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Optical fiber communications are essential for all types of long- and short-distance transmissions. The aim of this paper is to analyze the previously presented security risks and, based on measurements, provide the risk level evaluation. The major risk is the possibility of inserting a splitter into the optical distribution network and capturing a portion of the entire spectrum, i.e., all channels in the optical fiber. Another significant security risk is crosstalk on multiplexers in networks with wavelength division multiplexing. The paper covers the macrobend attenuation evaluation of fiber and back-reflection measurements. Based on the measurements, risks were evaluated for both point-to-point and point-to-multipoint networks and, lastly, the paper covers crosstalk measurements of an optomechanical switch. Finally, all individual risks are evaluated according to the severity, and a proposal for risk minimization is provided.

In this paper, the concept design of intelligent technical textile blocks implemented with optical fibers that include fiber Bragg gratings for strain and temperature sensing is briefly introduced. In addition to the main design of the system, a design of measurement blocks with integrated fiber Bragg grating elements for strain measurement is also presented. In the basic measurement, the created textile block was tested for deformation sensitivity when a load was applied. Moreover, a unique robust and low profile connector was designed, created and verified. The fibers are terminated with GRIN lenses, allowing easy manipulation and completion of the connector in the field, with an average insertion loss of 5.5 dB.

Fiber optic infrastructure is essential in the transmission of data of all kinds, both for the long haul and shorter distances in cities. Optical fibers are also preferred for data infrastructures inside buildings, especially in highly secured organizations and government facilities. This paper focuses on a reference measurement and analysis of optical fiber cables sensitivity to acoustic waves. Measurement was carried out in an anechoic chamber to ensure stable conditions of acoustic pressure in the range from 20 Hz to 20 kHz. The frequency response, the signal-to-noise ratio per frequency, and the Speech Transmission Index are evaluated for various types of optical fiber cables and different ceiling tiles, followed by their comparison. The influence of the means of fixing the cable is also studied. The results prove that optical fiber-based infrastructure in buildings can be exploited as a sensitive microphone.

In this study, we present a multimodal emotion recognition architecture that uses both feature-level attention (sequential co-attention) and modality attention (weighted modality fusion) to classify emotion in art. The proposed architecture helps the model to focus on learning informative and refined representations for both feature extraction and modality fusion. The resulting system can be used to categorize artworks according to the emotions they evoke; recommend paintings that accentuate or balance a particular mood; search for paintings of a particular style or genre that represents custom content in a custom state of impact. Experimental results on the WikiArt emotion dataset showed the efficiency of the approach proposed and the usefulness of three modalities in emotion recognition.

The primary goal in this paper is to verify the possibility of combining a quantum channel into a single optical fiber with other classical channels by using the so-called attenuation method. Since the quantum channel is very weak in terms of power, combining it into a single fiber with much more powerful classical channels is challenging. Thus, sufficiently high-quality filtering is important to avoid possible crosstalk. A second and more difficult problem to address is the interference caused by Raman noise, which increases with the fiber length and is also dependent on the input power of the classical channel. Thus, in this paper the focus is on the possibility of suppressing the Raman noise effect, both in advance by means of wavelength positioning and by means of installed optical components. Such phenomena must be considered in the route design, as the quantum channel must be placed at a suitable wavelength with respect to the classical channels. The influence of other nonlinear phenomena has been neglected. In this paper, a practical experiment aimed at building a fully functional multiplexed quantum key distribution link is also described.

