Optimal Beamforming for Hybrid Satellite Terrestrial Networks with Nonlinear PA and Imperfect CSIT

In hybrid satellite-terrestrial networks (HSTNs), spectrum sharing is crucial to alleviate the "spectrum scarcity" problem. Therein, the transmit beams should be carefully designed to mitigate the inter-satellite-terrestrial interference. Different from previous studies, this work considers the impact of both nonlinear power amplifier (PA) and large-scale channel state information at the transmitter (CSIT) on beamforming. These phenomena are usually inevitable in a practical HSTN. Based on the Saleh model of PA nonlinearity and the large-scale multi-beam satellite channel parameters, we formulate a beamforming optimization problem to maximize the achievable rate of the satellite system while ensuring that the inter-satellite-terrestrial interference is below a given threshold. The optimal amplitude and phase of desired beams are derived in a decoupled manner. Simulation results demonstrate the superiority of the proposed beamforming scheme.Comment: 5 pages, 5 figures, journa

Aerial small cells using coordinated multiple UAVs : an energy efficiency optimization perspective

Recently, unmanned aerial vehicle (UAV) communications have attracted great research interest. Due to the limited on-board energy, the optimization of energy efficiency (EE) is critical for UAV communications. In this paper, we propose an EE maximization scheme for UAV swarm-enabled small cell networks using large-scale channel state information at the transmitter (CSIT). The proposed scheme provides an agile coordination strategy for the UAVs in a swarm under energy constraints. We first formulate the EE maximization problem, where the objective function is defined as the ratio of the ergodic total data size to the total energy consumption. After that, an accurate approximation is derived to remove the intractable expectation operator in the objective function. As the newly formulated problem is non-convex, we decompose it into two subproblems to optimize the transmit power and the hovering time in an iterative way. Further by leveraging the max-min and linear optimization tools, both subproblems are efficiently solved. Simulation results demonstrate the superiority of our EE maximization scheme

Highly Sensitive Electrochemical Sensor for the Determination of 8-Hydroxy-2 \u27-deoxyguanosine Incorporating SWCNTs-Nafion Composite Film

8-Hydroxy-2\u27-deoxyguanosine (8-OHdG) is a typical biomarker of oxidative DNA damage and has attracted much attention in recent years since the level of 8-OHdG in body fluids is typically associated with various diseases. In this work, a simple and highly sensitive electrochemical sensor for the determination of 8-OHdG was fabricated incorporating single wall carbon nanotubes-(SWCNTs-) Nafion composite film coated on glassy carbon electrode. Nafion was chosen as an optimal adhesive agent from a series of adhesive agents and acted as a binder, enrichment, and exclusion film. Due to the strong cation-exchange ability of Nafion and the outstanding electronic properties ofSWCNTs, the prepared SWCNTs-Nafion film can strongly enhance the electrochemical response to oxidation of 8-OHdG and efficiently alleviate the interferences from uric acid and ascorbic acid. The oxidation peak currents are linear with the concentration of 8-OHdG in the range of 0.03 to 1.25 mu M with a detection limit of 8.0 nM (S/N = 3). This work demonstrates that SWCNTs-Nafion film can improve the sensitivity, selectivity, reproducibility, and stability, making it an ideal candidate for electrochemical detection of 8-OHdG

Nova+^+: Generative Language Models for Binaries

Generative large language models (LLMs) pre-trained on code have shown impressive effectiveness in code generation, program repair, and document analysis. However, existing generative LLMs focus on source code and are not specialized for binaries. There are three main challenges for LLMs to model and learn binary code: hex-decimal values, complex global dependencies, and compiler optimization levels. To bring the benefit of LLMs to the binary domain, we develop Nova and Nova$^+$, which are LLMs pre-trained on binary corpora. Nova is pre-trained with the standard language modeling task, showing significantly better capability on five benchmarks for three downstream tasks: binary code similarity detection (BCSD), binary code translation (BCT), and binary code recovery (BCR), over GPT-3.5 and other existing techniques. We build Nova$^+$ to further boost Nova using two new pre-training tasks, i.e., optimization generation and optimization level prediction, which are designed to learn binary optimization and align equivalent binaries. Nova$^+$ shows overall the best performance for all three downstream tasks on five benchmarks, demonstrating the contributions of the new pre-training tasks

