154 research outputs found
Biodiversity of macrozoobenthos in a large river, the Austrian Danube, including quantitative studies in a free-flowing stretch below Vienna: a short review
The Danube is ca. 2850 km in length and is the second largest river in Europe. The Austrian part of the Danube falls 156 metres in altitude over its 351 km length and, since the early 1950s, the river has been developed into a power-generating waterway, so that the continuity of the river is now interrupted by ten impounded areas. Only two stretches of the original free-flowing river are left, the Wachau region (above river-km 2005, west of Vienna) and the region downstream from the impoundment at Vienna (river-km 1921). Most of the recent theories and concepts related to invertebrates, in the context of the ecology of running waters, are based on studies on small streams, whereas investigations of large rivers have played a minor role for a long time, mainly due to methodological difficulties. The authors' recent detailed studies on macroinvertebrates in the free-flowing section of the Danube below Vienna, provide an excellent opportunity to survey or restate scientific hypotheses on the basis of a large river. In this review the main interest focuses on the investigation of biodiversity, i.e. the number of species and their relative proportions in the whole invertebrate community, as well as major governing environmental factors. The article summarises the species composition, the important environmental variables at the river cross-section and the effect of upstream impoundment on the riverbed and its fauna
A novel deep-learning based approach to DNS over HTTPS network traffic detection
Domain name system (DNS) over hypertext transfer protocol secure (HTTPS) (DoH) is currently a new standard for secure communication between DNS servers and end-users. Secure sockets layer (SSL)/transport layer security (TLS) encryption should guarantee the user a high level of privacy regarding the impossibility of data content decryption and protocol identification. Our team created a DoH data set from captured real network traffic and proposed novel deep-learning-based detection models allowing encrypted DoH traffic identification. Our detection models were trained on the network traffic from the Czech top-level domain maintainer, Czech network interchange center (CZ.NIC), and successfully applied to the identification of the DoH traffic from Cloudflare. The reached detection model accuracy was near 95%, and it is clear that the encryption does not prohibit the DoH protocol identification
Enhanced Low-Complexity FDD System Feedback with Variable Bit Lengths via Generative Modeling
Recently, a versatile limited feedback scheme based on a Gaussian mixture
model (GMM) was proposed for frequency division duplex (FDD) systems. This
scheme provides high flexibility regarding various system parameters and is
applicable to both point-to-point multiple-input multiple-output (MIMO) and
multi-user MIMO (MU-MIMO) communications. The GMM is learned to cover the
operation of all mobile terminals (MTs) located inside the base station (BS)
cell, and each MT only needs to evaluate its strongest mixture component as
feedback, eliminating the need for channel estimation at the MT. In this work,
we extend the GMM-based feedback scheme to variable feedback lengths by
leveraging a single learned GMM through merging or pruning of dispensable
mixture components. Additionally, the GMM covariances are restricted to
Toeplitz or circulant structure through model-based insights. These extensions
significantly reduce the offloading amount and enhance the clustering ability
of the GMM which, in turn, leads to an improved system performance. Simulation
results for both point-to-point and multi-user systems demonstrate the
effectiveness of the proposed extensions
Channel Estimation for Quantized Systems based on Conditionally Gaussian Latent Models
This work introduces a novel class of channel estimators tailored for coarse
quantization systems. The proposed estimators are founded on conditionally
Gaussian latent generative models, specifically Gaussian mixture models (GMMs),
mixture of factor analyzers (MFAs), and variational autoencoders (VAEs). These
models effectively learn the unknown channel distribution inherent in radio
propagation scenarios, providing valuable prior information. Conditioning on
the latent variable of these generative models yields a locally Gaussian
channel distribution, thus enabling the application of the well-known Bussgang
decomposition. By exploiting the resulting conditional Bussgang decomposition,
we derive parameterized linear minimum mean square error (MMSE) estimators for
the considered generative latent variable models. In this context, we explore
leveraging model-based structural features to reduce memory and complexity
overhead associated with the proposed estimators. Furthermore, we devise
necessary training adaptations, enabling direct learning of the generative
models from quantized pilot observations without requiring ground-truth channel
samples during the training phase. Through extensive simulations, we
demonstrate the superiority of our introduced estimators over existing
state-of-the-art methods for coarsely quantized systems, as evidenced by
