HI observations of three compact high-velocity clouds around the Milky Way

We present deep HI observations of three compact high-velocity clouds (CHVCs). The main goal is to study their diffuse warm gas and compact cold cores. We use both low- and high-resolution data obtained with the 100 m Effelsberg telescope and the Westerbork Synthesis Radio Telescope (WSRT). The combination is essential in order to study the morphological properties of the clouds since the single-dish telescope lacks a sufficient angular resolution while the interferometer misses a large portion of the diffuse gas. Here single-dish and interferometer data are combined in the image domain with a new combination pipeline. The combination makes it possible to examine interactions between the clouds and their surrounding environment in great detail. The apparent difference between single-dish and radio interferometer total flux densities shows that the CHVCs contain a considerable amount of diffuse gas with low brightness temperatures. A Gaussian decomposition indicates that the clouds consist predominantly of warm gas.Comment: 11 pages, 7 figures, accepted for publication by A&

Novel Satellite-Based Methodologies for Multi-Sensor and Multi-Scale Environmental Monitoring to Preserve Natural Capital

Global warming, as the biggest manifestation of climate change, has changed the distribution of water in the hydrological cycle by increasing the evapotranspiration rate resulting in anthropogenic and natural hazards adversely affecting modern and past human properties and heritage in different parts of the world. The comprehension of environmental issues is critical for ensuring our existence on Earth and environmental sustainability. Environmental modeling can be described as a simplified form of a real system that enhances our knowledge of how a system operates. Such models represent the functioning of various processes of the environment, such as processes related to the atmosphere, hydrology, land surface, and vegetation. The environmental models can be applied on a wide range of spatiotemporal scales (i.e. from local to global and from daily to decadal levels); and they can employ various types of models (e.g. process-driven, empirical or data-driven, deterministic, stochastic, etc.). Satellite remote sensing and Earth Observation techniques can be utilized as a powerful tool for flood mapping and monitoring. By increasing the number of satellites orbiting around the Earth, the spatial and temporal coverage of environmental phenomenon on the planet has in-creased. However, handling such a massive amount of data was a challenge for researchers in terms of data curation and pre-processing as well as required computational power. The advent of cloud computing platforms has eliminated such steps and created a great opportunity for rapid response to environmental crises. The purpose of this study was to gather state-of-the-art remote sensing and/or earth observation techniques and to further the knowledge concerned with any aspect of the use of remote sensing and/or big data in the field of geospatial analysis. In order to achieve the goals of this study, some of the water-related climate-change phenomena were studied via different mathematical, statistical, geomorphological and physical models using different satellite and in-situ data on different centralized and decentralized computational platforms. The structure of this study was divided into three chapters with their own materials, methodologies and results including: (1) flood monitoring; (2) soil water balance modeling; and (3) vegetation monitoring. The results of this part of the study can be summarize in: 1) presenting innovative procedures for fast and semi-automatic flood mapping and monitoring based on geomorphic methods, change detection techniques and remote sensing data; 2) modeling soil moisture and water balance components in the root zone layer using in-situ, drone and satellite data; incorporating downscaling techniques; 3) combining statistical methods with the remote sensing data for detecting inner anomalies in the vegetation covers such as pest emergence; 4) stablishing and disseminating the use of cloud computation platforms such as Google Earth Engine in order to eliminate the unnecessary steps for data curation and pre-processing as well as required computational power to handle the massive amount of RS data. As a conclusion, this study resulted in provision of useful information and methodologies for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage

ANALISIS PENGEMBANGAN OBJEK WISATA RELIGI DALAM MENINGKATKAN PENDAPATAN MASYARKAT (Studi Kasus Desa Babussalam Besilam Kabupaten Langkat)

