esa-sceva/satcom-chunk-collection huggingface dataset:
# The Fast Detection and Identification Algorithm of Optical Fiber Intrusion Signals
## 2 Design of Fast Detection and Identification Algorithm for Optical Fiber Intrusion Signals
### Feature Extraction and Identification  
The whole process of feature extraction and identification is shown in Figure 5. The features of OFPS signals are not obvious in the time domain. If these signals are transformed into the frequency domain, the corresponding features are clearer. In addition, FFT can extract the spectrum feature of a signal, which is often used in spectrum analysis. When FFT is performed on N points, the FFT results of N points can be obtained. Because of the symmetry of the FFT results, we usually only use the first half of the results in the frequency domain. The time domain and frequency domain of the data are analyzed, and the following conclusions are obtained: Mechanical signals have a stronger energy between 50 and 64 Hz in the FFT results, while other signals do not have this feature; The walking signal has stronger energy between 2-10 Hz in the FFT result, while other signals do not have this feature; The FFT result of the picking signal does not have obvious features, but its time domain duty cycle is significantly smaller than other signals.  
Figure 4: Extraction of identification data.  
We use a lot of experimental data (or you can edit the number) to find the energy ratio, and fit the obtained result to the probability density function. The probability density function curve is shown in the figure. As can be seen from the figure, thresholds selection is most appropriate at the intersection of the curves.  
The process of feature extraction and identification is shown in Figure 6. The 128 points FFT is applied for each column of the data. Next, the moduli of points 50-64 and points 11-50 are summed up, respectively. Then the former is divided by the latter and the quotient is compared with the recognition threshold of the mechanical signal. If the quotient is greater than the threshold as shown in Equation (3), where threshold_1 = 0.9, it is judged as mechanical signal.  
\[\sum_{n=50}^{64}\sqrt{{a_{n}}^{2}+{b_{n}}^{2}}/\sum_{n=11}^{50}\sqrt{{a_{n}}^{ 2}+{b_{n}}^{2}}>threshold\_1 \tag{3}\]  
If the quotient is smaller than the threshold_1, the signal is determined as walking signal. The discrimination method is shown in Equation (4), where threshold_2 = 0.55  
\[\sum_{n=2}^{10}\sqrt{{a_{n}}^{2}+{b_{n}}^{2}}/\sum_{n=11}^{50}\sqrt{{a_{n}}^{ 2}+{b_{n}}^{2}}>threshold\_2 \tag{4}\]  
Figure 5: Feature extraction and identification.  
Figure 6: The selection processing of thresholds.  
If the quotient in Equation (4) is larger than threshold_2, it is judged as walking signal. If the quotient is smaller than threshold_2, the signal will be further identified if it is picking signals.  
The picking signal is identified based on the number of alarm points rather than the result of FFT. And if the number of the alarm points is not within the range between 2 and 15, it is ultimately determined as false alarm.
## 2 Related Work
### Existing Shortcomings  
Currently, only [PERSON] and [PERSON] have proposed comprehensive measurement schemes for spatial information in area maps. Both scholars adopt a weighted summation approach to integrate geometric information and spatial distribution information, resulting in a comprehensive measurement of spatial information content in area maps. In their methods, due to a lack of systematic analysis of the quantitative relationship between geometric information and spatial distribution information, both models treat these two types of information as equally important. This paper argues that geometric morphological characteristics are the foundation of spatial distribution relationships, and the two types of information should interact with each other in the study of spatial information measurement. However, the weighted summation approach is unable to capture the diversity of this interaction.  
In summary, there are three main shortcomings in existing research that urgently need to be addressed:  
1. The existing methods for measuring the geometric information content of area objects lack a unified calculation standard and rely heavily on manual experience, resulting in high degrees of error and subjectivity in the calculation results.
2. The existing methods for measuring spatial distribution information content only consider the richness of spatial adjacency and spatial occupancy information, while failing to effectively consider the spatial morphological relationships, spatial distance relationships, and further spatial arrangement characteristics of area objects.
3. There is currently no theoretically feasible solution for comprehensively measuring the geometric information and spatial distribution information of area maps. The current approach of combining weights based on manual experience carries an uncertain degree of subjectivity.
