Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Transformer and Multilabel Classification for Remote Sensing Image Captioning

High-resolution remote sensing images are now available with the progress of remote sensing technology. With respect to popular remote sensing tasks, such as scene classification, image captioning provides comprehensible information about such images by summarizing the image content in human-readable text. Most existing remote sensing image captioning methods are based on deep learning-based encoder–decoder frameworks, using convolutional neural network or recurrent neural network as the backbone of such frameworks. Such frameworks show a limited capability to analyze sequential data and cope with the lack of captioned remote sensing training images. Recently introduced Transformer architecture exploits self-attention to obtain superior performance for sequence-analysis tasks. Inspired by this, in this work, we employ a Transformer as an encoder–decoder for remote sensing image captioning. Moreover, to deal with the limited training data, an auxiliary decoder is used that further helps the encoder in the training process. The auxiliary decoder is trained for multilabel scene classification due to its conceptual similarity to image captioning and capability of highlighting semantic classes. To the best of our knowledge, this is the first work exploiting multilabel classification to improve remote sensing image captioning. Experimental results on the University of California (UC)-Merced caption dataset show the efficacy of the proposed method. The implementation details can be found in https://gitlab.lrz. de/ai4eo/captioningMultilabel.
79-GHz SIW slot-coupled patch antenna array with low cross polarization and wide beamwidth

ABSTRACT A 79-GHz wide-beam patch antenna and 1×8 antenna array with low cross-polarization levels are presented for automotive radar applications. A substrate-integrated-waveguide slot-coupled scheme was employed to reduce the cross-polarization level of the patch antenna. Two parasitic elements were added to the main patch to form a three-element subarray for beamwidth enhancement. The simulated E-plane half-power beamwidth (HPBW) of the patch antenna is 148°, and the cross-polarization level is 32.5 dB less than the co-polarization level at the boresight direction. For further gain enhancement, a 1×8 patch array was designed and fabricated. The measured results show that the proposed design can offer a wide HPBW of 144° and a low cross-polarization level of 28.3 dB less than the co-polarization level at 79 GHz.
Cloud dynamical, microphysical and radiative properties retrieved from ground based radar/lidar synergy

In order to derive microphysical and radiative parameters an adaptation of Tinel et al. (2004) radar/lidar algorithm has been performed. Algorithms making use of the radar Doppler signal to derive dynamical properties (Protat et al. (2002) and Bouniol et al. (2004)) have been developped and implemented. The retrieval of TKE disipation rate is here the subject of a companion paper and this method will not be described here. This paper will be focussed on the determination of other dynamical caracteristics and on the microphysical and radiative property retrieval.
Learning Resource Scheduling with High Priority Users using Deep Deterministic Policy Gradients

Advances in mobile communication capabilities open the door for closer integration of pre-hospital and in-hospital care processes. For example, medical specialists can be enabled to guide on-site paramedics and can, in turn, be supplied with live vitals or visuals. Consolidating such performance-critical applications with the highly complex workings of mobile communications requires solutions both reliable and efficient, yet easy to integrate with existing systems. This paper explores the application of Deep Deterministic Policy Gradient (DDPG) methods for learning a communications resource scheduling algorithm with special regards to priority users. Unlike the popular Deep-Q-Network methods, the DDPG is able to produce continuous-valued output. With light post-processing, the resulting scheduler is able to achieve high performance on a flexible sum-utility goal.
An effective method of determining the drive-train effciency of wind turbines with high accuracy

Measurement of efficiency under various load conditions is an important prerequisite for the validation of wind turbine drive train systems. Current measurement methods rely highly on the accuracy of the mechanical torque measurement, which is substantially limited by the high level of torque as well as the available calibration capacity. This paper proposes a new method of test and measurement procedures, aiming to substantially reduce the dependency of the efficiency accuracy on the torque and electrical power measurement. Instead of measuring the efficiency directly, the new method focuses on measuring the loss of power and in this way reduces the uncertainty of the determined efficiency. At rated power, an uncertainty lower than 0.5 % is considered as achievable. The efficiency uncertainty is analysed in detail in the paper with considerations of different sources of measurement uncertainties and their contributions. An example of the uncertainty calculation with typical parameters is presented as a demonstration. The study shows that the new method can effectively reduce the effect of the torque (as well as the electrical power) uncertainty on the overall uncertainty of the determined efficiency, and therefore achieve a better accuracy despite of the unfavourable accuracy of the torque and electrical power measurement. Finally, experimental tests have been performed on a scaled dynamometer test bench, where the efficiencies at different operation points are determined with the new method. The efficiency results as well as their uncertainties are presented in the paper.
Spread Spectrum Technique for Decreasing EM Noise in High-Frequency APWM HB Resonant Converter With Reduced EMI Filter Size

