esa-sceva/satcom-chunk-collection huggingface dataset:
# An Approach to Data Analysis in 5G Networks
## 3 SELFNET Analyzer Module Design
### Initial Assumption and Requirements  
The following describes the most relevant requirements and the main initial assumptions considered in the design of the Analyzer Module:  
* _Scalability_. The approach must be allowed to add new capabilities (extensibility), according to SELFNET design principles. For this reason, the integration of additional analytic functionalities are done via plugins.
* _Use Case Driven_. Given the heavy reliance of the tasks performed with the characteristics of use cases, the basic definition of the observations to be studied (Knowledge-based objects, rules, prediction metrics, etc.) are provided by the use case operator, thus being the Analyzer Module scalable to alternative contexts.
* _Knowledge Acquisition_. It is well known that the most common disadvantage of the expert systems is the initial knowledge acquisition problem. Hence, to have skilled operators in novel use cases with the ability to properly specify rules is not always straightforward. This document does not address the issue of the innate knowledge acquisition. Our approach assumes that the use case knowledge-bases are provided by skilled operators or by accurate machine learning algorithms.
* _User-Friendly Symptom Definition Rules_. The definition of proper rule-sets is a tricky business. Thus, even the skilled operators often do coherence/ambiguity mistakes. In order to mitigate these  
Figure 1: Endsley vs. SELFNET Autonomic Layer.  
problems, the configuration and definition of new use cases should be user-friendly, as well as the scheme for building new rule-sets.
* _Uncertainly_. Classical logic permits only exact reasoning. It assumes that perfect knowledge always exists, but this remains far from the SELFNET reality. In order to improve the quality of the conclusions, the Analyzer Module manages the knowledge bearing in mind uncertainty. This is particularly appropriate for certain analytic features, such as studying observations based on decision thresholds or confidence intervals. In addition, closing the door on possible stochastic dependent definitions is against the SELFNET design principles, as these could be the keys to properly specify future use cases.
* _Filtering_. Initially, the filtering of symptom reports is not considered. Because of this, every inferred symptom, regardless of nature or uncertainty, is transmitted to the diagnosis/decision-making stages, where their impact and relevance are properly assessed.
## 4 Artificial Soiling Test with Pollen
### Results
Furthermore, while the soiling particles are known to hinder the glass transmission depending on their nature, size, and density [95], different pollutants at identical surface mass densities, even having comparable particle diameters, might lead to different transmittance impairments due to physical reasons (e.g., differences on the glass composition and thickness, on the particle stacking depending on the soiling particle characteristics, on particle settling constrains due to specific artificial soiling methods, etc.). As an important effect on PV generation, one further consideration: the intense optical absorption observed at shorter wavelengths (i.e., towards the UV range) suggests that a heavy soiling by pollen might impact the solar energy production differently depending on the PV module type because of the differences on the spectral response among the PV technologies [110]. Following this reasoning, wide-bandgap technologies (i.e., amorphous silicon and CdTe modules) would become more affected by pollen deposition than crystalline silicon and copper indium gallium diselenide (CIGS) modules.
# Group Authentication Scheme for Neighbourhood Area Networks (NANs) in Smart Grids
## 4 Security Analysis  
The authentication scheme is analysed against the two classes of threats mentioned in Section 4. A Sybil attack is an impersonation attack that is the result of eavesdropping and the node capture attack is the physical nodes capture which is very likely in the context of the SG network.  
The authentication scheme described in the previous sub-section is two-way secure, meaning that after the authentication process, both parties' new SM and GW can verify the authenticity of each other. The authenticity of GC is varied since the Authentication Request from the SM is encrypted using GW's key. Additionally, in the Authentication Response, GW sends the SN of the new SM. Only [PERSON] and SM know the mapping of SN to the ID number of SM. The authenticity of the new SM is verified in a similar way. First of all, the Authentication Response is encrypted using SM's key and, therefore, only SM is able to decrypt it using its shared key. Additionally, the new SM provides to proxy SM both its ID number and SN, which provide additional security.  
Denial of service (DoS) makes a node, as well as the service on it, inaccessible by others. An attacker sends a large number of packets, malicious or otherwise, addressed to the node and effectively at a rate which can block out all other communication. This causes the node receiving the packets to exhaust its storage and computing power, processing the packets that arrive from the attacker. Such a risk is imminent in a multi-hop network where the communication between two end points is routed via intermediate smart meters. The proposed authentication scheme authenticates every participant on the network before accepting any traffic from it. While this reduces the probability of spurious data on the network, the spurious traffic remains a problem. If such traffic targets a gateway node, then an attack can potentially incapacitate all nodes that communicate using that gateway. Additional means of detecting such intrusions and methods of isolating the attack traffic are necessary to handle such vulnerabilities.  
