We demonstrate a temperature sensor based on surface plasmon resonances supported by photonic crystal fibers (PCFs). Within the PCF, to enhance the sensitivity of the sensor, the air holes of the second layer are filled with a large thermo-optic coefficient liquid and some of those air holes are selectively coated with metal. Temperature variations will induce changes of coupling efficiencies between the fundamental core mode and the plasmonic mode, thus leading to different loss spectra that will be recorded. In this paper, variations of the dielectric constants of all components, including the metal, the filled liquid, and the fused silica, are considered. We conduct numerical calculations to analyze the mode profile and evaluate the power loss, demonstrating a temperature sensitivity as high as 720 pm/°C.

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With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion detection are urgently needed to defend against such attacks. Considering the serious class imbalance of the intrusion dataset, this paper proposes a method of using the synthetic minority oversampling technique (SMOTE) to balance the dataset and then uses the random forest algorithm to train the classifier for intrusion detection. The simulations are conducted on a benchmark intrusion dataset, and the accuracy of the random forest algorithm has reached 92.39%, which is higher than other comparison algorithms. After oversampling the minority samples, the accuracy of the random forest combined with the SMOTE has increased to 92.57%. This shows that the proposed algorithm provides an effective solution to solve the problem of class imbalance and improves the performance of intrusion detection.

To provide the basis and reference to further insights into the neural activity of the human brain in a microgravity environment, we discuss the amplitude changes of low-frequency brain activity fluctuations using a simulated microgravity model. Twelve male participants between 24 and 31 years old received resting-state fMRI scans in both a normal condition and after 72 hours in a −6° head down tilt (HDT). A paired sample t-test was used to test the amplitude differences of low-frequency brain activity fluctuations between these two conditions. With 72 hours in a −6° HDT, the participants showed a decreased amplitude of low-frequency fluctuations in the left thalamus compared with the normal condition (a combined threshold of P<0.005 and a minimum cluster size of 351 mm3 (13 voxels), which corresponded with the corrected threshold of P<0.05 determined by AlphaSim). Our findings indicate that a gravity change-induced redistribution of body fluid may disrupt the function of the left thalamus in the resting state, which may contribute to reduced motor control abilities and multiple executive functions in astronauts in a microgravity environment.

A compact temperature sensor based on a selectively liquid-filled photonic crystal fiber (PCF) is proposed using controlled hole collapse in PCF post-processing. The first ring around the core is filled with liquid of higher refractive index than the matrix, while the outer rings of holes are filled with air. The bandgap (BG)-like effect of the high refractive index ring is analyzed. Absorption loss spectra of the fiber are found to be quite sensitive to the refractive index of liquid when the liquid is lossy. Using the BG-like effect, a fiber temperature sensor is fabricated by selectively injecting a mixture of dimethyl sulfoxide and aqueous gold colloids with a high thermo-optic coefficient to the PCF. Temperature sensitivity up to -5.5 nm/°C is experimentally confirmed.