significant improvements in mean square error (MSE) and achievable rate
metrics
Leveraging Variational Autoencoders for Parameterized MMSE Channel Estimation
In this manuscript, we propose to utilize the generative neural network-based
variational autoencoder for channel estimation. The variational autoencoder
models the underlying true but unknown channel distribution as a conditional
Gaussian distribution in a novel way. The derived channel estimator exploits
the internal structure of the variational autoencoder to parameterize an
approximation of the mean squared error optimal estimator resulting from the
conditional Gaussian channel models. We provide a rigorous analysis under which
conditions a variational autoencoder-based estimator is mean squared error
optimal. We then present considerations that make the variational
autoencoder-based estimator practical and propose three different estimator
variants that differ in their access to channel knowledge during the training
and evaluation phase. In particular, the proposed estimator variant trained
solely on noisy pilot observations is particularly noteworthy as it does not
require access to noise-free, ground-truth channel data during training or
evaluation. Extensive numerical simulations first analyze the internal behavior
of the variational autoencoder-based estimators and then demonstrate excellent
channel estimation performance compared to related classical and machine
learning-based state-of-the-art channel estimators.Comment: 13 pages, 12 figure
Subjektive Repräsentation der Zusammenhänge zwischen Organisationsstruktur, soziomoralischer Atmosphäre und prosozialem Arbeitshandeln in demokratischen Unternehmen – eine Kreuzvalidierung
Theoretischer Hintergrund und Fragestellung Bei der Erforschung der individuellen moralischen Urteilskompetenz wurde zunehmend die Frage verfolgt, more about welche organisationalen Merkmale, buy Interaktionspraktiken und Interventionen in Institutionen sich fördernd bzw. hemmend auf die Genese von Kompetenzen und Handlungsorientierungen, die ethisch angemessenem Handeln zugrunde liegen, auswirken (z.B. Lempert, 2009; Oser & Althof, 2001; Power, Higgins, & Kohlberg, 1989
Endovascular therapy of direct dural carotid cavernous fistulas - A therapy assessment study including long-term follow-up patient interviews
Purpose Endovascular embolization nowadays is a well-established treatment option for direct carotid cavernous fistulas (dCCF, Barrow Type A). There are many publications on the complication and success rates of this method. However, little is known on the patients' opinion on the treatment result after several years. We report on this issue also including the "pioneer patients" treated almost two decades ago. Methods We retrospectively reviewed the records of all patient (n = 25) with a more than 24 months follow-up interval after endovascular treatment of a dCCF at our institution from 01/1999 to 08/2018. We determined primary therapy success, complication rate, state of the fistula in the last imaging follow-up and quoted the patient's subjective perception of the long-term treatment success using a standardized interview form. Results Occlusion rate in the last imaging follow up was 96% (24/25) with a complication rate of 8% (2/25). The response rate on our interview request was 96% (24/25) with a rate of considered feedback of 84% (21/25 patients). Duration of our observation interval for the patient reported outcome was 143 months / 11 years (median, range: 35-226 m/2-18 y). Most of them (21/25,84%) felt they benefited from the treatment. Conclusions Endovascular supply of dCCF is a highly effective treatment method leading to a sustainable therapy success with long-lasting stable subjective benefit even to our "pioneer patients" treated almost two decades ago
Pseudo-subarachnoid haemorrhage due to chronic hypoxaemia: case report and review of the literature
Background: The specificity of computed tomography (CT) for subarachnoid haemorrhage (SAH) is very high. However, physicians should be aware of rare false positive findings, also referred to as "pseudo-SAH". We present an unusual case in which such a finding was caused by chronic hypoxaemia. Case presentation: A 37-year-old male patient presented with headaches. His CT-scan showed multiple confluent subarachnoid hyperattenuations, which mimicked SAH. However, the headache was chronic and had no features typical for SAH. The patient suffered from severe chronic hypoxaemia due to congenital heart failure. On CT-angiography diffuse intracranial vessel proliferation was found and laboratory results revealed a highly raised level of haematocrit, which had both probably developed as compensatory mechanisms. A combination of these findings explained the subarachnoid hyperdensities. Magnetic resonance imaging (MRI) showed no signs of SAH and visualized hypoxaemia in cerebral veins. A diagnosis of pseudo-SAH was made. The patient's symptoms were likely due to a secondary headache attributed to hypoxia and/or hypercapnia. Therapy was symptomatic. Conclusions: Severe chronic hypoxaemia should be recognised as a rare cause of pseudo-SAH. Clinical evaluation and MRI help differentiate SAH from pseudo-SAH
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