Ag-Mg antisite defect induced high thermoelectric performance of α-MgAgSb

Engineering atomic-scale native point defects has become an attractive strategy to improve the performance of thermoelectric materials. Here, we theoretically predict that Ag-Mg antisite defects as shallow acceptors can be more stable than other intrinsic defects under Mg-poor-Ag/Sb-rich conditions. Under more Mg-rich conditions, Ag vacancy dominates the intrinsic defects. The p-type conduction behavior of experimentally synthesized ¿-MgAgSb mainly comes from Ag vacancies and Ag antisites (Ag on Mg sites), which act as shallow acceptors. Ag-Mg antisite defects significantly increase the thermoelectric performance of ¿-MgAgSb by increasing the number of band valleys near the Fermi level. For Li-doped ¿-MgAgSb, under more Mg-rich conditions, Li will substitute on Ag sites rather than on Mg sites and may achieve high thermoelectric performance. A secondary valence band is revealed in ¿-MgAgSb with 14 conducting carrier pockets

Research on deep hole segmented charge cut blasting of rock roadway based on numerical simulation

With the increase of the depth of the blast hole, the rock clamping effect at the bottom of the blast hole is enhanced, resulting in low rock breaking efficiency and blast hole utilization. The past continuous charging method can not solve the above problems. On this basis, this paper studies the rock roadway deep hole segmented charging cut blasting technology to improve the cut blasting efficiency. Using the smooth particle hydrodynamics-finite element method (SPH-FEM), a single-hole cut blasting model with different segmented charge structures was established, and the blasting speed of rock particles in the rock, the number of rock blasting and the characteristics of blasting cavity were analyzed in the blasting process under different models. The results show that different charge structures affect the damage range of the rock near the blast hole, and the damage area of the traditional continuous charge structure in the direction of the blast hole is larger than that of the segmented charge structure. In addition, the continuous charge structure makes the energy distribution of the explosive uneven because the explosive is concentrated at the bottom of the blast hole, resulting in poor blasting effect. The segmented charge structure can increase the number of rock fragments and optimize the blasting cavity, and the rock particles accelerate twice in the process of flying. The large or small proportion of the first segment charge obviously causes the unreasonable use of explosive energy and the poor effect of blasting cavity. Under the conditions of blast hole length, rock parameters and explosive performance set in the simulation, when the first stage charge ratio is 0.4, deep-hole rock tunnel excavation and blasting can make full use of explosive energy to achieve better cut blasting effect. The optimal subsection ratio obtained by numerical simulation was applied to the blasting construction of roadway excavation, and the delay initiation of two explosives in the cut hole was realized by using digital electronic detonator. The field test results show that the segmented charging can create good blasting effect and improve the utilization rate of blast holes in deep hole cut blasting

Evaluate how steaming and sulfur fumigation change the microstructure, physicochemical properties and in vitro digestibility of Gastrodia elata Bl. starch

The sulfur dioxide gas (SO2) generated by sulfur burning can improve the appearance quality of food and enhance the storage time. However, excessive sulfur dioxide will pollute the environment and cause deterioration of food quality, and even the high residual levels can increase the risk of cancer. As Gastrodia elata Blume is prone to corruption during processing, sulfur fumigation is often used for preservation. In this study, spectral analysis and Texture Profile Analysis (TPA) were used to investigate the effects of traditional sulfur fumigation processing on the morphology quality, edible quality and structural characteristics of G. elata. The results showed that compared with direct drying, the pH decreased by 0.399 of the sulfur fumigated after steamed treatment G. elata, and the morphology quality, pasting ability and gel edible quality of the starch were significantly improved. In addition, it was suggested that sulfur fumigation after steaming could promote the release of molecular chains from starch granules and thus enhance the cross-linking between molecules, which explained the reason for the improve of starch edible quality. This study can provide technical and theoretical support for improving the quality of starch rich foods, replacing sulfur fumigation and reducing potential environmental hazards

Applications of Nanomaterials in Electrogenerated Chemiluminescence Biosensors

Electrogenerated chemiluminescence (also called electrochemiluminescence and abbreviated ECL) involves the generation of species at electrode surfaces that then undergo electron-transfer reactions to form excited states that emit light. ECL biosensor, combining advantages offered by the selectivity of the biological recognition elements and the sensitivity of ECL technique, is a powerful device for ultrasensitive biomolecule detection and quantification. Nanomaterials are of considerable interest in the biosensor field owing to their unique physical and chemical properties, which have led to novel biosensors that have exhibited high sensitivity and stability. Nanomaterials including nanoparticles and nanotubes, prepared from metals, semiconductor, carbon or polymeric species, have been widely investigated for their ability to enhance the efficiencies of ECL biosensors, such as taking as modification electrode materials, or as carrier of ECL labels and ECL-emitting species. Particularly useful application of nanomaterials in ECL biosensors with emphasis on the years 2004-2008 is reviewed. Remarks on application of nanomaterials in ECL biosensors are also surveyed