# Condition Rating Prediction Using an Interactive Deterioration Model Development Package
## 4 Development of Deterioration Models
### Stochastic Deterioration Models Using Markov Chain  
The uncertainty of the condition state transition over time is modeled using a Markov chain process [22; 25]. A typical Markov chain problem involves developing a transition state matrix between any two inspection periods, which is defined as  
\[\mathbf{P}=\left[\begin{array}{cccc}p_{11}&1-p_{11}&0&\cdots&0\\ 0&p_{22}&1-p_{22}&\cdots&0\\ 0&0&p_{33}&\cdots&0\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&0&\cdots&1\end{array}\right] \tag{6}\]  
In Equation (6), \(p_{ii}\) is the probability that the condition rating of state \(i\) remains the same. Accordingly, the probability representing a decrease in the condition state is determined as \(1-p_{ii}\). The elements of \(\mathbf{P}\) are equal to zero when \(i<j\) because the states cannot be improved without intervention. Assume that the initial distribution probability of the bridges in each condition state vector is \(q_{0}\). Using total probability theory, the transition probability matrix for next inspection state can be obtained by the powers of \(\mathbf{P}\), and the probability condition state after the \(n\)th inspection periods is defined as  
\[q_{n}=q_{0}\mathbf{P}^{n} \tag{7}\]  
For most inspection data, the year built (age) is ranked highly in the set of explanatory variables. The zoning technique, in which the inspection data are grouped for identical age periods, is therefore used to consider the effects of the age and to improve the accuracy of the deterioration models [36]. The transition state matrices are estimated for each group and the initial vectors are updated accordingly. Several methods, including percentage prediction, logistic regression, and optimization-based approaches, have been developed to estimate the elements of the transition state matrix [11; 24; 25].
# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
## 6 Reliability Prediction of the Communication Module Under the Mission Profile
### Overall Reliability Prediction of the Communication Module
This approach avoids the error accumulation typically seen in traditional linear superposition methods, such as PoF-RBD, which can result in errors of up to \(\pm\)12%. In terms of the practical application, the method proposed here supports spacecraft-design optimization by identifying and mitigating early risks. For instance, the solder joint fatigue reliability curve highlights FPGA edge solder joints as weak points, prompting layout optimizations, like increasing the solder ball density or using higher-reliability solder materials. Additionally, the framework provides significant engineering scalability by supporting a multi-module cascade analysis. This enables the examination of interactions between management units and functional modules, helping to prevent complete module replacements due to unpredictable cascading failures, making it well-suited for system-level reliability optimization in next-generation spacecraft.
## 7 Experimental Description and Result Analysis
In our case, 0.25 Hz frame rate reaches the lower limit as during the U-turn the gaps of the image overlap arise for a frame interval of approximately 4 s. It is worth noting that the vision-based error characteristics are relevant to the processed images and the systematic error therefore varies with the frame rate as seen in Figure 12, particularly for the time period after U-turn.A higher image frame rate tends to yield better integration accuracy at the cost of more excessive computational load, whereas the acceptable processing speed inevitably depends on the available computational capability. In _Campus_0L_ segment, we used a 64-bit desktop computer with a 3 GHz processor to process the 2.5 Hz rate images with 1,024 \(\times\) 768 resolution based on a MATLAB platform. Our implementation requires a few seconds of processing for each pair of images and tens of milliseconds for all other filtering routines and, consequently, has yet to achieve real-time functionality. [PERSON]'s law, fortunately, has been appropriately indicating the rapid development of integrated circuits during the past decades and also has given us encouraging prediction on the exponentially growing processing capacity of semiconductor materials in the future. In addition, instead of using a CPU, a GPU module also facilitates speeding up the image processing task towards a real-time computation.
## 4 Performance Comparison between Protocols
### Simplified Security Analysis
If we use an autocompensated BBM92 protocol, longer distances are achieved (green curve); however, with the plug and play BSEP protocol, much longer transmission distances are obtained, which is the central result of this work.
