Application of Deep Neural Network Algorithm in the Analysis of Legal Precedent Citation Basis

In order to improve the retrieval efficiency of civil litigation cases, the research introduces the fuzzy neural network algorithm and constructs a targeted retrieval algorithm system. In the simulation verification, it is found that, in the artificial subjective evaluation results of the expert group, the comprehensive score of reference cases given by the retrieval scheme exceeds the level of reference cases in the cases promoted and studied by the Supreme Court. The use of this scheme can effectively save the preparation time of prelitigation documents and help to improve the fairness and justice of the court trial process. It is proved that the retrieval scheme has certain popularization value.

Antarctic snow-covered sea ice topography derivation from TanDEM-X using polarimetric SAR interferometry

Single-pass interferometric synthetic aperture radar (InSAR) enables the possibility for sea ice topographic retrieval despite the inherent dynamics of sea ice. InSAR digital elevation models (DEMs) are measuring the radar scattering center height. The height bias induced by the penetration of electromagnetic waves into snow and ice leads to inaccuracies of the InSAR DEM, especially for thick and deformed sea ice with snow cover. In this study, an elevation difference between the satellite-measured InSAR DEM and the airborne-measured optical DEM is observed from a coordinated campaign over the western Weddell Sea in Antarctica. The objective is to correct the penetration bias and generate a precise sea ice topographic map from the single-pass InSAR data. With the potential of retrieving sea ice geophysical information by the polarimetric-interferometry (Pol-InSAR) technique, a two-layer-plus-volume model is proposed to represent the sea ice vertical structure and its scattering mechanisms. Furthermore, a simplified version of the model is derived, to allow its inversion with limited a priori knowledge, which is then applied to a topographic retrieval scheme. The experiments are performed across four polarizations: HH, VV, Pauli 1 (HH + VV), and Pauli 2 (HH − VV). The model-retrieved performance is validated with the optically derived DEM of the sea ice topography, showing an excellent performance with root-mean-square error as low as 0.26 m in Pauli-1 (HH + VV) polarization.

Square transposition: an approach to the transposition process in block cipher

The transposition process is needed in cryptography to create a diffusion effect on data encryption standard (DES) and advanced encryption standard (AES) algorithms as standard information security algorithms by the National Institute of Standards and Technology. The problem with DES and AES algorithms is that their transposition index values form patterns and do not form random values. This condition will certainly make it easier for a cryptanalyst to look for a relationship between ciphertexts because some processes are predictable. This research designs a transposition algorithm called square transposition. Each process uses square 8 × 8 as a place to insert and retrieve 64-bits. The determination of the pairing of the input scheme and the retrieval scheme that have unequal flow is an important factor in producing a good transposition. The square transposition can generate random and non-pattern indices so that transposition can be done better than DES and AES.

Cooperative Cloud-Edge Feature Extraction Architecture for Mobile Image Retrieval

Mobile image retrieval greatly facilitates our lives and works by providing various retrieval services. The existing mobile image retrieval scheme is based on mobile cloud-edge computing architecture. That is, user equipment captures images and uploads the captured image data to the edge server. After preprocessing these captured image data and extracting features from these image data, the edge server uploads the extracted features to the cloud server. However, the feature extraction on the cloud server is noncooperative with the feature extraction on the edge server which cannot extract features effectively and has a lower image retrieval accuracy. For this, we propose a collaborative cloud-edge feature extraction architecture for mobile image retrieval. The cloud server generates the projection matrix from the image data set with a feature extraction algorithm, and the edge server extracts the feature from the uploaded image with the projection matrix. That is, the cloud server guides the edge server to perform feature extraction. This architecture can effectively extract the image data on the edge server, reduce network load, and save bandwidth. The experimental results indicate that this scheme can upload few features to get high retrieval accuracy and reduce the feature matching time by about 69.5% with similar retrieval accuracy.

