bluuebunny/crossref_metadata_2025 huggingface dataset:
AUTOMATIC GENERATION OF DEM USING SVM CLASSIFIER
Authors: Dubey R. , Bharadwaj S. , Biswas S.
Published: May 2022

Abstract. People are moving towards an automated strategy for building a Digital Elevation Model (DEM) from a high-resolution Google Earth photograph as technology advances. Manually terrestrial measurements are both time and money costly. The automatic creation of DEMs became possible because to recent advances in image matching algorithms and intelligent filtering. The goal of this work is to give an overview of how to create DEMs automatically in the tough situation of a build-up area with the extraction of building points. The calculation of building heights, is to produce a digital elevation model, which may be easily obtained by collecting automatic Lidar point cloud data The estimation of building height with its shadow plays important role in determination. In this detection of shadow and removal of false shadow is also very important. During picture segmentation, shadow detection is performed. Statistical features are used to extract the suspected shadow. Support vector machine on IOOPL matching could effectively remove the shadow for shadow removal. The approach can successfully detect shadows in high-resolution remote sensing photos of cities and can effectively repair shadows at a rate of over 95%. SVM classifier with the accuracy of 92% as compared to others on lower end. The average variation in height estimation comes out to be 1.64 m. First, the building with height more than 20 m and slope more than 3 m, give significantly improved precision in determining building height. Second, the strategy is cost-effective because most RS images from Google Earth are free to use with less knowledge of RS is needed for computation.
IMPROVING THE UAV-DERIVED DSM BY INTRODUCING A MODIFIED RANSAC ALGORITHM
Authors: Salehi B. , Jarahizadeh S.
Published: May 2022

Abstract. The process of finding correspondence points among the overlapping images is called matching. The matching process is one of the fundamental steps in photogrammetry and computer vision with primarily application in 3D model reconstruction. The main limitation with matching algorithms is finding all the correct matches, so-called inliers, and consequently, reducing the incorrect matches, so-called outliers. A number of algorithms have been developed to increase the inliers. One of the well-known algorithms is RANdom SAmple Consensus (RANSAC). RANSAC, however, has a few limitations in terms of the number of iterations, high false-positive rate (outliers), and computational time. To improve RANSAC we are proposing three enhancements steps. The enhancements utilise an Iterative Least-Squares-based Loop (ILSL), a Similarity Termination (ST) Criterion, and a Post-Processing (PoP) step. We tested our enhancements on unmanned aerial vehicles (UAV) images of a forested area. Results show that the proposed enhancements decrease the false-positive ratio (outliers) and increase the number of inliers, with a reduced computational time compared to the conventional RANSAC. This led to more accurate photogrammetry products including Digital Surface Model (DSM).
DETECTING OPENINGS FOR INDOOR/OUTDOOR REGISTRATION
Authors: Djahel R. , Vallet B. , Monasse P.
Published: May 2022

Abstract. Indoor/Outdoor modeling of buildings is an important issue in the field of building life cycle management. It is seen as a joint process where the two aspects collaborate to take advantage of their semantic and geometric complementary. This global approach will allow a more complete, correct, precise and coherent reconstruction of the buildings . The first issue of such modeling is thus to precisely register this data. The lack of overlap between indoor and outdoor data is the most encountered obstacle, more so when both data sets are acquired separately and using different types of sensors. As an opening in the façade is the unique common entity that can be seen from inside and outside, it can help the registration of indoor and outdoor point clouds. So it must be automatically, accurately and efficiently extracted. In this paper,we start by proposing a very efficient algorithm to detect openings with great precision in both indoor and outdoor scans. Afterwards, we integrate them in a registration framework. As an opening is defined by a rectangular shape composed of four segments, two horizontal and two vertical, we can write our registration problem as a minimization of a global robust distance between two segment sets and propose a robust approach to minimize this distance using the RANSAC paradigm.
EVALUATION OF A COMPACT HELMET-BASED LASER SCANNING SYSTEM FOR ABOVEGROUND AND UNDERGROUND 3D MAPPING
Authors: Li J. , Yang B. , Chen Y. , Wu W. , Yang Y. , Zhao X. , Chen R.
Published: May 2022

Abstract. As a strategic resource, urban underground space can be used for rail transportation, commercial streets, which has high economic and social benefits, and is of great significance to sustainable city development. Due to denied Global Navigation Satellite System (GNSS) signal, traditional mobile mapping systems have difficulty collecting accurate 3D point clouds in urban underground space. Thus, a helmet-based laser scanning system, named "WHU-Helmet", is integrated in this paper to make up for the shortcomings of the existing traditional mobile mapping systems. "WHU-Helmet" is mainly equipped with four types of sensors: a GNSS receiver (optional), an IMU, a laser scanner, and a global shutter camera. "WHU-Helmet" is not relying on GNSS signal and has the advantages of low cost, small volume and easy operation. Using "WHU-Helmet", a multi-scale Normal Distributions Transform (NDT) based LiDAR-IMU SLAM is implemented to collect underground 3D point cloud in real-time. To validate the performance of "WHU-Helmet" in aboveground and underground 3D mapping, experiments were conducted in a typical urban metro station. The experiments show that the average and RMSE of HLS point errors of "WHU-Helmet" are 0.44 meters and 0.23 meters, respectively, showing great potential of "WHU-Helmet" in the application of aboveground and underground 3D mapping.
DEEP LEARNING-BASED ROAD SEGMENTATION OF 3D POINT CLOUDS FOR ASSISTING ROAD ALIGNMENT PARAMETERIZATION
Authors: Soilán M. , Tardy H. , González-Aguilera D.
Published: May 2022

