An energy threshold of (220±10) eV was achieved at an efficiency of 50% with a four-channel ultra-low-energy germanium detector each with an active mass of 5 g. This provides a unique probe to WIMP dark matter with mass below 10 GeV. With a data acquisition live time of 0.338 kg-day at the Kuo-Sheng Laboratory, constraints on WIMPs in the galactic halo were derived. The limits improve over previous results on both spin-independent WIMP-nucleon and spin-dependent WIMPneutron cross-sections for WIMP mass between 3−6 GeV. Sensitivities for full-scale experiments are projected. This detector technique makes the unexplored sub-keV energy window accessible for new neutrino and dark matter experiments.PACS numbers: 95.35.+d, 98.70.Vc There is compelling evidence from cosmological and astrophysical observations that about one quarter of the energy density of the universe can be attributed to Cold Dark Matter(CDM), whose nature and properties are still unknown [1]. Weakly Interacting Massive Particles (WIMP, denoted by χ) are the leading candidates for CDM. There are intense experimental efforts[2] to look for WIMPs through direct detection of nuclear recoils in χN→χN elastic scattering or in the studies of the possible products through χχ annihilations. The Kuo-Sheng(KS) Laboratory[12] is located at 28 m from a 2.9 GW reactor core with an overburden of about 30 meter-water-equivalence. Limits on neutrino magnetic moments(µ ν )[13] with a 1.06-kg germanium detector(HPGe) at a threshold of 5 keV were reported [14]. These data also allowed the studies of reactor electron neutrinos [15] and reactor axions [16]. A background level of ∼ 1 event kg −1 keV −1 day −1 (cpkkd) at 20 keV, comparable with those of underground CDM experiments, was achieved. The current goal is to develop detectors with kg-scale target mass, 100 eV-range threshold and lowbackground specifications for the studies of WIMPs, µ ν and neutrino-nucleus coherent scatterings [17].Ultra-low-energy germanium detectors(ULEGe) is a matured technique for sub-keV soft X-rays measurements. They typically have modular mass of 5−10 g while detector arrays of up to 30 elements have been constructed. Compared with Al 2 O 3 , Ge provides enhancement in χN spin-independent couplings(σ SI χN ) due to the A 2 dependence [1,18], where A is the mass number of the target isotopes. The isotope 73 Ge (natural isotopic abundance of 7.73%) comprises an unpaired neutron such that it can provide additional probe to the spin-dependent couplings of WIMPs with the neutrons(σ SD χn ). The nuclear recoils from χN interactions in ULEGe only give rise to ∼20% of the observable ionizations compared with electron recoils at the same energy. The suppression ratio is called the quenching factor(QF) [19]. For clarity, all ULEGe measurements discussed hereafter in this article are electron-equivalent-energy, unless otherwise stated.The ULEGe array consists of 4-element each having an active mass of 5 g [20]. Standard ultra-low-background specifications were adopted in its construction and ch...
Huge overhead of beam training imposes a significant challenge in millimeter-wave (mmWave) wireless communications. To address this issue, in this paper, we propose a wide beam based training approach to calibrate the narrow beam direction according to the channel power leakage. To handle the complex nonlinear properties of the channel power leakage, deep learning is utilized to predict the optimal narrow beam directly. Specifically, three deep learning assisted calibrated beam training schemes are proposed. The first scheme adopts convolution neural network to implement the prediction based on the instantaneous received signals of wide beam training. We also perform the additional narrow beam training based on the predicted probabilities for further beam direction calibrations. However, the first scheme only depends on one wide beam training, which lacks the robustness to noise. To tackle this problem, the second scheme adopts long-short term memory (LSTM) network for tracking the movement of users and calibrating the beam direction according to the received signals of prior beam training, in order to enhance the robustness to noise. To further reduce the overhead of wide beam training, our third scheme, an adaptive beam training strategy, selects partial wide beams to be trained based on the prior received signals. Two criteria, namely, optimal neighboring criterion and maximum probability criterion, are designed for the selection. Furthermore, to handle mobile scenarios, auxiliary LSTM is introduced to calibrate the directions of the selected wide beams more precisely. Simulation results demonstrate that our proposed schemes achieve significantly higher beamforming gain with smaller beam training overhead compared with the conventional and existing deep-learning based counterparts.
This letter explores the positive side of the adversarial attack for the security-aware semantic communication system. Specifically, a pair of matching pluggable modules is installed: one after the semantic transmitter and the other before the semantic receiver. The module at transmitter uses a trainable adversarial residual network (ARN) to generate adversarial examples, while the module at receiver employs another trainable ARN to remove the adversarial attacks and the channel noise. To mitigate the threat of semantic eavesdropping, the trainable ARNs are jointly optimized to minimize the weighted sum of the power of adversarial attack, the mean squared error of semantic communication, and the confidence of eavesdropper correctly retrieving private information. Numerical results show that the proposed scheme is capable of fooling the eavesdropper while maintaining the high-quality semantic communication.
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