A Comprehensive Survey on Orbital Edge Computing: Systems, Applications, and Algorithms

The number of satellites, especially those operating in low-earth orbit (LEO), is exploding in recent years. Additionally, the use of COTS hardware into those satellites enables a new paradigm of computing: orbital edge computing (OEC). OEC entails more technically advanced steps compared to single-satellite computing. This feature allows for vast design spaces with multiple parameters, rendering several novel approaches feasible. The mobility of LEO satellites in the network and limited resources of communication, computation, and storage make it challenging to design an appropriate scheduling algorithm for specific tasks in comparison to traditional ground-based edge computing. This article comprehensively surveys the significant areas of focus in orbital edge computing, which include protocol optimization, mobility management, and resource allocation. This article provides the first comprehensive survey of OEC. Previous survey papers have only concentrated on ground-based edge computing or the integration of space and ground technologies. This article presents a review of recent research from 2000 to 2023 on orbital edge computing that covers network design, computation offloading, resource allocation, performance analysis, and optimization. Moreover, having discussed several related works, both technological challenges and future directions are highlighted in the field.Comment: 18 pages, 9 figures and 5 table

Advancements in 3D Lane Detection Using LiDAR Point Clouds: From Data Collection to Model Development

Advanced Driver-Assistance Systems (ADAS) have successfully integrated learning-based techniques into vehicle perception and decision-making. However, their application in 3D lane detection for effective driving environment perception is hindered by the lack of comprehensive LiDAR datasets. The sparse nature of LiDAR point cloud data prevents an efficient manual annotation process. To solve this problem, we present LiSV-3DLane, a large-scale 3D lane dataset that comprises 20k frames of surround-view LiDAR point clouds with enriched semantic annotation. Unlike existing datasets confined to a frontal perspective, LiSV-3DLane provides a full 360-degree spatial panorama around the ego vehicle, capturing complex lane patterns in both urban and highway environments. We leverage the geometric traits of lane lines and the intrinsic spatial attributes of LiDAR data to design a simple yet effective automatic annotation pipeline for generating finer lane labels. To propel future research, we propose a novel LiDAR-based 3D lane detection model, LiLaDet, incorporating the spatial geometry learning of the LiDAR point cloud into Bird's Eye View (BEV) based lane identification. Experimental results indicate that LiLaDet outperforms existing camera- and LiDAR-based approaches in the 3D lane detection task on the K-Lane dataset and our LiSV-3DLane.Comment: 7 pages, 6 figure

SARS-CoV-2 RapidPlex: A Graphene-Based Multiplexed Telemedicine Platform for Rapid and Low-Cost COVID-19 Diagnosis and Monitoring

The COVID-19 pandemic is an ongoing global challenge for public health systems. Ultrasensitive and early identification of infection is critical in preventing widespread COVID-19 infection by presymptomatic and asymptomatic individuals, especially in the community and in-home settings. We demonstrate a multiplexed, portable, wireless electrochemical platform for ultra-rapid detection of COVID-19: the SARS-CoV-2 RapidPlex. It detects viral antigen nucleocapsid protein, IgM and IgG antibodies, as well as the inflammatory biomarker C-reactive protein, based on our mass-producible laser-engraved graphene electrodes. We demonstrate ultrasensitive, highly selective, and rapid electrochemical detection in the physiologically relevant ranges. We successfully evaluated the applicability of our SARS-CoV-2 RapidPlex platform with COVID-19-positive and COVID-19-negative blood and saliva samples. Based on this pilot study, our multiplexed immunosensor platform may allow for high-frequency at-home testing for COVID-19 telemedicine diagnosis and monitoring

SARS-CoV-2 RapidPlex: A Graphene-Based Multiplexed Telemedicine Platform for Rapid and Low-Cost COVID-19 Diagnosis and Monitoring

The COVID-19 pandemic is an ongoing global challenge for public health systems. Ultrasensitive and early identification of infection is critical in preventing widespread COVID-19 infection by presymptomatic and asymptomatic individuals, especially in the community and in-home settings. We demonstrate a multiplexed, portable, wireless electrochemical platform for ultra-rapid detection of COVID-19: the SARS-CoV-2 RapidPlex. It detects viral antigen nucleocapsid protein, IgM and IgG antibodies, as well as the inflammatory biomarker C-reactive protein, based on our mass-producible laser-engraved graphene electrodes. We demonstrate ultrasensitive, highly selective, and rapid electrochemical detection in the physiologically relevant ranges. We successfully evaluated the applicability of our SARS-CoV-2 RapidPlex platform with COVID-19-positive and COVID-19-negative blood and saliva samples. Based on this pilot study, our multiplexed immunosensor platform may allow for high-frequency at-home testing for COVID-19 telemedicine diagnosis and monitoring

Touch100k: A Large-Scale Touch-Language-Vision Dataset for Touch-Centric Multimodal Representation