Abstract. The need for transportation infrastructure digitalization is becoming more important, and efficient data collection and processing workflows have to be established and pose a great research challenge. This paper presents a fully automated method for the geometric parametrization of the road alignment from 3D point clouds acquired with a low-cost mobile mapping system. It exploits the Point Transformer Deep Learning architecture in order to segment the 3D point cloud in four different classes, which include road markings. Those markings are then used as a reference to extract the alignment trajectory path, classify its geometries (straight lines, circular arcs, and clothoids) and then parametrize it, extracting data to easily generate alignment data that may follow the standard schema of the Industry Foundation Classes (IFC). Both the deep learning architecture and the geometry parametrization process show promising results to develop automatic workflows that extract precise as-built data of the infrastructure from 3D point clouds.
A FIELD-BASED METHOD FOR ESTIMATION OF OVERLAP FOR CONVERGENT IMAGES FOR VIEW PLANNING OF BUILDINGS
Authors: Dashora A. , Utla C. S. , Kulkarni A. V.
Published: May 2022

Abstract. Overlap between two convergent images for close-range terrestrial photogrammetry is a pre-requisite for view planning of building corners. Available tools determine overlap of images that are acquired by normal geometry at a constant distance. However, for the convergent images lengths of image footprints vary according to camera position. The paper proposes a field-based method that requires to measures only geometric dimensions of image footprints in field for assessing overlap fractions for convergent images. The paper first derives the overlap fraction of convergent images as a function of image footprints, which depend upon the camera position, object geometry, and camera FOV. Experiments are conducted in field for two building corner sites. The proposed method provides conservative estimates of overlap fractions compared to that provided by image-based methods. The errors in the overlap fractions are contributed by three sources, namely, the approximations of the proposed method, uncertainty in camera positions and alignment of camera optical axis, and placement of markers in field. Experimental results suggest that the proposed method can be used confidently in field for overlap estimation for convergent images for the view planning. Images acquired for the view planning of the two corners successfully generated 3D models.
DEEP-LEARNING-BASED POINT CLOUD UPSAMPLING OF NATURAL ENTITIES AND SCENES
Authors: Zhang T. , Filin S.
Published: May 2022

Abstract. Limited accessibility, occlusions, or sensor placement, can generate unevenly sampled laser scanning based point clouds. Such uneven coverage and partial lack of detail can affect the computation of geometric features therein and generate a visually unpleasant site description. The application of 3-D interpolation-driven solutions has been demonstrated to generate oversmoothed results as such algorithms ignore local patterns and variations within the surface. In that respect, the introduction of deep neural networks (DNN) has the potential to learn more complicated forms, typical of the rich morphological patterns that natural landforms and entities therein tend to exhibit. While existing research has focused on the upsampling of man-made objects, little has been devoted to natural scenes and the entities therein. To address that, we propose in this paper a DNN based approach that utilizes the self-similarity of geometric details as a means to address this generally ill-posed problem. Specifically, we treat two key elements that stand at the root of point-DNN-related design, the definition and selection of neighboring points, and the interpolation at a high dimensional feature space. We show how the introduction of a graph convolutional network and an attention unit helps address these matters and demonstrate how knowledge of densely sampled regions can be learned and transferred to sparsely sampled ones through geometric learning methods.
APPLICATION OF CLOSE-RANGE PHOTOGRAMMETRY FOR 3D MAPPING OF PARKING SPACE
Authors: Deka D. , Dashora A. , Raj D. , Gupta A. , Chanda A. , Kulkarni A. V.
Published: May 2022

Abstract. This paper explores and investigates potential of the close range terrestrial photogrammetry for urban parking space, which is occluded by trees and surrounded by buildings. The main challenge is to map boundaries of horizontal ground surface adjacent to vertical building structures. The study outlines a strategy for comprehensive mapping of such spaces at local scale. The methodology accounts for four criteria, namely, target characteristics, camera positions, overlap, and environmental conditions for planning of data acquisition. Further, target design and camera target geometry are considered to implement data acquisition with a consumer grade camera. Target design decides effective shape, orientation, and pattern of targets, and further determines the optimal distance with camera. Two-sided planar targets in vertical orientation with distinct and discriminate patterns, placed at a short distance to camera, are found appropriate. Camera target geometry designs the target and camera positions at specific distances in both longitudinal and lateral directions. Targets are placed in parallel lines, spaced at a constant distance in lateral direction on building surface and ground, and camera positions are interspaced geometrically in the same pattern. Camera positions acquire images that maintain designed overlap in lateral direction and contain minimum two layers of targets. This mechanism acquires images and generates 3D model that maps both building and horizontal ground surfaces simultaneously. A study site of 84,000 m2 requires a total of 6615 images, which are processed to generate the 3D photogrammetric model. The model is geo-registered to local reference frame. Area occupied by parking spaces, open areas, and buildings are calculated. Parking space is 6.85% of the total area of the study site and can accommodate 141 car vehicles. Apart from mapping of parking area, the study also prepares a comprehensive map of site that can be used for planning of other utilities and facilities. Though the developed strategy generates and processes voluminous data of images, yet it is an attractive alternate as it demands minimum resources and logistics.
3D RECONSTRUCTION OF BUILDING MODEL USING UAV POINT CLOUDS
Authors: Sani N. H. , Tahar K. N. , Maharjan G. R. , Matos J. C. , Muhammad M.
Published: May 2022

