esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Scheduling Optimization of Production Workshops Based on Digital Twin
## 1 Introduction
If we can predict the disturbance factors in advance and perform dynamic scheduling before the arrival of disturbance factors, such as faults, the scheduling effect will be improved. In recent years, with the rapid development of digital twin technology, digital twin workshop provides a basic guarantee for the application of dynamic scheduling. Digital twin technology is characterized by the combination of virtual and reality and real-time interaction, which provides a good platform for the timely acquisition of complex workshop status and the feedback of a scheduling plan with high response. The digital twin workshop system synchronizes the virtual model with the real state of the physical equipment by acquiring the dynamic and static information of the actual equipment or products in the physical workshop and mapping them to the corresponding digital twin virtual model. Therefore, it is valuable to study the applications of a hybrid shop floor scheduling system based on digital twin. Since [PERSON] et al. [12] first proposed the digital twin (DT) shop five-dimensional model of intelligent manufacturing, scheduling research based on digital twin has been widely carried out. [PERSON] et al. [13] proposed a DT-BASED super network shop scheduling framework. [PERSON] et al. [14] proposed the architecture and working principle of a new shop scheduling model based on DT. [PERSON] et al. [15] proposed a DT framework for production planning in terms of equipment and employee task scheduling. The framework takes into account the impact of uncertainties in production. [PERSON] and [PERSON] [16] built a DT-BASED planning and scheduling system model. [PERSON] et al. [17] analyzed the construction method and key implementation technologies of DT and pointed out that the application of DT in the production stage has not been carried out. The real-time data of DT provides a feasible idea for real-time online scheduling in production shop. However, most of the DT related research is based on the framework of the system, and the realization of dynamic production scheduling based on DT is still one of the key directions in urgent need of research. This study combined the technology of digital twin advantages. First, we establish a hi-fi digital twin virtual workshop, and realize the physical objects and the data exchange of the virtual model. Then, combined with the digital real-time acquisition of twin workshopsspindle vibration signal, the implementation is based on the signal of machine tool failure prediction, which enables us to predict the results as a heavy schedule of trigger dynamic scheduling in the workshop. The comparison case confirms that the dynamic scheduling results based on digital twin are significantly better than the scheduling results without digital twin technology. This study provides a new concept for the study of dynamic scheduling in the shop floor.
# Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights
## 4 Recurrent Neural Network Models
### BiLSTM Working Structure  
BiLSTM networks are extensions of the traditional LSTM architecture designed to store dependencies in sequential data in both directions. BiLSTMs employ two distinct LSTM layers (one for forward-processing input sequences and another for backward-processing) instead of traditional LSTMs, which process input sequences sequentially from the past to the future. The network's bidirectional processing allows future and past contexts, which facilitates a more full understanding of the input sequence. The internal organization of the BiLSTM is depicted in Figure 6b. For every time step in a BiLSTM, the output of each LSTM layer is merged, combining data from both directions. This concatenated form, which incorporates context from the past and future, represents the input sequence as a whole. Tasks requiring context from both sides, like named entity recognition, sentiment analysis, and part-of-speech tagging, are especially well-suited for BiLSTMs. Because BiLSTMs are bidirectional, they are effective at capturing contextual information and long-range relationships, which improves their performance in a variety of sequence modeling applications. But keep in mind that because BiLSTMs must process input sequences in both ways, they may add to the computational complexity. However, they are a popular option in many sequence prediction and natural language processing applications due to their capacity to take advantage of bidirectional context. BiLSTM can be expressed as follows [32]:  
\[\overrightarrow{h_{f}} =\sigma(W_{f}S_{t}+W_{f}h_{t-1}+b_{f}), \tag{16}\] \[\overleftarrow{h_{r}} =\sigma(W_{r}S_{t}+W_{r}h_{t+1}+b_{r}), \tag{17}\]  
where the activation function is expressed as \(\sigma\), the forward and backward time steps are represented as \(t-1\) and \(t+1\), the transmitted signal is \(S_{t}\), the hidden state of the previous step is \(h_{t-1}\), the hidden state of next state is \(h_{t+1}\), the weights for the forward and reverse direction are \(W_{f}\) and \(W_{r}\), learnable biases of forward and reverse directions are \(b_{f}\) and \(b_{r}\), \(h_{f}\rightarrow\) is the forward direction of network output, and \(h_{r}\leftarrow\) is the backward direction of the LSTM network output, respectively. A dropout layer comes after the BiLSTM layer receives the input data. The input data are then classified using a fully connected layer and a classification layer. This architecture improves the model's capacity to effectively  
Figure 6: (**a**) Internal structure of LSTM [29], (**b**) internal structure of BiLSTM [30], (**c**) internal structure of GRU [31].  