Our scheme does not handle jamming attacks. Jamming attacks are DoS attacks that targets the wireless communication frequency in the smart grid. When nodes are in close range, large amounts of noise may be generated in these appliances. It is difficult to avoid jamming in our scheme because the victim and its client may not catch the attack. In this kind of attack, the attacker prevents legal users from having access to information and services by targeting the victim's device and the network connection. This attack stops the user from making outgoing connections on the smart grid. The communication can be jammed so as to make the signal noise very low, and this could lead to the failure of specific portions of the smart grid [20].
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
###### Abstract  
System-level radiation testing of electronics is evaluated, based on test examples of the System-in-Package (SiP) module irradiations. Total ionizing dose and single event effects tests are analyzed to better understand the opportunities and limitations of the system-level approach in the context of the radiation qualification of electronics. Impact on the SiP product development is discussed.  
system-level tests; radiation hardness assurance; total ionizing dose; single event effects Article
## 3 Experiments and Results
### Comparative Experiment  
To verify the performance and feasibility of this paper's proposed model in the task of fault detection for water pumps in IRASs, another experiment was conducted. This compared the performance of our model vis-a-vis a suite of commonly used models in the vibration signal classification task: KNN, SVM, random forest, LSTM, BiLSTM, KNN-GRU, Swin-transformer, CNN-attention, and CNN-LSTM. These comparative results are presented in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Acc (\%)** & **P (\%)** & **R (\%)** \\ \hline KNN & 75.05 & 75.16 & 75.05 \\ Random Forest & 80.63 & 80.63 & 80.63 \\ SVM & 82.87 & 82.90 & 82.87 \\ LSTM & 85.10 & 85.31 & 85.10 \\ BiLSTM & 87.34 & 87.36 & 87.34 \\ Swin-Transformer & 87.15 & 87.14 & 87.15 \\ KAN-GRU & 88.83 & 88.81 & 88.83 \\ CNN-Attention & 84.54 & 84.70 & 84.54 \\ CNN-LSTM & 89.57 & 89.55 & 89.57 \\ CNN-Transformer- & 91.43 & 91.52 & 91.43 \\ BiGRU & & & \\ \hline \hline \end{tabular} Note: Acc, accuracy; P, precision; R, recall.  
\end{table}
Table 7: Results for the accuracy (Acc), precision (P), and recall (R) of different tested models.
## 4 Performance Analysis of Islanding Detection Methods
### Reliability  
The islanding detection method has to be reliable and accurate for tripping the system only during the islanding condition without providing any unwanted trip. Therefore, islanding detection methods have to be able to distinguish the variation of voltage and frequency which is caused by reasons other than the islanding, such as under- or overloaded conditions that the system may face. However, when the system faces some disturbances, such as load switching, the system parameters may exceed the allowed limits. In this case, islanding detection methods may present a false detection, which is known as the error detection ratio (E) and calculated as [54]:  
\[\mathrm{E}=\mathrm{N}_{\mathrm{error}}/(\mathrm{N}_{\mathrm{error}}+\mathrm{ N}_{\mathrm{correct}}), \tag{9}\]  
where \(\mathrm{N}_{\mathrm{error}}\) and \(\mathrm{N}_{\mathrm{correct}}\) are the times of error detection and correct detection respectively.
## 3 System Design
### QAT Offloading Module  
To address the practical challenges of direct offloading, this section proposes an asynchronous offloading design, as shown in Figure 6. After the request is preprocessed by the QZRAM main thread, the worker thread continues with the time-consuming part of the process. Multiple worker threads simultaneously invoke unified compression algorithms of the compression module. Then, the compression module selects the submitted GZIP compression request to the QAT driver and sleeps until it receives the interrupt signal from the QAT driver. This approach not only avoids the CPU's idle wait time after submitting the compression request but also enables QZRAM to process multiple requests concurrently and multiple computing units of QAT hardware to work simultaneously. Finally, after processing the compression request, the QAT accelerator sends an interrupt signal to the CPU indicating that the compression is complete. Then, the QAT driver uses the callback function to wake up the worker, and it resumes the compression task and continues the remaining write process.  
Figure 6: QAT asynchronous offloading design.
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 8 Conclusions and Future Work
### Some Limitations of the Proposed Method  
Some limitations of the proposed method can be listed, as follows:  
* Data-driven methods rely heavily on the availability of training data. These approaches depend on historical system data (e.g., training datasets) to identify correlations, establish patterns, and analyze trends that can lead to failure predictions [50].
* A recursive neural network requires a laborious and computational intensive training process, with large amounts of data required from the system-of-interest.