Abstract. Photo-realistic 3D model of object cannot be created by using conventional Total Station method. The aim of this study is to generate 3D building model using UAV’s dense point clouds. In this research, the ability of remotely detected information for the construction of 3D building models is found to be as specific as UAV datasets, Tacheometry Survey and Traverse Survey. Pix4D and MeshLab software were used in this study to perform image processing of 3D building. The building was built up upon using the information needed from capturing UAV images. This procedure requires the height of building to be measured through conventional method such that of UAV and the height is then compared with a 3D building model. At the end of the process, the comparison of height of building, also known as RMSE, was conducted. The images of building were then processed in Photogrammetry which is the Pix4D software. Using Pix4D, this study proceeded with several processing stages such as camera calibration, photo uploading, building of dense cloud, mesh generation, and model texturing. Those processes were done automatically by using Pix4D tools. High quality of photo-realistic 3D model of building was then successfully constructed. The accuracy assessment was done by comparing the measurement of 3D model of building. Direct measuring of building by using total station had taken place to compare the measurements between 3D model details with actual measurements. The measurement of 3D model of building was done in Pix4D software. The RMSE achieved in this study is 0.015 m which is under reasonable accuracy limit. This study proves that UAV point cloud is capable to produce a very fine 3D model.
IMPACT OF GNSS SIGNAL OUTAGE ON EOPS USING FORWARD KALMAN FILTER AND SMOOTHING ALGORITHM
Authors: Jouybari A. , Bagherbandi M. , Nilfouroushan F.
Published: May 2022

Abstract. The global navigation satellite system (GNSS) has been playing the principal role in positioning applications in past decades. Position robustness degrades with a standalone receiver due to GNSS signal outage in mobile mapping systems. The GNSS and inertial measurement unit (IMU) integration is used to solve positioning degradation. This article studies the GNSS/IMU integration processing (i.e., forward Kalman filter (KF) and smoothing algorithm) using a single or a network of nearby GNSS reference stations. In addition, we analyze the impact of simulated GNSS signal outage on exterior orientation parameters (EOPs). The outcomes confirm that the smoothing algorithm works better than the forward KF and improves the accuracy for position and orientation in the case when there is no GNSS signal outage. Also, it improves the position and orientation accuracy by about 95% and 60% when there is a 180 second GNSS signal outage, respectively.
AERIAL TRIANGULATION WITH LEARNING-BASED TIE POINTS
Authors: Remondino F. , Morelli L. , Stathopoulou E. , Elhashash M. , Qin R.
Published: May 2022

Abstract. Aerial triangulation (AT) has reached outstanding progress in the last decades, and now fully automated solutions for nadir and oblique images are available. Usually, image correspondences (tie points) are found using hand-crafted methods, such as SIFT or its variants. But in the last years, there were many investigations and developments to promote the use of machine and deep learning solutions within the photogrammetric processing pipeline. The paper explores learning-based methods for the extraction of tie points in aerial image blocks. Image correspondences are used to perform aerial triangulation (AT) and successively generate dense point clouds. Two different datasets are used to compare conventional hand-crafted detector/descriptor methods with respect to learning-based methods. Accuracy analyses are performed using GCPs as well as ground truth LiDAR point clouds. Results confirm the potential of learningbased methods in finding reliable image correspondences in the aerial block, still showing space for improvements due to camera rotations.
MOTIF: MULTI-ORIENTATION TENSOR INDEX FEATURE DESCRIPTOR FOR SAR-OPTICAL IMAGE REGISTRATION
Authors: Yao Y. , Zhang B. , Wan Y. , Zhang Y.
Published: May 2022

Abstract. The inherent speckle noise in synthetic aperture radar (SAR) images and the significant differences between SAR and optical images in nonlinear radiation give rise to the great difficulty in computing similarity between image features, improving detection accuracy of corresponding points and the efficiency of image matching, thus making the registration of SAR and optical images a long-standing challenging task. To address these issues, a new SAR-optical image registration method was proposed in this paper, namely, Multi-orientation Tensor Index Feature (MoTIF), which is characterized by a lightweight feature descriptor. Specifically, we firstly established a diffusion tensor model based on the information of image gradient orientation. Then, the model was parameterized using polar coordinates to help identify the MoTIF and get the array of indices of maximum value, with which we could draw a multi-orientation index map and thereupon construct the feature vector descriptors. To evaluate the proposed method, seven representative SAR-optical image pairs were tested along with a comparison with other four state-of-the-art methods. Results show that our MoTIF method outperforms the other methods in that it substantially de-speckles SAR images, overcomes nonlinear radiation distortions caused by the differences between SAR and optical images, and achieves high precision and efficiency in image registration. The average number of correct matches (NCM) of 151.0 and the root of mean-squared error (RMSE) of 1.66 pixels obtained by utilizing MoTIF with lower time consumption adds more evidence to its superior performance. The time consumption of the MoTIF method is better than that of the other four methods, and the calculation speed is 4 times faster than that of the LGHD method. Executable code and test data are published in the link https://skyearth.org/publication/project/MoTIF/
URBAN GROWTH MONITORING – REMOTE SENSING METHODS FOR SUSTAINABLE DEVELOPMENT
Authors: Jenerowicz A. , Bielecka E.
Published: May 2022