minimize interference by allowing it to capture context information from the past and the future.
# HPS-Net: Multi-Task Network for Medical Image Segmentation with Predictable Performance
###### Abstract
To reduce this type of uncertainty, a feasible method is to provide the corresponding measure predictions along with each segmentation hypothesis. The measures can be precision, accuracy, the true positive rate, the true negative rate, or other measures. These predictive measures can assist radiologists to evaluate the segmentation performance and decide whether the segmentation hypothesis should be accepted. To the best of our knowledge, there exists no other work that has considered providing the corresponding measure predictions along with each segmentation hypothesis; and there exists no other work that has considered generating segmentation hypotheses based on specified measurement values. To fill these two gaps, we propose a hierarchical predictable segmentation network (HPS-Net). An illustration of the application case of the proposed HPS-Net is shown in Figure 1. HPS-Net can learn a complex probability distribution of the samples in the latent space and has the ability to predict the measurement values; therefore, it can generate an unlimited number of segmentation hypotheses along with their measure predictions. From another perspective, HPS-Net can provide the segmentation hypothesis based on a specified measurement value by continually generating segmentation hypotheses until the predicted measurement value is extremely close to the specified value. HPS-Net is a hierarchical multi-task network for medical image segmentation and segmentation performance prediction, which consists of a new network structure, a new loss function, and a cooperative training mode. As a multi-task network, HPS-Net includes four sub-networks, namely, the posterior network, the prior network, the likelihood network, and the measure network. These subnetworks have multiple symmetrical structures in them, which enable the networks to extract the features of images at different scales. To demonstrate the efficacy of HPS-Net, experiments were conducted on two public datasets--the LIDC-IDRI dataset [11] and the ISIC2018 dataset [12]. The results show that the proposed HPS-Net can provide effective measure predictions while keeping the maximum segmentation performance in terms of segmentation accuracies of multiple annotations and a single annotation per image. The remainder of the article is organized as follows. In Section 2, we review related work including U-Net, probabilistic U-Net, and PHiSeg. We describe problem formulation and the HPS-Net structure in Section 3. In Section 4, we evaluate the performance of HPS-Net on the LIDC-IDRI dataset [11] and the ISIC2018 dataset [12]. Finally, we conclude our article in Section 5.
## 2 Protocol Design and Equipment  
The first goal is achieved by conducting in situ questionnaires using a slightly altered version of Method A (questionnaire) from Annex C of the ISO/TS 12913-2:2018 technical specification [5] collected either via handheld tablets or paper copies of the questionnaire. Typically, a minimum of 100 responses are collected at each location during multiple 2-5-h sessions over several days. During the survey sessions, acoustic data are collected via a stationary class 1 or class 2 Sound Level Meter (SLM) (as defined in IEC 61672-1:2013 [32]) running throughout the survey period and through binaural recordings taken next to each respondent. These acoustic and response data are linked through an indexing system so that features of the acoustic environment can be correlated with individual responses or with the overall assessment of the soundscape, as required by researchers.  
The second goal is achieved by making First-Order (or higher) Ambisonic recordings simultaneously with 360\({}^{\circ}\) video which can be reproduced in a virtual reality environment. It has been shown that head-tracked binaural and multi-speaker ambisonic reproduction of recorded acoustic environments recorded in this way have high ecological validity [33], particularly when paired with simultaneous head-tracked virtual reality video [22; 34; 35].  
