bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimation of Water Coverage in Permanent and Temporary Shallow Lakes and Wetlands by Combining Remote Sensing Techniques and Genetic Programming: Application to the Mediterranean Basin of the Iberian Peninsula
Authors: Doña Carolina , Morant Daniel , Picazo Antonio , Rochera Carlos , Sánchez Juan Manuel , Camacho Antonio
Published: February 2021

This work aims to validate the wide use of an algorithm developed using genetic programing (GP) techniques allowing to discern between water and non-water pixels using the near infrared band and different thresholds. A total of 34 wetlands and shallow lakes of 18 ecological types were used for validation. These include marshes, salt ponds, and saline and freshwater, temporary and permanent shallow lakes. Furthermore, based on the spectral matching between Landsat and Sentinel-2 sensors, this methodology was applied to Sentinel-2 imagery, improving the spatial and temporal resolution. When compared to other techniques, GP showed better accuracy (over 85% in most cases) and acceptable kappa values in the estimation of water pixels (κ ≥ 0.7) in 10 of the 18 assayed ecological types evaluated with Landsat-7 and Sentinel-2 imagery. The improvements were especially achieved for temporary lakes and wetlands, where existing algorithms were scarcely reliable. This shows that GP algorithms applied to remote sensing satellite imagery can be a valuable tool to monitor water coverage in wetlands and shallow lakes where multiple factors cause a low resolution by commonly used water indices. This allows the reconstruction of hydrological series showing their hydrological behaviors during the last three decades, being useful to predict how their hydrological pattern may behave under future global change scenarios.
iCOR Atmospheric Correction on Sentinel-3/OLCI over Land: Intercomparison with AERONET, RadCalNet, and SYN Level-2
Authors: Wolters Erwin , Toté Carolien , Sterckx Sindy , Adriaensen Stefan , Henocq Claire , Bruniquel Jérôme , Scifoni Silvia , Dransfeld Steffen
Published: February 2021

To validate the iCOR atmospheric correction algorithm applied to the Sentinel-3 Ocean and Land Color Instrument (OLCI), Top-of-Atmosphere (TOA) observations over land, globally retrieved Aerosol Optical Thickness (AOT), Top-of-Canopy (TOC) reflectance, and Vegetation Indices (VIs) were intercompared with (i) AERONET AOT and AERONET-based TOC reflectance simulations, (ii) RadCalNet surface reflectance observations, and (iii) SYN Level 2 (L2) AOT, TOC reflectance, and VIs. The results reveal that, overall, iCOR’s statistical and temporal consistency is high. iCOR AOT retrievals overestimate relative to AERONET, but less than SYN L2. iCOR and SYN L2 TOC reflectances exhibit a negative bias of ~−0.01 and −0.02, respectively, in the Blue bands compared to the simulations. This diminishes for RED and NIR, except for a +0.02 bias for SYN L2 in the NIR. The intercomparison with RadCalNet shows relative differences < ±6%, except for bands Oa02 (Blue) and Oa21 (NIR), which is likely related to the reported OLCI “excess of brightness”. The intercomparison between iCOR and SYN L2 showed R2 = 0.80–0.93 and R2 = 0.92–0.96 for TOC reflectance and VIs, respectively. iCOR’s higher temporal smoothness compared to SYN L2 does not propagate into a significantly higher smoothness for TOC reflectance and VIs. Altogether, we conclude that iCOR is well suitable to retrieve statistically and temporally consistent AOT, TOC reflectance, and VIs over land surfaces from Sentinel-3/OLCI observations.
A Response of Snow Cover to the Climate in the Northwest Himalaya (NWH) Using Satellite Products
Authors: Choudhury Animesh , Yadav Avinash Chand , Bonafoni Stefania
Published: February 2021