## 4 Discussion
[17] reported that the performance of the SSF process on cottonseed hulls initiated by the _Pleutrotus_ species reduced the presence of FG. [PERSON] et al. [12] stated that the fermentation by _P. chrysosporium_, _T. reesei_, _A. niger_, and their consortium displayed a significant reduction of gossypol in the CSC, which could be presumably explained by the interaction between the secreted enzymes of the used consortium. The latter enzymes could be a potent means for the degradation and transformation of highly polymerized carbohydrates and proteins in CSC. Additionally, according to [PERSON] et al. [38], the application of the SSF process on cottonseed meal by _B. coagulans_ S17 revealed a significant reduction in FG's presence. [PERSON] et al. [39] described that _L. agilis_ WWK129, which was firstly isolated from the rumen contents, could be characterized as a potential gossypol degradation agent since the latter decreased gossypol's levels in cottonseed meal up to 80% by applying anaerobic fermentation for 5 days. [PERSON] et al. [40] reported that the performance of the SSF procedure initiated by a mixed fungal culture consisting of _C. tropicals_ and _S. cerevisiae_ on CSC displayed a reduced gossypol profile, including FG and BG, with a parallel upgrade of the latter's physicochemical properties. In our study, the amount of unwanted FG and TG was reduced as a consequence of SSF utilization, since _P. ostreatus_ secretues various specific enzymes (cellulolytic, amylases, proteases, lipolytic) that are capable of releasing BG and breaking down the FG, resulting in the reduction of both FG and TG [40,41]. Another possible explanation that could justify the observed reduction in the FG level is connected with microbial activity, since the utilized microorganism has the potential to degrade FG or convert FG into BG [42]. Additionally, it has been reported that this conversion of FG to BG facilitates the reduction in overall toxicity, probably due to the microbial processes that are carried out during SSF. The observed significant reduction in the TG level at the end of the procedure can be rationalized considering that FG is consumed by _P. ostreatus_ since it constitutes a carbon source that supports its biochemical activities [43]. It must be noted however that several literature reports indicate that BG does not have a direct impact on animals' health since it is not assimilable by the animals through the digestive tract. Nevertheless, [PERSON] et al. [44] stated that although BG is considered a non-toxic compound, its presence poses a significant threat, especially in high concentrations since during the digestion period where it can be transformed into its free form. Therefore, our study is of high significance since our results reveal a significant reduction in TG levels, exceeding 9-fold at the end of the process, which also results in BG's depletion.
## 3 HDELC Algorithm
### Classifier Selection and Ensemble Models  
The classifier selection and ensemble part of the HDELC algorithm is firstly based on the traditional Stacking ensemble strategy for the high-dimensional-data classification problem to be solved in this paper, and secondly, it is optimized based on it. The basic idea of the Stacking ensemble algorithm is to train the first-layer classifier with the initial dataset. A new dataset is generated to train the second layer of classifiers. The output of the first-layer classifier is the input feature of the second-layer classifier, while the original labels are still used for the new dataset.  
After the reconstruction of the feature space in the previous subsection, the classifier selection and ensemble model in this chapter is based on the Stacking algorithm. The main two parts are classifier selection and classifier ensemble. The subspace classifier integration algorithm called CSE algorithm (CSE stands for subspace classifier ensemble process) describes the process of subspace classifier integration for high-dimensional data.  
Figure 2: Illustration of the process of feature space selection and reconstruction.
## 2 Methodology
### Coarse-to-Fine Spatiotemporal Correlation Computation Scheme  
As shown in Figure 4, the proposed scheme uses binocular stereo [36] as the basic architecture. The infrared speckle projector continuously projects a set of binary random speckle patterns to the measured face, and the binocular infrared camera simultaneously collects stereo image pairs. Because the camera parameters are obtained through [PERSON]'s [38] pre-calibration method, the image pairs are rectified according to its parameters to ensure that homologous points are searched along the epipolar line and matched. During the disparity computation procedure, the coarse-to-fine spatiotemporal correlation algorithm is adopted. Meanwhile, a spatiotemporal box filter (STBF) is used to accelerate correlation computation. After the subpixel disparity computation is completed, a fine-grained 3D face model can be obtained.  
During the process of disparity computation in this study, a coarse-to-fine two-level spatiotemporal correlation matching strategy based on a grid search is adopted.
## 3 Proposed Methodology
### Feature Selection  
Genetic algorithm (GA) is a search technique that depends on the basics of genetics and natural selection. GA is considered one of the most effective and efficient global optimization techniques. Optimization problems can be solved by means of the GA, by using the object function \(f(x)\) where N-dimensional vector of parameters of optimization is indicated by \(x=[x_{1},\ x_{2},\ \ldots,\ x_{n}]\). The GA in contrast to the traditional searching technique is based on the population of candidate solutions. The size of the population is a parameter that should be selected by the user and affects the scalability and performance of the GA.  
In this study, GA is used as a feature selection method to reduce the number of features extracted from the ROI of mammogram images. The main purpose of the process is to reduce the overlapping between cancer classes that are benign and malignant, as well as to speed up the classification process. In this study, from each block, 25 features were extracted, for a total of 125 features, and then we used GA to select the most relevant 10 features from each block; as a result, the most relevant 50 features were selected.