Abstract. Urban areas account for a small fraction of the Earth's surface but have a disproportionate impact on its surroundings regarding mass, energy, and resources. An exponential increase in the urban population has been observed since the mid-20th century. As expected by the United Nations (UN), by the year 2050, 68.4% of the world population will live in cities with a population of 20,000 or more. Due to enormous socio-economic pressures resulting from population expansion, urbanization and intensive changes in the landscape, an urban development program to make cities and human settlements inclusive, safe, resilient, and sustainable has become of the utmost importance since the 2005 World Summit in Rio and further adopted in 2015 by UN as the "2030 Agenda" for Sustainable Development. The study focused on monitoring the SDG 11 target 11.3.1. they are defined as a ratio of land consumption rate to the population growth rate because mapping urban land quickly and accurately is indispensable for watershed run-off prediction and other planning applications. There is no well-established, consistent way to measure either urban land sprawl or population growth. However, remote sensing methods and satellite-derived data make it possible to monitor urban growth rates over large areas in a relatively short time. There are many techniques for urban land cover automatically mapping. These techniques can be broadly grouped into two general types: those based on the input data classification, including pixel- and object-based classifications and those based on directly segmenting the indices, such as the commonly used normalized difference vegetation index (NDVI), normalized build-up area index (NDBI), and their modifications.The authors used classical pixel (supervised classification with Spectral Angle Mapper classification and KNN methods) and objectbased classification in the presented research. In addition, spectral indices, i.e., NDVI, NDBI and their modifications to derive buildup areas, were applied. Moreover, the authors focused on the recent deep learning and machine learning methods, i.e., the utilization of spatial-context information in multi-temporal data to learn hierarchical feature representations. All methods of detecting built-up areas were compared and assessed based on the available cartographic data.
A 3D CNN APPROACH FOR CHANGE DETECTION IN HR SATELLITE IMAGE TIME SERIES BASED ON A PRETRAINED 2D CNN
Authors: Meshkini K. , Bovolo F. , Bruzzone L.
Published: May 2022

Abstract. Over recent decades, Change Detection (CD) has been intensively investigated due to the availability of High Resolution (HR) multi-spectral multi-temporal remote sensing images. Deep Learning (DL) based methods such as Convolutional Neural Network (CNN) have recently received increasing attention in CD problems demonstrating high potential. However, most of the CNN-based CD methods are designed for bi-temporal image analysis. Here, we propose a Three-Dimensional (3D) CNN-based CD approach that can effectively deal with HR image time series and process spatial-spectral-temporal features. The method is unsupervised and thus does not require the complex task of collecting labelled multi-temporal data. Since there are only a few pretrained 3D CNNs available that are not suitable for remote sensing CD analysis, the proposed approach starts with a pretrained 2D CNN architecture trained on remote sensing images for semantic segmentation and develops a 3D CNN architecture using a transfer learning technique to jointly deal with spatial, spectral and temporal information. A layerwise feature reduction strategy is performed to select the most informative features and a pixelwise year-based Change Vector Analysis (CVA) is employed to identify changed pixels. Experimental results on a long time series of Landsat 8 images for an area located in Saudi Arabia confirm the effectiveness of the proposed approach.
HOW CHALLENGING IS THE DISCRIMINATION OF FLOATING MATERIALS ON THE SEA SURFACE USING HIGH RESOLUTION MULTISPECTRAL SATELLITE DATA?
Authors: Mikeli P. , Kikaki K. , Kakogeorgiou I. , Karantzalos K.
Published: May 2022

Abstract. Developing a remote sensing-based monitoring system for detecting marine plastics requires a thorough investigation of their discrimination possibilities from other floating objects. To this end, this study aims to explore the ability to discriminate marine debris from other floating materials and sea features using high-resolution multispectral satellite data. To perform our analysis, we utilized the open-access Marine Debris Archive (MARIDA), which contains several marine classes on Sentinel-2 (S2) data and benchmarks machine learning frameworks. In particular, we investigated well-established spectral indices, GrayLevel Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) texture and other spatial features at multiple scales. A Random Forest (RF) classifier was also applied for the classification procedure, and the spectral and spatial features which contributed the most were underlined. The quantitative and qualitative assessment indicated that the spectral information alone is insufficient to distinguish marine plastic from other floating materials which exhibit similar spectral behavior, such as vessels. However, a strong potential even for challenging discrimination tasks is presented when combined with spatial information. By further evaluating our results qualitatively, significant insights are gained, and specific combinations are proposed for challenging floating materials discrimination.
APPLICATION OF PRE-TRAINED REAL-WORLD SUPER RESOLUTION MODELS TO OPTICAL SATELLITE IMAGE
Authors: Shinohara T. , Ito R. , Kobayashi Y. , Satoh T. , Shimazaki Y. , Nakamura S.
Published: May 2022

