Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges of Software Requirements Quality Assurance and Validation: A Systematic Literature Review

Validation of software requirements is a primary phase in requirements engineering that ensures requirements match the target system with the intended needs of the acquirer. It aims to detect and correct errors that prevail in the specified requirements. Although there are tremendous requirements validation approaches, some software may fail because of limited or ineffective requirements validation techniques and unreliable requirements’ quality characteristics. In this study, a systematic literature review of requirements validation is performed. The study analyzes the most adopted validation techniques, reports requirements quality characteristics, and discovers significant challenges of validation techniques. The review identified 66 relevant primary studies analyzed to derive deep insights into the following aspects of requirements validation: trends of requirements validation methods, including their subtechnique strengths and weaknesses, requirements quality characteristics categories, and adopted tools and datasets in these techniques. We grouped validation techniques into categories: prototyping, inspection, knowledge-oriented, test-oriented, modeling and assessment, and formal models. The analysis reported 19 validation techniques, 27 tools, new requirements validation characteristics, and several challenges that prevailed through validation techniques. The trend of validation techniques is to those methods that apply machine learning techniques with knowledge from dictionaries and ontologies. Most challenges are about how to express the requirements and how to revert clients’ feedback. There is a strong relationship between validation techniques, software application domain, and requirements validation quality attributes. Thus, there is an immense need to unify the quality characteristics and domain-specific validation methods.
Multi-Modal Self-Supervised Representation Learning for Earth Observation

Self-Supervised learning (SSL) has reduced the performance gap between supervised and unsupervised learning, due to its ability to learn invariant representations. This is a boon to the domains like Earth Observation (EO), where labelled data availability is scarce but unlabelled data is freely available. While Transfer Learning from generic RGB pre-trained models is still common-place in EO, we argue that, it is essential to have good EO domain specific pre-trained model in order to use with downstream tasks with limited labelled data. Hence, we explored the applicability of SSL with multi-modal satellite imagery for downstream tasks. For this we utilised the state-of-art SSL architectures i.e. BYOL and SimSiam to train on EO data. Also to obtain better invariant representations, we considered multi-spectral (MS) images and synthetic aperture radar (SAR) images as separate augmented views of an image to maximise their similarity. Our work shows that by learning single channel representations through non-contrastive learning, our approach can outperform ImageNet pre-trained models significantly on a scene classification task. We further explored the usefulness of a momentum encoder by comparing the two architectures i.e. BYOL and SimSiam but did not identify a significant improvement in performance between the models.
Geographically Weighted Regression Modeling Using Optical and Lidar Data to Map Aboveground Biomass of Urban Trees

Aboveground biomass (AGB) of trees refers to the dry weight of organic matter per unit area. Estimating the AGB of urban trees is helpful for urban ecosystem assessment. Due to the high heterogeneity of urban land use, the AGB of trees is greatly affected by geographical location, which brings difficulties to the estimation of AGB of urban trees. In this study, we use geographically weighted regression model(GWR) to take the geographical location into account in the regression model. In order to obtain a large number of samples, high-resolution optical data and airborne LiDAR data are used in combination. Taking Hengqin in Guangdong Province as an example, the experimental results show that the GWR model has good performance in estimation accuracy.
Bias correction of satellite retrievals of orographic precipitation

Satellite remote sensing observations at high spatial and temporal resolutions have advantages for precipitation retrievals. However, it is still challenging to produce reliable precipitation estimates over complex terrain regions. This paper proposes a deep learning framework to correct the bias associated with satellite revivals of orographic precipitation, focusing on the influence of terrain on precipitation estimation. Using precipitation product derived by the NOAA Climate Prediction Center morphing technique (i.e., CMORPH-CRT) in the western United States as an example, the deep learning model is trained and tested, and the ground-based Stage IV rainfall estimates are used as target labels in the training phase. Independent validation results show that the deep learning-based bias correction model can significantly enhance the performance of satellite precipitation products over complex terrain.
AgriFusion: An Architecture for IoT and Emerging Technologies Based on a Precision Agriculture Survey

