Monopisthocotylan parasites have been proposed as tags for studying host population structure due to their direct life cycles and short generation times. However, their effectiveness in reflecting host population connectivity remains under scrutiny. The poorly-understood connectivity of fisheries stocks in Lake Tanganyika, the second deepest lake in the world with a permanently stratified, relatively species-poor and well-delimited pelagic zone, serves as a case-study to test utility of parasites for fish stock identification. This study investigates the population structure of two Kapentagyrus species parasitizing dorosomatid fish in Lake Tanganyika, by analyzing variation in mitochondrial protein-coding genes across a geographic gradient. The study comprised 12 population genomic datasets from the central and southern subbasins of the lake sampled within the same week to account for host migration. We observed differences in geographic population structure of the two parasite species, with restricted gene flow in Kapentagyrus limnotrissae infecting a single dorosomatid species and smaller hosts preferring the littoral zone. Conversely, K. tanganicanus exhibited no geographical structure, reflecting its broader host range and preference for larger hosts preferring the pelagic zone. The results also highlight that other mitochondrial genes such as those from the dehydrogenase family or atp6 provide higher resolution for population genetic studies in these parasites than the frequently used cox1. The study also compared two sequencing strategies—individual versus pooled sequencing (PoolSeq)—for assessing population structure of monopisthocotylan parasites, and found that PoolSeq yielded similar results with lower demands on individual DNA quantity and sequencing costs. This work supports the use of host-specific, directly transmitted parasites as ecosystem tags and provides valuable insights into the role of host ecology and parasite life-history traits in shaping population dynamics.
In this letter, we experimentally demonstrate 400 Gbps coherent transmission over a turbulent free-space optical (FSO) channel for optical feeder links using a commercially available fiber-optimized coherent transponder. Controlled atmospheric turbulence (AT) with a refractive-index structure parameter up to C-n(2)=5.51 & times;10(-11)m(-2/3) is generated within a 2 m long atmospheric chamber to assess the impact of AT on the performance of the FSO link. The AT spectra, auto-correlation functions and scintillation indices are measured for six turbulence levels, demonstrating robust coherent transmission above a receiver sensitivity of -29.5 dBm and a corresponding electrical signal-to-noise ratio of 7.5 dB.
The evolution toward open and partially disaggregated optical networks has introduced new, to our knowledge, requirements on how transmission performance is evaluated and compared across technologies, vendors, and deployment scenarios. In this context, sound benchmarking practices are essential to ensure that quality-of-transmission (QoT) assessments are reproducible, transparent, and meaningful beyond isolated experimental demonstrations. QoT estimation plays a central role in these practices, as it directly impacts network planning, commissioning, automation, and long-term technology selection in heterogeneous optical infrastructures. This paper discusses benchmarking practices for optical transmission in open networks using the open-source GNPy library as a reference digital model. The contribution of this work lies in formalizing how a transparent, vendor-agnostic QoT estimator can be used as a common benchmarking baseline across research and industry. Representative experimental validations spanning short-reach, multiband, and multi-vendor flex-grid transmission scenarios are reviewed and reframed as benchmarking baselines, establishing evidence-based expectations on achievable accuracy and applicability limits under realistic operating conditions. Finally, the paper illustrates how reference QoT models are employed in industry-facing benchmarking workflows, including closed-loop interactions with standardization bodies, multi-vendor planning and automation, procurement processes, and strategic network evolution toward emerging architectures.
Forecast-based models are fundamental to network traffic management and anomaly detection. However, it is essential to evaluate these forecasting models over extended periods of time in order to assess their adaptability to unexpected phenomena and shifts in underlying patterns. This kind of long-term evaluation requires multi-year data to account for yearly seasonality and behavioral drift. Existing public datasets are too short to support such long-horizon forecasting tasks. We introduce CESNET-TimeSeries26, comprising 995 days of continuous traffic time series from the CESNET3 network, together with a single subnet spanning approximately 4.5 years. The dataset captures 278,466 IP addresses, 285 institutions, and 566 institutional subnets at three aggregation intervals. The dataset provides a definitive longitudinal benchmark for evaluating forecasting models in both operational management and security monitoring. We demonstrate that annual seasonality is the dominant periodic component. Moreover, we show that a forecasting model can exploit the annual component only when its training window covers at least one complete annual cycle, which no sub-year dataset provides.
This paper presents results of optical precise time transfer using the White Rabbit (WR) protocol on shared fiber with high speed coherent data transfers, where bidirectional optical amplification using EDFA amplifiers has been used for reach extension. Both laboratory setup on the fiber spools and deployment in the field over real fiber line have been verified.