The Himalayan region is one of the most crucial mountain systems across the globe, which has significant importance in terms of the largest depository of snow and glaciers for fresh water supply, river runoff, hydropower, rich biodiversity, climate, and many more socioeconomic developments. This region directly or indirectly affects millions of lives and their livelihoods but has been considered one of the most climatically sensitive parts of the world. This study investigates the spatiotemporal variation in maximum extent of snow cover area (SCA) and its response to temperature, precipitation, and elevation over the northwest Himalaya (NWH) during 2000–2019. The analysis uses Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra 8-day composite snow Cover product (MOD10A2), MODIS/Terra/V6 daily land surface temperature product (MOD11A1), Climate Hazards Infrared Precipitation with Station data (CHIRPS) precipitation product, and Shuttle Radar Topography Mission (SRTM) DEM product for the investigation. Modified Mann-Kendall (mMK) test and Spearman’s correlation methods were employed to examine the trends and the interrelationships between SCA and climatic parameters. Results indicate a significant increasing trend in annual mean SCA (663.88 km2/year) between 2000 and 2019. The seasonal and monthly analyses were also carried out for the study region. The Zone-wise analysis showed that the lower Himalaya (184.5 km2/year) and the middle Himalaya (232.1 km2/year) revealed significant increasing mean annual SCA trends. In contrast, the upper Himalaya showed no trend during the study period over the NWH region. Statistically significant negative correlation (−0.81) was observed between annual SCA and temperature, whereas a nonsignificant positive correlation (0.47) existed between annual SCA and precipitation in the past 20 years. It was also noticed that the SCA variability over the past 20 years has mainly been driven by temperature, whereas the influence of precipitation has been limited. A decline in average annual temperature (−0.039 °C/year) and a rise in precipitation (24.56 mm/year) was detected over the region. The results indicate that climate plays a vital role in controlling the SCA over the NWH region. The maximum and minimum snow cover frequency (SCF) was observed during the winter (74.42%) and monsoon (46.01%) season, respectively, while the average SCF was recorded to be 59.11% during the study period. Of the SCA, 54.81% had a SCF above 60% and could be considered as the perennial snow. The elevation-based analysis showed that 84% of the upper Himalaya (UH) experienced perennial snow, while the seasonal snow mostly dominated over the lower Himalaya (LH) and the middle Himalaya (MH).
Improved YOLOv3 Based on Attention Mechanism for Fast and Accurate Ship Detection in Optical Remote Sensing Images
Authors: Chen Liqiong , Shi Wenxuan , Deng Dexiang
Published: February 2021

Ship detection is an important but challenging task in the field of computer vision, partially due to the minuscule ship objects in optical remote sensing images and the interference of clouds occlusion and strong waves. Most of the current ship detection methods focus on boosting detection accuracy while they may ignore the detection speed. However, it is also indispensable to increase ship detection speed because it can provide timely ocean rescue and maritime surveillance. To solve the above problems, we propose an improved YOLOv3 (ImYOLOv3) based on attention mechanism, aiming to achieve the best trade-off between detection accuracy and speed. First, to realize high-efficiency ship detection, we adopt the off-the-shelf YOLOv3 as our basic detection framework due to its fast speed. Second, to boost the performance of original YOLOv3 for small ships, we design a novel and lightweight dilated attention module (DAM) to extract discriminative features for ship targets, which can be easily embedded into the basic YOLOv3. The integrated attention mechanism can help our model learn to suppress irrelevant regions while highlighting salient features useful for ship detection task. Furthermore, we introduce a multi-class ship dataset (MSD) and explicitly set supervised subclass according to the scales and moving states of ships. Extensive experiments verify the effectiveness and robustness of ImYOLOv3, and show that our method can accurately detect ships with different scales in different backgrounds, while at a real-time speed.
Editorial for Underwater 3D Recording & Modelling
Authors: Skarlatos Dimitrios , Bruno Fabio , Menna Fabio , Nocerino Erica
Published: February 2021

The Special Issue “Underwater 3D recording and modelling” is focused on challenges for 3D modeling and ways to overcome them in the underwater environment [...]
End-to-End Super-Resolution for Remote-Sensing Images Using an Improved Multi-Scale Residual Network
Authors: Huan Hai , Li Pengcheng , Zou Nan , Wang Chao , Xie Yaqin , Xie Yong , Xu Dongdong
Published: February 2021

Remote-sensing images constitute an important means of obtaining geographic information. Image super-resolution reconstruction techniques are effective methods of improving the spatial resolution of remote-sensing images. Super-resolution reconstruction networks mainly improve the model performance by increasing the network depth. However, blindly increasing the network depth can easily lead to gradient disappearance or gradient explosion, increasing the difficulty of training. This report proposes a new pyramidal multi-scale residual network (PMSRN) that uses hierarchical residual-like connections and dilation convolution to form a multi-scale dilation residual block (MSDRB). The MSDRB enhances the ability to detect context information and fuses hierarchical features through the hierarchical feature fusion structure. Finally, a complementary block of global and local features is added to the reconstruction structure to alleviate the problem that useful original information is ignored. The experimental results showed that, compared with a basic multi-scale residual network, the PMSRN increased the peak signal-to-noise ratio by up to 0.44 dB and the structural similarity to 0.9776.
Spatial and Temporal Differences in Alpine Meadow, Alpine Steppe and All Vegetation of the Qinghai-Tibetan Plateau and Their Responses to Climate Change
Authors: Duan Hanchen , Xue Xian , Wang Tao , Kang Wenping , Liao Jie , Liu Shulin
Published: February 2021

