry stands out as an effective technique that democratizes the process of metric documentation, encouraging the participation of individuals with no prior geomatic experience [5]. Through the application of multi-image photogrammetry, comprehensive 3D metric surveys can be obtained with a fast and intuitive approach [6]. This technique has versatile applications, ranging from the architectural field to the monitoring of structures and environmental assessment [6]. Specifically, algorithms based on structure-from-motion (SfM) and integrated into multi-image photogrammetry allow for the generation of precise and consistent point clouds, highlighting their suitability for metric documentation [7]. Technological evolution has further facilitated the metric documentation landscape with the introduction of miniaturized and affordable LiDAR (Light Detection And Ranging) sensors [8]. These sensors, such as those integrated into Apple devices since 2020, like the iPhone 12 Pro and the iPad Pro, have popularized the acquisition of 3D data for buildings and historical-cultural heritage assets [9]. This LiDAR, unlike traditional sensors, offers direct time-of-flight measurements (dToF), eliminating mechanical components and ensuring accurate and reliable measurements [10]. To fully harness this advancement, specific applications available in the Apple App Store are required. These applications allow for the acquisition and export of point clouds and meshes in various formats [11]. Both free and paid versions, with more advanced features oriented towards metric documentation, are available [12]. Since the integration of LiDAR into Apple devices, various applications and research groups have explored its geometric potential and practical applications. Nevertheless, despite the critical role that scan parameters--such as scanning speed, sensor angle, lighting, or the distance from the scanned object--may play, there is a lack of detailed studies examining their effects in the literature. To address this gap, this paper employs [PERSON]'s orthogonal arrays to define the optimal scan parameters for the LiDAR sensor on the 2022 iPad Pro. Through this initiative, efficient practices for performing scans can be established that yield useful results, raising the question of which parameters influence scan quality and what the optimal levels are for each [4]. The optimized parameters are then used to scan a historical building. The remining paper is structured as follows. In Section 2, the background of this research is presented in order to summarize the current knowledge in the usage of the iPad for structures scanning. Section 3 comprises the stages, activities, tools and deliverables of the research method conducting this work. A methodology for selecting the optimal levels of scanning parameters for the iPad Pro (Apple Inc., Cupertino, CA, USA), using orthogonal arrays, is also presented. In Section 4, this methodology is applied to three objects with different geometries to determine the ideal scanning parameters. These parameters are then used in Section 5 to monitor the facade of a heritage structure.