Miracle-dz/UniFAR-FTUs huggingface dataset:
CRAG at the SBFT 2023 Tool Competition - Cyber-Physical Systems Track

CRAG is a combinatorial testing-based generator of road scenarios for autonomous driving testing. The parameters of the combinatorial model are used for determining lengths and curvatures of a road in the Frenet frame representation. CRAG first generates a test suite of roads achieving pairwise coverage, and then incrementally searches for roads achieving combinatorial strength k, but only for the k-tuples that contain (k-1)-tuples having optimal test performance in the previously generated roads.
Quality inspection of Titanium-dioxide blocking layers for efficient Dye sensitized solar cells photoanode

Dye sensitized solar cells are prospective devices for the usage as additional energy sources, since their fabrication is simple, they are low cost and they have low impact on the environment. One of the main parts of these cells is photoanode, which converts sunlight into electricity. The process that has the negative influence on the photoanode efficiency is the process of recombination. The role of the blocking layer is to prevent this process. In dye sensitized solar cells, blocking layer are usually made of titanium dioxide, TiO2. There are many methods of deposition of the blocking TiO2 layer on the substrate. In this work we examined dip-coating technique, which is widely used in the literature. The samples were prepared under different conditions, in order to find the best parameters for efficient blocking of recombination. Afterwards, the samples were investigated with the electrochemical impedance spectroscopy for inspection of the surface coverage and charge transfer through the layers. Also, the transparency of the deposited layer was examined by UV-VIS. The best results are obtained with the samples that were dip-coated in a 40 mmol water solution of TiCl4 and treated in a water bath for 30 minutes, at 70 °C. In future work, solar cells which will differ only in the blocking layers will be fabricated, in order to see if there is a correlation between the cell’s efficiency and the results obtained in this work. As a result, validation of the electrochemical impedance spectroscopy as a method for photoanode quality inspection will be achieved.
Sobolev gradient type preconditioning for the saint-venant model of elasto-plastic torsion

In this paper a suitable Laplacian preconditioner is proposed for the numerical solution of the nonlinear elasto-plastic torsion problem. The aim is to determine the tangential stress in cross-sections under a given torsion, for which the physical model is based on the Saint Venant model of torsion and the single curve hypothesis for the connection of strain and stress. The proposed iterative solution of the arising nonlinear elliptic problem is achieved by combining the advantages of Laplacian preconditioners with the qualitatively favourable aspects of the strong formulation. Error estimate is given for the convergence of the method. Finally, a numerical example is given.
Hyperspectral Image Segmentation and Entropy Calculation for Qualitative Analysis of Grassland

Hyperspectral imaging provides high spectral resolution images of a scene in hundreds of narrow spectral bands. This technique proves to be very useful in Earth Observation applications such as land cover mapping, agriculture crop health assessment and other remote sensing tasks. The qualitative analysis of grassland areas is manually performed by computing the Shannon-Weaver biodiversity index. In this paper we propose a semi-automatic estimation of this biodiversity index in remotely-sensed hyperspectral images. Starting from a spectral reflectance curve chosen to be representative for grassland, we perform the image segmentation based on histogram thresholding of spectral angle mapper (SAM) values. We then compute the entropy for the pixels belonging to the segmented grassland areas. In order to apply the classic Shannon definition of entropy, we perform a clustering for data dimensionality reduction. The evaluation of the proposed method is performed considering a manually generated ground truth.
House heating system thermal dynamics assessment using Petri Net approach

Energy efficiency is important as a way towards achieving energy savings and lowering harmful greenhouse gas emissions, and it also opens opportunities for employment and growth for new areas of activity boosting investment. Residential buildings have an important share in the energy used for different activities. As most of the energy used in residential buildings is aimed for space heating, the heating system must be assessed in terms of energy efficiency related to low heat losses. The paper proposed a Petri Net approach for modelling and simulating the dynamics of thermal heating system of a house in order to reduce the energy consumption based on appropriated heating system behavior.
LCP: A Lightweight Cache Partition Approach Based on Machine Learning

How to improve the quality of service of software is a hot issue for high-performance computing. For the multi-core CPU, the last level cache (LLC) is public and shared. When multiple running software access LLC in parallel, access conflicts will inevitably occur and result in QoS degradation. At present, the resolution of access conflicts mainly depends on cache partition. The existing cache partition methods mainly rely on heuristic algorithms that seeks the optimal partition scheme with the best performance by continuously verifying different partitions, in other words, by a “trial and error” way. However, this way makes the implementation of the algorithm uncertain and difficult to estimate the overhead. More seriously, the partition procedure may not converge. Therefore, we try to introduce intelligent methods into cache architecture and proposed LCP, which is a cache partition mechanism based on machine learning. This mechanism uses the decision tree (DT) and sequence to sequence (Seq2seq) model to predict the performance of the remaining cache partition schemes with the aid of several running parameters in the cache partition schemes, and directly find the optimal partition scheme, effectively avoiding the problem of excessive overhead caused by uncertain partition. Experiments show that LCP can increase the system throughput by 7.21% and save 26.2% of the partition overhead compared with the existing methods.
An Empirical Study of Storj DCS: Ecosystem, Performance, and Security

