esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability Analysis of Hybrid Laser INS Under Multi-Mode Failure Conditions
## 5 Calculation Results and Analysis
### Dual Random Uncertainty Analysis of Outer Frame Component Based on Structural Strength Failure
#### 5.2.2 Impact of Design Parameters on Failure Probability When Considering Series of Failure Modes  
When considering three failure modes in series, the failure probabilities of each design parameter for both composite materials at specified thresholds are illustrated in Figure 23a-f. The results indicate that the overall failure probability of the structure also follows a trend of \"initial increase followed by decrease\", exhibiting a maximum value. Specifically, the maximum failure probability for the ZL107 traditional material is approximately 37.6%, while for the SiCp/Al composite material, it is about 15.26%. The failure probability for SiCp/Al in series with three failure modes is significantly lower than that for the ZL107 material, reflecting its excellent reliability.  
Figure 23: Impact of design parameters on failure probability under series failure modes.  
It is expected that the reliability analysis results of the outer frame structure considering three failure modes in series will differ from those considering a single failure mode. Additionally, the influence of each failure mode on the reliability of the outer frame structure varies, necessitating further research.
## 3 Results and Discussion
The study has been summarized and listed in Table 2. The comparative study suggest that the antenna design based on proposed method is simpler, compact in size, low-cost in manufacturing (as it can be implemented over the single-layer substrate) and having relatively higher gain performance. Figure 7: Designed antenna’s simulated and the measured far field performance graph including axial-ratio (AR) and peak-gain vs. frequency. Here the solid line represents the measured results and the dashed line stands for the simulated results.
# AutoFL: Towards AutoML in a Federated Learning Context
## 3 Analysis of a Contemporary AutoML Framework
Examples include the number of clients to involve in each training round (all clients or only a subset), the method to select these clients (random or adaptive), the relative impact of each model update (uniform, weighted by the amount of data), the size of each model update (full model update or only slices of a model update), etc. There are many more hyperparameters to explore in federated learning. Some of them might be decided upon upfront by the MLOps engineer, whereas others are subject to optimization in a given deployment context. 7. Security and privacy are important concerns, and various defenses have been proposed to counter threats. Whether these threats are relevant to the application and data at hand is something that cannot be decided upon automatically. If an MLOps engineer or data scientist identifies a certain threat, a multitude of countermeasures may be available that influence not only the result but also how efficiently the resulting ML model was obtained. For example, differential privacy may be less resource intensive during training compared to cryptographic techniques, such as MPC and HE, but the final model may be less accurate due to the introduction of noise. Unfortunately, these countermeasures are not part of auto-sklearn but are (partially) available in state-of-practice federated learning frameworks, such as PySyft [33]. 8. Building upon the previous challenge, in a federated learning scenario that deals with sensitive information, it is paramount that the AutoML framework never explores models without proper countermeasures. Otherwise, the Bayesian optimization might pick a configuration from the search space where the data or the model updates are not properly protected, such that the optimization process itself might leak sensitive information during the automated machine learning. Addressing all the above concerns within a single framework is beyond the scope of this work, but it clearly shows there are many more hyperparameters and trade-offs to be considered in federated learning scenarios. In the following subsection, we will highlight the approach behind our framework and how it can be further extended.
# Simultaneous Transmission of Photonic Services over One Fiber with an ITU 100 GHz Grid
## 3 Measurement Setup
### Coriant Groove  
Coriant Groove\({}^{TM}\) G30 represents the network disaggregation platform. The increasing traffic of streaming, cloud content delivery, and data transmission has provided an opportunity for vendors. The Coriant Groove G30 is an open-line system (OLS) with industry-leading density, flexibility, and low power consumption. It supports a line capacity up to \(4.8\,\mathrm{Tbps}\) in a \(1\,\mathrm{U}\) rack mount. We use Coriant as a data source with \(100\,\mathrm{Gbps}\) transmission speed. On the other hand, Coriant provides \(100\,\mathrm{G}\), \(150\,\mathrm{G}\), and \(200\,\mathrm{G}\) in different modulation formats, such as dual-polarization quadrature phase shift keying (DS-QPSK), dual polarization \(8\) quadrature amplitude modulation (DP-8 QAM), and dual polarization \(16\) quadrature amplitude modulation (DP-16 QAM), respectively.
# Reproducibility and Data Storage for Active Learning-Aided Systematic Reviews
## 5 Data Storage Recommendations for Systematic Screening
### Labeling Phase
#### 5.3.2 Data Produced by the Model  
Most notably, the data produced by the model are where we need to strike a balance between reproducibility and storage size. Storing all the model data will result in enormous file sizes, whilst storing no model data means that we cannot reproduce the model's behavior.  
