esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Inference of Electronic Equipment Based on Multi-State Fuzzy Bayesian Network
## 3 Multi-State Fuzzy BN
### Fuzziness of Prior Probability of Faults of Nodes  
Prior probability of nodes in fault inference is generally obtained based on statistical data of historical maintenance. However, due to small statistical magnitude or extreme operating environment of electronic equipment, certain fuzziness is inevitable in values of prior probability. According to a small amount of statistical information, the central value \(p_{0}\) of fault probability is given and the interval \([p_{1},p_{2}]\) of fault probability is presented based on experts' experience. After that, the simple triangular membership function is used to represent fuzzy number of probability, referring to [32; 33], Membership function of triangular fuzzy number shown as Figure 3, that is, the fuzzy set of faults of node \(F_{i}\) in the fault state \(F_{i}{}^{a_{i}}\) is expressed as \(\overline{P}(S_{i}{}^{j})\), namely \(\overline{P}(S_{i}{}^{j})=\{p_{1},p_{0},p_{2}\}\).  
The fuzzy membership degree can be expressed as  
\[\mu(p)=\begin{cases}0&0<p<p_{1}\\ \frac{p-p_{1}}{p_{0}-p_{1}}&p_{1}\leq p\leq p_{0}\\ \frac{p_{2}-p_{0}}{p_{2}-p_{0}}&p_{0}<p\leq p_{2}\\ 0&p_{2}<p\end{cases} \tag{7}\]  
where, \(p_{0}\) indicates the central value of fault probability; \(p_{1}\) and \(p_{2}\) separately represent the upper and lower limits of fault probability.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 6 Analysis of Security Functionalities of ECSSP-SC
### Analysis of Resistance Capability of ECSSP-SC against Attacks
#### 6.2.3 Resistance to Server Impersonation  
If an _Attacker_\(A\) tries to impersonate the \(SVR\), then it has to create the accurate message \(\{A_{s},\ T_{S}\}\). As mentioned in point (ii), the _Attacker_\(A\) is unable to obtain the key \(K\) and they cannot access the secret key \(K_{RS}\). Therefore, the _Attacker_\(A\) fails to impersonate the \(SVR\), and the ECSSP-SC is safe from server impersonation. Resistance to server impersonation by ECSSP-SC has been shown in Figure 8.
# Frost Resistance of Coal Gangue Aggregate Concrete Modified by Steel Fiber and Slag Powder
## 3 Results and Discussion  
### Effect of Steel Fiber on Frost Resistance  
#### 3.1.1 Compressive Strength Loss  
Figure 5 illustrates the impact of the type and content of steel fibers on compressive strength of CGAC before and after F-T cycles. According to the Figure, the mass loss increased rapidly with the increase of freeze-thaw cycles. After 500 freeze-thaw cycles, compared to the R0 mixture with a mass loss of 24.46%, HE1.5 and HE2 mixtures had a mass loss of 27%, while that of HE0.5 and HE 1 were relatively small. The water in the capillary pores of CGAC expanded during its freezing, resulting in formation of microcracks when the expansion force was greater than the tensile strength of the concrete. With the increase of F-T cycles, the microcracks propagated and formed macroracks, decreasing the concrete strength. In this case, the steel fibers incorporated into CGAC could partially bear the expansion force, which limited the propagation of microcracks, and decreased the strength loss of the concrete. However, the increase of steel fiber content decreased the workability of concrete, and led to steel fiber balling and free water trapping, causing higher air void ratios and more internal defects, thus increasing compressive strength loss of CGAC after F-T cycles [41].  
For CGAC with 1% steel fiber content, as shown in Figure 5, after 500 F-T cycles, the compressive strength loss of CGAC with 1% USF content was 13.23%, which was the smallest of all the samples. The compressive strength loss of HESF reinforced concrete, CPSF-reinforced concrete, and the reference group were 20.4%, 21.2%, and 24.46%, respectively. It can be seen that, compared with the other two kinds of steel fibers, USF plays a better role in reducing the compressive strength loss.
# IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities
## 2 Materials and Methods
### Evaluation of Results  
The evaluation of results is essential to determining the effectiveness of anomaly detection algorithms in the critical infrastructure of a smart city. In this work, several evaluation metrics are used to measure the performance of the models, including accuracy, completeness, and F1-score. Determining the number of anomalies is based on the knowledge and analysis of the critical infrastructure system under study. A combination of historical operating data, records of previous incidents, and advice from experts in the vital infrastructure domain are used to estimate this value. This estimate is based on a deep understanding of standard operating patterns and possible deviations that can be considered anomalies. Additionally, in some cases, the availability of reference data, such as past incident records or audit reports, can contribute to a more accurate estimate of the actual number of anomalies. Due to the dynamic nature of critical infrastructure and evolving threats, estimating the number of anomalies can be a continuous and adaptive  
Figure 2: Process flow is used to train and evaluate AI models.  
process that is regularly reviewed and updated as new data is obtained and knowledge of the system is refined.  
* _Accuracy:_ This metric measures the proportion of correctly classified instances, both anomalies and regular models. It is calculated as: \[Accuracy=\frac{Number\;of\;correct\;predictions}{Total\;number\;of\;predictions}\] (1)
* _Recall:_ Completeness measures the proportion of anomalies that were correctly identified. It is calculated as: \[Recall=\frac{True\;positives}{True\;positives+False\;negatives}\] (2)
* \(F1\)-_score_: The \(F1\)-_score_ combines precision and completeness into a single metric and is calculated as: \[F1\;Score=\frac{2*Accuracy*Recall}{Accuracy+Recall}\] (3)  
In addition to general metrics and visualizations, discussing specific use cases where models demonstrated outstanding performance or faced challenges is relevant. For example, in the context of the city's smart power grid, the models performed exceptionally well in identifying attempts to tamper with the power grid, significantly contributing to the prevention of cyber-attacks. However, challenges were also observed in detecting subtle anomalies that required a deep understanding of normal infrastructure behavior.
## 5 Discussion: Model Advantages, Limitations, and Generalizability
Ideally, these data should include detailed traffic measurements similar to those used in Paris, such as the number of vehicles per road segment and occupancy rates obtained from sensors (e.g., electromagnetic) and open data platforms. These traffic parameters are crucial for enabling the model to learn the relationship between traffic intensity and temporal variations (e.g., hours, days of the week, etc. ), ensuring an effective adaptation to the specific urban context at hand. Publicly available scripts and configuration files can be provided to streamline this process. Additionally, employing dedicated frameworks (e.g., TensorFlow Federated) will facilitate the implementation of federated learning, allowing other researchers to easily reproduce and adapt our methodology to diverse urban environments while maintaining data quality and consistency;
* Computational Requirements and Comparison with Simpler Models: The CNN-LSTM model has higher computational requirements due to its hybrid structure, which combines convolutional layers to capture spatial relationships between road segments and LSTM layers to model temporal dependencies. In contrast, simpler models like MLPs or standard LSTMs handle only static correlations (MLP) or temporal sequences (LSTM), making the CNN-LSTM significantly more resource-intensive. This is because it processes a larger volume of data and multi-dimensional feature matrices, resulting in much higher parameters and more significant memory and computational capacity needs. This difference in computational costs makes the CNN-LSTM less suitable for environments with limited resources. However, the additional complexity is justified by a significant improvement in prediction accuracy, especially in urban scenarios where capturing spatio-temporal dynamics is crucial for accurately modeling CO\({}_{2}\) emissions. Thus, choosing a CNN-LSTM and simpler architectures will depend on the trade-off between available hardware resources and the required prediction accuracy;
* Future Improvements: Transfer learning mechanisms could enhance the model's generalizability, allowing it to be adapted to different urban environments with minimal retraining. Techniques like federated learning also enable model sharing without transferring data between regions, increasing the model's scalability across cities.
## 2 Materials and Methods
### Research Methods  
#### 2.3.1 Trend Analysis and Significance  
Theil-Sen median trend analysis, also known as Sen slope estimation, is a robust non-parametric statistical method. This method has high computational efficiency and is insensitive to measurement errors and outlier data. It is commonly used for trend analysis of long time series data. In the present study, the raster data of NPP mean values from 2001 to 2021 in the study area were used to perform a Theil-Sen median trend evaluation and a Mann-Kendall statistical test, using the R programming language. The aim was to calculate and analyze the spatial pattern of the annual average NPP and to evaluate the significance of the series' long-term variations. The formula used is used to calculate the Sen trend was as follows:  
\[\text{Slope}=\text{median}\frac{x_{i}-x_{i}}{j-i}\ (1<i<j<n) \tag{1}\]  
where 'Slope' represents the slope, and \(x_{i}\) and \(x_{j}\) represent the annual average values of NPP in NW Liaoning for years \(i\) and \(j\), respectively. A _Slope_ > 0 indicates that there has been a rising trend in the annual average NPP value over the course of the 21-year period, as opposed to a declining trend.  