Alpine meadow and alpine steppe are the two most widely distributed nonzonal vegetation types in the Qinghai-Tibet Plateau. In the context of global climate change, the differences in spatial-temporal variation trends and their responses to climate change are discussed. It is of great significance to reveal the response of the Qinghai-Tibet Plateau to global climate change and the construction of ecological security barriers. This study takes alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau as the research objects. The normalized difference vegetation index (NDVI) data and meteorological data were used as the data sources between 2000 and 2018. By using the mean value method, threshold method, trend analysis method and correlation analysis method, the spatial and temporal variation trends in the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau were compared and analyzed, and their differences in the responses to climate change were discussed. The results showed the following: (1) The growing season length of alpine meadow was 145~289 d, while that of alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau was 161~273 d, and their growing season lengths were significantly shorter than that of alpine meadow. (2) The annual variation trends of the growing season NDVI for the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau increased obviously, but their fluctuation range and change rate were significantly different. (3) The overall vegetation improvement in the Qinghai-Tibet Plateau was primarily dominated by alpine steppe and alpine meadow, while the degradation was primarily dominated by alpine meadow. (4) The responses between the growing season NDVI and climatic factors in the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau had great spatial heterogeneity in the Qinghai-Tibet Plateau. These findings provide evidence towards understanding the characteristics of the different vegetation types in the Qinghai-Tibet Plateau and their spatial differences in response to climate change.
FOD Detection Method Based on Iterative Adaptive Approach for Millimeter-Wave Radar
Authors: Wan Yangliang , Liang Xingdong , Bu Xiangxi , Liu Yunlong
Published: February 2021

Using millimeter-wave radar to scan and detect small foreign object debris (FOD) on an airport runway surface is a popular solution in civil aviation safety. Since it is impossible to completely eliminate the interference reflections arising from strongly scattering targets or non-homogeneous clutter after clutter cancellation processing, the consequent high false alarm probability has become a key problem to be solved. In this article, we propose a new FOD detection method for interference suppression and false alarm reduction based on an iterative adaptive approach (IAA) algorithm, which is a non-parametric, weighted least squares-based iterative adaptive processing approach that can provide super-resolution capability. Specifically, we first obtain coarse FOD target information by data preprocessing in a conventional detection method. Then, a refined data processing step is conducted based on the IAA algorithm in the azimuth direction. Finally, multiple pieces of information from the two steps above are used to comprehensively distinguish false alarms by fusion processing; thus, we can acquire accurate FOD target information. Real airport data measured by a 93 GHz radar are used to validate the proposed method. Experimental results of the test scene, which include golf balls with a diameter of 43 mm, were placed about 300 m away from radar, which show that the proposed method can effectively reduce the number of false alarms when compared with a traditional FOD detection method. Although metal balls with a diameter of 50 mm were placed about 660 m away from radar, they also can obtain up to 2.2 times azimuth super-resolution capability.
Event-Triggering State and Fault Estimation for a Class of Nonlinear Systems Subject to Sensor Saturations
Authors: Huang Cong , Shen Bo , Zou Lei , Shen Yuxuan
Published: February 2021

This paper is concerned with the state and fault estimation issue for nonlinear systems with sensor saturations and fault signals. For the sake of avoiding the communication burden, an event-triggering protocol is utilized to govern the transmission frequency of the measurements from the sensor to its corresponding recursive estimator. Under the event-triggering mechanism (ETM), the current transmission is released only when the relative error of measurements is bigger than a prescribed threshold. The objective of this paper is to design an event-triggering recursive state and fault estimator such that the estimation error covariances for the state and fault are both guaranteed with upper bounds and subsequently derive the gain matrices minimizing such upper bounds, relying on the solutions to a set of difference equations. Finally, two experimental examples are given to validate the effectiveness of the designed algorithm.
Nanostructures for Biosensing, with a Brief Overview on Cancer Detection, IoT, and the Role of Machine Learning in Smart Biosensors
Authors: Banerjee Aishwaryadev , Maity Swagata , Mastrangelo Carlos H.
Published: February 2021

Biosensors are essential tools which have been traditionally used to monitor environmental pollution and detect the presence of toxic elements and biohazardous bacteria or virus in organic matter and biomolecules for clinical diagnostics. In the last couple of decades, the scientific community has witnessed their widespread application in the fields of military, health care, industrial process control, environmental monitoring, food-quality control, and microbiology. Biosensor technology has greatly evolved from in vitro studies based on the biosensing ability of organic beings to the highly sophisticated world of nanofabrication-enabled miniaturized biosensors. The incorporation of nanotechnology in the vast field of biosensing has led to the development of novel sensors and sensing mechanisms, as well as an increase in the sensitivity and performance of the existing biosensors. Additionally, the nanoscale dimension further assists the development of sensors for rapid and simple detection in vivo as well as the ability to probe single biomolecules and obtain critical information for their detection and analysis. However, the major drawbacks of this include, but are not limited to, potential toxicities associated with the unavoidable release of nanoparticles into the environment, miniaturization-induced unreliability, lack of automation, and difficulty of integrating the nanostructured-based biosensors, as well as unreliable transduction signals from these devices. Although the field of biosensors is vast, we intend to explore various nanotechnology-enabled biosensors as part of this review article and provide a brief description of their fundamental working principles and potential applications. The article aims to provide the reader a holistic overview of different nanostructures which have been used for biosensing purposes along with some specific applications in the field of cancer detection and the Internet of things (IoT), as well as a brief overview of machine-learning-based biosensing.
Drowsiness Detection Based on Intelligent Systems with Nonlinear Features for Optimal Placement of Encephalogram Electrodes on the Cerebral Area
Authors: Hong Seunghyeok , Baek Hyun Jae
Published: February 2021

