Communications

The Unified Multimodal Network Intrusion Detection System (UM-NIDS) dataset is a comprehensive, standardized dataset that integrates network flow data, packet payload information, and contextual features, making it highly suitable for machine learning-based intrusion detection models. This dataset addresses key limitations in existing NIDS datasets, such as inconsistent feature sets and the lack of payload or time-window-based contextual features.

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The dataset consists of uplink channel gains, downlink channel gains and uplink to downlink channel gains along with corresponding power allocations for uplink users and downlink users across all subcarriers. Additionally, it consists of NOMA decoding order for successful implementation of SIC at NOMA receiver. The number of UL users and DL users are considered as N=M=6, and subcarriers are S=9. Each column in the dataset is a sample for fading channel realization and it should be converted back to the matrix to compute sumrate.

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Today, the anywhere, anyhow and anytime application scenarios of 5G system force designer to challenge on electromagnetic interference (EMI) requirements. Despite the technological progress, relevant test techniques are necessary to minimize the future communication system EMI risk. In this paper, the EMI characterization from nonlinearity (NLT) of 5G system Gallium Nitride (GaN) power amplifier (PA) is studied. Firstly, the PA NLT is evaluated by 1-dB/3-dB/6-dB compression point and 3rd-order intermodulation distortion (IMD3).

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The softwarization and virtualization of the fifth-generation (5G) cellular networks bring about increased flexibility and faster deployment of new services. However, these advancements also introduce new vulnerabilities and unprecedented attack surfaces. The cloud-native nature of 5G networks mandates detecting and protecting against threats and intrusions in the cloud systems.

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This dataset contains audio recordings and transcriptions of toxic speech derived from Indonesian conversations during YouTube videos where scammers are confronted. The dataset captures two separate interactions that escalate into toxic exchanges. Each interaction has been verified by native Indonesian speakers and labeled into two classes: toxic and non-toxic. The dataset includes both the original and preprocessed versions of the speech and text data. The original speech files total 136MB, while the preprocessed speech files are 111,7MB.

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Currently, cybersecurity in digital substations is a topic of wide interest for companies and the academic community, which demands for its study the analysis of datasets (i.e., traffic collected during the operation of a substation).

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This image proposes a new class of multi-frequency band-pass frequency selective surfaces (FSSs) with ultra-high angular stability and polarization insensitivity. The proposed design consists of multiple ring slots coupled with a complementary structure on the opposite side of a single substrate layer. This approach effectively enables the design of multiband band-pass FSSs, ranging from dual-passband to penta-passband, using a simplified design process and achieving an ultra-low thickness of 0.004λ.

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Dataset containing real word data related to the informations receive on a downlink and an uplink channels. The information related to the data flowing on the channel are expressed by means of complex numbers. Some processing can be performed to convert the complex number into integer numbers in order to represent the channel as preferred. This dataset can be used to develop new compression and channel estimation algorithms.

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Test vectors are given as a part of the simulations on Gaussian noise channel. For all zero codeword transmission, a received noisy vector with length 128, the most probable L=49 messages with length 64 bit (in 16 hexadecimal), Euclidean distances and also the 64 bit indices in a 128 bit vector are provided.

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