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A celestial autonomous positioning sensor using a multi-spectral star sensor, dual-axis inclinometer, and precision clock, is introduced in this study. We propose a multi-spectral star detection scheme to resolve sky background variation challenges during cross-domain flight operations. Multiple coordinate systems are systematically linked to achieve autonomous positioning, with joint calibration methods used to quantify and correct inclinometer installation errors, which significantly improves cross-domain autonomous positioning accuracy, achieving 156.11 m (3 σ ) precision.

Human motions, such as joint/spinal bending or stretching, often contain information that is useful for orthopedic/neural disease diagnosis, rehabilitation, and prevention. Here, we show a badge-reel-like stretch sensing device with a grating-structured triboelectric nanogenerator exhibiting a stretching sensitivity of 8 V mm −1 , a minimum resolution of 0.6 mm, a low hysteresis, and a high durability (over 120 thousand cycles). Experimental and theoretical investigations are performed to define the key features of the device. Studies from human natural daily activities and exercise demonstrate the functionality of the sensor for real-time recording of knee/arm bending, neck/waist twisting, and so on. We also used the device in a spinal laboratory, monitoring human subjects’ spine motions, and validated the measurements using the commercial inclinometer and hunchback instrument. We anticipate that the lightweight, precise and durable stretch sensor applied to spinal monitoring could help mitigate the risk of long-term abnormal postural habits induced diseases. Human motions often contain information that is useful for orthopedic/neural disease diagnosis, rehabilitation, and prevention. Here, the authors show a badge-reel-like stretch sensing device with a grating-structured triboelectric nanogenerator for joints/spine bending or stretching sensing.

In-place automatic inclinometers are typical devices used to monitor displacements of extremely slow to slow-moving landslides. The significance of these measurements requires methodologies able to distinguish real measures from anomalous ones, to quantify significant moments of acceleration in deformation trends and to determine the main factors that influence the kinematic behavior measured by an automatic inclinometer. This work aimed at developing a novel method, which allows to cover all the steps of analysis of data acquired by automatic inclinometers. The methodology is composed by five steps: (I) evaluation of the reliability of the instruments; (II) identification and elimination of anomalous measures from displacement time-series; (III) recognition of significant moments of acceleration in the rate of displacement, through thresholds based on the mean rate of displacement and on the cumulated amount of the deformation; (IV) clustering of the events of significant acceleration, to characterize different typologies of events according to different landslides kinematic behaviors; (V) identification of the main meteorological and groundwater parameters influencing the deformation pattern measured by an automatic inclinometer. The methodology was developed and tested using displacement time-series of 89 automatic inclinometers, belonging to the regional monitoring network of Piemonte region (northern Italy), managed by Arpa Piemonte. Two representative inclinometric time-series were selected to validate all the steps of the methodology for different types of monitored slow-moving landslides. The developed method is reliable in the estimation of anomalous measures and in the identification of significant accelerations, helping in the comprehension of the response of displacement trends during activity phases. Moreover, it is able to identify the factors which influence more the deformation pattern measured in correspondence of an automatic inclinometer.

The necessity of early warning systems to ensure people’s safety requires the usage of real-time monitoring instrumentation. To meet the required real-time monitoring performance, in-place inclinometer systems represent one of the most common solutions to obtain accurate measures over time. This paper presents the results of a laboratory tests campaign performed on the prototypes and preproduction samples of an in-place inclinometer chain for structural and geotechnical monitoring applications. First, each element sensor has been calibrated to reach a proper level of measure accuracy. Eventually, laboratory tests are carried out on both a single instrument (element) and on the complete measurement chain (system). The adopted centering device, obtained as a combination of a Cardan joint and four spring plungers avoids bending of elements by preventing fictitious displacement measurements and permits the creation of a kinematic chain that accommodates the displacements of a grooveless tube. A specially designed and constructed test set-up that permits assigning a movement to each node has been employed to test a specifically designed centering device and check the system stability over time. Different scenarios have been investigated to determine the accuracy and repeatability of the measures in replicating real cases. The results demonstrated the necessity of validating a measurement chain by analyzing its overall behavior and not limiting the study on the performances of a single element.

New radar satellites provide global coverage and the possibility of long-term, regular frequency (days-weeks) surface displacement measurements through the application of high precision multi-temporal InSAR (Synthetic Aperture Radar Interferometry) techniques. This represents an excellent opportunity to investigate and improve our understanding of the behavior of extremely slow landslides, as well as of the long- to short-term controls of their activity. In urban settings, such landslides deserve special attention, as their cumulative movements can cause significant socio-economic damage. Here, we re-examine the case of a long-lived, deep-seated landslide in the Apennine Mountains (Italy) which was urbanized between the late 1970s and early 2000s. The case provides a rare opportunity to highlight the benefits of the integrated analysis of long-term (several years) borehole inclinometer measurements with 15 years of multi-temporal InSAR displacement data. We present evidence of the landslide composite nature and asymmetry, and draw attention to the recent period of accelerated movement that coincided with the foot failure event. This helps constraining the interpretation of the borehole and InSAR data and demonstrating the predominantly rotational landslide mechanism. We show how a detailed analysis of sparse inclinometer and more spatially continuous InSAR measurements, when combined with local rainfall records, can reveal long- to short-term patterns of temporal variability in landslide motions and allow anticipating the consequences of future landslide activity.