In the age of pervasive computing, traditionally centralized cloud storage (CCS) services may not fit in well due to their centralized architecture, limited worldwide availability, high expense, and security and privacy concerns. To address these issues, decentralized cloud storage (DCS) services emerged recently. However, previous works focus on analyzing the technical design of DCS services, e.g., their incentive mechanisms. Little is known regarding how well these DCS services work in real-world operations. In this paper, we fill this gap by providing the first empirical measurement of Storj, one of the most extensive in-operation decentralized cloud storage services, focusing on its ecosystem, performance, and security implications. Our study is made possible through multiple measurement techniques to automatically capture storage nodes, profile Storj's quality of service, understand co-located network threats, and evaluate potential attacks in a simulated environment. Leveraging these techniques, a set of insightful findings have been distilled. Particularly, we have observed over 32K storage nodes as well as 155K unique node IP addresses, which are widely distributed in 122 countries, 2,418 ASNs, and 205 /8 IPv4 prefixes. Regarding performance, storage customers located in Europe or the United States tend to enjoy a better storage performance than those in Asia-Pacific, likely due to the imbalanced distribution of storage nodes in different regions. Lastly, what is concerning is that 4.48% of IPs of storage nodes were found to have been associated with various malicious activities, especially botnets and cryptomining. Another vulnerability is that a malicious storage node could exploit multiple channels to boost its storage reputation while demoting that of benign nodes.
Emergency Message Broadcasting Scheme For Urban VANET

As an important part of the advanced Intelligent Traffic System (ITS) in the Internet of Vehicles (IoV), V2X communication technology provides reliable end-to-end connection and efficient packet transmission. Currently, the main challenge is design and implementation of the multi-hop broadcast protocol. Especially the network topology is constantly changing because of the high-speed vehicle and diversified road structure, which troubles selection of forward nodes. Therefore, we propose an Emergency Message Broadcast Protocol (EMBP). In this method, we select the optimal relay node according to the directionality and mobility, channel fading and link connectivity and apply suboptimal relay node selection mechanism to guarantee the reliability of broadcast. Simulation results show that EMBP is more efficient and stable than part classical protocols.
A Balun-Integrated On-Chip Differential Pad for Full/Multi-Band mmWave/THz Measurements

A set of balun-integrated 100/300 GHz differential on-chip pads for broadband characterization (small/large-signal) of active circuits fabricated in SiGe HBT technology is presented. A general theory of asymmetric coupled-lines in inhomogeneous medium was applied to design a Marchand-like balun consisting of multiple sub-λ/4 long nonuniformly-sized line sections with a close-proximity ground-plane to compensate for the frequency-dependent pad impedance.
Tree Object Detection And Classification Algorithms – Review

Just a few years ago, the only way to gather data on plant density and condition from a distance was through expensive flights and satellite imagery. However, now there’s a more affordable and innovative option available: the use of Unmanned Aerial Vehicles (UAVs) to capture high-resolution images that can be used for estimation purposes. Images obtained in this way represent a high-quality source of information and could be further used as an input dataset for various object detection and classification algorithms. In recent years, there has been a proliferation of studies in which images obtained in this way are used for training different object detection and classification algorithms for usage in the process of improving agricultural production. Precisely for this reason, the main goal of this research is to present a review of the scientific results of application of different algorithms in the process of plant detection on agricultural plantations. Furthermore, a comparison of the obtained results of the application of different algorithms is given.
High Capacity Dual-Polarization THz-Wireless Transmission in the 300 GHz Band using a Broadband Orthomode Transducer

We report on the broadband, dual-polarized THz-wireless transmission with >100 Gb/s net data rate in the 300 GHz band, employing a new broadband orthomode transducer. The orthomode transducer is realized by a new microstrip probe-fed circular waveguide transition. It omits the use of hybrids for the commonly used balanced 4-port excitation and, for that reason, is more suitable for a planar active integration with transceiver MMICs. The characterization of the orthomode transducer in back-to-back configuration shows very low losses of less than 1.5 dB per transition over a bandwidth of more than 40 GHz with a total back-to-back cross polarization better than 25 dB. Constellation diagrams and signal spectra from the dual-polarized wireless transmission experiments are compared to single-polarized transmission, showing only small degradations due to cross-polarization effects. The paper demonstrates that this simplified polarization multiplexer is a potential candidate for use in a wireless extender of a coherent fiber-optical transmission system.
A Novel Multi-Functional Coupled Resonator Based Balun Filter in Waveguide Technology

