esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In conclusion, we demonstrated a passively Q-switched tunable Er\({}^{3+}\):ZBLAN fiber laser around 2.8 \(\upmu\)m. A Fe\({}_{3}\)O\({}_{4}\)-nanoparticle SAM is used as an efficient broadband mid-infrared Q-switcher. Stable Q-switched pulses are observed with a tunable wavelength of 2710-2810 nm. At a wavelength of 2760 nm, a maximum output power of 188 mW is obtained with a repetition rate of 115.8 kHz and a pulse width of 1.3 \(\upmu\)s. The corresponding pulse energy is 1.68 \(\upmu\)J. The wavelength-tunable ability is helpful for applications such as material processing and laser surgery since the output can be tuned to match the absorption peak of different materials. This demonstration shows that Fe\({}_{3}\)O\({}_{4}\) nanoparticles are a promising broadband SA material for the mid-infrared region.  
Conceptualization, K.C. and X.Z.; methodology, K.C., X.Z. and X.W.; validation, K.C.; formal analysis, K.C.; investigation, K.C., X.Z. and X.W.; resources, C.T.; data curation, K.C.; writing--original draft preparation, K.C.; writing--review and editing, K.C., X.Z., C.T. and L.W.; visualization, K.C.; supervision, C.T. and L.W.; project administration, C.T. and L.W.; funding acquisition, C.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (61790584 and 62025506).  
Not applicable.  
[PERSON] would like to thank [PERSON] for helping with the grammar.  
The authors declare no conflict of interest.  
Figure 6: (**a**) Output spectrum (colored lines) and output power (triangle) as a function of wavelength at a pump power of 1.4 W. (**b**) Repetition rate (triangle) and pulse width (circle) as a function of wavelength at a pump power of 1.4 W.
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 3 Related Work and State-of-the-Art
### Dealing with Missing Values  
When our reputation model is viewed as a horizontally-sampled ensemble (in Equation (2) below), there are many missing values (Table 4) that require careful treatment.  
* Complete-case analysis: Also known as \"list-wise deletion\". In this method, the entire record is excluded from analysis if a single value is missing. This method is known to behave well even if values are not missing completely at random [37]. In our case, only the hostnames that are found in \"all\" of the networks would be used for analysis. This is difficult because such a set of hostnames is actually empty. A way to get a non-empty set is to use only a subset of available networks. This leads to a very small resultant dataset which in turn leads to large estimation errors.
* Available-case analysis: Also known as \"pairwise deletion\". For many models, the parameters of interest can be expressed using population mean, variance, correlation and other population statistics. In available-case analysis, each of these is estimated using all of the available data for each variable or pair of variables. In our case, by assuming the mean and standard deviation estimates are required, the entirety of the available data would be used, but the sets of hostnamesthat each network normalization is dependent on would be different. Intuitively, this would give better results than complete-case analysis due to the much bigger dataset available but it also introduces additional bias if the probability of a value being missing is correlated to the anomaly score and it introduces bias in estimating standard deviation due to the different sample sizes.
* Data imputation: Many methods fall into this category; the common trait is that these methods guess or estimate the missing values before doing the analysis. A popular approach is simply to substitute means of existent values for missing values. Another method is to estimate the missing values using the maximum likelihood method or draw the missing values from a chosen distribution multiple times and aggregate the results. If the substituted values are biased, the results of analysis based on them will also be biased. Here, due to the extremely large number of missing values in our case, the data imputation seems not to be the right choice. The methods with strong theoretical guarantees (maximum likelihood and multiple imputation) need precise probabilistic models of the data which are unfortunately not available in our case.
# Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model
## 3 Methodology
### Dataset Augmentation  
CNN models are prone to overfitting when the training dataset is limited. To alleviate the problem, the easiest and most common method is to artificially enlarge the dataset. Dataset augmentation is an effective way to reduce the effect of overfitting for CNN, which generates more training data by making minor alterations (flip, translation, and rotation) to an existing dataset. Moreover, the architecture of CNNs enables extraction of scale, translation, and rotation tolerant features for classifying images or object categories [9]. The key concept of dataset augmentation is that the method applied to the labeled data does not change the semantic meaning of the labels while producing additional training data. When an additional dataset was trained in the network, the network can become invariant to those deformations and generalizes better for unseen data [27].