Kabupaten Langkat memiliki potensi wisata yang potensial untuk dikembangkan. Salah satu tempat wisata yang sering didatangi pengunjung adalah Desa Religi Babussalam Besilam. Berbagai upaya dilakukan pemerintah daerah untuk menarik perhatian wisatawan untuk berkunjung kekabupaten langkat termasuk pengembangan sektor pariwisatanya. Penelitian ini bertujuan untuk mengetahui, pertama Bagaimana Pengembangan Objek Wisata Religi Pada Desa Babussalam Besilam, kedua untuk mengetahui Dampak Pengembangan Objek Wisata Religi Dalam Meningkatkan Pendapatan Masyarakat. Pendekatan penelitian yang dilakukan pada penelitian ini adalah kualitatif deskriptif dengan metode pengumpulan data yaitu menggunakan teknik observasi, wawancara dan dokumentasi. Hasil penelitian menyimpulkan bahwa Pertama, Pengembangan yang dilakukan pada Objek Wisata Religi Desa Babussalam Besilam. Kerjasama yang dilakukan pemerintah provinsi dengan pemerintah desa untuk membangun sarana dan prasarana seperti masjid, lapangan, lapak usaha umkm, jalan raya dan pembangunan drainase, kedua Adanya objek wisata ini memberikan dampak positif bagi masyarakat sekitar mereka dapat memanfaatkan peluang yang ada untuk berwirausaha serta menciptakan lapangan pekerjaan, walaupun objek wisata religi memberikan dampak positif ternyata tidak meningkatkan pendapatan masyarakat, walaupun pengunjung yang datang setiap harinya ramai, tetapi kebanyakan pengunjung yang datang hanya untuk berziarah, menyampaikan hajat maupun mengikuti kegiatan agama lainnya.Dengan adanya Pengembangan yang dilakukan pada kawasan wisata religi hendaknya baik pemerintah setempat maupun pengurus wisata religi untuk, Mengoptimalkan sarana dan prasarana Objek Wisata Religi yang belum diadakan, Membuat fasilitas penunjang agar objek wisata religi tidak monoton dan Para pedangang hendaknya menciptakan produk-produk baru pada dagangannya

Can the Rise of Dual-Earning Households Explain Gentrification of US Central Cities?

The last four decades have seen a return of high-earning households to central cities. The consequences are urban renewal on the one hand and soaring inner-city rents on the other. In this paper I extend a monocentric city model of income sorting and urban rents to examine whether increases in the number of two-earner households can explain recent patterns of gentrification. I then present evidence from Washington DC that, among the young and married, the rich are shortening their commutes while the poor are lengthening theirs. However, among the unmarried, no such trend is discernible. These facts support the model\u27s prediction that two-earner households have reshaped the landscape of urban income group sorting

A Study of Short-Spacing Correction for Galactic and Extragalactic Objects

Radio telescopes can be divided into single-dish instruments and interferometer arrays. Their respective observational approaches are fundamentally different. Single dishes can make an accurate measurement of flux. However, due to practical limits in the size of the dish which can be constructed, they have correspondingly limited resolution. On the other hand, interferometric arrays can be as large as the earth, with corresponding resolution, yet cannot accurately measure the total flux, or resolve extended objects. This is because they are not able to measure zero- and short-spacings. The technique of short-spacing correction (SSC) bridges the gap between the single-dish and array approaches. It combines the high angular resolution from interferometric observations with the total flux measurement from single-dish observations. Hence, the final data product combines the best of both worlds. The correction is of great importance for the Galactic objects as well as a number of nearby galaxies with considerable amount of diffuse and extended structure. For neutral atomic hydrogen (HI) observations, each of these instruments provides different information regarding different gas phases present in the interstellar medium (ISM). The single-dish instrument provides information regarding the large angular scale structures, whereas the interferometric array provides information about the small angular scale structures. Only after combining the two data sets is it possible to study all gas phases in detail. In the near future, several large interferometer instruments will become available. The new instruments provide large amounts of data (for instance, in the case of the Australia’s Square Kilometer Array Pathfinder (ASKAP, Duffy et al. 2012), the expected data rate is about 2.8 GB/s). Considering the large amount of data, it is very difficult to store the raw data indefinitely. SSC will continue to be a desirable technique, and any viable SSC algorithm needs to be designed to cope with the anticipated features and problems of the new instruments. An important requirement for a future-oriented SSC algorithm is therefore that it operates on science-ready images, not requiring any raw data input. The present dissertation presents an automated pipeline which is able to perform the SSC in the image domain. The algorithm takes the peculiarities of future instruments into account, namely, large amounts of data and restricted access to raw data. It operates on cleaned and calibrated interferometric data in combination with single-dish observations. The impacts on the algorithm of weighting schemes and pixel size in combination with different deconvolution procedures have also been studied. The latter parameters were found to affect the result of the combination significantly. The pipeline has been evaluated with both Galactic and extragalactic spectral line data sets obtained from different instruments. In all the cases, the combination fulfills the expectations, i.e., the measured total flux in the combined data set is in good agreement with the measured value in the single-dish data, while the interferometric resolution is preserved in the combination. The study underlines the importance of SSC for a detailed study of different gas phases