The Mann-Kendall test is a non-parametric statistical test method proposed by [PERSON] and [PERSON] and further refined in the mid-20 th century. Its advantage is that it does not require the measured values to follow a normal distribution, nor does it require the trend to be linear, and it is not affected by missing values and outliers. This method has been widely applied in trend significance testing and can be combined with Theil-Sen median trend analysis to determine the trend of long-term data changes. It is commonly used in fields such as climate, hydrology, and vegetation. In addition, it can also verify whether there have been any mutations in the time series. The Mann-Kendall significance test was computed as follows.  
Given the assumption that \(x_{1}\), \(x_{2}\),..., \(x_{n}\) are time series elements, the test formula for the statistic \(S\) is as follows:  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}f\big{(}x_{j}-x_{i}\big{)} \tag{2}\]  
\[f\big{(}x_{j}-x_{i}\big{)}=\left\{\begin{aligned} &+1,x_{j}-x_{i}>0\\ & 0,x_{j}-x_{i}=0\\ &-1,x_{j}-x_{i}<0\end{aligned}\right. \tag{3}\]  
The \(Z_{s}\) test statistic is determined as follows:  
\[Z_{s}=\left\{\begin{aligned} &\frac{S-1}{\sqrt{\text{Var}\ (S)}}S>0\\ & 0\quad\quad S=0\\ &\frac{S+1}{\sqrt{\text{Var}\ (S)}}S<0\end{aligned}\right. \tag{4}\]  
where \(S\) denotes a normal distribution, and \(Var(S)\) denotes variance; when \(\mid Z_{s}\mid>Z_{1-\alpha/2}\), it means that the change in this series is significant at the \(\alpha\) level, and when \(\mid Z_{s}\mid\) exceeds 1.65,1.96, and 2.58, it indicates that there has been a substantial shift in the annual NPP mean's trend at the 0.1, 0.05, and 0.01 confidence levels, respectively.
# A Query Understanding Framework for Earth Data Discovery
## 5 System Design and Experiment  
### System Design  
The system works in the following nine steps:  
1. In pre-processing, the system collects raw Web usage logs and metadata.
2. Vocabulary similarity database is computed from query logs and metadata [30].
3. Bigram and trigram models are trained from description field in the metadata with Gensim.
4. A NER model is trained with Standford CoreNLP APIs for select entities including variable, term, processing level, version, collection, and sensor.
5. With these pre-trained models, the query understanding framework can parse corresponding information from a query.
6. After receiving a query, the framework extracts the spatial and temporal bounding box from the query calling Google Geocoding API and Dateparser function.
7. The corresponding spatial and temporal phrases are removed from the query and other parts of the query are passed to the bigram model, trigram model, and NER model for phrases and name entities recognition.
8. Detected phrases are expanded with highly related terms by calling the Restful query expansion API in the query expansion module.
9. The query understanding framework converts the original query to a rewritten query, which define the spatial-temporal context and real intent of a query (Figure 7)\".  
We intentionally made this system process independent from ocean science so the metadata, domain phrases, name entities, and synonyms can be easily changed for other domains for adaptability.
# Intelligent Dynamic Backlash Agent: A Trading Strategy Based on the Directional Change Framework
## 4 Dynamic Backlash Agent
### DBA: Basic Trading Rules  
In this section, we describe the trading strategy named Dynamic Backlash Agent (DBA), as introduced in [14]. DBA is only applicable when the market is in a downtrend. DBA consists of two rules:  
_Rule DBA.1: (generate buy signal)_  
If the current OS event is on a downtrend and \(OSV\leq down\_ind\), then generate buy signal.  
_Rule DBA.2: (generate sell signal)_  
If \(P_{c}\geq P_{DCC\uparrow}\) and a buy order has been fulfilled, then generate sell signal.  
In _Rule DBA.1: \(OSV\)_ is the variable previously defined in Section 2.2 and \(down\_ind\) is a trading parameter. In simple terms, DBA generates a buy signal when the Overshoot Value (\(OSV\)) drops below a certain threshold, namely \(down\_ind\), during a downtrend's OS event. DBA generates a sell signal when the DC confirmation point of the next upward DC event is confirmed.  