Abstract. The single image super-resolution (SISR) technique refers to improving the resolution over the original image. In recent years, we use deep learning-based convolutional neural networks to improve the spatial resolution of images more reasonably. To train such deep learning models, we use training samples consisting of the original HR images and LR images obtained by bicubic downsampling. However, this method of training data using downsampling has a negative impact when applying the trained model to real images. That is because the downsampling function that occurs in the real image is unknown, and the hypothetically created LR image does not represent the resolution degradation that can realistically occur. Therefore, SISR methods that use realistic degradation called real-world super-resolution (RWSR) have been proposed. In this paper, we investigate how such RWSR methods using realistic degradation affect the SISR performance of satellite images. The results of applying the trained model to optical satellite images show that the RWSR method is not the most effective way to handle optical satellite images when compared to the deep learning method without modeling the degradation. In particular, we showed that the effect of RWSR with not only upsampling but also noise and blur removal is significant in the visibility of optical satellite images. Moreover, pre-trained RWSR models can be an aid in visually deciphering ground objects.
Tracer and observationally derived constraints on diapycnal diffusivities in an ocean state estimate
Authors: Trossman David S. , Whalen Caitlin B. , Haine Thomas W. N. , Waterhouse Amy F. , Nguyen An T. , Bigdeli Arash , Mazloff Matthew , Heimbach Patrick
Published: May 2022

Abstract. Use of an ocean parameter and state estimation framework – such as the Estimating the Circulation and Climate of the Ocean (ECCO) framework – could provide an opportunity to learn about the spatial distribution of the diapycnal diffusivity parameter (κρ) that observations alone cannot due to gaps in coverage. However, we show that the inclusion of misfits to observed physical variables – such as in situ temperature, salinity, and pressure – currently accounted for in ECCO is not sufficient, as κρ from ECCO does not agree closely with any observationally derived product. These observationally derived κρ products were inferred from microstructure measurements, derived from Argo and conductivity–temperature–depth (CTD) data using a strain-based parameterization of fine-scale hydrographic structure, or calculated from climatological and seafloor data using a parameterization of tidal mixing. The κρ products are in close agreement with one another but have both measurement and structural uncertainties, whereas tracers can have relatively small measurement uncertainties. With the ultimate goal being to jointly improve the ECCO state estimate and representation of κρ in ECCO, we investigate whether adjustments in κρ due to inclusion of misfits to a tracer – dissolved oxygen concentrations from an annual climatology – would be similar to those due to inclusion of misfits to observationally derived κρ products. We do this by performing sensitivity analyses with ECCO. We compare multiple adjoint sensitivity calculations: one configuration uses misfits to observationally derived κρ, and the other uses misfits to observed dissolved oxygen concentrations. We show that adjoint sensitivities of dissolved oxygen concentration misfits to the state estimate's control space typically direct κρ to improve relative to the observationally derived values. These results suggest that the inclusion of oxygen in ECCO's misfits will improve κρ in ECCO, particularly in (sub)tropical regions.
The Effect of Pilgrimage to Waliyullah's Tomb on the Spiritual Motivation Student of Jagad 'Alimussirry
Authors: Mahmudah Ani , Hartono Djoko
Published: May 2022

The modern world, which is full of increasing human needs, is more easily met with the rapid advancement of science and technology. However, in reality the desired happiness is getting further away and causes a spiritual crisis. As for the signs of a spiritual crisis, material difficulties are replaced by mental difficulties which cause the mental burden to become heavier, anxiety and tension and feeling pressure are felt more often and are more pressing, thereby reducing happiness.

Making a pilgrimage to the tomb of waliyullah’s is like being in the house of someone who is loved by Allah so that it can be a medicine for those who make pilgrimages and understand their pain and suffering. There he complained about all the problems he faced. In that house, he will feel at ease because he feels no longer alone and is under the auspices of Allah's grace and power.

The results of previous research related to the research to be carried out are Fitriana's Thesis, (2014) entitled The Impact of Pilgrimage to the Tomb of Habib Sholeh Tanggul on Community Morals, and Selvi Raudatut Tilawah's Thesis, (2015) entitled Study of Living Hadith: The Phenomenon of Pilgrimage to the Grave in Village Author Kab. Bondowoso.

so the author is interested in researching and studying more deeply about this problem by proposing the title of the research, namely the influence of pilgrimage to the tomb of waliyullah’s on the spirituality student of Jagad ‘Alimussirry. This type of research includes quantitative research that collects, collects and processes data in numerical form. The target population of the researcher is the active students of the Jagad 'Alimussirry Islamic Boarding School. Collecting data using questionnaires and interviews.
Effects of Sand-Based Plyometric-Jump Training in Combination with Endurance Running on Outdoor or Treadmill Surface on Physical Fitness in Young Adult Males
Authors: Singh Gaurav , Kushwah Gaurav Singh , Singh Tanvi , Thapa Rohit Kumar , Granacher Urs , Ramirez-Campillo Rodrigo
Published: May 2022