A spread spectrum technique (SST) has been proposed as a solution to mitigate electromagnetic (EM) interference in power converters. In the previous research, the SST has been widely applied to the power converters that have insensitive voltage gain according to switching frequency variation, such as buck, boost and flyback topologies. However, resonant converters have sensitive voltage gain according to the switching frequency variation, which cannot regulate the output voltage with the SST. In this paper, a high-frequency (HF) half-bridge (HB) resonant converter employing the SST is developed for power supply applications with 100-W power capability. The design methodology of its resonant tank and the SST is proposed to obtain the tight output voltage regulation performance and the EM noise reduction. In addition, the size reduction of EM noise filters is analyzed by employing the SST. The output voltage regulation performance and the filter size reduction will be experimentally verified using a 100-W prototype HF HB resonant converter.
TBTOA: A DAG-Based Task Offloading Scheme for Mobile Edge Computing

Mobile Edge Computing (MEC) is an emerging computation paradigm that enables mobile devices to offload computation-intensive tasks to edge servers in order to speed up task processing. However, there are still a few challenging problems with MEC before it can be widely adopted. For instance, due to storage and computation constraints, only a limited set of services can be deployed on an edge server. If a mobile device requires a specific service, it can only offload its task to the edge servers that provide the corresponding service. In addition, a task might be composed of several dependent subtasks. The subtask that depends on other subtasks cannot be offloaded until the prerequisite subtasks are completed. Finally, due to the heterogeneity of edge servers, offloading a task to different edge servers could lead to varied energy consumption performance. Few studies consider both of these scenarios and to fulfill the gap, we investigate the task offloading problem in MEC under the dependency and service caching constraints. Specifically, we propose a heuristic algorithm named Table Based Task Offloading Algorithm (TBTOA), which is capable of predicting the impact of offloading decisions. Our experimental results indicate that TBTOA outperforms the existing offloading schemes for MEC in terms of makespan and energy consumption.
Weighted Interpretive Structural Modeling for Supply Chain Risk Management: An Application to Logistics Service Providers in Turkey

Background: The aim of this paper is to introduce weighted interpretive structural modeling approach to supply chain risk management efforts by presenting an application to identify micro risks of logistics service providers at the industry level in Turkey. Methods: In this research, eighteen risk factors in the logistics sector have been identified through both literature review and recommendations from a group of academicians and experts in the sector. A survey was conducted to rank these risks. They were further analyzed through a weighted interpretive structural modeling (WISM) approach in order to demonstrate mutual relationships among these risks. Results: Finally, using a WISM approach, an analysis was conducted to identify the driving and dependence power of the risk factors. This study covers a variety of micro-risk factors of logistics service providers and demonstrates the relationships among them and clusters them based on their driving and dependence power. Conclusions: Such a clustering of the risk factors helps us identify those that affect the others and are of paramount importance in risk management and mitigation.
Serum proteomics in giant cell arteritis in response to a three-day pulse of glucocorticoid followed by tocilizumab monotherapy (the GUSTO trial)

Objective Proteome analyses in patients with newly diagnosed, untreated giant cell arteritis (GCA) have not been reported previously, nor are changes of protein expression upon treatment with glucocorticoids (GC) and/or tocilizumab (TCZ) known. The GUSTO trial allows to address these questions, provides the opportunity to learn about the differential effects of GC and TCZ on proteomics and may help to identify serum proteins to monitor disease activity. Methods Serum samples obtained from 16 patients with new-onset GCA at different time points (day 0, 3, 10, and week 4, 24, 52) during the GUSTO trial (NCT03745586) were examined for 1436 differentially expressed proteins (DEPs) based on proximity extension assay technology. The patients received 500 mg methylprednisolone intravenously for 3 consecutive days followed by TCZ monotherapy. Results When comparing day 0 (before the first GC infusion) with week 52 (lasting remission), 434 DEPs (213↑, 221↓) were identified. In response to treatment, the majority of changes occurred within 10 days. GC inversely regulated 25 proteins compared to remission. No difference was observed between weeks 24 and 52 during established remission and ongoing TCZ treatment. Expression of CCL7, MMP12, and CXCL9 was not regulated by IL6. Conclusion Disease-regulated serum proteins improved within 10 days and were normalized within 24 weeks, showing a kinetic corresponding to the gradual achievement of clinical remission. The proteins inversely regulated by GC and TCZ shed light on the differential effects of the two drugs. CCL7, CXCL9, and MMP12 are biomarkers that reflect disease activity despite normalized C-reactive protein levels.
Barriers to Data-Driven Decision-Making Among Online Retailers