In _Rule DBA.2, \(P_{DCC\uparrow}\)_ denotes the minimum price required to confirm the observation of the subsequent uptrend DC event (see Section 2.2). The condition of _Rule DBA.2_ denotes the case under which we confirm the DCC point of the next uptrend DC event of threshold \(theta\). Note that _Rule DBA.2_ is applicable only if a buy signal has been triggered. In other words, no short selling is allowed. _DBA.2_ plays two roles at the same time: _take-profit_ and _stop-loss_. When _DBA.2_ triggers a sell signal, it may incur losses (hence, functioning as _stop-loss_) or generate profits (thus, working as _take-profit_). Table 2 illustrates an example of a DC summary. We use Table 2 to provide an example of how the trading rules of DBA function by examining the downward DC event [CC\({}^{0.1}\)], of threshold 0.10%, which starts at time 21:41:00.  
1. Suppose that the trader has chosen \(down\_ind=-0.45\).
2. At time 21:43:00 (shown in column \"Time\"), the value of \(OSV\) is \(-0.48006847\) (shown in column \"\(OSV\)\"), which is less than \(down\_ind\) (\(-0.45\)). In this example, \(OSV\) is computed as follows: \(\bigcirc\) C is the extreme point of the downward DC event [CC\({}^{0.1}\)]. As \(theta=0.001\), based on Equation (2) (Section 2.2), we get: \(PDCC=P_{EXT}\times(1-\ 0.001)=\)1.48690 \(\times\) 0.999 = 1.48541310. At time 21:43:00, the mid-price is 1.48470. Thus, based on Equation (4) (Section 2.2): \(OSV=((P_{c}-PDCC\ )/PDCC)/theta\) \[OSV=((1.48470\ -\ 1.4854131)/1.4854131)/0.001=-0.48006847.\] (3) Based on (a) and (b), both conditions of \(Rule\)\(DBA.1\) are fulfilled. Therefore DBA generates a buy signal at time 21:43:00.
4. [DD\({}^{0.1}\)] is the upward DC event, which immediately follows the downward DC event [CC\({}^{0.1}\)]. At time 22:01:00, we confirm the DCC point of [DD\({}^{0.1}\)], which is D\({}^{0.1}\). Based on \(Rule\)\(DBA.2\), DBA will generate a sell signal at time 22:01:00.
## 2 Related Works
### Noise Prediction  
The authors proposed a signal-to-noise ratio (SNR) estimation method based on the sounding reference signal (SRS) and a deep learning network in [17]. The proposed deep learning network, called the denoising and image restoration network (DINet), combines a denoising convolutional neural network (DrCNN) and an image restoration convolutional neural network (IRCNN) in parallel. The method was compared to other algorithms, and the results demonstrated superior performance in SNR estimation. The evaluation metric used was the normalized mean square error (NMSE) calculated over 200 test samples, yielding an NMSE value of 0.0012. This result was significantly better than the performance achieved by other algorithms.  
In [11], the authors presented a method for estimating SNR in long-term evolution (LTE) systems and fifth-generation (5G) networks. They employed a combination of a CNN and long short-term memory (LSTM), known as a CNN-LSTM neural network. The CNN was utilized to extract spatial features, while the LSTM was used to extract temporal features from the input signal. Data were generated using MATLAB R2020b LTE and 5G toolboxes, taking into consideration modulation types, path delays, and Doppler shifts. The evaluation metric used was NMSE. The NMSE achieved a value of zero in the time domain for SNR ranging from \(-4\) to 32 dB, demonstrating very low latency. However, in the frequency domain, the proposed method exhibited lower performance.  
In [18], the authors proposed a noise denoising residual network (NDR-Net), a novel neural network for channel estimation in the presence of unknown noise levels. NDR-Net comprises a noise level estimation subnet (NLES), a DnCNN, and a residual learning cascade (RLC). The NLES determines the noise interval, followed by the DnCNN, which processes the pure noise image. Subsequently, RLC is applied to extract the noiseless channel image. The evaluation metric used for assessing the model's performance was the mean square error. The experiments conducted across different channel models--tapped delay line-A (TDL-A), TDL-B, and TDL-C--consistently demonstrated low mean square error values. However, it is worth noting that the proposed model's performance was evaluated within an SNR range of 0 to 35 dB. This limited range does not provide a comprehensive understanding of the model's robustness, particularly in scenarios with high levels of noise.  
