In this paper, we propose RFUAV as a new benchmark dataset for radio-frequency based (RF-based) unmanned aerial vehicle (UAV) identification and address the following challenges: Firstly, many existing datasets feature a restricted variety of drone types and insufficient volumes of raw data, which fail to meet the demands of practical applications. Secondly, existing datasets often lack raw data covering a broad range of signal-to-noise ratios (SNR), or do not provide tools for transforming raw data to different SNR levels. This limitation undermines the validity of model training and evaluation. Lastly, many existing datasets do not offer open-access evaluation tools, leading to a lack of unified evaluation standards in current research within this field. RFUAV comprises approximately 1.3 TB of raw frequency data collected from 37 distinct UAVs using the Universal Software Radio Peripheral (USRP) device in real-world environments. Through in-depth analysis of the RF data in RFUAV, we define a drone feature sequence called RF drone fingerprint, which aids in distinguishing drone signals. In addition to the dataset, RFUAV provides a baseline preprocessing method and model evaluation tools. Rigorous experiments demonstrate that these preprocessing methods achieve state-of-the-art (SOTA) performance using the provided evaluation tools. The RFUAV dataset and baseline implementation are publicly available at https://github.com/kitoweeknd/RFUAV/.

The use of unmanned aerial vehicles (UAVs) for smart agriculture is becoming increasingly popular. This is evidenced by recent scientific works, as well as the various competitions organised on this topic. Therefore, in this work we present a system for automatic fruit counting using UAVs. To detect them, our solution uses a vision algorithm that processes streams from an RGB camera and a depth sensor using classical image operations. Our system also allows the planning and execution of flight trajectories, taking into account the minimisation of flight time and distance covered. We tested the proposed solution in simulation and obtained an average score of 87.27/100 points from a total of 500 missions. We also submitted it to the UAV Competition organised as part of the ICUAS 2024 conference, where we achieved an average score of 84.83/100 points, placing 6th in a field of 23 teams and advancing to the finals.

Fire and smoke phenomena pose a significant threat to the natural environment, ecosystems, and global economy, as well as human lives and wildlife. In this particular circumstance, there is a demand for more sophisticated and advanced technologies to implement an effective strategy for early detection, real-time monitoring, and minimizing the overall impacts of fires on ecological balance and public safety. Recently, the rapid advancement of Artificial Intelligence (AI) and Computer Vision (CV) frameworks has substantially revolutionized the momentum for developing efficient fire management systems. However, these systems extensively rely on the availability of adequate and high-quality fire and smoke data to create proficient Machine Learning (ML) methods for various tasks, such as detection and monitoring. Although fire and smoke datasets play a critical role in training, evaluating, and testing advanced Deep Learning (DL) models, a comprehensive review of the existing datasets is still unexplored. For this purpose, we provide an in-depth review to systematically analyze and evaluate fire and smoke datasets collected over the past 20 years. We investigate the characteristics of each dataset, including type, size, format, collection methods, and geographical diversities. We also review and highlight the unique features of each dataset, such as imaging modalities (RGB, thermal, infrared) and their applicability for different fire management tasks (classification, segmentation, detection). Furthermore, we summarize the strengths and weaknesses of each dataset and discuss their potential for advancing research and technology in fire management. Ultimately, we conduct extensive experimental analyses across different datasets using several state-of-the-art algorithms, such as ResNet-50, DeepLab-V3, and YoloV8.

Al Jazeera's Charles Stratford reports on the method being used to stop Russian drones from attacking Ukrainian vehicles near the border, not far from the Russian region of Kursk where fierce fighting is ongoing.