Drowsiness while driving can lead to accidents that are related to the loss of perception during emergencies that harm the health. Among physiological signals, brain waves have been used as informative signals for the analyses of behavioral observations, steering information, and other biosignals during drowsiness. We inspected the machine learning methods for drowsiness detection based on brain signals with varying quantities of information. The results demonstrated that machine learning could be utilized to compensate for a lack of information and to account for individual differences. Cerebral area selection approaches to decide optimal measurement locations could be utilized to minimize the discomfort of participants. Although other statistics could provide additional information in further study, the optimized machine learning method could prevent the dangers of drowsiness while driving by considering a transitional state with nonlinear features. Because brain signals can be altered not only by mental fatigue but also by health status, the optimization analysis of the system hardware and software will be able to increase the power-efficiency and accessibility in acquiring brain waves for health enhancements in daily life.
An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces
Authors: Zhu Fangkun , Jiang Lu , Dong Guoya , Gao Xiaorong , Wang Yijun
Published: February 2021

Brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenge to the practical application. This study provides an open dataset, which is collected based on a wearable SSVEP-based BCI system, and comprehensively compares the SSVEP data obtained by wet and dry electrodes. The dataset consists of 8-channel EEG data from 102 healthy subjects performing a 12-target SSVEP-based BCI task. For each subject, 10 consecutive blocks were recorded using wet and dry electrodes, respectively. The dataset can be used to investigate the performance of wet and dry electrodes in SSVEP-based BCIs. Besides, the dataset provides sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.
The Use of Pulse Oximetry in the Assessment of Acclimatization to High Altitude
Authors: Dünnwald Tobias , Kienast Roland , Niederseer David , Burtscher Martin
Published: February 2021

Background: Finger pulse oximeters are widely used to monitor physiological responses to high-altitude exposure, the progress of acclimatization, and/or the potential development of high-altitude related diseases. Although there is increasing evidence for its invaluable support at high altitude, some controversy remains, largely due to differences in individual preconditions, evaluation purposes, measurement methods, the use of different devices, and the lacking ability to interpret data correctly. Therefore, this review is aimed at providing information on the functioning of pulse oximeters, appropriate measurement methods and published time courses of pulse oximetry data (peripheral oxygen saturation, (SpO2) and heart rate (HR), recorded at rest and submaximal exercise during exposure to various altitudes. Results: The presented findings from the literature review confirm rather large variations of pulse oximetry measures (SpO2 and HR) during acute exposure and acclimatization to high altitude, related to the varying conditions between studies mentioned above. It turned out that particularly SpO2 levels decrease with acute altitude/hypoxia exposure and partly recover during acclimatization, with an opposite trend of HR. Moreover, the development of acute mountain sickness (AMS) was consistently associated with lower SpO2 values compared to individuals free from AMS. Conclusions: The use of finger pulse oximetry at high altitude is considered as a valuable tool in the evaluation of individual acclimatization to high altitude but also to monitor AMS progression and treatment efficacy.
Image Captioning Using Motion-CNN with Object Detection
Authors: Iwamura Kiyohiko , Louhi Kasahara Jun Younes , Moro Alessandro , Yamashita Atsushi , Asama Hajime
Published: February 2021

Automatic image captioning has many important applications, such as the depiction of visual contents for visually impaired people or the indexing of images on the internet. Recently, deep learning-based image captioning models have been researched extensively. For caption generation, they learn the relation between image features and words included in the captions. However, image features might not be relevant for certain words such as verbs. Therefore, our earlier reported method included the use of motion features along with image features for generating captions including verbs. However, all the motion features were used. Since not all motion features contributed positively to the captioning process, unnecessary motion features decreased the captioning accuracy. As described herein, we use experiments with motion features for thorough analysis of the reasons for the decline in accuracy. We propose a novel, end-to-end trainable method for image caption generation that alleviates the decreased accuracy of caption generation. Our proposed model was evaluated using three datasets: MSR-VTT2016-Image, MSCOCO, and several copyright-free images. Results demonstrate that our proposed method improves caption generation performance.
Remote Photoplethysmography for Evaluation of Cutaneous Sensory Nerve Fiber Function
Authors: Marcinkevics Zbignevs , Aglinska Alise , Rubins Uldis , Grabovskis Andris
Published: February 2021