Linear estimation of average global effects

We study the problem of estimating the average causal effect of treating every member of a population, as opposed to none, using an experiment that treats only some. This is the policy-relevant estimand when deciding whether to scale up an intervention based on the results of an RCT, for example, but differs from the usual average treatment effect in the presence of spillovers. We consider both estimation and experimental design given a bound (parametrized by $\eta > 0$) on the rate at which spillovers decay with the ``distance'' between units, defined in a generalized way to encompass spatial and quasi-spatial settings, e.g. where the economically relevant concept of distance is a gravity equation. Over all estimators linear in the outcomes and all cluster-randomized designs the optimal geometric rate of convergence is $n^{-\frac{1}{2+\frac{1}{\eta}}}$, and this rate can be achieved using a generalized ``Scaling Clusters'' design that we provide. We then introduce the additional assumption, implicit in the OLS estimators used in recent applied studies, that potential outcomes are linear in population treatment assignments. These estimators are inconsistent for our estimand, but a refined OLS estimator is consistent and rate optimal, and performs better than IPW estimators when clusters must be small. Its finite-sample performance can be improved by incorporating prior information about the structure of spillovers. As a robust alternative to the linear approach we also provide a method to select estimator-design pairs that minimize a notion of worst-case risk when the data generating process is unknown. Finally, we provide asymptotically valid inference methods

Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition

Speech Emotion Recognition (SER) plays a pivotal role in enhancing human-computer interaction by enabling a deeper understanding of emotional states across a wide range of applications, contributing to more empathetic and effective communication. This study proposes an innovative approach that integrates self-supervised feature extraction with supervised classification for emotion recognition from small audio segments. In the preprocessing step, to eliminate the need of crafting audio features, we employed a self-supervised feature extractor, based on the Wav2Vec model, to capture acoustic features from audio data. Then, the output featuremaps of the preprocessing step are fed to a custom designed Convolutional Neural Network (CNN)-based model to perform emotion classification. Utilizing the ShEMO dataset as our testing ground, the proposed method surpasses two baseline methods, i.e. support vector machine classifier and transfer learning of a pretrained CNN. comparing the propose method to the state-of-the-art methods in SER task indicates the superiority of the proposed method. Our findings underscore the pivotal role of deep unsupervised feature learning in elevating the landscape of SER, offering enhanced emotional comprehension in the realm of human-computer interactions

A modified version of the SMAR model for estimating root-zone soil moisture from time-series of surface soil moisture

Root-zone soil moisture at the regional scale has always been a missing element of the hydrological cycle. Knowing its value could be a great help in estimating evapotranspiration, erosion, runoff, permeability, irrigation needs, etc. The recently developed Soil Moisture Analytical Relationship (SMAR) can relate the surface soil moisture to the moisture content of deeper layers using a physically-based formulation. Previous studies have proved the effectiveness of SMAR in estimating root-zone soil moisture, yet there is still room for improvement in its application. For example, the soil water loss function (i.e. deep percolation and evapotranspiration), assumed to be a linear function in the SMAR model, may produce approximations in the estimation of water losses in the second soil layer. This problem becomes more critical in soils with finer textures. In this regard, the soil moisture profile data from two research sites (AMMA and SCAN) were investigated. The results showed that after a rainfall event, soil water losses decrease following a power pattern until they reach a minimum steady state. This knowledge was used to modify SMAR. In particular, SMAR was modified (MSMAR) by introducing a non-linear soil water loss function that allowed for improved estimates of root zone soil moisture.Keywords: surface soil moisture, root-zone soil moisture, SMAR, soil water loss function, MSMA