## 5 Conclusions  
Recent artificial intelligence and machine learning technologies enable the use of online information of electrical and electromechanical conditions to be used to diagnose and predict the operating status of power systems. A high-accuracy conservative transient stability prediction model is proposed in this paper. Compared with the existing models, our model contains four improvements. (1) The sampled data containing multiple time stages are used as input features for the SVM classifier. It is found that the sampled data containing all three-time stages, namely the pre-fault, during-fault and post-fault, can better characterize the transient stability of the power systems; (2) the new feature of generators' electromagnetic power is found to be highly correlated to system stability. The SVM classification results show that the prediction accuracy of electromagnetic power feature is higher than the conventional generator rotor angle, generator rotor speed and bus voltage amplitude features; (3) electromagnetic power and voltage amplitude are determined as two superior features so as to reduce computational burden, based on mRMR feature selection; (4) a high-precision WTA ensemble  
Figure 13: Comparison of accuracy in different models.  
learning model based on the two selected features is established for power system transient stability prediction, which improves the accuracy for unstable situations from 90.38%, 92.31% to 99.26%. The WTA ensemble learning can significantly improve the conservativeness of the prediction model, and the overall prediction accuracy will also be increased slightly. All the research results are verified by the simulated samples on the IEEE 39-Bus system.  
Conceptualization, J.L. and H.S.; methodology, J.L.; software, H.S.; validation, H.S. and Y.L.; formal analysis, W.F.; data curation, S.N.; writing--original draft preparation, H.S.; writing--review and editing, J.L. and W.F.; supervision, W.F.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under grant 51507126, in part by the Key Research and Development Program of Shaanxi under Grant 2017 ZDCXL-GY-02-03, and in part by the Fundamental Research Funds for the Central Universities of China under Grant xjj2017145.  
The authors declare no conflict of interest.
## 2 Related Work  
Several projects have introduced diverse mechanisms for distributed data querying, some of which include the Cougar system and the TinyDB. The Cougar system is a query processing mechanism that consists of three layers: front end, cluster leader, and the cluster node [16]. Each cluster node keeps its sensor data in the form of a time-series [20]. When the user sends a query to the network, the queries are transmitted to the front end, a cluster leader, and eventually delivered to the cluster node, which transmits the queried data in the reverse manner [16]. The TinyDB is a query processing system for extracting information from a network of TinyOS sensor nodes. When the user requests certain data with an SQL-like query language, the nodes take readings from the current node and merge it with sub-aggregates from the subtree, returning the node's aggregate data up the tree [21].  
Both of these systems, however, lack query optimization schemes, and thus, are very vulnerable to heavy network traffic caused when many users submit multiple queries simultaneously. To alleviate network traffic and reduce energy waste, many researchers have studied data aggregation schemes as in the directed diffusion methods studied by [22] or the data centric routing schemes proposed by [23]. Several studies on effective data routing protocols and localization architectures were conducted by [19, 22, 24-27] as well. However, these researches were focused on efficient data handling without much focus on query or query result managements.  
Some of the most closely related works on query management are [19, 25]. Document [19] suggests a query aggregation mechanism for location-based queries. In [19], a multi-layer query aggregation method was introduced, in which the query manager aggregates queries and sends the merged query to each of the access points, where the queries are distributed among the target nodes. This project, however, only takes snap shot queries into account and disregards continuous queries (requests for data transmissions over a certain period of time).  
In [28], a concept of condition-based query merging similar to our approach is introduced. The project, however, is immature and lacks theoretical grounds. Furthermore, in [19, 28] no management methodologies for continuous queries are provided and their naive approach to merging queries bears significant flaws and inefficiencies that are addressed and resolved in our paper. The project also lacks a generalized query processing scheme.