For each iteration of the active learning cycle, we believe it is essential to store two components: the type of model that was used (instead of the model itself) and information to reconstruct the training set of labeled records on which the model was trained. Withthis information, it is transparent what information has been used in each iteration of the active learning cycle and it is possible to recompute the relevance scores at any point in the active learning cycle. For deterministic models, or for probabilistic models where the random seed is stored, it is possible to recompute these scores exactly. The amount of time this takes is the same as the amount of time it took to train the original model and compute the scores.  
Similarly, we do not need to store all the records used in the training set for each iteration. The model will be trained again on each iteration's current set of currently labeled records. Since we can already find which records were labeled in the annotator data and in which order, we do not need to store this information again during the model training. Instead of storing all the records in the training set, it is enough to store the number of records. For example, if we know that there are 50 records in the training set, then the training set consists of the first 50 labeled records. We can find exactly which 50 records these are in the annotator data. If the model data are stored in this way, the size will depend linearly on the size of the input dataset. This is much better than the quadratic scaling one would obtain by naively storing all the model data. Moreover, using the same hardware, reconstructing the model of a specific iteration takes approximately the same time as it took to train the original model. If we also stored the random seeds used in any of the probabilistic training algorithms, then we can almost exactly reproduce the original model. Most certainly, we will be able to reproduce the ranking produced by the original model. It turns out that this amounts to surprisingly little extra information since we already stored the data from the set-up phase and all data produced by the annotator.  
Users may opt to store all the models learned during the AL process. While these models can often be reconstructed from other stored data, direct storage of the models can facilitate ease of reproducibility, especially in cases where computational resources are limited or when strict reproducibility is required for scrutiny or detailed analysis. We suggest software creators offer this as an optional feature, allowing users to make an informed choice based on their specific needs and constraints.
# A Formula-Structure-Embedding-Based GNN for Formula Equivalence Determination
## 3 Method
### Encoder
#### 3.1.2 Multi-Level Node Feature Embedding Module  
In mathematics, formulas can be represented as graphs, where nodes represent operators and operands, and edges represent operational relations between them. This graph preserves the hierarchical structure and logical relations of formulas. However, if we use the GNN model for formula equivalence determination directly, nodes are represented by their IDs. This representation fails to adequately use use the positional information of each node, resulting in an imprecise understanding of the formula's overall structure. In order to solve this problem, we improve the representation of formulas based on the node structure information. In this paper, each operator or operand, such as '+','sin', etc., is represented by a fixed number which is called the Node Type Identifier T); Node Level Information (L) refers the level at which the node is located in its formula tree; Node Intra-Level Number (\(N\)) records the relative position of the node at its level. The operations of formula structure embedding are as follows:  
1. Each node's 'Token T', 'Level L', and 'Number N' are converted into corresponding vectors by embedding, such that each embedding vector contains all structure information of corresponding node.
2. The three embedding vectors of a node are fused to form a node feature vector that contains all structure information of corresponding node (see Figure 5).
3. A breadth-first traversal is performed on the formula tree to aggregate all node vectors, generating a vector that represents the structure information of the formula tree (see Figure 6).  
Figure 4: AST reordering based on the commutative law.  
Figure 5: Feature embedding and fusion.  
Figure 6: Breadth first traversal and generation of formula vector.  
Take '\(x+2y+2 xy\)' as an example, Table 1 shows its structure information of each node. By encoding such structural information into node representations, the model incorporates hierarchical and positional details into computation, ensuring the accuracy and reliability of formula equivalence determination.
# Time Domain Investigation of Hybrid Intelligent Controllers Fed Five-Phase PMBLDC Motor Drive
## 3 Design of Controllers
### Adaptive Hybrid Fuzzy-PI Controller  
The conventional fixed gain PI controller cannot provide good response for all operating conditions due to the fixing of Kp and Ki values, irrespective of test conditions. Therefore, the proportional and integral constants should be adjusted online to obtain a better transient and steady state response. The adaptive hybrid controller, along with the fuzzy membership function, is depicted in Figure 3; the fuzzy logic controller tunes the constants of the PI controller, Kp, and Ki online. The fuzzy input sets are error (e) and change in error (\(\Delta\)e), which are taken as a triangular function. The linguistic variables are negative (N), zero (Z), and Positive (P). The fuzzy output sets are the tunable parameter (h) with a triangular membership function. Big (B) and small (S) are the linguistic variables. A total of nine rules were framed based on expert knowledge.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\Delta\)M & **NL** & **NM** & **NS** & **Z** & **PS** & **PM** & **PL** \\ \hline
**NL** & NL & NL & NL & NM & NS & NS & Z \\
**NM** & NL & NM & NM & NM & NS & Z & PS \\
**NS** & NL & NM & NS & NZ & PS & PM & PL \\
**Z** & NL & NM & NS & Z & PS & PM & PL \\
**PS** & NM & NS & Z & PS & PS & PM & PL \\
**PM** & NS & Z & PS & PM & PM & PM & PL \\
**PL** & Z & PS & PS & PM & PM & PL & PL \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fuzzy Associative Memory Table.  