About 2% of the world’s population suffers from small nerve fiber dysfunction, neuropathy, which can result in severe pain. This condition is caused by damage to the small nerve fibers and its assessment is challenging, due to the lack of simple and objective diagnostic techniques. The present study aimed to develop a contactless photoplethysmography system using simple instrumentation, for objective and non-invasive assessment of small cutaneous sensory nerve fiber function. The approach is based on the use of contactless photoplethysmography for the characterization of skin flowmotions and topical heating evoked vasomotor responses. The feasibility of the technique was evaluated on volunteers (n = 14) using skin topical anesthesia, which is able to produce temporary alterations of cutaneous nerve fibers function. In the treated skin region in comparison to intact skin: neurogenic and endothelial component of flowmotions decreased by ~61% and 41%, the local heating evoked flare area decreased by ~44%, vasomotor response trend peak and nadir were substantially reduced. The results indicate for the potential of the remote photoplethysmography in the assessment of the cutaneous nerve fiber function. It is believed that in the future this technique could be used in the clinics as an affordable alternative to laser Doppler imaging technique.
A Comparative Survey of Feature Extraction and Machine Learning Methods in Diverse Acoustic Environments
Authors: Bonet-Solà Daniel , Alsina-Pagès Rosa Ma
Published: February 2021

Acoustic event detection and analysis has been widely developed in the last few years for its valuable application in monitoring elderly or dependant people, for surveillance issues, for multimedia retrieval, or even for biodiversity metrics in natural environments. For this purpose, sound source identification is a key issue to give a smart technological answer to all the aforementioned applications. Diverse types of sounds and variate environments, together with a number of challenges in terms of application, widen the choice of artificial intelligence algorithm proposal. This paper presents a comparative study on combining several feature extraction algorithms (Mel Frequency Cepstrum Coefficients (MFCC), Gammatone Cepstrum Coefficients (GTCC), and Narrow Band (NB)) with a group of machine learning algorithms (k-Nearest Neighbor (kNN), Neural Networks (NN), and Gaussian Mixture Model (GMM)), tested over five different acoustic environments. This work has the goal of detailing a best practice method and evaluate the reliability of this general-purpose algorithm for all the classes. Preliminary results show that most of the combinations of feature extraction and machine learning present acceptable results in most of the described corpora. Nevertheless, there is a combination that outperforms the others: the use of GTCC together with kNN, and its results are further analyzed for all the corpora.
Learning for a Robot: Deep Reinforcement Learning, Imitation Learning, Transfer Learning
Authors: Hua Jiang , Zeng Liangcai , Li Gongfa , Ju Zhaojie
Published: February 2021

Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured environment. In view of the existing problem, this paper presents a state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning. The paper first reviews the main achievements and research of the robot, which were mainly based on the breakthrough of automatic control and hardware in mechanics. With the evolution of artificial intelligence, many pieces of research have made further progresses in adaptive and robust control. The survey reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots. Furthermore, deep reinforcement learning, imitation learning, and transfer learning in robot control are discussed in detail. Finally, major achievements based on these methods are summarized and analyzed thoroughly, and future research challenges are proposed.
Data Driven Model-Free Adaptive Control Method for Quadrotor Formation Trajectory Tracking Based on RISE and ISMC Algorithm
Authors: Yuan Dongdong , Wang Yankai
Published: February 2021

In order to solve the problems of complex dynamic modeling and parameters identification of quadrotor formation cooperative trajectory tracking control, this paper proposes a data-driven model-free adaptive control method for quadrotor formation based on robust integral of the signum of the error (RISE) and improved sliding mode control (ISMC). The leader-follower strategy is adopted, and the leader realizes trajectory tracking control. A novel asymptotic tracking data-driven controller of quadrotor is used to control the system using the RISE method. It is divided into two parts: The inner loop is for attitude control and the outer loop for position control. Both use the RISE method in the loop to eliminate interference and this method only uses the input and output data of the unmanned aerial vehicle(UAV) system and does not rely on any dynamics and kinematics model of the UAV. The followers realize formation cooperative control, introducing adaptive update law and saturation function to improve sliding mode control (SMC), and it eliminates the general SMC algorithm controller design dependence on the mathematical model of the UAV and has the chattering problem. Then, the stability of the system is proved by the Lyapunov method, and the effectiveness of the algorithm and the feasibility of the scheme are verified by numerical simulation. The experimental results show that the designed data-driven model-free adaptive control method for the quadrotor formation is effective and can effectively realize the coordinated formation trajectory tracking control of the quadrotor. At the same time, the design of the controller does not depend on the UAV kinematics and dynamics model, and it has high control accuracy, stability, and robustness.
Electrical and Electromagnetic Geophysical Prospecting for the Monitoring of Rock Glaciers in the Dolomites, Northeast Italy
Authors: Pavoni Mirko , Sirch Fabio , Boaga Jacopo
Published: February 2021