# Facial Expression Recognition Based on Vision Transformer with Hybrid Local Attention
## 3 Methodology
### Convolutional Feature Extraction Network  
CNNs are often used as the backbone for extracting image features; ResNet [5] is proposed to solve the problem of gradient dispersion caused by the deepening of the network. The residual unit proposed by ResNet makes it more efficient than normal networks by adding a \"short-circuiting\" mechanism between each of the two layers to obtain the feature parts without redundancy, and then using nonlinear activation for the redundant information to form the residual learning. The backbone network used in this paper is Improved ResNet-50 (IR50) [22]. IR50 optimizes the problems that exist in ResNet. For the residual module, the ReLU activation function and the last BN layer on the main propagation path are moved to the beginning of the Residual Block, and the BN layer is added at the end of the main propagation path. For the projection shortcut module, the maximum pooling is added to the beginning of the \(1\times 1\) convolution to avoid the information loss problem caused by the dimensionality reduction. In this paper, the input image size is 3 \(\times\) 224 \(\times\) 224, then downsampled to 3 \(\times\) 112 \(\times\) 112, and then fed into the model.  
IR50 has four convolution stages to extract features, similar to ResNet, but they differ in the number of output feature maps. In this paper, only the first three stages of IR50 are utilized to extract the feature maps, and the size of the extracted feature maps is \(256\times 14\times 14\), ensuring that the subsequent input to the Vision Transformer meets the conditions for patch embedding.
## 5 Conclusions  
This paper used the application of exponential smoothing and the ARIMA algorithm to analyze and study the pressure gain (Mpa/mm), internal leakage (L/min), and other indicators of the slide-valve-type direct-drive electro-hydraulic servo valve, as well as carrying out the work of constructing a servo valve life prediction model based on the experimental data of the spool and sleeve of the slide-valve-type direct-drive electro-hydraulic servo valve in order to make predictions on the life of the slide-valve-type direct-drive electro-hydraulic servo valve The error analysis of the prediction results was carried out on the basis of the Copula function and the joint probability density distribution theory to verify the feasibility and universality of the algorithm used in this paper. It was also shown that the algorithm can be fully applied to the aerospace industry by using 16 sets of experimental data under the operating conditions of servo valves commonly used in the aerospace industry, and the prediction results indicated that this servo valve also met the life prediction requirements of the institute.  
The research in this paper enables the pre-maintenance of servo valves, providing important references and guidance for engineering practice in related fields. The study also has a wide application prospect. It can be applied to various mechanical equipment's life prediction and reliability analysis, providing technical support and guarantee for engineering practice and manufacturing.  
Through the analysis and study of the experimental data, although the exponential smoothing method prediction theory, ARIMA, and other theories were discussed and models were constructed, the results needed to be more accurate. The causes of the problems were analyzed, and it was realized that the number and breadth of the samples selected were inadequate. In future research and optimization of direct-drive electro-hydraulic servo valves, a more comprehensive selection of pieces is needed, including the selection of models and categories for the whole range of servo valve applications to ensure a complete prediction theory of servo valve service life.  
[PERSON] proposed the research topic and direction, gave theoretical guidance, revised the paper, and provided some valuable suggestions; [PERSON] surveyed the literature and collected data, built the experimental bench and performed the experiments and data recording and processing, performed some simulations, analyzed the data, and then conceived and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
Funding: The authors would like to thank the National Key R&D Program of China (grant no. 2019 YFB1703600) and the National Natural Science Foundation of China (grant no. 62033001). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data sharing is not applicable. Conflicts of Interest: The authors declare no conflict of interest.