Precision Agriculture (PA) is a management strategy that utilizes communication and information technology for farm management. It is a key to improve productivity by using the best agricultural practices and optimal usage of resources. Agriculture faces diverse challenges due to soil degradation, climate variation, and increasing costs. To unfold these challenges, PA uses Wireless Sensor Networks(WSNs) and exploits acquisition, communication, and processing of the data as basic enabling technologies to amplify the crop yield. Also, many other multidisciplinary technologies are supporting PA in finding the most novel use cases for PA. The use of Machine Learning (ML) and Artificial Intelligence (AI) has transformed PA at almost every level. The fog/edge paradigm is mitigating many challenges such as network bandwidth and security by bringing computation closer to the deployed network. At the same time, Software Defined Networks (SDN) brings flexibility, big data assists in handling data, and nanotechnology plays a crucial part in driving the innovation in PA. This paper delves into ways these technologies are transforming PA in respective tracks, exhibiting the significance of integrating multidisciplinary approaches towards the future of PA. In addition to a comprehensive survey, this paper proposes a multidisciplinary architecture: AgriFusion, for efficient and cost-effective agriculture solutions. A list of industrial solutions for different aspects of farm management and their underlying focused technology have been highlighted. This can help to align research and industrial goals for PA. Furthermore, this paper defines a step approach to describe the performance dichotomy between resource availability and objectives for PA. In addition, solution architecture is proposed for designing Key Performance Indicators (KPI) in PA. In the end, some open research issues in implementing PA and respective future scopes have been presented.
Multi-Temporal Changes Analysis of Natural Vegetation Cover Using Serial NDVI and Metric Indices: Case of Tlemcen National Park (Northwest of Algeria)

Assessing the evolution of vegetation cover fragmentation over time is a key-issue for monitoring biodiversity and sustainable development in land ecosystems. In the present study, landscape spatial metrics were investigated to assess changes of multitemporal vegetation cover within the Tlemcen National Park (TNP) in Northwest of Algeria over 31 years. To achieve this, five landscape metrics including number of patches (NP), landscape shape index (LSI), class area (CA), largest patch index (LPI), and percentage of landscape (PLAND) were applied and analysed to assess the fragmentation at class level. While, only NP and LSI were analyzed at the landscape level. Three Landsat images acquired during September 1987, 1999 and 2018 with TM, ETM+ and OLI sensors, respectively, were used. After rigorous preprocessing steps, the images were transformed and threshold into NDVI maps representing the major land use classes in TNP, such as forest, shrub lands, open shrub, scattered vegetation and bare soil. For validation purposes, Kappa coefficient analysis was carried out based on 120 ground control points representing these land use classes. The obtained results revealed that the generated confusion matrix ($\mathrm{p} < 0.05$) yield a Kappa coefficient of 0.77 and a significant concordance coefficient of 0.83 between the observed (ground truth) and predicted classes by NDVI-2018. These results indicate that the thresholding procedure is very appropriate and extendable to the other NDVI maps derived for the years 1987 and 1999. Moreover, at the landscape level, the NP and LSI metrics have increased over time by 67.6% in 1999 and 27.9% in 2018, revealing a gradual and important fragmentation of the landscape at TNP. In addition, this trend is corroborated at the class level by the five considered metrics. Accordingly, the synergy between metric indices and remote sensing data offers great potential for sustainable and efficient management policies to address the issues of spatiotemporal fragmentation and degradation across national parks.
Recent Trends of Drought Using Remotely Sensed and In-Situ Indices: Towards an Integrated Drought Monitoring System for South Africa