Figure 3: Adaptive Fuzzy-PI controller. (**a**) Structure of Adaptive Hybrid Fuzzy-PI Controller. (**b**) Membership Function.  
The values of \(\mathrm{Kp}\) and \(\mathrm{Ki}\) for the hybrid adaptive controllers are obtained [34] from the following:  
\[\mathrm{Kp}=\mathrm{h}\;\mathrm{Kp};\;\mathrm{Ki}=\mathrm{h2}\;\mathrm{Kim};\;0.0< \mathrm{h}\leq 1.0 \tag{9}\]  
where \(\mathrm{Kp}\) and \(\mathrm{Kim}\) are the maximum possible proportional and integral constants obtained from the Ziegler-Nichols method, respectively, and '\(\mathrm{h}\)' is an empirical constant. Here, the value of the PI controller has been tuned online according to the current error using a fuzzy controller.
## 6 Conclusions  
The damage potential of ground motions is represented by the \(S_{d}\), and the \(S_{d}\) under different \(T\) and \(C_{y}\) conditions is analyzed based on a bilinear and modified Clough hysteretic model. Not only the variation of the \(S_{d}\) values and discreteness under different \(T\) and \(C_{y}\) conditions are analyzed, but more importantly, the variation of \(S_{d}\) ranking is also analyzed. The conclusions are as follows.  
1. When \(C_{y}\) is constant, the average value of \(S_{d}\) increases with the increase in \(T\). When \(T\) is constant, the smaller the \(C_{y}\) in the short- and medium-period ranges, the larger the average value of \(S_{d}\). On the contrary, the smaller the \(C_{y}\), the smaller the average value of \(S_{d}\) in the long-period range.
2. The correlation coefficient of the \(S_{d}\) ranking under different \(C_{y}\) conditions is less than 0.8 in the short-period range. Especially when the difference of \(C_{y}\) is large, the correlation of the corresponding \(S_{d}\) rankings is poor, which indicates that \(C_{y}\) has a great effect on the \(S_{d}\) rankings. In the medium- and long-period range, the correlation coefficient of \(S_{d}\) values corresponding to different \(C_{y}\) is higher, and the correlation is very good, indicating that \(C_{y}\) has almost no effect on the \(S_{d}\) ranking.
3. In the medium- and long-period range, with the change of \(T\), and when the change of \(T\) is small, the \(S_{d}\) value changes obviously, but the change of \(S_{d}\) ranking is very small. When \(T\) changes greatly, the \(S_{d}\) value and \(S_{d}\) ranking change greatly.  
It is worth noting that only the SDOF system is used to analyze the damage potential ranking in this paper, and the multi-degree-of-freedom structures need to be used in the next study.  
Conceptualization, Q.L. and J.H.; methodology, L.X.; software, Q.L.; validation, Q.L., J.H. and L.X.; formal analysis, Q.L.; investigation, Q.L.; resources, Q.L.; data curation, Q.L.; writing--original draft preparation, Q.L.; writing--review and editing, J.H.; visualization, J.H.; supervision, L.X.; project administration, Q.L.; funding acquisition, L.X. All authors have read and agreed to the published version of the manuscript.  
The work is supported by the Transportation Science and Technology Project of Sichuan Province (Grant No. 2018-ZL-01), Institute local cooperation project of Chinese Academy of Engineering (Grant No. HB2022B18) and the National Natural Science Foundation of China (Grant No. U2139207; U1939210).  
Not applicable.  