This study aimed at examining the effects of nine weeks of sand-based plyometric-jump training (PJT) combined with endurance running on either outdoor or treadmill surface on measures of physical fitness. Male participants (age, 20.1 ± 1.7 years) were randomly assigned to a sand-based PJT combined with endurance running on outdoor surface (OT, n = 25) or treadmill surface (TT, n = 25). The endurance running intervention comprised a mixed training method, i.e., long slow distance, tempo, and interval running drills. A control group was additionally included in this study (CG, n = 25). Participants in CG followed their regular physical activity as OT and TT but did not receive any specific intervention. Individuals were assessed for their 50-m linear sprint time, standing long jump (SLJ) distance, cardiorespiratory fitness (i.e., Cooper test), forced vital capacity (FVC), calf girth, and resting heart rate (RHR). A three (groups: OT, TT, CG) by two (time: pre, post) ANOVA for repeated measures was used to analyze the exercise-specific effects. In case of significant group-by-time interactions, Bonferroni adjusted paired (within-group) and independent (between-group comparisons at post) t-tests were used for post-hoc analyses. Significant group-by-time interactions were found for all dependent variables (p < 0.001 – 0.002, ɳp2 = 0.16 – 0.78). Group-specific post-hoc tests showed improvements for all variables after OT (p < 0.001, Hedges’g effect size [g] = 0.05 – 1.94) and TT (p < 0.001, g = 0.04 – 2.73), but not in the CG (p = 0.058 – 1.000, g = 0.00 – 0.34). Compared to CG, OT showed larger SLJ (p = 0.001), cardiorespiratory fitness (p = 0.004), FVC (p = 0.008), and RHR (p < 0.001) improvements. TT showed larger improvements in SLJ (p = 0.036), cardiorespiratory fitness (p < 0.001), and RHR (p < 0.001) compared with CG. Compared to OT, TT showed larger improvements for SLJ (p = 0.018). In conclusion, sand-based PJT combined with either OT or TT similarly improved most measures of physical fitness, with greater SLJ improvement after TT. Coaches may use both concurrent exercise regimes based on preferences and logistical constrains (e.g., weather; access to treadmill equipment).
Comparison of A Single Vibration Foam Rolling and Static Stretching Exercise on the Muscle Function and Mechanical Properties of the Hamstring Muscles
Authors: Reiner Marina Maren , Tilp Markus , Guilhem Gaël , Morales-Artacho Antonio , Konrad Andreas
Published: May 2022

Knee extension and hip flexion range of motion (ROM) and functional performance of the hamstrings are of great importance in many sports. The aim of this study was to investigate if static stretching (SS) or vibration foam rolling (VFR) induce greater changes in ROM, functional performance, and stiffness of the hamstring muscles. Twenty-five male volunteers were tested on two appointments and were randomly assigned either to a 2 min bout of SS or VFR. ROM, counter movement jump (CMJ) height, maximum voluntary isometric contraction (MVIC) peak torque, passive resistive torque (PRT), and shear modulus of semitendinosus (ST), semimembranosus (SM), and biceps femoris (BFlh), were assessed before and after the intervention. In both groups ROM increased (SS = 7.7%, P < 0.01; VFR = 8.8%, P < 0.01). The MVIC values decreased after SS (-5.1%, P < 0.01) only. Shear modulus of the ST changed for -6.7% in both groups (VFR: P < 0.01; SS: P < 0.01). Shear modulus decreased in SM after VFR (-6.5%; P = 0.03) and no changes were observed in the BFlh in any group (VFR = -1%; SS = -2.9%). PRT and CMJ values did not change following any interventions. Our findings suggest that VFR might be a favorable warm-up routine if the goal is to acutely increase ROM without compromising functional performance.
Estado crítico de las emisoras municipales en Castilla-La Mancha: su gobernanza a través del Indicador de Rentabilidad Social en Comunicación (IRSCOM)
Authors: Peralta García Lidia , Sanz-Hernando Mª Clara , Álvarez-Peralta Miguel
Published: May 2022

Este trabajo aplica el Indicador de Rentabilidad Social en Comunicación (IRSCOM) a las emisoras municipales de Castilla-La Mancha, con el fin de medir el estado de salud de su funcionamiento interno. Se estudian 33 variables relacionadas con la gestión, el capital social, la articulación territorial, la programación, el entorno comunicativo y las infraestructuras. El indicador se aplica a las 40 emisoras operativas de la comunidad autónoma a través de un cuestionario telefónico y entrevistas semiestructuradas a personas al cargo de alcaldías, concejalías y de las propias emisoras. Los resultados demuestran la vulnerabilidad del sector, ya que de las 108 licencias concedidas a corporaciones locales en Castilla-La Mancha, sólo un 37% están operativas. Además, la nota obtenida se califica de “muy baja”, y algunas de ellas se encuentran en peligro de extinción. Ante las importantes carencias que presentan en cada una de las variables testadas se proponen caminos de mejora.
Atentados de Las Ramblas y Cambrils (2017): análisis de la cobertura televisiva pública de TVE, TV3 y Betevé y propuestas de mejora
Authors: Sancho-Ligorred Belén , Rodríguez Pepe
Published: May 2022

Los atentados terroristas acontecidos en Las Ramblas de Barcelona y Cambrils en agosto de 2017 acapararon una gran atención mediática. El papel de los medios de comunicación es fundamental en situaciones de crisis, y las televisiones públicas son especialmente importantes para servir a la ciudadanía y garantizar su derecho a la información. En este estudio se analiza la cobertura informativa sobre estos atentados realizada por tres canales de televisión pública (TVE, TV3 y Betevé). Se describe su implicación, sus estrategias informativas y sus malas praxis y, finalmente, se postula una serie de propuestas de acción y de mejora dirigidas a obtener una mejor capacitación profesional y ética de los/as periodistas que interactúan con escenarios de catástrofe.
Impacto de la Operación Balmis en las cuentas de Facebook de las unidades militares implicadas
Authors: Mena Muñoz Sergio , Martínez Arias Santiago María , Parra Valcárce David , Gamonal Arroyo Roberto
Published: May 2022