The monitoring of rock glaciers plays a relevant role in relation to natural hazards in high mountain environments. Due to the climate warming, mountain permafrost is thawing, and its degradation is influencing the triggering and the evolvement of processes such as rockfalls, landslides, debris flows and floods. Therefore, the study and monitoring of these periglacial forms have both a scientific and economic importance. We tested electrical and electromagnetic measurements along the same investigation lines, in two different sites of the Dolomites area (Northeast Italy). Electrical prospecting exploits the high resistivity contrast between frozen and non-frozen debris. However, these measurements have high logistic demands, considering the complex rock glaciers surface and the need of ground galvanic contact. For this reason, we tried to compare electrical measurements with electromagnetic contactless ones, that theoretically can be used to define the distribution of electrical resistivity in the first subsoil in a quicker and easier way. The obtained results show that the joint use of the two methods allows us to characterize a rock glacier subsoil with good confidence. Finally, the advantages and disadvantages of both the techniques are discussed.
Experimental Comparison of Radon Domain Approaches for Resident Space Object’s Parameter Estimation
Authors: Ghio Selenia , Martorella Marco , Staglianò Daniele , Petri Dario , Lischi Stefano , Massini Riccardo
Published: February 2021

The fast and uncontrolled rise of the space objects population is threatening the safety of space assets. At the moment, space awareness solutions are among the most calling research topic. In fact, it is vital to persistently observe and characterize resident space objects. Instrumental highlights for their characterization are doubtlessly their size and rotational period. The Inverse Radon Transform (IRT) has been demonstrated to be an effective method for this task. The analysis presented in this paper has the aim to compare various approaches relying on IRT for the estimation of the object’s rotation period. Specifically, the comparison is made on the basis of simulated and experimental data.
Methods and Limits for Micro Scale Blood Vessel Flow Imaging in Scattering Media by Optical Feedback Interferometry: Application to Human Skin
Authors: Quotb Adam , Atashkhooei Reza , Magaletti Simone , Jayat Francis , Tronche Clement , Goechnahts Julien , Perchoux Julien
Published: February 2021

At the micrometric scale, vessels or skin capillaries network architecture can provide useful information for human health management. In this paper, from simulation to in vitro, we investigate some limits and interests of optical feedback interferometry (OFI) for blood flow imaging of skin vascularization. In order to analyze the tissue scattering effect on OFI performances, a series of skin-tissue simulating optical phantoms have been designed, fabricated and characterized. The horizontal (2D) and vertical (depth penetration) sensing resolution of the OFI sensor have been estimated. The experimental results that we present on this study are showing a very good accordance with theoretical models. In the case of a skin phantom of 0.5 mm depth with a scattering coefficient from 0 to 10.8 mm−1, the presented OFI system is able to distinguish a pair of micro fluidic channels (100 µm × 100 µm) spaced by 10 µm. Eventually, an in vivo test on human skin is presented and, for the first time using an OFI sensor, a 2D blood flow image of a vein located just beneath the skin is computed.
Multimodal Signal Analysis for Pain Recognition in Physiotherapy Using Wavelet Scattering Transform
Authors: Badura Aleksandra , Masłowska Aleksandra , Myśliwiec Andrzej , Piętka Ewa
Published: February 2021

Fascial therapy is an effective, yet painful, procedure. Information about pain level is essential for the physiotherapist to adjust the therapy course and avoid potential tissue damage. We have developed a method for automatic pain-related reaction assessment in physiotherapy due to the subjectivity of a self-report. Based on a multimodal data set, we determine the feature vector, including wavelet scattering transforms coefficients. The AdaBoost classification model distinguishes three levels of reaction (no-pain, moderate pain, and severe pain). Because patients vary in pain reactions and pain resistance, our survey assumes a subject-dependent protocol. The results reflect an individual perception of pain in patients. They also show that multiclass evaluation outperforms the binary recognition.
Application of Measurement Sensors and Navigation Devices in Experimental Research of the Computer System for the Control of an Unmanned Ship Model
Authors: Szelangiewicz Tadeusz , Żelazny Katarzyna , Antosik Andrzej , Szelangiewicz Maciej
Published: February 2021