**Acknowledgments:** The work is supported by the National Natural Science Foundation of China (Grant No. U2139207; U1939210). The support is gratefully acknowledged. The authors would also like to thank the NGA-West2 database for providing strong ground motion data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 AR/VR Applications
### Travel and Tourism  
As the outbreak of COVID-19 has resulted in a huge loss in revenue for the tourism industry, leaders are resorting to AR/VR devices as a powerful tool for boosting the post-pandemic tourism industry [49]. With the rise in demand for travel and hospitality services, travel companies are willing to invest in AR/VR technology to provide an immersive experience to their customers. VR has been proposed as a substitution for travel, which implies that it can be used as a replacement for physically visiting certain destinations, thus providing numerous benefits [50]. AR/VR technologies generate a realistic, easy, and detailed navigation of the tourism places and enable travelers to experience a bird's-eye view of their desired destination [51]. VR can be used as an effective tool in planning a trip by allowing the tourist to communicate with other members via social media apps to gain insights into the destination through feedback from other travelers regarding their previous experiences. An AR-/VR-based application known as VirtualCruiseTour [52] is a technology dedicated to promoting shore excursions by allowing future customers to experience virtual 360\({}^{\circ}\) pictures or videos either in an immersive VR or non-immersive mode of their sites of interest while on a cruise. This technology has an AR module that augments a map to help the customers with the cruise route and by locating their current position and the next stop at the desired location. A marker-based approach has been used to create this AR module, where the marker (typically an ad hoc 2D image) is in the camera frame to locate the virtual content superimposed on the real world. According to Accord Marketing [53], a marketing agency, VR tourism has been rated as the third-most-popular virtual activity after virtual museum visits and virtual gaming. Many luxury hotels and properties have the option of virtual tours of hotel rooms and luxury suites so that customers can view these before making their purchase.
# Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks
## 4 Theoretical Analysis and Experimental Results  
In this section, we will evaluate the performance of the proposed routing protocol APCDBRP and compare it with several existing protocols, including DBR [6], EEDBR [7], and EAVARP [14]. We will implement our protocol and the comparative protocols using AquaSim-Next Generation [26], which is a simulator for underwater sensor networks based on NS3 [27] and its libraries. We have made modifications to the physical layer design in AquaSim-Next Generation to enable dynamic adjustment of the transmission power for each packet.
## 3 Blind Adaptive Beamforming Based on Direction Lock Loop
### The Procedure of the Proposed Method  
The diagram of the proposed algorithm is shown in Figure 4 with the following steps.  
Figure 4: Block diagram of the proposed method, \((\cdot)^{*}\) denotes complex conjugation.  
Figure 3: The S-curves of the coherent and non-coherent DiLL.  
1. The inverse matrix of the covariance matrix of the array signal was solved and multiplied by the array signal to achieve interference suppression.
2. The array signal after interference suppression was correlated with the local copied pseudo-code signal to obtain the correlation accumulation vector.
3. The DiLL method was used to realize the stable tracking processing of the DOA parameters of navigation signals. The spatial correlation values corresponding to the \(\Delta\theta\) bias besides the current DOA estimation were constructed, and the revised DOA estimate was obtained after processing and filtering by the discriminator.
4. The beamformer was constructed according to the estimated DOA value, the array was weighted, and the weighted output signal was used for follow-up tracking processing.
# Defect Prediction for Capacitive Equipment in Power System
## 4 Experiments
### Data Collection and Preprocessing
#### 4.1.1 Data Cleaning  
The raw data may contain a substantial number of invalid samples, and direct utilization in analysis and modeling may substantially impact the performance of the prediction model. Thus, data cleaning is essential. To understand the situation of missing features, we utilized Missingno [36], a visualization tool from the Pyecharts package, to graphically display the extent of missing features. The significance and extent of missing values were manually evaluated. Following this assessment, missing values in the samples were addressed. For instance, the latitude and longitude features of the equipment are crucial and should be retained whenever possible. In cases where these features have a relatively high missing rate, we match a missing value sample with high similarity to a complete dataset sample. Subsequently, we utilized the corresponding value from the complete dataset to fill in the missing value in the sample with missing data. The data format was then standardized for subsequent processing. For Chinese encoding, data storage documents were encoded in Chinese internal code specification (GBK) and saved as comma-separated value (CSV) files for ease of programming and readability. Subsequently, incorrectly recorded data were rectified, abnormal data were removed, and duplicate values were manually evaluated for retention or deletion.  
After data cleaning, a total of 24 variables were selected as input variables, including equipment name, substation, equipment type, full name, equipment type remarks, equipment model, manufacturer, equipment longitude, equipment latitude, equipment altitude, running state, voltage level, voltage type, year, month, and day of production, week of the production date, year, month, and day of commissioning date, week of the commissioning date, year of operation, power supply bureau, and topography. The first seven variables are of string type, while the remaining variables are of floating-point or integer type.