In Table 1, some techniques for noise prediction are summarized and compared. The methodology proposed in this article considers several variables that influence signal quality for data generation. The study incorporates noise power density across a wide range of values and explores a spectrum sensing environment where multiple users are in motion at fixed speeds over time. Several regression models are compared using various metrics to highlight the robustness of the proposed method.
## 2 Principles and Methods
### Selection of Paramater Indicators
#### 2.3.2 Signal-to-Noise Ratio (SNR)  
The SNR definitions are as follows:  
\[SNR=10\lg\frac{\sum\limits_{i=1}^{N}f^{2}(i)}{\sum\limits_{i=1}^{N}\left[f(i )-g(i)\right]^{2}} \tag{8}\]  
where \(f(i)\) and \(g(i)\) are the same as described above. A larger \(SNR\) indicates a better denoising effect.
# A Study of Gender Bias in Face Presentation Attack and Its Mitigation
## 2 Related Work
### Mitigating Bias in Face Recognition Systems
#### 2.2.1 Reducing Bias by Improving Recognition Model  
Das et al. [19] presented a solution to reduce bias that involves applying a multi-task convolution neural network (MTCNN). The MTCNN uses specific joint dynamic loss weight to enhance classification of some soft biometrics such as gender and age, and make it less bias. The MTCNN was applied on two large datasets: the UTKFace and the bias estimation in face analytics (BEFA) dataset, which was the dataset used in BEFA-challenge of the European Conference on Computer Vision (ECCV) 2018. The results showed a rise in the performance and the MTCNN algorithm won the first place in the BEFA-challenge.  
In order to reduce such bias, Wang et al. [20] focused on using domain adaption and proposed a deep information maximization adaptation network to improve the generalization of the models. Moreover, researchers applied an innovative mutual information loss function to increase the model ability to discriminate different classes.  
Two solutions were suggested by Klare et al. [13] to improve the accuracy of face recognition. The first one uses dynamic face matcher selection in which the algorithms trained with different demographical groups are combined. The second solution is to use more balanced training dataset in terms of demographic groups.
## 4 Experimental Results
### Comparison with State-of-the-Art Methods  
To compare ESTD-GZSL with the recent GZSL methods, we select the recent state-of-the-art generative-based methods such as f-CLSWGAN [8], CANZSL [36], cycle-CLSWGAN [14], FREE [7], LisGAN [37], CADA-VAE [17], f-VAEGAN-D2 [18], TF-VAEGAN [6], CE-GZSL [20], RPGN [21], and SDGZSL [10]. Table 2 shows the GZSL performance of the compared methods and ours with and without finetuning the backbone on the datasets.  
Our method achieves better performances than the baseline on CUB, FLO, and APY, and it obtains the second-best \(H\) results of AwA2, where SDGZSL decomposes visual features into those that are semantic-consistent and semantic-unrelated. However, we believe that using only a single encoder to extract semantic-consistent features is not sufficient, and the visual features synthesized by VAE do not express their semantics well, which can lead to semantic and visual biases. Therefore, to improve disentanglements, we introduced a semantic decoder module that maps the visual space to the semantic space by using cycle-consistent loss to reduce bias. We combine the semantic decoder's output with the disentanglement encoder output for the reconstruction of the original image to improve generalization. In the classification phase, we transform semantic-consistent features decomposed by the semantic decoder output and the disentanglement module into enhanced semantic-consistent discriminative features for classification. Compared with the performance of SDGZSL, as shown in Table 2, for all the datasets without finetuning, our method outperforms SDGZSL in \(H\) results, which improved by 1.2% on the AwA2 dataset, improved by 1.9% on the CUB dataset, improved by 1.8% on the FLO dataset, and improved by 0.5% on the APY dataset. For all the finetuned datasets, our method outperforms SDGZSL in \(H\) results on CUB, FLO, and APY, and comparable \(H\) results were also obtained on AwA2, which improved by 2.3% on the CUB, improved by 0.7% on the FLO, and improved by 0.3% on the APY. The proposed method achieves better results because it is able to generate semantically aligned visual features for unseen classes and extract more sufficient semantic-consistent features. The experimental results show that the proposed method can improve the effect of the baseline, which proves the effectiveness of the method.