## 4 Simulation Analysis
### Simulation and Result Analysis
#### 4.2.2 INS+LEO2-Satellite Alternate Switching Ranging Integrated Navigation Algorithm
The intuitive effect is better than that of the same orbital surface in terms of the position and velocity error and final trajectory error. Similarly, to quantitatively analyze the algorithm's performance on the adjacent orbit surface, we calculate the final navigation trajectory error, and the results are shown in Table 5 (the INS error statistics are shown in Table 4, which is not listed here). From Table 5, we can see that as the switching time increases, the longitude, latitude, and altitude errors also generally show an increasing trend. Compared with simple INS navigation, when the switching time is 5 s, 10 s, 30 s, 60 s, the mean values for longitude error increased by 99.57%, 99.42%, 98.13% and 96.65%, respectively, and the corresponding standard deviations increased with 96.04%, 94.43%, 90.34% and 86.06%, respectively. The mean latitude performance increased by 99.77%, 99.43%, 96.54% and 92.39%, respectively, the standard deviations increased by 98.01%, 97.28%, 95.37% and 93.32%, respectively, the standard deviations are more concentrated, and the performance is also improved to some extent. In terms of altitude error, the mean error increased by 99.93%, 99.42%, 98.94% and 98.88%, respectively, and the corresponding standard deviation increased by 99.30%, 97.50%, 96.25% and 94.64%, respectively, while both the mean and standard deviation of the altitude performance also improved by at least 94%. From this data analysis, it can be concluded that the INS+LEO2-satellite alternate switching ranging integrated navigation algorithm based on adjacent orbital surfaces can also effectively overcome the INS divergence problem. Based on the results presented in Figure 4, we can preliminarily conclude that the algorithm for adjacent orbits is generally better than that of the same orbits, and therefore is more suitable for real-time location service requirements in challenging environments.
## 4 Experiment and Results  
### Experiment Environments  
The experimental environment used in this study is described below. Table 1 lists the specifications of the main hardware and software used in the experiment. We used an Intel Core i7-10,700F 2.90 GHz CPU (Intel, Santa Clara, CA, USA), a high-performance multicore processor that performs complex calculations and data processing tasks. An NVIDIA GeForce RTX 3070 GPU (NVIDIA, Sanata Clara, CA, USA) was used to perform accelerated training and inference of deep learning models, making it ideal for training AI models. We used 32 GB RAM to support data processing and complex algorithms. The operating system was Windows 10 Pro, and Python version 3.11.9 was used for compatibility. Pytorch version 2.4.0+cu121 was used for model development and training optimization, and CUDA version 12.1 was used to run the experiments.
## 4 Experiments Analysis
### Performance Metric  
The accuracy, precision, recall, and F1 score of the proposed were assessed through cross-validation. By using the confusion matrix, the following matrices could be calculated: true positives (_TP_) are predictions that are positive and their actual data are positive; true negatives (_TN_) are predictions that are negative and their actual data are also negative; false positives (_FP_) are predictions that are positive but their actual results are negative; and false negatives (_FN_) are predictions that are negative but their actual results are positive. Equations (10)-(13) can be used to measure the accuracy, precision, recall, and _F1-score_[64].  
\[Accuracy=\frac{(TP+TN)}{(TP+TN+FN+FP)} \tag{10}\]  
\[Recall=\frac{(TP)}{(TP+FN)} \tag{11}\]  
\[Precision=\frac{(TP)}{(TP+FP)} \tag{12}\]  
\[F1-Score=\frac{(2\times P\times R)}{(P+R)} \tag{13}\]
## 2 Materials and Methods
### Experimental Setup  
The ball burnishing experiments were performed on a lathe machine (Baoji Machine Tool Group, CS6250B, Baoji, China) equipped with a specially designed ball burnishing tool holder. Figure 1 illustrates the experimental setup used in this study. The burnishing force was adjusted using the lathe's clamping mechanism, and a force gauge was used to ensure precise and consistent application of force during the experiments. The burnishing tool featured a hardened steel ball with a diameter of 10 mm, mounted on a specially designed holder that allowed for both dry and MQL burnishing conditions. Figure 2a presents the picture with the experimental setup of the workpiece used in this experiment; Figure 2b shows the burnishing tool.  
This study implemented a MQL system, which has gained significant attention in recent manufacturing applications as the cooling system. The MQL system was configured to deliver 0.0036 mL of coolant oil per spray at 5 bar pressure, with two sprays per second. This configuration resulted in a total oil consumption of 25.92 mL per hour. The cooling fluid was precisely applied at the contact point between the burnishing apparatus and the workpiece surface.  