The on-site procedure to collect these data are separated into two stages, which will be outlined in detail in Section 4. The stage during which the spatial audio-visual recordings are made for lab experiments is called the **Recording Stage**, while the stage during which questionnaires and environmental data are captured is called the **Questionnaire Stage**.  
The procedure has been designed to include multiple levels of data and metadata redundancy, making it robust to on-site issues and human error. The most crucial aspect of the redundancy is ensuring perceptual responses can be matched with the appropriate corresponding environmental and acoustic data even when some information is lost or forgotten.
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 2 Wireless Communication Technologies for Agriculture
### Bluetooth (BT) Wireless Protocol  
The BT standard has been utilized to establish a communication link between movable and portable devices, such as laptops, over a short distance of up to 10 m. Given its pervasive nature and availability in most mobile devices, BT has been employed to satisfy multi-level agricultural requirements [5]. Weather information, soil moisture, sprinkler position, and temperature are monitored remotely using Global Positioning System (GPS) and BT technologies. The proposed system in [27] was developed for irrigation application to increase field productivity and conserve water. The irrigation application proposed in [28] collects field information in real time via the BT wireless communication protocol. Several software and hardware were developed in [29] to monitor the relative humidity and temperature in greenhouses based on BT. The BT module was employed in integrated control method [30] to control the irrigation system in greenhouses based on soil and weather information, and this technology improved the leaf number, height, dry weight, and fresh weight of red and romaine lettuce in greenhouses. Result of estimation for water usage and electricity also improved by 90% based on integrated control method using BT technology relative to the traditional method (i.e., timer control strategy). Given its low energy consumption, wide availability, and ease of use for farmers, smartphone-based BT has been employed in different agricultural applications [31; 32; 33; 34; 35], such as controlling irrigation systems, monitoring soil and weather conditions, and controlling the use of fertilizers and pesticides.
## 5 Simultaneous Localization of AUV and Mobile Docking Station
\[\Sigma_{x,k}=\left[H_{x}^{T}S_{k}^{-1}H_{x}+P_{1}^{-1}\right]^{-1} \tag{26}\]  
\[\mu_{x,k}=\Sigma_{x,k}H_{x}^{T}S_{k}^{-1}\left(z_{k}-\hat{z}_{k}\right)+\hat{z}_ {k} \tag{27}\]  
where \(\mu_{x,k}\) is the estimated state of AUV at \(k\)th time. \(\Sigma_{x,k}\) is the estimated variance of AUV at \(k\)th time. Based on the sampling mechanism, the posterior probability of M-DS is calculated by the following formula:  
\[P\left(\Theta_{k}\mid x_{k},x_{k-1},\Theta_{k-1},z_{k},u_{x,k},u_{\Theta,k}\right)\]  
\[=P\left(z_{k}\mid\Theta_{k},x_{k},x_{k-1},\Theta_{k-1},z_{k},u_{x,k},u_{\Theta,k}\right)\cdot P\left(\Theta_{k}\mid x_{k},x_{k-1},\Theta_{k-1},z_{k-1},u_{x,k},u_{\Theta,k}\right) \tag{28}\]  
Using the same method as calculating the sate of AUV, the estimated state and variance of M-DS are as follows:  
\[\Sigma_{\Theta,k}=H_{\Theta}^{T}R^{-1}H_{\Theta}+P_{2}^{-1} \tag{29}\]  
\[\mu_{\Theta,k}=\Sigma_{\Theta,k}H_{\Theta}^{T}R^{-1}\left(z_{k}-\hat{z}_{k} \right)+\hat{\Theta}_{k} \tag{30}\]  
where \(\mu_{\Theta,k}\) is the estimated state of M-DS. \(\Sigma_{\Theta,k}\) is the estimated variance of M-DS. Due to the deviation between the proposed distribution and the expected distribution, this difference is corrected by importance sampling. In the state probability distribution of AUV, the importance weight of each particle is calculated as follows:  
\[L_{x}=H_{x}P_{1}H_{x}^{T}+H_{\Theta}^{T}R^{-1}H_{\Theta}+R \tag{31}\]  
\[w_{x}=\left|2\pi L_{x}\right|^{-\frac{1}{2}}exp\left\{-\frac{1}{2}\left(z_{k} -\hat{z}_{k}\right)^{T}L_{x}^{-1}\left(z_{k}-\hat{z}_{k}\right)\right\} \tag{32}\]  
In the state probability distribution of M-DS, the importance weight of each particle is calculated as follows:  
\[L_{\Theta}=H_{\Theta x}P_{2}H_{\Theta}^{T}+R \tag{33}\]  
\[\Sigma_{x,k}=\left[H_{x}^{T}S_{k}^{-1}H_{x}+P_{1}^{-1}\right]^{-1} \tag{26}\]0  
After calculating the weight of each particle, the random resampling is used to resample. The purpose of resampling is to retain weighted particles in order to reduce the degree of particle degradation.
