Backscatter communication is emerging as a promising solution for enabling low-power and large-scale IoT applications. However, it faces challenges in terms of widespread deployment, wireless resource management, and quality of service (QoS). In this paper, we first propose a secure transmission architecture to integrate the backscatter network with the existing 5G/IoT infrastructure. Next, we introduce a multi-device scheduling and time-switching strategy aimed at optimizing both capacity and secure throughput. In the time-switching scheme, BDs primarily operate in symbiotic mode without requiring additional spectrum, but can dynamically switch to opportunistic spectrum access mode when necessary, with adaptive time allocation to improve QoS. For multi-device scheduling, BDs are assigned to function as a master transmission node, cooperation node, or spoofing/jamming node, thereby enhancing the system’s resistance to proactive eavesdropping. The optimization problem is formulated to minimize spectrum resource usage while ensuring QoS and following the energy constraint. To solve this, we introduce an enumeration-based interior-point algorithm (EIA) and design a novel progressive greedy algorithm (PGA). The EIA method provides optimal solutions, while the PGA algorithm achieves high-quality suboptimal solutions with lower complexity. Extensive simulation results demonstrate that the proposed strategy stands out in ensuring QoS, enhancing security, and reducing spectrum resource usage.

Early detection of wildfires is essential to prevent large-scale fires resulting in extensive environmental, structural, and societal damage. Uncrewed aerial vehicles (UAVs) can cover large remote areas effectively with quick deployment requiring minimal infrastructure and equipping them with small cameras and computers enables autonomous real-time detection. In remote areas, however, detection methods are limited to onboard computation due to the lack of high-bandwidth mobile networks. For accurate camerabased localisation, segmentation of the detected smoke is essential but training data for deep learning-based wildfire smoke segmentation is limited. This study shows how small specialised segmentation models can be trained using only bounding box labels, leveraging zero-shot foundation model supervision. The method offers the advantages of needing only fairly easily obtainable bounding box labels and requiring training solely for the smaller student network. The proposed method achieved 63.3% mIoU on a manually annotated and diverse wildfire dataset. The used model can perform in real-time at ~25 fps with a UAV-carried NVIDIA Jetson Orin NX computer while reliably recognising smoke, as demonstrated at real-world forest burning events. Code is available at: https://gitlab.com/fgi_nls/public/wildfire-real-time-segmentation

Over the years, space-based remote sensing of nighttime light has mostly utilized panchromatic or multispectral sensors. The hyperspectral mission EnMAP, primarily intended for daytime observations, can also produce hyperspectral data of nighttime lighting. EnMAP data from the Las Vegas Strip was analyzed by detecting locations of certain lighting types using matched filtering and detection of sharp emission spikes at known wavelengths. Additionally, images from different nights were compared to determine how changes in observation geometry affect the observed spectra. The results indicate that corrections for geometric effects would be necessary to produce robust time-series data. The EnMAP data were also used to approximate two in-dices related to the efficiency and spectral quality of the light, the luminous efficiency of radiation (LER) and the spectral G index. Future developments will include ana-lyzing scenes from other cities using similar approaches. Program code used in this work is available at https://github.com/silmae/EnMAP_nightlights.

Finnish rock paintings from Neolithic Stone Age are located on open-air bedrock panels, being subject to the effects biotic and climatic influences. Consequently, these paintings have predominantly faded and are challenging to discern. Through hyperspectral (HS) imaging and computational unmixing methods, both the delineation of these drawings and the identification of their pigments can be enhanced. In this study, we developed an unmixing method for detecting hematite and compared various techniques for delineating figures from HS images captured from Halsvuori rock painting. The results indicate that HS cameras combined with unmixing methods can successfully identify pigments and render the figures more discernible than before. HS data and source code are open and available in Zenodo [1].

Miniaturized hyperspectral imaging sensors are becoming available to small unmanned airborne vehicle (UAV) platforms. Imaging concepts based on frame format offer an attractive alternative to conventional hyperspectral pushbroom scanners because they enable enhanced processing and interpretation potential by allowing for acquisition of the 3-D geometry of the object and multiple object views together with the hyperspectral reflectance signatures. The objective of this investigation was to study the performance of novel visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral frame cameras based on a tunable Fabry-Pérot interferometer (FPI) in measuring a 3-D digital surface model and the surface moisture of a peat production area. UAV image blocks were captured with ground sample distances (GSDs) of 15, 9.5, and 2.5 cm with the SWIR, VNIR, and consumer RGB cameras, respectively. Georeferencing showed consistent behavior, with accuracy levels better than GSD for the FPI cameras. The best accuracy in moisture estimation was obtained when using the reflectance difference of the SWIR band at 1246 nm and of the VNIR band at 859 nm, which gave a root mean square error (rmse) of 5.21 pp (pp is the mass fraction in percentage points) and a normalized rmse of 7.61%. The results are encouraging, indicating that UAV-based remote sensing could significantly improve the efficiency and environmental safety aspects of peat production.