Unmanned autonomous transport vessels (MASS) are the future of maritime transport. The most important task in the design and construction of unmanned ships is to develop algorithms and a computer program for autonomous control. In order for such a computer program to properly control the ship (realizing various functions), the ship must be equipped with a computer system as well as measurement sensors and navigation devices, from which the recorded parameters are processed and used for autonomous control of the ship. Within the framework of conducted research on autonomous ships, an experimental model of an unmanned ship was built. This model was equipped with a propulsion system not commonly used on transport vessels (two azimuth stern thrusters and two bow tunnel thrusters), but providing excellent propulsion and steering characteristics. A complete computer system with the necessary measuring sensors and navigation devices has also been installed in the model of the ship, which enables it to perform all functions during autonomous control. The objective of the current research was to design and build a prototype computer system with the necessary measurement sensors and navigation devices with which to autonomously control the unmanned ship model. The designed computer system is expected to be optimal for planned tasks during control software tests. Tests carried out on open waters confirmed the correctness of the operation of the computer system and the entire measurement and navigation equipment of the built model of the unmanned transport vessel.
Leveraging Deep Learning for Visual Odometry Using Optical Flow
Authors: Pandey Tejas , Pena Dexmont , Byrne Jonathan , Moloney David
Published: February 2021

In this paper, we study deep learning approaches for monocular visual odometry (VO). Deep learning solutions have shown to be effective in VO applications, replacing the need for highly engineered steps, such as feature extraction and outlier rejection in a traditional pipeline. We propose a new architecture combining ego-motion estimation and sequence-based learning using deep neural networks. We estimate camera motion from optical flow using Convolutional Neural Networks (CNNs) and model the motion dynamics using Recurrent Neural Networks (RNNs). The network outputs the relative 6-DOF camera poses for a sequence, and implicitly learns the absolute scale without the need for camera intrinsics. The entire trajectory is then integrated without any post-calibration. We evaluate the proposed method on the KITTI dataset and compare it with traditional and other deep learning approaches in the literature.
Minimizing Lentil Harvest Loss through Improved Agronomic Practices in Sustainable Agro-Systems
Authors: Yang Tony , Liu Kui , Poppy Lee , Mulenga Alick , Gampe Cindy
Published: February 2021

Lentils are one of the most common legume crops used to diversify the cereal-oilseed cropping system in semi-arid environments. Lentils are a major source of protein and fiber for human consumption worldwide. However, the morphological characteristics of lentil plants—such as a short stem and low pod positioning—and complicated combine harvesting methods often result in yield loss. This also increases the susceptibility of a lentil crop to disease and render it less competitive against weeds. As a result, producers have resorted to using pesticides in order to mitigate the effects of weeds and disease. As a consequence, there have been undesirable negative environmental impacts on sustainable agroecosystems. Although land rolling, stubble management, and pesticide usage are common agronomic practices used to increase lentil yield and mitigate the issues associated with its morphology, their comprehensive effects on lentil growth and harvest loss are still not fully understood. In this study, we examined the impact of stubble management, the timing of land rolling, and the application of common fungicides and herbicides on lentil growth and yield. We found that stubble management and the timing of rolling modified lentil morphological structures, and thus impacted lentil yield and seed loss. These results were influenced by environmental factors, such as precipitation during the growing season. Although the results did not show significant interaction between fungicide application and lentil growth and yield, herbicide applications, stubble management, and the timing of rolling, along with common pesticide application strategies tested in our study, showed effects that were dependent on environmental conditions. Based on our results, we concluded that stubble management and the timing of rolling, combined with pesticide applications, can affect lentil seed loss and yield by modifying plant morphology. This was largely influenced by environmental conditions such as precipitation.
On the Convergence of Collaborative and Social Economy: A Quality Model for the Combined Effects
Authors: Moreno Chacón Pierina , Selvam Rejina M. , Marimon Frederic
Published: February 2021

This article has two aims: the first is to propose a definition for social collaborative companies that encompasses their main characteristics, and once the companies to which we refer is settled, the second aim is to propose a quality model for social collaborative companies. These companies are of a particular type based on a collaborative business model and with a social focus as part of their mission. They employ a democratic style of governance, whether or not they are cooperatives, and operate through a platform in the collaborative environment. The quality model has three main categories: management, operations, and continuous improvement in a cyclical manner to ensure continuous improvement. The findings may be interesting for academics due to the fact that it is the first attempt to provide a quality model for these social collaborative companies and provide a conceptual framework for these organizations. Additionally, some managerial implications can be evident, such as when (i) the framework functions as a management guide for excellence, (ii) a tool for benchmarking, and (iii) a tool for internal and external communication.
Agile Beeswax: Mobile App Development Process and Empirical Study in Real Environment
Authors: Alrabaiah Hazem Abdelkarim , Medina-Medina Nuria
Published: February 2021