This paper presents a novel multi-functional balun filter designed at 10 GHz in rectangular waveguide technology. The balun filter is realized using the modified coupling matrix of the coupled resonator-based band-pass filter. The balun filter integrates the functionality of balun and band-pass filter within a single filtering component, thus enabling system miniaturization (due to functional integration). In-addition, it enhances the system performance by eliminating additional insertion loss and imbalance loss arising from the balun. For the proof of the concept, a 4th order waveguide balun filter is designed, fabricated and tested at a center frequency of 10 GHz with 350 MHz bandwidth. The measured peak insertion loss is 0.25 dB and rejection is better than 40 dB at ± 650 MHz offset. The amplitude imbalance is within 0.2 dB and the phase imbalance is within 1.25 degrees over the entire bandwidth. The proposed design methodology is scalable to higher order balun filters.
Query Processing on Attributed Graphs

An attributed graph is a powerful tool for modeling a variety of information networks. It is not only able to represent relationships between objects easily, but it also allows every vertex and edge to have its attributes. Hence, a lot of data, such as the web, sensor networks, biological networks, economic graphs, and social networks, are modeled as attributed graphs. Due to the popularity of attributed graphs, the study of attributed graphs has caught attentions of researchers. For example, there are studies of attributed graph OLAP, query engine, clustering, summary, constrained pattern matching query, and graph visualization, etc. However, to the best of our knowledge, the studies of topological and attribute relationships between vertices on attributed graphs have not drawn much attentions of researchers. Given the high expressive power and popularity of attributed graph, in this thesis, we define and study the processing of three new attributed graph queries, which would help users to understand the topological and attribute relationships between entities in attributed graphs. For example, a reachability query on a social network can tell whether two persons can be connected given certain attribute constraints; a reachability query on a biological network can tell whether a compound can be transformed to another compound under given chemical reaction conditions; a How-to-Reach query can tell why the answers of the above two reachability query are negative; a visualizable path summary query can offer an overall picture of topological and attribute relationship between any two vertices in attributed graphs. Except for the proposed query types in this thesis, we believe that there is still penalty of meaningful attributed graph query types that have not been proposed and studied by the database and data mining community since an attributed graph is a very rich source of information. Through this thesis, we hope to draw people's attentions on attributed graph query processing so that more hidden information contained in attributed graphs can be queried and discovered.
Boosting Optical Camera Communication via 2D Rolling Blocks

Optical Camera Communication (OCC) appears as a promising technology to provide secure and pervasive wireless services with users' daily smart devices. Rolling shutter based modulations can improve the frequency response of the camera. This paper introduces a 2D Rolling Block (2DRB) based OCC modulation to use un-exploited spatial diversity to improve OCC's data rate for real-world applications. 2DRB outperforms traditional 1D strip based modulations. Using our 2DRB prototype with commercial devices, we show a significant data rate enhancement. We also discuss one promising real-world use case: indoor office integrated lighting and communication.
Non-decimated Wavelet Transform for a Shift-invariant Analysis

Due to the ability of time-frequency location, the wavelet transform has been applied in several areas of research involving signal analysis and processing, often replacing the conventional Fourier transform. The discrete wavelet transform has great application potential, being an important tool in signal compression, signal and image processing, smoothing and denoising data. It also presents advantages over the continuous version because of its easy implementation, good computational performance and perfect reconstruction of the signal upon inversion. Nevertheless, the downsampling required in the discrete wavelet transform calculous makes it shift variant and not appropriated to some applications, such as for signals or time series analysis. On the other hand, the Non-Decimated Discrete Wavelet Transform is shift-invariant because it eliminates the downsampling and, consequently, is more appropriate for identifying both stationary and nonstationary behaviors in signals. However, the non-decimated wavelet transform has been underused in the literature. This paper intends to show the advantages of using the non-decimated wavelet transform in signal analysis. The main theorical and pratical aspects of the multiscale analysis of time series from non-decimated wavelets in terms of its formulation using the same pyramidal algorithm of the decimated wavelet transform was presented. Finally, applications with a simulated and real time series compare the performance of the decimated and non-decimated wavelet transform, demonstrating the superiority of non-decimated one, mainly due to the shift-invariant analysis, patterns detection and more perfect reconstruction of a signal.
Optimal Strategy Selection for Cyber Deception via Deep Reinforcement Learning