Figure 1: The diagrammatic representation of the experimental setup.  
The coolant oil used in this study was CUTTEX SYN-10 (Belgin Oil, Kocaeli, Turkiye), a high-performance metal-working fluid produced by blending synthetic vegetable-based oils with high-performance additives. This environmentally friendly oil is characterized by its chlorine-free and heavy metal-free composition, making it biodegradable. The oil appears light yellow and transparent, with a refractive index of 1.4580 \(\pm\) 0.01 (ASTM D 1218), allowing for clear workpiece visibility during processing. Its technical specifications include a kinematic viscosity of 10 \(\pm\) 2 cSt at 40 \({}^{\circ}\)C (ASTM D 445), a minimum flash point of 200 \({}^{\circ}\)C (ASTM D 92), and an excellent copper corrosion resistance rating of 1a (ASTM D 130).  
This specific coolant oil offered several advantages for the ball burnishing process. Its excellent lubrication capabilities and special additives provided superior surface quality, while its low viscosity and high wetting properties resulted in reduced consumption rates. The oil's synthetic-based composition contributed to extended service life, reducing the frequency of oil changes. Its high flash point also minimized oil mist formation and associated fire hazards. The anti-wear additives in the oil helped minimize tool wear and improve tool life, making it particularly suitable for precision manufacturing processes like ball burnishing.
## 5 Numerical Results and Analysis
### The NFE Resistant Ability Comparison  
The BER performances of these algorithms without power control, named the near-far effect (NFE), are discussed in this simulation. In this DS-UWB system, there are also 10 users and the communication channel is AWGN.  
Figure 7 shows the BER performance curves of the first user, when the energy per bit of this user is fixed with its \(E_{b1}/N_{0}\) equal to 5 dB while that of other nine users \(E_{b2-10}/N_{0}\) varies from 0 dB to 20 dB synchronously. That is, the ratio \(E_{b2-10}/E_{b1}\) changes from \(-5\) dB to 15 dB.  
From this figure, we can see that the NFE resistant ability (no sense with \(E_{b2-10}/E_{b1}\)) of OMD is the best among them. Those of CA-DFC and VL-DFC are evidently much better than those of other sub-optimal detectors, and even close to that of OMD. Moreover, those of O-DFC and MV-DFC are still not better than that of MMSE for the same reason as well as Figure 6 shows.  
Figure 6: The BER _versus_\(E_{b}/N_{0}\) curves in AWGN channel when \(K=10\) and perfect power control is employed.  
Furthermore, the BER performance curve of SIC has an inflexion at the point where \(E_{b2\sim 10}/E_{b1}=0\) dB, due to its detection method in Equations (9) and (10). On one hand, when the energy per bit of users 2\(\sim\)10 calculated by Equation (10) is smaller than that of the first user, that is \(E_{b2\sim 10}/N_{0}<5\) dB and \(E_{b2\sim 10}/E_{b1}<0\) dB, then the information bits of user1 will be detected at first, the same as CD does. This is the reason the BER performance curve of SIC is identical with CD until \(E_{b2\sim 10}/E_{b1}=0\) dB. On the other hand, when \(E_{b2\sim 10}/N_{0}>5\) dB and \(E_{b2\sim 10}/E_{b1}>0\) dB, the information bits of users 2\(\sim\)10 will be detected before those of the first user, with more reliability. Consequently, after the interfering signal subtracted from the original received signal by Equation (9), the BER performance of SIC is improved dramatically.
## 2 Background
### Driving Technologies of Industry 4.0
#### 2.2.1 Big Data  
Big data refers to the use of Information and Communication Technology (ICT) and advanced analytical tools to analyze, process, and disseminate a large volume of data, making the process more efficient and aiding in decision-making [66; 72]. Big data is characterized by the 5 Vs--volume, variety, velocity, veracity, and value--which, when combined, offer a wide range of information through data mining [63; 92].