The frequency, intensity and longevity of droughts is a major concern globally for multiple sectors, as they compromise water and food security and other socioeconomic imperatives. We assessed and compared recent drought trends with various indices (i.e., Standardised Precipitation Index-SPI, Palmer-Drought Index-PDSI, and Vegetation Condition Index-VCI) and hydrological parameters using linear regression (LR) trend analysis and Mann-Kendall (MK) Trend Test. The results showed that PDSI, SPI-12 and VCI were more consistent (r > 0.5), thus showed the greatest potential for integration, to characterise hydrological and agroecological drought. Moreover, all drought indices showed significantly worsening drought conditions (p < 0.005) in Western Cape province of South Africa. This study showed the potential of intergrating insitu and satellite-based indices for assessing drought, significant for developing multi-index, multi-scale drought monitoing system.
Integrated Monitoring of a Slowly Moving Landslide Based on Total Station Measurements, Multi-Temporal Terrestrial Laser Scanning and Space-Borne Interferometric Synthetic Aperture Radar

Continuous landslide monitoring is a crucial task for the management of natural hazards for identifying suitable mitigation measures, including nature-based solutions. In the present study, three monitoring techniques including (i) an automated tracking total station (ATTS), (ii) multi-temporal terrestrial laser scanning (TLS) and (iii) space-borne interferometric synthetic aperture radar (InSAR) are applied to monitor the spatio-temporal displacement patterns of the Vögelsberg landslide (Tyrol, Austria) between 2016/05 and 2020/06. The landslide shows spatially and temporally varying displacement rates with up to 12 cm/a and a mean annual displacement of 4 cm/a. The results show that only the ATTS provides sufficient temporal resolution and spatial accuracy for assessing the temporal behaviour of the landslide's movement. However, ATTS measurements are only available at the installed 53 retro-reflecting prisms. Multi-temporal TLS can provide additional insight into the spatial displacement pattern at various man-made and natural objects such as walls, fences, poles and tree stems. But the respective accuracy and data acquisition intervals do not allow to draw conclusions about the temporal dynamics of the landslide's movement. Results of the InSAR technique based on Sentinel-1 imagery show good agreement with ATTS measurements, but cannot provide real-time information on the landslide's acceleration and deceleration phases. However, in combination, the measurement techniques provide vital information in both the spatial and temporal domain.
Semiconductor Manufacturing Final Test Yield Optimization and Wafer Acceptance Test Parameter Inverse Design Using Multi-Objective Optimization Algorithms

In the semiconductor industry, many previous optimization studies have been carried out at the integrated circuit front-end design phase to identify optimal circuit elements’ size and design parameters. With the scaling of device dimensions, semiconductor manufacturing back-end Final Test (FT) yield is increasingly influenced by systematic or random process variations. As a result, the FT yield is not fully guaranteed by design phase optimization due to existing limitations of yield simulation models and coverage. Very few studies have attempted to incorporate the production FT yield data into process variation optimization and inverse design. In this paper, we introduce a novel framework for FT yield optimization and Wafer Acceptance Test (WAT) parameter inverse design using multi-objective optimization algorithms. This provides a solution to monitor and quickly adjust process variations to maximize FT yield without expensive characterization or design correction after products are released into the production phase. Both yield optimization and process shift feasibility are to be taken into consideration by formulating these factors into a two objective optimization problem. One objective is to minimize FT yield loss, wherein the yield loss is predicted by a machine learning model. The other objective is to minimize the total WAT parameters shift distance from the current standard setting. Three widely used multi-objective algorithms NSGA-II, SMPSO and GDE3 are applied, compared and discussed. An automatic parameter tuning approach using Sequential Model-based Algorithm Configuration (SMAC) and entropy-based termination criterion is applied to reduce the execution time whilst maintaining the optimization algorithms’ performance. Real production data for CMOS 55nm chip are used to validate the framework and the results point to the effectiveness of yield improvement and accurate identification of the optimal WAT parameter combination.
Optimizing Barrier Synchronization on ARMv8 Many-Core Architectures