This study investigates the barriers to data-driven decision-making among online retailers. The study seeks to deepen the previous knowledge by considering data-driven decision-making as a process and identifying the critical obstacles within its different (6) stages. Qualitative interview data (N=10) collected from Finnish e-commerce professionals are analyzed. The findings show that barriers to data-driven decision-making occur at all stages during the decision-making process. The barriers are mostly related to the employees’ and the management’s expertise in collecting, organizing, analyzing, summarizing, synthesizing, and prioritizing data. In addition, attitudinal, technical, and strategy-related barriers can hinder data-driven decision-making. The findings suggest that all the stages included in the data-driven decision-making process and the obstacles that prevent data-driven decision-making during the different stages should be carefully investigated and considered by online retailers.
Application of Orange Peel Waste as Adsorbent for Methylene Blue and Cd2+ Simultaneous Remediation

Pollution by dyes and heavy metals is one of the main concerns at the environmental level due to their toxicity and inefficient elimination by traditional water treatment. Orange peel (OP) without any treatment was applied to effectively eliminate methylene blue (MB) and cadmium ions (Cd2+) in mono- and multicomponent systems. Although the single adsorption processes for MB and Cd2+ have been investigated, the effects and mechanisms of interactions among multicomponent systems are still unclear. Batch experiments showed that in monocomponent systems, the maximum adsorption capacities were 0.7824 mmol g−1 for MB and 0.2884 mmol g−1 for Cd2+, while in multicomponent systems (Cd2+ and MB), both contaminants competed for the adsorption sites on OP. Particularly, a synergic effect was observed since the adsorption capacity of Cd2+ increased compared to the monocomponent system. Results of desorption and adsorbent reuse confirmed that the adsorbent presents good regeneration performance. The low cost of this material and its capacity for the individual or simultaneous removal of Cd2+ and MB in aqueous solutions makes it a potential adsorbent for polluted water treatment processes.
Towards Trust and Friendliness Approaches in the Social Internet of Things

The Internet of Things (IoT) is an interconnected network of heterogeneous entities, such as sensors and embedded devices. During the current era, a new field of research has emerged, referred to as the social IoT, which mainly includes social networking features. The social IoT refers to devices that are capable of creating interactions with each other to independently achieve a common goal. Based on the structure, the support of numerous applications, and networking services, the social IoT is preferred over the traditional IoT. However, aspects like the roles of users and network navigability are major challenges that provoke users’ fears of data disclosure and privacy violations. Thus, it is important to provide reliable data analyses by using trust- and friendliness-based properties. This study was designed because of the limited availability of information in this area. It is a classified catalog of trust- and friendliness-based approaches in the social IoT with important highlights of important constraints, such as scalability, adaptability, and suitable network structures (for instance, human-to-human and human-to-object). In addition, typical concerns like communities of interest and social contacts are discussed in detail, with particular emphasis on friendliness- and trust-based properties, such as service composition, social similarity, and integrated cloud services.
SERVNET: Path Validation with Authenticated Packet Modification in Service Function Chains

A sequence of composable network functions constitutes a service function chain (SFC). Network function virtualization and software-defined networking have made SFCs feasible. With SFCs, a fundamental security requirement is that traffic, directed to traverse a path of network functions, actually follows the specified path. Current path validation enables path enforcement and verification, with on-path nodes able to verify that packets have indeed traversed the specified path. However, there is a problem — on-path service nodes, that implement network functions, might modify packets, whereas current path validation techniques do not allow packet modification.We propose SERVNET, a path validation scheme which supports authenticated packet modification. SERVNET uses a cryptographic primitive called chameleon hash function which allows packet modification without changing its associated hash value. To the best of our knowledge, SERVNET is the first work on path validation that allows authenticated packet modification by designated nodes.
Dirty Paper Coding for Waveform Synthesis in Integrated Sensing and Communications: A Broadcast Channel Approach