The low battery autonomy of Unnamed Aerial Vehicles (UAVs or drones) can make smart farming (precision agriculture), disaster recovery, and the fighting against dengue vector applications difficult. This article considers two approaches, first enumerating the characteristics observed in these three IoT application types and then modeling an UAV's battery recharge coordination using the Agent-Based Simulation (ABS) approach. In this way, we propose that each drone inside the swarm does not communicate concerning this recharge coordination decision, reducing energy usage and permitting remote usage. A total of 6000 simulations were run to evaluate how two proposed policies, the BaseLine (BL) and ChargerThershold (CT) coordination recharging policy, behave in 30 situations regarding how each simulation sets conclude the simulation runs and how much time they work until recharging results. CT policy shows more reliable results in extreme system usage. This work conclusion presents the potential of these three IoT applications to achieve their perpetual service without communication between drones and ground stations. This work can be a baseline for future policies and simulation parameter enhancements.

Israeli forces killed at least nine Palestinians in Gaza in strikes on Saturday, according to the Gaza health ministry, the latest in a string of Israeli attacks on the enclave that have kept up despite a roughly two-month-old truce with Hamas. Since the cease-fire went into effect in mid-January, the military has conducted constant strikes in Gaza. Israel has accused militants of threatening its forces by laying explosive devices, flying drones or by approaching where Israeli troops are deployed. Hamas has claimed those attacks have killed more than 150 people since the truce took effect, at least some of them civilians. And it has accused Israel of repeatedly violating the agreement by continuing military operations.

At least nine people, including three journalists, have been killed and several others wounded in an Israeli drone attack on Beit Lahiya in northern Gaza, according to Palestinian media. The attack on Saturday reportedly targeted a relief team that was accompanied by journalists and photographers. At least three local journalists are among the dead. The Palestinian Journalists' Protection Center said in a statement that "the journalists were documenting humanitarian relief efforts for those affected by Israel's genocidal war" and called on Gaza ceasefire mediators to pressure Israeli Prime Minister Benjamin Netanyahu to move forward with implementing the agreed truce and prisoner exchange. Israel has rejected opening talks on the second phase of the ceasefire between it and Hamas, which would require it to negotiate over a permanent end to the war, a key Hamas demand.

British Prime Minister Keir Starmer stresses the United Kingdom's intention to provide "robust and credible" security guarantees to prevent Russia from attacking Ukraine again as Russian President Vladimir Putin has yet to sign up to a US-brokered truce proposal as fighting rages on the ground. "We will build up Ukraine's own defences and armed forces and be ready to deploy as a'coalition of the willing' in the event of a peace deal to help secure Ukraine on the land, at sea and in the sky," Starmer said on Saturday after a virtual meeting with 25 European Union and other world leaders as well as Ukrainian President Volodymyr Zelenskyy. "We agreed military planners would convene again in the UK this week to progress practical plans for how our militaries can support Ukraine's future security," Starmer added. The meeting was held after Putin on Friday agreed in principle to an immediate 30-day ceasefire put forward by Washington and already accepted by Ukraine. But on Friday, Putin also said there were issues to work out.

A team of charity workers has been killed in Israeli strikes in northern Gaza, the UK-registered Al Khair Foundation has told the BBC. The charity said eight workers - including volunteers and journalists documenting their activities - were killed when their vehicles were targeted on Saturday in what Hamas described as a "blatant violation" of the ceasefire agreement with Israel. The Israeli military has said it had struck "two terrorists who were identified operating a drone that posed a threat to Israeli troops", adding that it then targeted "additional terrorists" who arrived at the scene. The charity rejects the allegation that members of its team were terrorists.

At Tehran's Amirkabir University, authorities installed facial recognition software at its entrance gate to also find women not wearing the hijab, the report said. Surveillance cameras on Iran's major roads are also being used to search for uncovered women. Investigators also said they obtained the "Nazer" mobile phone app offered by Iranian police, which allows "vetted" members of the public and the police to report on uncovered women in vehicles, including ambulances, buses, metro cars and taxis. "Users may add the location, date, time and the licence plate number of the vehicle in which the alleged mandatory hijab infraction occurred, which then'flags' the vehicle online, alerting the police," the report said. According to the report, a text message is then sent to the registered owner of the vehicle, warning them they had been found in violation of the mandatory hijab laws.