Synchronization operations are commonly seen in OpenMP programs where a parallel construct often works with an explicit or implicit barrier operation. While OpenMP synchronization has been extensively studied on the traditional x86 CPU architectures, there is little work on understanding OpenMP barrier synchronization operations on ARMv8 high-performance many-cores. This paper presents the first comprehensive performance study on OpenMP barrier implementations on emerging ARMvS-based many-cores. We evaluate seven representative barrier algorithms on three distinct ARMv8 architectures: Phytium 2000+, ThunderX2, and Kunpeng920. We empirically show that the existing synchronization implementations exhibit poor scalability on ARMv8 architectures compared to the x86 counterpart. We then propose various optimization strategies for improving these widely used synchronization algorithms on each platform. We showcase that our optimizations yield 12.6x performance improvement over the GCC implementation and 4.7x improvement over the LLVM implementation, translating to 1.6x improvement over the state-of-the-art best-performing algorithm. We share our experience and practical insights on optimizing OpenMP synchronization operations on emerging ARMv8 multi-core CPU architectures.
CloudSat Cloud Length, Thickness Distributions Again Confirm the 23/9 (2.55 D) Scaling, Stratified, Turbulence Model

Nearly 100 years ago, Richardson proposed that his “scaling first”, 4/3 law of turbulent advection holds from dissipation up to planetary scales. Starting in the 1960s - due primarily to its theoretical simplicity - the atmosphere was instead modelled as an “isotropy first” hybrid of isotropic 2D and isotropic 3D turbulence. In the 1980's Schertzer and Lovejoy pointed out that since the atmosphere is stratified, scaling first implies different horizontal and vertical scaling exponents and proposed the ratio = 5/9 implying an in-between dimension $D=2+5/9= 23/9=2.55\ldots$. By 2013 (the review [1]), the (meagre) aircraft support for the isotropic first model had been shown to be spurious, while numerous scaling first analyses (precipitation, aerosols, cloud densities, horizontal wind) vindicated Richardson and confirmed the 23/9D model over most of the horizontal and vertical ranges. I discuss new analyses of CloudSat cloud length distributions up to ≈ 2000 km (data from [2]) that yield the estimate $D=2.53\pm 0.02$ giving even more support for Richardson and the 23/9D model and excluding the (still) fashionable isotropy first ($D=2$, large, $D=3$, small) model at the level of many, many, many standard deviations. Applications to remote pollution modelling and measurements are also discussed.
Polarimetric Coherence Analysis for Mangrove Types Discrimination of Pichavaram, India using Sentinel-1 Satellite Data

This study showcased the capabilities of dual-pol synthetic aperture radar (SAR) remote sensing dataset to characterize Rhizophora and Avicennia types of mangroves in Pichvaram, India. The focus of the study was to utilize the Sentinel-1A derived polarimetric coherence pattern in the rotation domain to differentiate mangrove types. The polarimetric coherence for both species was generated at a specific interval in the rotation domain and analyzed for different rotation angles for various seasons in 2019. Both the mangrove types demonstrated the similar type of polarimetric coherence pattern with differences in the range of values. The discrimination between the species was investigated temporally, revealing the importance of the season and suitable interval of the rotation angle for mangrove type characterization.
A Deep Learning Model for Subsurface Mesoscale Eddy Detection Based on Remote Sensing Images

Subsurface-intensified eddies are ubiquitous in the world ocean, and they can transport large amounts of heat and salt and influence the three-dimensional distribution of oceanic tracers at a global scale. There is a lack of knowledge of subsurface-intensified eddies due to the lack of global in situ measurement. The correlations between sea surface height (SSH) and sea surface temperature (SST) within eddies can be used to identify whether they are subsurface-intensified eddies. The paper proposes a deep learning-based model for subsurface-intensified eddy detection from multi-source remote sensing images. The newly proposed method can distinguish surface- and subsurface-intensified eddy using fusion data of satellite SSH and SST images.
Copernicus Sentinel-6 Michael Freilich Satellite Mission: Overview and Preliminary in Orbit Results