Touch holds a pivotal position in enhancing the perceptual and interactive capabilities of both humans and robots. Despite its significance, current tactile research mainly focuses on visual and tactile modalities, overlooking the language domain. Inspired by this, we construct Touch100k, a paired touch-language-vision dataset at the scale of 100k, featuring tactile sensation descriptions in multiple granularities (i.e., sentence-level natural expressions with rich semantics, including contextual and dynamic relationships, and phrase-level descriptions capturing the key features of tactile sensations). Based on the dataset, we propose a pre-training method, Touch-Language-Vision Representation Learning through Curriculum Linking (TLV-Link, for short), inspired by the concept of curriculum learning. TLV-Link aims to learn a tactile representation for the GelSight sensor and capture the relationship between tactile, language, and visual modalities. We evaluate our representation's performance across two task categories (namely, material property identification and robot grasping prediction), focusing on tactile representation and zero-shot touch understanding. The experimental evaluation showcases the effectiveness of our representation. By enabling TLV-Link to achieve substantial improvements and establish a new state-of-the-art in touch-centric multimodal representation learning, Touch100k demonstrates its value as a valuable resource for research. Project page: https://cocacola-lab.github.io/Touch100k/

Coherent Precipitates with Strong Domain Wall Pinning in Alkaline Niobate Ferroelectrics

High‐power piezoelectric applications are predicted to share approximately one‐third of the lead‐free piezoelectric ceramic market in 2024 with alkaline niobates as the primary competitor. To suppress self‐heating in high‐power devices due to mechanical loss when driven by large electric fields, piezoelectric hardening to restrict domain wall motion is required. In the present work, highly effective piezoelectric hardening via coherent plate‐like precipitates in a model system of the (Li,Na)NbO₃ (LNN) solid solution delivers a reduction in losses, quantified as an electromechanical quality factor, by a factor of ten. Various thermal aging schemes are demonstrated to control the average size, number density, and location of the precipitates. The established properties are correlated with a detailed determination of short‐ and long‐range atomic structure by X‐ray diffraction and pair distribution function analysis, respectively, as well as microstructure determined by transmission electron microscopy. The impact of microstructure with precipitates on both small‐ and large‐field properties is also established. These results pave the way to implement precipitate hardening in piezoelectric materials, analogous to precipitate hardening in metals, broadening their use cases in applications

Biofuel-powered soft electronic skin with multiplexed and wireless sensing for human-machine interfaces

Existing electronic skin (e-skin) sensing platforms are equipped to monitor physical parameters using power from batteries or near-field communication. For e-skins to be applied in the next generation of robotics and medical devices, they must operate wirelessly and be self-powered. However, despite recent efforts to harvest energy from the human body, self-powered e-skin with the ability to perform biosensing with Bluetooth communication are limited because of the lack of a continuous energy source and limited power efficiency. Here, we report a flexible and fully perspiration-powered integrated electronic skin (PPES) for multiplexed metabolic sensing in situ. The battery-free e-skin contains multimodal sensors and highly efficient lactate biofuel cells that use a unique integration of zero- to three-dimensional nanomaterials to achieve high power intensity and long-term stability. The PPES delivered a record-breaking power density of 3.5 milliwatt·centimeter⁻² for biofuel cells in untreated human body fluids (human sweat) and displayed a very stable performance during a 60-hour continuous operation. It selectively monitored key metabolic analytes (e.g., urea, NH₄⁺, glucose, and pH) and the skin temperature during prolonged physical activities and wirelessly transmitted the data to the user interface using Bluetooth. The PPES was also able to monitor muscle contraction and work as a human-machine interface for human-prosthesis walking

Expression and clinical value of CXCR4 in high grade gastroenteropancreatic neuroendocrine neoplasms

BackgroundCXC chemokine receptor 4 (CXCR4) is associated with the progression and metastasis of numerous malignant tumors. However, its relationship with Gastroenteropancreatic Neuroendocrine Neoplasms Grade 3 (GEP-NENs G3) is unclear. The aim of this study was to characterize the expression of CXCR4 in GEP-NENS and to explore the clinical and prognostic value of CXCR4.MethodsThis study retrospectively collected clinical and pathological data from patients with GEP-NENs who receiving surgery in Qilu Hospital of Shandong University from January 2013 to April 2021, and obtained the overall survival of the patients based on follow-up. Immunohistochemistry (IHC) was performed on pathological paraffin sections to observe CXCR4 staining. Groups were made according to pathological findings. Kaplan-Meier (K-M) curve was used to evaluate prognosis. SPSS 26.0 was used for statistical analysis.Results100 GEP-NENs G3 patients were enrolled in this study. There was a significant difference in primary sites (P=0.002), Ki-67 index (P<0.001), and Carcinoembryonic Antigen (CEA) elevation (P=0.008) between neuroendocrine tumor (NET) G3 and neuroendocrine carcinoma (NEC). CXCR4 was highly expressed only in tumors, low or no expressed in adjacent tissues (P<0.001). The expression level of CXCR4 in NEC was significantly higher than that in NET G3 (P=0.038). The K-M curves showed that there was no significant difference in overall survival between patients with high CXCR4 expression and patients with low CXCR4 expression, either in GEP-NEN G3 or NEC (P=0.920, P=0.842. respectively).ConclusionDifferential expression of CXCR4 was found between tumor and adjacent tissues and between NET G3 and NEC. Our results demonstrated that CXCR4 can be served as a new IHC diagnostic indicator in the diagnosis and differential diagnosis of GEP-NENs G3. Further studies with multi-center, large sample size and longer follow-up are needed to confirm the correlation between CXCR4 expression level and prognosis