* The trained model is typically tailored to a specific system. Therefore, if the system dynamics, internal states, or input-output data profiles change, the model will likely need to be retrained to adapt to the new conditions [51].
## 5 Training an Arabic Transformer Model Using the AraFast Corpus
### Segmented and Unsegmented Versions of the AraFast Corpus  
The findings presented in the following subsection respond to RQ3, \"Does pretraining a transformer model on a segmented Arabic corpus lead to better performance than training the model on an unsegmented Arabic corpus?\"  
In order to answer RQ3, in the following study, the web-scraped data were removed from the AraFast corpus to make improvements for training the model. The size of the updated corpus was roughly 112 GB. This version of the corpus was used to determine how segmenting the text would affect training performance.  
The Arabic language is characterized by lexical sparsity due to its complicated concatenative system [67]. This characteristic results in words taking various forms while retaining the same meaning. For example, the word \"<<<<<<<<\", which translates to \"will suffice you against them\" in English, is composed of multiple parts within a single word. In Arabic morphology, the definite article \"  
loss value demonstrated the improved capability of the model to grasp and internalize the linguistic nuances of Arabic, thus facilitating a more accurate and efficient learning curve. This decrease indicates that the model was able to learn more as the number of steps increased. Figure 9 represents a sample of segmented text. It is a part of an Arabic text discussing ignorance and how to overcome it. The red dotted lines, as a caution, are because the words are segmented using the Farasa tool, which segments text into parts using the (+) symbol to indicate if the part follows or is followed by another part of the same word.  
A loss value that decreases during training is preferable. In machine learning (ML) and DL, especially when training language models such as transformers, the loss value is a measure of how well the model is performing with respect to its training objective. A low loss value indicates that the predictions of the model are close to the actual outcomes, highlighting the accuracy and effectiveness of the model [70]. Therefore, a decreasing loss value during training is a positive sign, as it means that the model is learning and that its ability to make predictions or generate text accurately is improving over time.  
Figure 8: Training loss of the base model with segmentation.  
Figure 7: Training loss of the model without segmentation.  
The findings of this comparative study underscore the critical role of segmentation in Arabic language modeling and the effectiveness of Farasa in optimizing model performance. The contrast between the performance levels of the model with and without Farasa emphasizes the need to incorporate advanced linguistic tools for languages with complex morphological structures such as Arabic.
# Hybrid Electrostatic-Atomic Accelerometer for Future Space Gravity Missions
## 2 Materials and Methods
### Hybrid Instrument Simulator
#### 2.3.2 Electrostatic Accelerometer  
The principle of operation of an EA was already described in Section 1.3. The chosen HybridSTAR instrument (see Section 3.4.2 for details on the design) uses a cubic proof-mass involving, by construction, all three axes having the same performance in-orbit. The software simulator models the acceleration measured by the EA, that is to say, the dynamics of the proof-mass relative to the center of the electrostatic cage. The dynamics is computed in two parts: the first block computes the dynamics of the electrostatic cage in the reference frame of the satellite and the second one the dynamics of the proof-mass in the reference frame of the electrostatic cage.  
The proof-mass of the electrostatic accelerometer is surrounded by several sets of electrode pairs. Its motion induces a variation of the gaps between the mass and the electrodes, and therefore, a variation of the corresponding capacity, which is measured by the capacitive detector (see Figure 3a). In the simulator, the capacity is computed taking into account the position of the proof-mass, and the detector is modeled by a gain, a first-order transfer function, and a saturation, as well as its noise. By combining the outputs of the  
Figure 9: Global view of the software simulator for the in-orbit operation of the hybrid accelerometer.  
different electrode pairs, the PM position is deduced along the six degrees of freedom (three translations and three rotations). The PID-type corrector computes the voltage to be applied to the electrodes in order to compensate for the PM motion and to keep it centered. The command is amplified by the drive voltage amplifier (DVA), whose transfer function, saturation, and noise are taken into account. This corrective voltage is applied to the PM by the same electrodes used for the position detection. The electrostatic stiffness of the control loop is taken into account, resulting in a perturbative term proportional to the PM position in the actuation. The resulting electrostatic actuation keeps the PM motionless in the satellite, and therefore, provides a measurement of the acceleration of the PM relative to the satellite. The simulation includes a secondary input for commanding the proof-mass position. This input is used when the proof-mass of the electrostatic instrument acts as the CAI reference mirror for the atom interferometer in order to compensate for the satellite rotation, as discussed in the following Section 2.3.3 describing the CAI part of the simulator. The main contributors to the EA noise were included in the simulation: detectors' noise, analog-to-digital converter (ADC) measurement noise, thermal fluctuation of the bias, and wire damping.