In integrated sensing and communications (ISAC), both functions are tightly integrated using the same spectrum, waveform and hardware. Analogy is identified between ISAC and broadcast channel in communications, since ISAC transmitter broadcasts signals for both communication receivers and sensing, despite the functional overlap between communications and sensing. Although the communication signal can also be used for sensing, the intrinsic randomness of communications impairs the reliability of sensing. Therefore, a dedicated and deterministic sensing waveform is added, in order to improve the sensing performance. To remove the interference of the dedicated sensing waveform to communication receivers, dirty paper coding is employed, which exploits precoding for interference cancellation. Meanwhile, the trade-off between communications and sensing is identified. Numerical results are used to demonstrate the proposed ISAC scheme and the performance trade-off.
A Home-Based Dyadic Music-with-Movement Intervention for People with Dementia and Caregivers: A Hybrid Type 2 Cluster-Randomized Effectiveness-Implementation Design

Background Integrating evidence-based music-with-movement into routine practices for people with dementia requires effective implementation strategies. The objectives of this study were to evaluate the clinical efficacy of the intervention and to examine the effectiveness of the implementation strategies in promoting home-based adoption. Methods This was a cluster randomized controlled trial adopting a clinical effectiveness-implementation hybrid design. The 12-week music-with-movement intervention aims to promote the well-being of people with dementia and their caregivers. Clinical outcomes were assessed to evaluate the efficacy of the intervention. These included the symptoms of anxiety and depression of people with dementia; caregivers’ perception of their stress and the positive aspects of caregiving; and the quality of the dyadic relationship. Caregivers, staff, and volunteers were interviewed and objective figures on reach and adoption were collected to reflect the outcomes of the implementation. Results Compared to the waitlist control, there was a significant reduction in the anxiety and the symptoms of depression of those in the intervention group. There was also a significant reduction in the perceived stress level of the caregivers. Six focus group interviews were conducted. The intervention and the appropriateness of the implementation strategies were perceived positively by the dyad participants, the staff of the centres, and volunteers. Of the collaborating centres, 57% continued to use the intervention and, after six months, served six times more people than were recruited in this study. Over half of the participating families continued to use the intervention for at least six more months. Conclusion Music-with-movement interventions showed promise for improving the well-being of people with dementia and their caregivers. Effective strategies facilitated the implementation process, such as integrating communication technology to provide instant support and involving volunteers in engaging the collaborating centres and families. This study sheds light on perceptions of the intervention, and on its scalability for a broader population. Trial Registration NCT03575026, Registered 2 July 2018.
Research on GSSA Model of Wireless Power Transfer System Using a quasi Z-source Converter

Due to the large number of dynamic components and the complexity, the dynamic modeling of wireless power transmission(WPT) systems has always been a difficult problem. The generalized state-space average(GSSA) modeling method is suitable for wireless power transfer systems because of its fast time-varying characteristics. Based on this, Fourier coefficients and switching functions is introduced to connect the state variables of the circuit with the generalized state variables, a large-signal model of the Quasi-Z-source WPT system is established in the shoot-through and phase-shift control mode, and the transfer function model of the system is further derived with the help of the small-signal perturbation method. The model is used to analyze the steady-state and dynamic characteristics of the system, which provides an important reference for the design of the system controller. In this paper, an experimental platform for wireless power transfer system using Quasi-Z-source converters is built, and the basic principles and theoretical models of the system are experimentally verified through digital PI controllers. The results show that the theoretical model has sufficient accuracy.
The Expression and Function of lincRNA-154324 and the Adjoining Protein-Coding Gene vmp1 in the Caudal Fin Regeneration of Zebrafish

Caudal fin regeneration is regulated by a variety of mechanisms, but the role of long non-coding RNA (lncRNA) has rarely been studied. The present study aimed to describe the landscape of lncRNAs during caudal fin regeneration using whole transcriptome sequencing, and then to conduct a functional study on the target lncRNAs using real-time fluorescent quantitative PCR (RT-qPCR), in situ hybridization, and the CRISPR/Cas9 method for lncRNA gene knockout. The results of the transcriptome sequencing showed that a total of 381 lncRNAs were differentially expressed, among which ENSDART00000154324 (lincRNA-154324) was found to be highly related to caudal fin regeneration, and thus it was chosen as the target lncRNA for the subsequent functional study. The results regarding the temporal and spatial expression of lincRNA-154324 and the gene knockout results from CRISPR/Cas9 indicated that lincRNA-154324 is involved in the caudal fin regeneration of zebrafish. Importantly, we serendipitously discovered that the cis correlation coefficient between lincRNA-154324 and its neighboring gene vacuole membrane protein 1 (vmp1) is extremely high, and they are essential for the process of caudal fin regeneration. Moreover, studies have found that vmp1 plays an important role in protein secretion, organelle formation, multicellular development, and autophagy. Collectively, our result may provide a framework for the identification and analysis of lncRNAs involved in the regeneration of the zebrafish caudal fin.