Mobile application development is a highly competitive environment; agile methodologies can enable teams to provide value faster, with higher quality and predictability, and a better attitude to deal with the continuous changes that will arise in the mobile context application (App), and the positive impact of that on sustainable development through continuous progress. App development is different from other types of software. For this reason, our objective is to present a new agile-based methodology for app development that we call Agile Beeswax. Agile Beeswax is conceived after identifying the mobile development process’s issues and challenges, and unique requirements. Agile Beeswax is an incremental, iterative development process composed of two main iterative loops (sprints), the incremental design loop and the incremental development loop, and one bridge connecting these two sprints. Agile Beeswax is structured in six phases, idea and strategy, user experience design, user interface design, design to development, handoff and technical decisions, development, and deployment and monitoring. One of its main strengths is that it has been created with academic and business perspectives to bring these two communities closer. To achieve this purpose, our research methodology comprises four main phases: Phase 1: Extensive literature review of mobile development methodologies, Phase 2: Interviews with mobile application developers working in small to medium software companies, Phase 3: Survey to extract valuable knowledge about mobile development (which was carefully designed based on the results of the first and the second phases), and Phase 4: Proposal of a new methodology for the agile development of mobile applications. With the aim of integrating both perspectives, the survey was answered by a sample of 35 experts, including academics and developers. Interesting results have been collected and discussed in this paper (on issues such as the development process, the tools used during this process, and the general issues and challenges they encountered), laying the foundations of the methodology Agile Beeswax proposed to develop mobile apps. Our results and the proposed methodology are intended to serve as support for mobile application developers.
A Battery of Soil and Plant Indicators of NBS Environmental Performance in the Context of Global Change
Authors: Andrés Pilar , Doblas-Miranda Enrique , Mattana Stefania , Molowny-Horas Roberto , Vayreda Jordi , Guardiola Moisès , Pino Joan , Gordillo Javier
Published: February 2021

Nature-based solutions (NBSs) for risk reduction produce environmental effects that must be assessed to evaluate their performance. In a context of climate change and growing concern about the loss of biodiversity, indicators informing about ecosystem complexity, resilience and stability are required. NBS projects hardly ever include environmental monitoring programs and, at best, NBS performance is evaluated based on elementary indicators that provide poor information about ecosystem functions and services. Within the framework of the PHUSICOS (EU, H2020) project, a NBS approach is being applied to reduce the hydrometeorological risks (rock falls and snow avalanches) that threaten a transnational road and a very populated town in the Pyrenees range. In both cases, the planned actions are modifying soil and vegetation structure and functioning as well as the environmental services they provide. Here we present a set of soil and plant indicators designed to be included in the postoperation monitoring plan of both NBS projects. We provide a description and information about the range of values of every indicator measured in the study sites together with indications about analytical methods and sampling calendar. We discuss the trade-offs between monetary cost, expertise requirements and meaningfulness of the indicators.
Optimization of Daylighting Pattern of Museum Sculpture Exhibition Hall
Authors: Huang Xianfeng , Zhu Shangyu
Published: February 2021

In this study, based on the current daylighting situation of a museum sculpture exhibition hall, the exhibition space is classified according to the daylighting requirements of the sculptures. Additionally, the daylighting pattern for the sculpture exhibition hall and the display layout of the exhibits are summarized. The daylighting parameters of the exhibition space under different scenarios are calculated. The exhibition space is simulated and analyzed under three daylighting conditions (flat skylights, flat skylights with side windows, and flat skylights with high side windows), and the daylighting parameters are optimized based on the daylighting patterns and components. It is discovered that with the combination of flat skylights and high side windows, the daylight factor (DF) and uniformity of daylighting (UD) of the sculpture exhibition as well as glare rating of the windows are the most favorable. Therefore, the appropriate daylighting pattern and components are determined, and the corresponding optimization strategy for daylighting is proposed. The results show that the daylighting optimization strategy proposed herein can improve the daylighting quality of the museum sculpture exhibition space and yield a suitable light environment.
Purpose Implementation: Conceptualization and Measurement
Authors: Lleo Alvaro , Bastons Miquel , Rey Carlos , Ruiz-Perez Fernando
Published: February 2021

Understanding what drives effective purpose implementation is the key to making more sustainable organizations. Most studies on the subject of purpose focus on two of its dimensions: formulation (knowledge) and practical application (contribution). However, for it to be effectively implemented in the organization, purpose also involves motivating and exciting people. In this article, we propose a three-dimensional formative second-order construct of purpose implementation based on the knowledge, internalization and contribution of purpose. In this conceptualization of purpose, for effective purpose implementation to occur, its three dimensions must be implemented intensively and consistently in a balanced way. Two separate studies were combined to develop a validated scale for measuring the intensity of purpose implementation and for demonstrating that the more intensely and consistently purpose is implemented within a company, the greater its impact on organizational citizenship behaviors.