This study examines the deformation of high arch dams during construction, focusing on temperature, self-weight, and creep effects. Using the project as a case study, a finite element model was created and simulations were run to analyse deformation patterns under various loads. The analysis combines principal component and multiple regression analyses to assess each factor’s influence. Results show these three factors significantly impact deformation in different ways. Temperature mainly causes upstream and downstream deformation, with later-stage downstream deformation being notable. Placing monitoring instruments at mid-elevations reduces temperature-related errors. Combined loads of self-weight, temperature, and creep have minimal impact on measurement precision. Maximum deformation occurs at specific dam heights, with the highest deformation gradient near 1/3 and 2/3 dam heights. A flexible inclinometer with 0.1mm precision is needed for effective measurement. These findings provide valuable theoretical and technical guidance for monitoring and controlling deformation in high arch dams during construction.

Excavation-induced deformations of earth-retaining walls (ERWs) can critically affect the safety of surrounding structures, highlighting the need for reliable prediction models to support timely decision-making during construction. This study utilizes traditional statistical ARIMA (Auto-Regressive Integrated Moving Average) and deep learning-based LSTM (Long Short-Term Memory) models to predict earth-retaining walls deformation using inclinometer data from excavation sites and compares the predictive performance of both models. The ARIMA model demonstrates strengths in analyzing linear patterns in time-series data as it progresses over time, whereas LSTM exhibits superior capabilities in capturing complex non-linear patterns and long-term dependencies within the time series data. This research includes preprocessing of measurement data for inclinometer, performance evaluation based on various time series data lengths and input variable conditions, and demonstrates that the LSTM model offers statistically significant improvements in predictive performance over the ARIMA model. In addition, by combining LSTM with attention mechanism, attention-based LSTM (ATLSTM) is proposed to improve the short- and long-term prediction performance and solve the problem of excavation site domain change. This study presents the advantages and disadvantages of major time series analysis models for the stability evaluation of mud walls using geotechnical inclinometer data from excavation sites, and suggests that time series analysis models can be used effectively through comparative experiments.

In view of the soft coal seam drilling construction of small and medium-sized rotary drilling rigs in coal mines, the soft coal seam is usually used to support the hole wall due to the loose coal quality. The traditional wireless storage measurement trajectory instrument has a large outer diameter, which cannot realize the measurement while drilling in the drilling construction while the screen is lowered. In this paper, a reduced storage borehole trajectory measuring instrument is proposed, and the outer diameter and length of the probe tube are improved to be 20 mm and 692 mm. A fixed support structure for the probe pipe is designed, which can be applied to the drill pipe with an outer diameter of φ≥73mm, which is convenient for the measurement of the trajectory while drilling of the soft coal seam in the construction of the screen pipe. The industrial test shows that the measurement accuracy of the inclinometer is ± 0.2° in azimuth and plus or minus 0.1° in inclination, which realizes the process requirements of screen lowering without redrilling measurement.

This paper presents soil mass movement monitoring for landslides detection using low-cost MEMS accelerometer as inclinometer. Commercial inclinometers for geotechnical ground observations are quite expensive. This research aims to study and develop low-cost inclinometer as an alternative using accelerometer. The output of the low-cost accelerometer is noisy and fluctuated make it not suitable for accurate measurement device. We solved this problem in this paper using moving average filter. The digital filter algorithm was tested and showed promising results.

The accuracy of landslide displacement prediction can effectively prevent casualties and economic losses. To achieve accurate prediction of the Majiagou landslide displacement in the Three Gorges Reservoir (TGR), China, a hybrid machine learning prediction model considering the deformation hysteresis effect is proposed. The real-time deep displacement measurements were captured by using in-place inclinometers with Fiber Bragg grating (FBG) sensors. The time series method was adopted to divide the total displacement into a trend term and periodic term. Trend displacement was determined by the geological condition and predicted by the fitting method. Periodic displacement was controlled by external factors such as rainfall and fluctuation of reservoir water level. Before making the prediction, the grey correlation analysis was adopted to confirm that the fluctuation of the reservoir water level was the main influence factor. In view of the deficiency that current prediction methods could not quantitatively determine the lag time of landslide deformation and thus select the influencing factors empirically, the dynamic analysis of the correlation between periodic influence factors and periodic displacement was carried out in this paper, and the deformation lag time was identified to be 18 days by using set pair analysis (SPA) method. Finally, the optimal influence factors were selected and the prediction model of Majiagou landslide based on support vector machine optimized by particle swarm optimization (SPA-PSO-SVM) was established. Results showed that the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the proposed SPA-PSO-SVM prediction model are 0.28 and 12.8, respectively. Compared with the PSO-SVM model, the prediction accuracy of the proposed model had been improved significantly. The reliability and effectiveness of the SPA-PSO-SVM prediction model is verified and it has apparent advantages while predicting landslide displacement with deformation hysteresis effect involved.