Machine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these hyperparameter configurations and their complex interactions, it is common to use optimization techniques to find settings that lead to high predictive performance. However, insights into efficiently exploring this vast space of configurations and dealing with the trade-off between predictive and runtime performance remain challenging. Furthermore, there are cases where the default hyperparameters fit the suitable configuration. Additionally, for many reasons, including model validation and attendance to new legislation, there is an increasing interest in interpretable models, such as those created by the decision tree (DT) induction algorithms. This paper provides a comprehensive approach for investigating the effects of hyperparameter tuning for the two DT induction algorithms most often used, CART and C4.5. DT induction algorithms present high predictive performance and interpretable classification models, though many hyperparameters need to be adjusted. Experiments were carried out with different tuning strategies to induce models and to evaluate hyperparameters’ relevance using 94 classification datasets from OpenML. The experimental results point out that different hyperparameter profiles for the tuning of each algorithm provide statistically significant improvements in most of the datasets for CART, but only in one-third for C4.5. Although different algorithms may present different tuning scenarios, the tuning techniques generally required few evaluations to find accurate solutions. Furthermore, the best technique for all the algorithms was the Irace. Finally, we found out that tuning a specific small subset of hyperparameters is a good alternative for achieving optimal predictive performance.

Distributed acoustic sensing (DAS) is an emerging technology with diverse applications in monitoring infrastructure, security systems, and environmental sensing. This study presents a dataset comprising acoustic vibration patterns recorded by a commercial DAS system, providing valuable insights into the acoustic sensitivity of optical fibers. The data are crucial for evaluating the performance of DAS systems, particularly in scenarios related to security and eavesdropping. The dataset offers the possibility to develop and test algorithms aimed at enhancing signal-to-noise ratio (SNR), detecting anomalies, and improving speech intelligibility. Additionally, this resource facilitates the validation of de-noising techniques through the calculation of the speech transmission index (STI). The experimental setup, measurement procedures, and the characteristics of the DAS system employed are comprehensively documented for researchers in the field of optical fiber sensing and signal processing.

Fibre optic infrastructures are very important, and therefore, it is necessary to protect them from fibre cuts. Most fibre cuts are caused by digging activity, and many network operators seek appropriate solutions enabling detection of possible unexpected events (predict these cuts) and subsequent network outages. In most cases, there is no need to locate events, and only information regarding the occurrence of the event is sufficient. Direct detection-based distributed fibre optic sensing systems appear to be an appropriate solution, allowing digging to be detected before the fibre breaks. The average power of such signals is relatively small, and there is no interference with other signals in the fibre. We performed laboratory measurements to compare the sensitivity and accuracy of interferometric and polarization systems for acoustic vibrations. In the case of interferometric systems, the reference and sensing arms were in the same cable, and both were subjected to acoustic vibrations.

Distributed Acoustic Sensing (DAS) technology leverages optical fibers to detect acoustic signals over long distances, offering high-resolution data critical for applications such as seismic monitoring, structural health monitoring, and security. A significant challenge in DAS systems is the accurate classification of detected events, which is crucial for their reliability. Traditional signal processing methods often struggle with the high-dimensional, noisy data produced by DAS systems, making advanced machine learning techniques essential for improved event classification. However, the lack of large, high-quality datasets has hindered progress. In this study, we present a comprehensive labeled dataset of DAS measurements collected around a university campus, featuring events such as walking, running, and vehicular movement, as well as potential security threats. This dataset provides a valuable resource for developing and validating machine learning models, enabling more accurate and automated event classification. The quality of the dataset is demonstrated through the successful training of a Convolutional Neural Network (CNN).

This work is focused on creating an open-source software-based solution for monitoring traffic transmitted through gigabit passive optical network. In this case, the data are captured by the field-programmable gate array (FPGA) card and reassembled using parsing software from a passive optical network built on the International Telecommunication Unit telecommunication section (ITU-T) G.984 gigabit-capable passive optical network GPON recommendation. Then, the captured frames are converted by suitable software into GPON frames, which will be further processed for analysis. Due to the high transfer rate of GPON recommendations, the work describes the issue of writing to the Mongo database system. In order to achieve the best possible results and minimal loss of transmitted frames, a series of tests were performed. The proposed test scenarios are based on different database writing approaches and are implemented in the Python and C# programming languages. Based on our results, it has been shown that the high processing speed is too high for Python processing. Critical operations must be implemented in the C# programming language. Due to rapid application development, Python can only be used for noncritical time-consuming data processing operations.