Multi-scale Information Retrieval for BIM using Hierarchical Structure Modelling and Natural Language Processing

Building Information Modelling (BIM) captures numerous information the life cycle of buildings. Information retrieval is one of fundamental tasks for BIM decision support systems. Currently, most of the BIM retrieval systems focused on querying existing BIM models from a BIM database, seldom studies explore the multi-scale information retrieval from a BIM model. This study proposes a multi-scale information retrieval scheme for BIM jointly using the hierarchical structure of BIM and Natural Language Processing (NLP). Firstly, a BIM Hierarchy Tree (BIH-Tree) model is constructed to interpret the hierarchical structure relations among BIM data according to Industry Foundation Class (IFC) specification. Secondly, technologies of NLP and International Framework for Dictionaries (IFD) are employed to parse and unify the queries. Thirdly, a novel information retrieval scheme is developed to find the multi-scale information associated with the unified queries. Finally, the retrieval method proposed in this study is applied to an engineering case, and the practical results show that the proposed method is effective.

Update on CO2 and temperature profiles retrievals from NOMAD-SO on board ExoMars TGO

<ul> <li>The SO channel of the NOMAD instrument</li> </ul> <p>The NOMAD-SO channel [1] is an infrared spectrometer working in the 2.2 to 4.3 µm spectral range (2200-4500 cm<sup>-1</sup>) and started to perform solar occultation measurement on April 21, 2018. The instrument is composed of an echelle grating coupled to an Acousto-Optical Tunable Filter for the diffraction order selection. As TGO is on a quasi-circular orbit at around 400 km of altitude, it performs one orbit every two hours. During a solar occultation measurement, SO scans six diffraction orders each second. These diffraction orders are recorded on four bins leading to a vertical sampling lower than one km. The calibration of the SO channel is described in [2] and is still being fine-tuned.</p> <ul> <li>CO<sub>2</sub> density and temperature profiles retrievals</li> </ul> <p>Several diffraction orders probe different altitude ranges as they contain CO<sub>2</sub> lines with different intensities that appear and saturate at different altitudes. Correct temperature profiles are necessary for the retrieval of several species and the profiles have to be carefully retrieved as their inversion is very sensitive to noise. We use the following retrieval scheme:</p> <p>For each solar occultation measurement, we derive a slant column profile of CO<sub>2</sub> using ASIMUT-ALVL [3]. ASIMUT is a radiative transfer program developed at BIRA-IASB and uses the Optimal Estimation Method for regularization [4]. The GEM-Mars GCM provides the <em>a priori</em> profiles of CO<sub>2</sub> local density, pressure and temperature. We then apply an iterated Tikhonov regularization to derive a regularized local density profile using an improved version of the algorithm described in [5]. This method requires the selection of a regularization parameter to reduce as much as possible the presence of noise in the profile while keeping the real variations. This retrieval scheme allows a fine-tuning of the reguralization parameter. We finally apply the hydrostatic equilibrium equation to derive the temperature profiles [6]. We derived the NOMAD-SO CO<sub>2</sub> and temperature profiles for MY34 and 35.</p>

Characterising the interior structures and atmospheres of multiplanetary systems.

<div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>The increasing number of well characterised low-mass planets, combined with the valuable informations from stellar and planetary spectroscopy, opens the way to the modeling of planetary structures and compositions, which can be obtained with theoretical and numerical works. This approach gives a valuable insight to understand the formation of planetary systems in the low-mass range. We present a 1D planetary model where the interior is coupled with the atmosphere in radiative-convective equilibirum within a Bayesian retrieval scheme. In addition to a Fe core and a silicate mantle, we take into account water in all its possible phases, including steam and supercritical phases, which is necessary for systems with a wide range of stellar irradiations. </p> </div> </div> </div> <p>Our interior-atmosphere model calculates the compositional and atmospheric parameters, such as Fe and water content, surface pressures, scale heights and albedos. We analyse the multiplanetary systems K2-138 and TRAPPIST-1, which present six low-mass planets with different densities and irradiations. From the individual composition of their planets, we derive a similar trend for both systems: a global increase on the water content with increasing distance from the star in the inner region of the systems, while the planets in the outer region present a constant water mass fraction. This trend reveals the possible effects of migration, formation location and atmospheric mass loss during their formation history.</p>