Plant phenotyping attempts to objectively measure a plant's reaction to its environment as encoded by its genotype. It has become an essential tool for deepening our understanding of plant responses to environmental stimuli. Understanding the plant's reaction to, for example, a warmer climate is crucial to ensure food production for future generations. Breeders and researchers rely on automated high-throughput phenotyping for optimizing crops. Ideally, above- and below-ground traits are observed simultaneously. The newly established controlled environment facility at the Technology Center for Phenotyping of the Fraunhofer IIS in Merkendorf provides several climate chambers with individually controllable conditions for up to 400 individual plants to allow simulation of even extreme climatic conditions all year around. Comprehensive measurement of plant structures using X-ray as well as optical cameras provide highly detailed 2D and 3D information to researchers and breeders worldwide. In combination with automated data pipelines, distinct plant traits can be extracted from the sensor data. By bridging above- and below-ground phenotyping, this facility not only advances plant science but also contributes to the breeding of more resilient and productive crops. Collaborators are welcome to unlock the transformative potential of these unique phenotyping capabilities, exploring traits such as root length, leaf area, biomass, and more. Unter Pflanzenph & auml;notypisierung versteht man die m & ouml;glichst objektive Erfassung der genotypabh & auml;ngigen Reaktion einer Pflanze auf ihre Umwelt. Sie ist zu einem wichtigen Instrument geworden, um unser Verst & auml;ndnis der Reaktionen von Pflanzen auf Umweltreize zu vertiefen. Das Verst & auml;ndnis der Reaktion der Pflanze auf z. B. ein w & auml;rmeres Klima ist der Schl & uuml;ssel zur Sicherung der Nahrungsmittelproduktion f & uuml;r k & uuml;nftige Generationen. Z & uuml;chter und Forscher verlassen sich bei der Optimierung von Kulturpflanzen auf die automatisierte Ph & auml;notypisierung im Hochdurchsatzverfahren. Im Idealfall werden ober- und unterirdische Merkmale gleichzeitig beobachtet. Das neu er & ouml;ffnete Technologiezentrum f & uuml;r Ph & auml;notypisierung des Fraunhofer IIS in Merkendorf bietet in kontrollierter Umgebung mehrere Klimakammern mit individuell steuerbaren Bedingungen f & uuml;r bis zu 400 Einzelpflanzen, um selbst extreme Klimabedingungen ganzj & auml;hrig simulieren zu k & ouml;nnen. Die ganzheitliche Erfassung von Pflanzenstrukturen mit R & ouml;ntgen- und optischen Kameras liefert hoch detaillierte 2D- und 3D-Informationen f & uuml;r Forscher und Z & uuml;chter weltweit. In Kombination mit automatisierten Datenpipelines k & ouml;nnen aus den Sensordaten Pflanzeneigenschaften extrahiert werden. Durch die Verkn & uuml;pfung von ober- und unterirdischer Ph & auml;notypisierung bringt diese Einrichtung nicht nur die Pflanzenforschung voran, sondern tr & auml;gt auch zur Z & uuml;chtung widerstandsf & auml;higerer und produktiverer Nutzpflanzen bei. Partner sind willkommen, um das transformative Potenzial dieser einzigartigen Ph & auml;notypisierungskapazit & auml;ten zu erschlie ss en und Merkmale wie Wurzell & auml;nge, Blattfl & auml;che und Biomasse zu erforschen.
Interference poses a significant challenge to satellite-based positioning systems, making it essential to accurately quantify the effects of specific interference types on receiver performance and the resulting reliability of position computation. In current practice, interference effects are often quantified using receiver-independent metrics, with receiver-specific front-end characteristics either idealized or only implicitly considered. In this paper, we address this limitation by explicitly incorporating receiver-specific front-end characteristics into the computation of interference effects and validating the resulting receiver-dependent analysis experimentally. Therefore, we record a real-world open-field dataset comprising 210 distinct interference scenarios and compute the receiver-dependent spectral separation coefficient (SSC) and interference impact for a specific receiver module. Furthermore, we verify the computation using a controlled dataset generated with a radio frequency constellation simulator (RFCS), employing the same receiver module and replaying similar interferences classes. The comparison of results obtained in both environments demonstrates the robustness of the interference impact computation.
Jamming and spoofing threaten wireless and satellite navigation by disrupting or manipulating radio frequency (RF) signals, undermining availability, integrity, and trust. Robust interference monitoring (i.e., detection, classification, characterization, and direction finding) is therefore essential to identify and localize anomalous signals. While machine learning (ML) promises improved performance in complex environments, its development and validation depend on large-scale datasets that capture realistic signal and channel variability. Collecting such data in the real world is difficult because intentional jamming is illegal and ground-truth attribution is confounded by propagation, hardware, and environmental effects. To address this gap, we create and publish S-ICDF, a large-scale indoor interference dataset generated with Sionna, a GPU-accelerated simulation library for physical-layer wireless communications. S-ICDF covers 102 interference configurations, including diverse antenna array patterns, bandwidths, and simulation settings such as noise level and reflection depth. We further provide baseline results by benchmarking S-ICDF with classical estimation and direction finding (DF) methods (MUSIC, ESPRIT, and CAPON) and with modern ML approaches. The dataset is publicly available at: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/sicdf_dataset
Intentional radio-frequency interference from low-cost GNSS jammers increasingly threatens the accuracy and reliability of satellite-based positioning. Mitigating this threat requires not only detection but also robust waveform classification and characterization, direction-of-arrival inference for localization, and impact estimation on receiver performance under realistic operating conditions; all of which are challenged by strong distribution shifts across devices, sensors, environments, and satellite geometries. We address these challenges by compiling a dedicated real-world dataset recorded during Jammertest 2025 (Andoya, Norway), covering two outdoor test areas with parallel measurements from a single-antenna E1/E5 receiver module and a 2x2 CRPA-array. The dataset spans diverse jamming, spoofing, and meaconing scenarios, including CW, PRN, sweep/chirp, and multi-emitter configurations, and is complemented by per-recording metadata enabling time-aligned ground truth. Methodologically, we benchmark 17 machine learning (ML) architectures for interference-modulation recognition and multi-task characterization (type, occupied bandwidth, and signal strength), and we quantify navigation-relevant degradation via a receiver-aware spectral separation coefficient (SSC) that maps measured spectra to effective (C_s/N_0)_eff loss. To enable multi-source analysis, we transfer a YOLOv8s and RF-DETR detector pretrained on a labeled spectrogram dataset to localize multiple simultaneous interferers in GNSS spectrograms and subsequently characterize each detected component. Results show near-99
Subjective evaluation remains the most reliable way of testing speech and audio coding techniques. Crowdsourcing the listening task is a cost-efficient and fast way of conducting this evaluation, but the quality of the results tends to be inferior to that of conventional listening tests done in the controlled environment of a laboratory. In this paper, classical and neural speech codecs are evaluated to compare P.808 against P.800 DCR tests. A statistical analysis is conducted to investigate the effectiveness of selected screening methods. The analysis shows that the crowdsourced evaluation can be improved by employing postscreening methods based on anchor ordering and rating span, and continuous screening methods like traps and gold standard questions, thus giving more value to the ratings obtained for the codecs under test. Based on these outcomes, a set of suitable screenings is proposed, for cost-effective, simplified, and bias-free enhancement of listening results.