La pandemia de COVID-19 ha supuesto el mayor despliegue de las Fuerzas Armadas españolas en suelo propio desde 1977. El Gobierno de España decretó el 14 de marzo de 2020 el estado de alarma y puso en marcha una misión militar de apoyo y contención donde las cuentas de las redes sociales de las unidades implicadas han ido dando cuenta de su evolución. Este estudio tiene como objetivo conocer el impacto de las publicaciones subidas por cada organismo cada día de ese estado de alarma en sus cuentas de Facebook. Se ha medido cuantitativa y cualitativamente el número de publicaciones, «likes» y comentarios hechos durante el estado de alarma tanto positivos como negativos observándose que todos los organismos han dado una cobertura muy amplia de su trabajo, que ha habido unas cuentas más «acaparadoras» que otras que y el público ha agradecido y reconocido mayoritariamente su esfuerzo.
Comunicación y violencia política en Venezuela: las elecciones a la asamblea nacional constituyente según los diarios El Impulso y Correo del Orinoco (2017)
Authors: Camacho Rodríguez Francisco , Montes Biondi Génesis
Published: May 2022

Se presenta un análisis de las noticias publicadas en las primeras páginas de los diarios El Impulso y Correo del Orinoco sobre el proceso de convocatoria y elecciones a la Asamblea Nacional Constituyente de 2017 en Venezuela. Se compararon los titulares y leads aplicando la técnica del análisis de contenido. Se realizó un cuadro de categorías y subcategorías, siguiendo los criterios de pertinencia, representatividad, homogeneidad, exhaustividad. La investigación está sustentada en las teorías de la comunicación política, la personificación de la política y la posverdad. En medio de la polarización y la violencia en las calles, el Correo, controlado por el gobierno de Nicolás Maduro, fue el que más apeló a las emociones de su audiencia, mientras que la personificación política en torno a la figura del fallecido Hugo Chávez en medios oficialistas no tuvo la intensidad de los años anteriores. El Impulso dedicó más espacio a la confrontación del proceso Constituyente que el Correo del Orinoco.
Periodismo deportivo en plataformas crecientes: análisis de las retransmisiones futbolísticas en Twitch a través de LaLiga Casters
Authors: Marín-Sanchiz Cristian Ramón , Valero-Pastor José María , Rojas-Torrijos José Luis
Published: May 2022

Twitch se ha consolidado como la plataforma digital de referencia para las emisiones en directo y, en este contexto, el periodismo deportivo ha experimentado un gran crecimiento. Para examinar esta tendencia, la presente investigación elabora un estudio de caso sobre LaLiga Casters, un proyecto de retransmisión de partidos liderado por el creador de contenidos Ibai Llanos. El estudio se lleva a cabo a través de un análisis de contenido y de una encuesta a heavy users (n=50). Los resultados revelan que estas retransmisiones guardan amplias similitudes con espacios de radio y televisión, y también se observan los primeros pasos de un proceso de convergencia mediática, en el que los usuarios de las retransmisiones deportivas convencionales pueden seguir los encuentros con un tono más desenfadado en determinados momentos, como los postpartidos, y los streamers de Twitch incrementan la calidad periodística de sus contenidos gracias a fórmulas propias de los medios tradicionales.
Entre la competencia y la supervivencia: nuevas estrategias digitales de la radiotelevisión pública vasca (EITB)
Authors: Mendieta Bartolomé Ana
Published: May 2022

Con la implantación de las plataformas de distribución online de contenidos (OTT) se ha creado una fuerte competencia entre los operadores de la industria audiovisual española en un mercado cada vez más fragmentado y personalizado, a lo que se añade una transformación radical de los hábitos de consumo televisivo. En cuanto a las audiencias jóvenes, cuatro de cada diez no ven las cadenas de televisión autonómicas, y solo el 15% las conectan todos los días. Las televisiones autonómicas parten de una situación de desventaja por su constante desplome de audiencias desde 2004. Este trabajo analiza las estrategias digitales utilizadas por la radiotelevisión pública vasca (EiTB) para poder competir con las plataformas y atraer audiencias, sobre todo jóvenes vascoparlantes, con contenidos en euskera. En las conclusiones se reflexiona sobre la viabilidad de las iniciativas de EiTB para evitar la huida de usuarios y cumplir su misión de servicio público.
¿Unidos o fragmentados? Organizaciones de periodistas como respuesta a la violencia contra la prensa en México
Authors: González Rubén Arnoldo , Díaz-Cerveró Elba , Barredo Ibáñez Daniel
Published: May 2022

El ejercicio del periodismo en México es una actividad de alto riesgo, dadas las condiciones de violencia e inseguridad que imperan en todo el país. Ante la creciente diversidad de ataques, algunos informadores han encontrado que el trabajo colectivo a través de organizaciones de periodistas es una vía para mejorar las condiciones con las que desarrollan su profesión. En ese sentido, el objetivo de este artículo es analizar la percepción que los propios reporteros tienen de dichas agrupaciones. Para lograrlo, el estudio parte de una serie de entrevistas semiestructuradas con 93 periodistas que laboran en los 23 estados más peligrosos de México. Los resultados indican que, si bien hay una opinión favorable sobre tales asociaciones, su estructura y operación igualmente generan dudas. No obstante, también cuestionan la manera en la que se desempeñan y los limitados alcances que tienen.
Sitcoms familiares estadounidenses en España (1990-2005): el efecto de la nostalgia en la teoría del cultivo
Authors: Cabeza José , Vicent-Ibáñez Mireya , Mayagoitia Ana
Published: May 2022