Cyber deception has emerged as an effective approach to enhance the security of computer systems. One important factor that affects the security effectiveness of cyber deception is the deployment strategy. In this paper, we develop a honeypot-based defense mechanism to deploy honeypot intelligently. We propose a Stackelberg game model to simulate the interaction between network attackers and defenders. We use attack graph to generate the attacker’s strategy and find the optimal honeypot deployment strategy in a constant confrontation. We develop a reinforcement learning (RL) based approach to train the attack strategy and the honeypot deployment strategy. To cope with the large strategy space problem, we modify the neural network structure for the attacker and the defender according to the deception game rules. We conduct experiments with real network data and various game settings to evaluate the performance of our approach. Experiment results show that our RL-based approach outperforms baselines, and the approach is robust to the uncertain security environment.
An Overview and Ontology of Privacy to Preserve Privacy in Ultra-Wideband Networks

The Internet of Things (IoT) has become increasingly popular due to the growing number of IoT devices and the adoption of numerous communication protocols. With the renewed interest in Ultra-Wideband (UWB) positioning and recent reports on privacy infringements through UWB-enabled spyware, the consideration of privacy in UWB applications has become paramount. Currently, an IoT-centric security database is under development, VarIoT, however, there is no filter for privacy-or protocol-based vulnerabilities, risks, or threats and current UWB literature does not focus on privacy.Thus, this work formalizes privacy risks as attack patterns, based on the UWB protocol and presents it in an ontology. The effectiveness of this ontology is exemplified by a case study that receives UWB artifacts as input and derives a set of privacy risks by relying on the presented formalized knowledge graph. By exhibiting the ontology’s ability to automatically derive threats for an applied scenario an increased privacy preservation in UWB networks and solutions is reached.
CATS: A Computation-Aware Transaction Processing System with Proactive Unlocking

With the increasing complexity of network applications and high demands for QoS, transaction processing systems have received more attentions due to salient features of simplicity and atomicity. Computation operations play an important role in transaction processing systems. However, conventional QoS-based mechanisms become inefficient due to the limited concurrent support upon computation operations, thus causing high time consumption in the critical path of concurrency control. In order to efficiently offer concurrent computations, we propose CATS, a Computation Aware Transaction processing System, to mitigate performance impacts caused by computation operations. CATS further leverages program semantics to defer the execution of transaction operations in the commit phase to alleviate unnecessary conflicts caused by computations. Extensive evaluation results demonstrate that CATS significantly outperforms state-of-the-art designs, including 2PL and OCC based transaction processing systems on high-contended and computation-intensive workloads. We have released the open-source codes in GitHub for public use.
Gap Approaching Intelligent Driver Model for Interactive Simulation of Merging Scenarios

As an important part of automated vehicle development and testing, simulation makes heavy use of driver models to reproduce the behavior of traffic participants. Due to their simplicity, most models fail to capture driver behavior in interactive situations like lane changes or merging, where drivers need to consider multiple vehicles simultaneously and smoothly approach gaps. We propose the Gap APproaching Intelligent Driver Model (GAP-IDM), an extension of IDM that takes an arbitrary number of target vehicles into account and produces realistic behavior for approaching traffic gaps, even when the ego vehicle has to overtake or fall behind target vehicles. To this end, we use a target distance rectification to produce smooth behaviors even for small or negative distances, and to enforce time or distance limits on the maneuver. We evaluate the proposed model in an optional and a necessary lane change scenario and demonstrate that it generates realistic driving behavior. Possible applications of our model include simulations of interactive scenarios, development of complex driver models with multiple target vehicles, or the use as a low-level policy in a high-level behavior planning module.
Symmetrical Multiport mmWave Chalcogenide Phase-Change RF Switches

This article demonstrates ultra-compact multiport chalcogenide phase-change germanium telluride (GeTe)-based RF switches. RF series switches in a single-pole single-throw (SPST), single-pole double-throw (SPDT), single-pole triple-throw (SP3T), and a single-pole eight-throw (SP8T) are developed in-house using an eight-layer microfabrication process. The devices are designed with design symmetry to achieve consistent RF performance across all output RF ports. All the multiport switches have chip area less than 0.36 mm2 including all the RF and DC terminals. The SPST, SPDT, and SP3T switches are measured over DC to 67 GHz with less than 0.25 for SPST and lower than 1 dB loss for SPDT and SP3T, respectively. The SP8T switch is measured from DC to 40 GHz with loss lower than 1.5 dB and isolation better than 20 dB.