## 4 Discussion
Developing an in-house ASR system allowed us not only to customize the post-analysis of the speech without the black-box and pricing limitations of the general-purpose gASR system, but also to neither pre-discard specific words (e.g., out-of-context, infrequent, and very short words) nor worry about the data privacy and preparation costs. Moreover, future research studies might follow the same procedure to develop a similar ASR system for minimal pairs focusing on specific sounds. Despite the positive results reported about the kASR, the training corpus was limited in both quantity and variety of words and the experiment was carried out under a controlled environment. Data augmentation, noise reduction, and a systematic study of the non-native speech data gathered to find pronunciation mistakes associated with key features of proficiency-level characterization with the help of experts for its automatic characterization [4; 17] must be considered in the future to expand the project. Finally, we compared the scores provided by kASR to the gASR ones, obtaining moderate positive correlations between them (Table 7 and Figure 3). The post-test values of both gASR and kASR systems also strongly correlated with the final scores provided by the CAPT tool of the experimental group speakers (Table 7 and Figure 4). In other words, although the training words in Japanol were not the same as the pre/post-test ones, the phonemes trained were actually the same and the speakers were able to assimilate the lessons learned from the training sessions to the final post-test. Therefore, we were able to ensure that both scoring alternatives are valid and can be used for assessing Spanish minimal pairs for certain phonemes and contexts (e.g., availability of resources, learning, place, data privacy, or costs), even though our specific-purpose ASR system is not as accurate as gASR (30.0% vs. 44.22% WER values, Table 4). Future work will consist of fine-tuning our kASR system with more speech data and retraining techniques, such as deep or recurrent neural networks, combining both native and non-native speech in order to improve the current results and to obtain a better customization of the ASR system to the specific phoneme-level tasks. Thus, researchers, scholars, and developers can decide which one to integrate into their CAPT tools depending on the tasks and resources available.
## 3 ML Application in O-RAN
### AI/ML Workflow Implementation in O-RAN  
The O-RAN specification addresses the overall architecture and solution for AI/ML Workflow-related requirements in [22] for the O-RAN use cases. These requirements allow automating AI scaling, where Data, Model Training, and Model Evaluation pipelines are key points to make AI faster, easier to deploy, and able to scale to larger and more complex problems.  
In [36], the authors adopt the importance of ML training pipeline automation to propose ML pipeline automation techniques to apply the MLOps level 1 (ML pipeline automation) to the RIC platform, where they use Kubeflow for supporting the end-to-end lifecycle of the model management and propose the training pipeline automation to the RIC Platform to conduct the online training process. However, the authors use the KSFerving inference service to deploy Kubeflow's trained model. The new ML xApp type structure removed the RMR/gRPC adapter and replace it by using a Shared Data Layer (SDL) and RIC Message Router (RMR) libraries directly from the ML xApp. The authors show that the round trip time of the inference request between assisted xApp and ML xApp reduced significantly when the number of requests is more than 300. To find how an RL model application performs under the proposed RIC AI/ML workflow, the authors trained it to solve the resource allocation in the DU optimization problem using PPO to show the improvement in user throughput.  
\begin{table}
\begin{tabular}{c p{341.4 pt}} \hline \hline
**Year** & **Ref** & **Contribution** \\ \hline
2022 [15] & Proposed a centralized/federated learning approach for the deployment of deep learning mechanisms in the O-RAN architecture, while the integration of RL mechanisms is associated with the Near-RT Rice, O-CU, O-DU, and O-RU modules. The E2 and O1 interfaces are explored for RL-based resource allocation and scheduling. \\ \hline
2022 [35] & Proposed a joint DRL-based solution using PPO to solve an optimization problem aiming to intelligently admit and place network slices in the O-RAN environment considering the available resources and different network loads. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of current efforts on ML applications for O-RAN deployment.
## 2 Materials and Methods
### MTInSAR Datasets  
Table 1 reports schematically the datasets used in the present work. S1 data are acquired in interferometric wide-swath (IW) mode, with a ground pixel size of about \(5\times 20\) m\({}^{2}\) (range \(\times\) azimuth). CSK data are acquired in stripmap mode, with a ground pixel size of about 3 \(\times\) 3 m\({}^{2}\). CSK scenes are acquired with an average time interval  
Figure 1: Location of Montescalioso and Pomarico villages.  
of 16 days, whereas the S1 acquisition schedule is more frequent, with an acquisition interval of 12 days, and reaching 6 days from 2017, when the second satellite of the constellation, Sentinel-1B, was launched. In fact, S1 series cover a shorter time interval than CSK, as they all start in October 2014, whereas CSK series start two years earlier, but with a considerably higher number of acquisitions, and almost double in the case of the Montescalioso ascending dataset.  
All data series cover a time period that ends in 2018 since they are those available at the time when the MTInSAR processing was carried out. Although several more years would have been useful for assessing the ground instability of the case studies at a more recent time, the results derived by exploiting the available datasets are suitable for the time series analysis we present in the following, and perfectly suit the aim of this work.