Phase sensitive Optical Time Domain Reflectometer (Φ-OTDR), usually addressed as Distributed Acoustic Sensor (DAS), is an emerging IoT-based sensing technology that generates large volumes of data during real-time acquisition. Artificial intelligence-driven models and methods have made accurate and real-time analysis of its disturbance events possible in recent years. However, identifying novel disturbance events still poses a key challenge. This challenge is further exacerbated under semi-supervised conditions due to the scarcity of labeled data, and becomes particularly severe in open-world scenarios, where the model must not only recognize known categories but also actively discover novel ones in the complete absence of supervision for unknown classes. This paper proposes an effective semi-supervised method—ODAS (Open-world Semi-Supervised DAS)—to adaptively recognize new categories of sensing events. The method jointly trains on class probability and embeddings to mitigate confirmation bias, leveraging graph-based contrastive learning with graph neural network-based regularization to explicitly enforce structural consistency among sample embeddings, thereby effectively alleviating representation degeneration and enhancing model robustness under sparse-label conditions. An adaptive synchronized threshold is applied to balance the learning rates of known and novel classes, accompanied by an uncertainty-guided temperature scaling to generate more reliable pseudo-labels and feature embeddings. Experimental results demonstrate that our proposed method achieves over 98% accuracy for known event classes and over 85% for novel event classes, with an overall classification accuracy exceeding 93%. This approach introduces open-world semi-supervised learning to the Φ-OTDR open application environment, overcoming the limitations of traditional closed-world scenarios. It significantly reduces data labeling costs, providing the system with enhanced recognition capabilities when encountering new types of disturbances. Particularly in unlabeled or unknown disturbance environments, this method provides superior robustness and generalization ability to sensing event classification of Φ-OTDR.

Large-scale matrix multiplication is a critical operation in various fields such as machine learning, scientific computing, and graphics processing, but performing it on a single machine introduces significant computational latency. Therefore, matrices are partitioned along different dimensions, decomposed into multiple subtasks, and executed in distributed systems. However, the presence of stragglers in distributed systems can severely impact the speed of matrix multiplication. So, coding schemes are introduced to mitigate the straggler problem. Recently, coding schemes for three-dimensional matrix partitioning have gained increasing attention, including DEP codes. However, these schemes have not focused on decoding accuracy and job completion time. In this paper, with the aim of enhancing decoding accuracy and reducing job completion time, we combine the grouping strategy with Systematic Matdot codes to propose Grouped Systematic Matdot (GSM) codes. Experimental results demonstrate that, compared to DEP codes, GSM codes ensure 100% decoding accuracy and achieve shorter encoding time, communication time, and local computation time, thereby reducing job completion time by at least 45%. Moreover, GSM codes consume fewer memory resources, and as the matrix size increases, their time advantage becomes more pronounced.

Wireless sensor network (WSN) is communicating method with each other without the assistance of communication cable. The circular environment is an important application scenario in WSN. As a core technology, routing algorithm is the brain of WSN monitoring system. In this paper, we propose a new routing algorithm named Equal Area Partition Based Energy Efficient Multipath Algorithm (EAPEEM), which tries to exploit sector equal area model in node deployment, data transfer, and route maintenance. Existing algorithms, as compared to our existing models, do not fully match with routing and nodes. One attractive feature of our protocol is its balanced network transmission capacity and improved node energy utilization, which can reduce the possibility of system damage. Simulations show that EAPEEM can retain at least 28.6% of batteries and expand stability by more than 43%.

The Earth Radiation Measurement (ERM) instrument onboard FengYun (FY)-3B satellite observes the Earth's atmosphere through a narrow scanning field of view (NFOV) and a wide nonscanning FOV. For each field of view, the measurements are made from two broadband channels: a total waveband channel covering 0.2-50 μm and a shortwave (SW) band covering 0.2-4.3 μm. The validation to the ERM calibration was carried out by comparing the unfiltered longwave (LW) and SW radiances from ERM with those from Clouds and Earth's Radiation Energy System (CERES) flight model 3 onboard Earth Observing System Aqua satellite. While the ERM LW and SW radiances have a good correlation with CERES data, there is a systemic bias between the two data sets. A spectral correction is made for the ERM data using the CERES data. After the correction, the error of the ERM LW radiance is reduced from -3.00 to -0.60 W/sr · m2. For the SW radiance, the bias is reduced from 6.00 to 4.00 W/sr · m2. Based on the ERM in-orbit calibration data, the stability of the ERM LW radiometric response is analyzed, and it is shown that the gains are stable with a variation of less than 1.5% during its first 9 months in orbit. However, the gains at the SW channels have larger changes and exceed 3%. These drifts might be caused by the detector degradation. Also, the NFOV scanner at the SW channel is no longer working after its 8 months in orbit.

In this paper, we mainly get three results. First, let p, q be distinct primes with ((p-1)p,(q-1)q)=(p-1,q-1)=e ; we give a method to compute the linear complexity of Whiteman's generalized cyclotomic sequences of period pm+1qn+1. Second, if e=4, we compute the exact linear complexity of Whiteman's generalized cyclotomic sequences. Third, if p≡q 5 (mod 8), gcd(p-1, q-1)=4, and we fix a common primitive root g of both p and q, then 2∈H0=(g), which is a subgroup of the multiplicative group Z*pq, if and only if Whiteman's generalized cyclotomic numbers of order 4 depend on the decomposition pq=a2+4b2with 4|b.