Se analizan las prácticas de programación de las sitcoms familiares estadounidenses en la programación infantil y juvenil de cadenas de televisión españolas durante la edad de oro de la televisión comercial (1990-2005). Se valora también la popularidad que alcanzaron en su momento de emisión, así como el recuerdo que mantienen, en la actualidad, espectadores de aquella época. Se observa que existe, por parte de la audiencia de aquel momento, una identificación de los contenidos de estas sitcoms y una idealización tanto de los contenidos como del momento en el que se percibieron, lo cual puede afectar al efecto cultivo. Las sitcoms familiares americanas se ven como una serie única por su capacidad para producir una realidad claramente distinta a la real, pero que se acepta como buena: no se rechaza, se asimila. El efecto de la nostalgia permite, con el tiempo, crear una visión acrítica y positiva de estas series.
La programación radiofónica en la era del consumo bajo demanda. Evolución de la oferta generalista en España (1999-2020)
Authors: Pedrero-Esteban Luis Miguel , Marín-Lladó Carles , Martínez-Otón Laura
Published: May 2022

La plena conectividad digital de la población y el acceso ubicuo e instantáneo a la información y el entretenimiento han diluido la trascendencia de las parrillas de radio, que no obstante siguen resultando imprescindibles para transmitir la marca sonora identificativa de cada operador. Esta investigación realiza un estudio diacrónico de las programaciones generalistas en las principales cadenas comerciales españolas (SER, COPE y Onda Cero) entre 1999 y 2020 para constatar si su diseño se ha adaptado al nuevo entorno. A través de un análisis cuantitativo y cualitativo de variables formales y de contenido en las tres cadenas, la investigación constata una progresiva reducción en la oferta y el tratamiento expresivo de los programas, que tienden a una simplificación y homogeneidad en los temas y géneros poco competitiva con la eclosión y cada vez mayor diversidad de las plataformas de audio bajo demanda.
Discovery of a disjunct Iberian population and revision of the distribution of the Western Mediterranean endemic Carex olbiensis (Cyperaceae)
Authors: Luceño Modesto , De la Peña Begoña Quirós , Sánchez-Villegas Rogelio , Sánchez-Villegas Manuel , Rodríguez Fernando Estévez , Martín-Bravo Santiago
Published: May 2022

In this paper we report the presence of Carex olbiensis (Cyperaceae) in the CW Iberian Peninsula (Sierra de Gredos, Extremadura region), which represents an important disjunction in relation to the known range of the species and its westernmost new limit. We revised the distribution of this species, which resulted in the likely exclusion of the species from the floras of Bosnia-Herzegovina and Morocco. Finally, we provide an illustration of the species and discuss some aspects of its ecology, morphology, and conservation status.
Vida y pluralidad. El recorrido de la reflexión nietzscheana en Zambrano y Arendt
Authors: Nájera Pérez Elena
Published: May 2022

El presente trabajo se ocupa del efecto que la propuesta de Nietzsche de una forma de vida que aspira a la autorrealización del individuo tiene en María Zambrano y Hannah Arendt. La lectura que cada una de ellas hace del autor alemán permite establecer con respecto a este continuidades y divergencias e hilvanar también un paralelismo entre sus propios planteamientos. Al igual que Nietzsche, Zambrano y Arendt asumen la pluralidad como rasgo constitutivo de lo humano y parten por ello de la crítica del paradigma platónico que uniformiza la existencia. Sin embargo, tanto la noción de persona que perfila Zambrano como la de acción que desarrolla Arendt se desvían del pathos individualista que anima la obra nietzscheana y de la persistente reprobación de la igualdad que contiene. Ambas autoras acaban señalando la insuficiencia de este enfoque para pensar la pluralidad en el horizonte de la democracia.
Persona e realtà: L’essere “super-stante “ nella realtà “sub-stante”
Authors: Irias Alfaro Bryan Jesus
Published: May 2022

Il presente lavoro ha come obiettivo ragionare sul tema della realtà in sé rispetto al tema della persona umana. Detto obiettivo sarà esaminato dalla filosofia spagnola del ‘900, specialmente della filosofia metafisica di Xavier Zubiri, che tenendo presente il dibattito fenomenologico-esistenziale contemporaneo, tratta di attualizzare la relazione “sostanziale” tra la realtà come fondamento dell’essere, e la persona umana come realtà “super-stante” rispetto alla realtà soggettiva.
La naturaleza del color en la Grecia clásica. De la percepción al concepto
Authors: Unanua Garmendia Miguel Ángel
Published: May 2022

El color despierta muy pronto el interés de la filosofía, aunque las posiciones teóricas adoptadas acerca de su naturaleza difieran entre ellas. El estudio fisiológico de la aísthesis les brinda un denominador común, descubriendo por su medio maneras nuevas de categorizar la experiencia, distanciándose de la propiamente estética. Ese proceso llega a su apogeo cuando se concede al color cierta primacía empírica, por contribuir a idear una manera de acceder al conocimiento y sistematización de los propios fenómenos naturales. La clave para entenderlo se encuentra en la prioridad que la Antigüedad concedió al sentido de la vista (junto al sentido del oído) en calidad de órgano superior, capaz de dar un testimonio ostensivo de las cosas. Este problema necesita ser discutido en los términos en que hizo acto de presencia.