# Combined Navigation and Tracking with Applications to Low Earth Orbit Satellites +
## 5 Conclusions  
A hardware Xona PULSAR(tm) receiver for the XL signal based on the STMicroelectronics STAs15 GA (TeseoV) chipset has been demonstrated. Autonomous Xona-only acquisition and data download and navigation were verified to prove practical system redundancy in case of MEO GNSS outage, with Xona+GNSS scenarios also shown to be effective. This shows not just PULSAR compatibility with existing GNSS but also interoperability. Furthermore, PULSAR LEO signals were shown to be effective in achieving high accuracy, with rapid convergence of Precise Point Positioning (PPP), achieving 11 cm, 68 th percentile.  
Combined navigation and tracking in the form of prediction aiding can be effective in reducing the stress on the receiver control loops induced by LEO high orbit dynamics. The proposed algorithms improved the tracking sensitivity and availability at low CN0. Observables quality and, eventually, positioning accuracy are also expected to benefit from loop aiding, especially under difficult signal conditions and automotive land scenarios.  
All authors contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData sharing is not applicable to this article.  
The test scenarios used in this paper were generated by Safran's Skydel(tm) simulation engine and simulator with the support of Xona Space Systems Inc. STMicroelectronics GNSS products, chipsets and software, baseband, and radio front-end are developed by a distributed team in Italy (Milan, Naples, Catania), France (Le Mans), and Asia (Shenzhen, Taiwan). The contribution of all these teams is gratefully acknowledged. Xona Space Systems would like to thank the Canadian Space Agency for supporting this work as part of the smartEarth 2021 initiative.  
Author [PERSON], [PERSON] and [PERSON] are employed by the company STMicroelectronics. Author [PERSON], [PERSON] and [PERSON] are employed by the company Xona Space Systems Inc. Author [PERSON] is employed by the company Safran Trusted 4D Inc. All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Experiments
### Comparison with State-of-the-Art Methods  
THUMOS14: We compared our model with several recent state-of-the-art methods including one-stage, two-stage, and Transformer models on the THUMOS14 dataset. Table 2 summarizes the performance. It can be seen intuitively that our model outperformed all previous methods, establishing the new state-of-the-art of 67.2% [EMAIL_ADDRESS] on THUMOS14. In particular, our model achieved an improvement of 11.7% (from 55.5% to 67.2%) on [EMAIL_ADDRESS] and 10.9% (from 52.0% to 62.9%) on the average mAP ([0.3:0.1:0.7]) compared with AFSD [33], which is currently the best-performing one-stage detector. We outperformed MUSES [48], which is the strongest two-stage competitor by 10.3% (from 56.9% to 67.2%) on [EMAIL_ADDRESS] and 9.5% on the average mAP (from 53.4% to 62.9%). Moreover, our model achieved up to 1.6% (from 65.6% to 67.2%) on [EMAIL_ADDRESS] and 0.3% (from 62.6% to 62.9%) on the average mAP over the Transformer method [16], which is the current state-of-the-art method in TAD tasks. The excellent performance proved that for TAD, simultaneous modeling of local multi-scale features and long-range temporal dependencies can effectively improve its performance.  