This article describes the Copernicus Sentinel-6 satellite mission and early in-orbit results. The mission is designed to address the needs of the European Copernicus programme for precision sea level, near-real-time measurements of sea surface height, significant wave height, and other products tailored to operational services in the climate, ocean, meteorology and hydrology domains. It is designed to provide enhanced continuity to the very stable time series of mean sea level measurements and ocean sea state started in 1992 by the TOPEX/Poseidon (T/P) mission and follow-on Jason-1, Jason-2 and Jason-3 satellite missions. The mission is implemented through a unique international partnership with contributions from NASA, NOAA, ESA, EUMETSAT, and the European Union (EU). It includes two satellites that will fly sequentially (separated in time by 5 years). The first satellite, named Sentinel-6 Michael Freilich (S6-MF), launched from Vandenburg Air Force Base, USA on 21st November 2020. The main payload is the Poseidon-4 dual frequency (C/Ku-band) nadir-pointing radar altimeter providing synthetic aperture radar (SAR) processing in Ku-band to improve the signal through better along-track sampling and reduced measurement noise. The altimeter has an innovative interleaved mode enabling radar data processing on two parallel chains, one with the SAR enhancements and the other furnishing a “Low Resolution Mode” that is fully backward-compatible with the historical TOPEX/Poseidon and Jason measurements, so that complete inter-calibration between the state-of-the-art data and the historical record can be assured. Early in-orbit performance data are presented.
Energy Efficiency Implement Soft Switching at Zero Voltage of Transistors in Push-Pull Converter, with a Throttle at the Input

The operation of a push-pull converter with an input choke and an autotransformer current divider operating in the soft switching mode of all transistors, which is provided by reversing the instantaneous value of the inductor current, is considered. The implementation of the soft switching condition of all transistors, except for reducing dynamic losses on them, leads to an in-crease in dynamic losses in the inductor and static losses in the circuit due to an increase in the effective value of the inductor current. As a result of the experiment, it was shown that an increase in the variable component of the inductor current leads to an increase in the converter efficiency to a certain level of the average inductor current.
Thick Cloud Removal from Remote Sensing Images Using Double Shift Networks

Clouds greatly reduce the available ground information in optical remote sensing images. This paper proposed a double shift network to remove the thick clouds from multitemporal remote sensing images. The proposed networks are divided into two shift steps. In the first shift, the moment match and style transfer play the role of multi-temporal image normalization to obtain more reliable training images. In the second shift, in order to improve the network architecture's ability of capturing global semantics and local details, the shift connection layer and depthwise separable convolutions are introduced into U-Net. These two shift steps can not only improve the visual effect of cloud removal, but also further improve the quantitative evaluation. Experiments prove that the double shift network shows great advantages in cloud removal.
Infllunce of Irrigation on the Bias Between Orchidee and Fluxcom Evapotranspiration Products

Terrestrial Evapotranspiration (ET) is a key component of the surface energy balance and it is the second largest component of an intensified global hydrological cycle. In this study, we evaluated ORCHIDEE ET simulations with different configurations: three offline simulations with different forcing datasets and one simulation coupled to LMDz atmospheric model. The ORCHIDEE simulations were compared to the satellite-based FLUXCOM ET products. Within this evaluation, we also investigated the impact of irrigation on the biases between ORCHIDEE and the FLUXCOM ET values during the 1981 to 2010 period. While the ORCHIDEE offline simulations underestimated the FLUXCOM ET products over almost all latitudes, the ORCHIDEE simulation coupled with LMDz generally overestimated the FLUXCOM over the southern hemisphere. A structure of systematic negative bias over regions largely irrigated was found for all simulations. This deficiency in evapotranspiration seems not sensitive to the forcing datasets whether for ORCHIDEE or the FLUXCOM product over small and moderately irrigated areas except over highly irrigated areas where ORCHIDEE with GSWP3 forcing had larger bias than other offline configurations but similar to the coupled simulation. Our study highlights the importance of taking into account human activities such as irrigation in order to improve models simulation and therefore long or short-term forecasts.