ActivityNet 1.3: The performances on the ActivityNet 1.3 dataset are shown in Table 3. On the average mAP, GLFormer reached an mAP of 36.3%, which is 0.7% higher than the current state-of-the-art of 35.6% by ActionFormer [18]. Our method achieved 37.7% [EMAIL_ADDRESS], outperforming all previous methods by at least 1.5%. GLFormer achieved 54.5% [EMAIL_ADDRESS], but did not perform as well as the previous method ContextLoc [49] (54.5% vs. 56.0%), but outperformed it on other evaluation metrics. Our method achieved 7.6% [EMAIL_ADDRESS] and had no advantage over other methods. Considering that the evaluation index mAP@avg is the average result of multiple tight tIoUs (such as tIoU = 0.95), it requires higher accuracy, so the performance of 36.3% is also commendable. This demonstrates the effectiveness and generalizability of fusing multi-scale context and long-range features.
## 3 Results and Discussion
### Effect of Blending on the Sensory Profile of WSs  
The free sorting task analysis is based on grouping together the samples with perceived sensory similarities. In this experiment, the tasters categorised/gathered the WSs according to their colour, flavour, taste, and overall quality.  
The results of MDS for the studied WSs are shown in Figure 9, revealing the lack of differentiation (proximity) between the sensory profiles of WSs produced by the natural blending and by the blending operation in each ageing technology. Conversely, two different clusters (distance), including the WSs from the two ageing technologies, were clearly established. These outcomes corroborate the aforementioned results of the physicochemical analyses, showing that natural blending is an adequate technology for the WSs production because it afforded sensory profiles similar to those resulting from the blending operation.  
Figure 8: Heat map plotting of physicochemical characteristics and blending modalities of WSs in each ageing technology. Gall—gallic acid; Scop—scopoletin; L*—lightness; VA—volatile acidity; TA—total acidity; 5 Mfurt—5-methylfurfurural; HMF—5-hydroxymethylfurural; Umb—umbelliferone; Ferul—ferulic acid; Ellag—ellagic acid; Sipde—sinapaldehyde; Cofde—configeraldehyde; Syde—syringaldehyde; Vanil—vanillin; Furf—furfurural; TPI—total phenolic index; Syrg—syringic acid; C—chroma; a* and b*—chromaticity coordinates; A 470—absorbance at 470 nm; DE—total dry extract; FA—fixed acidity; AS—alcoholic strength.  
Figure 9: Projection of the WSs from natural blending and from the blending operation of each ageing technology in two-dimensional MDS plots: (**a**) colour; (**b**) flavour; (**c**) taste; (**d**) overall quality.
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 1 Introduction
### Security Requirements  
The research on the security of communication for GLOMONET has shown that the security requirements are essential to produce a secure and efficient authentication protocol. Table 1 shows the security requirements for authenticaiton and key agreement protocol.
# Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
## 4 SS-FGSM Performance Analysis
### Receiver Complexity
Therefore, the complexity computation of MLSS-FGSM can be expressed as follows:  
\[\text{TNCO}_{MLSS-FGSM}=8\mathcal{N}N_{T}\Big{(}2\Big{[}\frac{N_{t}}{2}-1 \Big{]}\Big{)}(2)^{R_{SS-FGSM}}, \tag{39}\]  
In the case, the SS-FGSM is based on DL-detection, the complexity contribution is computed in different ways since the DL-based detector is used. As shown in (19), the input of the DNN model is first multiplied by the weights of the first hidden layers and summed with the biases of that layer. The resultant signal passed through the activation function, and similarly multiplied and summed with weights and biases of the next layer, respectively. Using that way, the complexity of the DNN model obtained by the training can be calculated by multiplying the output of the input layer with the size of the next layer and then add the biases whose size is similar to the number of nodes of the hidden layer, i.e., the real and imaginary signals of the received data \(\mathbf{r}_{j}(t)\) signal is multiplied by the weight of first hidden layer \(\mathbf{Q}\). Then every node bias is added, this process is repeated at each hidden layer. Based on that, the DNN complexity can be expressed as:  
\[\text{TNCO}_{\text{DLSS-FGSM}}=\ (z*\mathbf{Q}\ +\ \mathbf{Q})+\ (d*\mathbf{Q}\ +\ d), \tag{40}\]  
where \(z\) is the number of nodes of the input layer.