## 3 Experimental Tests and Results
### The Time-Variant Experimental Tests for Model Validation
The obtained results underlined the effectiveness of the FBG sensors in detecting the structure stresses due to external loads (e.g., temperature variation). The application of FBG sensors shows many advantages, in particular, they are able to measure stresses from different types of sources (e.g., load, temperature, accelerations) in real-time. This fact allows for the ability to obtain a model that is able to predict the TTP position when any type of stress occurs on the MT structure. On the other hand, the model needs to be optimized in order to reduce the predicted errors, increasing the number of sensors and improving the regression technique. Table 2 shows a summary of the main advantages and limitations of the FBG sensor application. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coefficients** & **Description** & **Value** & **Standard Error** \\ \hline \(\alpha_{0}\) & Intercept & \(-0.002\) & \(0.005\) \\ \(\alpha_{1}\) & FBG Sensor 1 & \(10.097\) & \(2.552\) \\ \(\alpha_{2}\) & FBG Sensor 2 & \(1.836\) & \(4.626\) \\ \(\alpha_{3}\) & FBG Sensor 3 & \(-10.063\) & \(4.575\) \\ \(\alpha_{4}\) & FBG Sensor 4 & \(0.678\) & \(5.896\) \\ \(\alpha_{5}\) & FBG Sensor 5 & \(-5.138\) & \(6.137\) \\ \(\alpha_{6}\) & Delta \(T_{bulk}\) & \(0.002\) & \(0.020\) \\ \(\alpha_{7}\) & Delta \(T_{surface}\) & \(-0.028\) & \(0.022\) \\ \(\alpha_{8}\) & Delta \(T_{flange}\) & \(0.010\) & \(0.013\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Multiple Regression Equation coefficients.
## 2 Materials and Methods
### Classification Models  
Seven machine learning models were chosen to classify the similarity scores into normal and abnormal classes.  
The first three classifiers were simple models, as follows. The first classifier is logistic regression [27], which is simple and effective for linearly separating the input features into two groups. More specifically, the log odds between the two classes are related to a linear function of the input features, resulting in a linear decision boundary between the two classes. The second classifier is Linear Discriminant Analysis (LDA) [28]. LDA is another linear classifier, but based on different assumptions about the input distributions, the input features are normally distributed with different means for different classes and the same covariance. The third classifier is quadratic discriminant analysis (QDA) [29]. Similar to LDA, QDA assumes that the input features are normally distributed with different means for different classes. In contrast, QDA allows the covariances of the classes to be different, resulting in non-linear decision boundaries.  
Four more advanced classifiers were chosen to capture more complex patterns in the feature. The Naive Bayes classifier [30] was used to investigate the effect of potential correlations between the input features. The Naive Bayes classifier modeled the input features independently of each other, providing a baseline accuracy for ignoring the interactions between input features. Next, a support vector classifier (SVC) with a linear kernel [31] was used to determine the hyperplane that separated the input features with different classes with the maximum margin. In addition, the random forest classifier [32] was used to capture complex and potentially non-linear patterns in the data using an ensemble of randomly selected decision trees. The final model was the k-nearest neighbor (KNN) classifier [33]. KNN could capture more complex and local structures in the data because the class label of the input sample was determined by the class labels of the KNNs.  
The hyperparameters of the SVC, random forest, and KNN classifiers varied for a wide range of values, and the highest accuracy was reported for each classifier. In the SVC, the level of robustness to outliers was controlled by the weight (C) of the penalty for misclassification during training. The value of C varied from \(10^{-5}\) to \(10^{5}\) by the factor of \(10^{-0.25}\). The complexity level of the random forest was controlled by the number of estimators (n). Increasing the number of estimators enabled the classifier to consider more complex data patterns. However, excessive estimators would result in overfitting and poor generalization to new inputs. The value of n increased from 2 to 30 in steps of 2. In the KNN classifier, the number of neighbors (k) controlled the smoothness of the decision boundary. The value of k increased from 1 to 25 in steps of 2.
## 5 Empirical Model
### The Statistical Reliability of the Proposed Model
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**[PERSON] and**} & \multicolumn{3}{c}{**[PERSON] and**} & \multicolumn{3}{c}{**[PERSON] et**} \\
**No.** & \multicolumn{3}{c}{**Authors model,**} & \multicolumn{3}{c}{**[PERSON] (2018)**} & \multicolumn{3}{c}{**[PERSON] (2018)**} & \multicolumn{3}{c}{**al. (2018)**} & \multicolumn{3}{c}{**[PERSON] et al. (2018)**} \\  & \multicolumn{3}{c}{**Equation (5)**} & \multicolumn{3}{c}{**Equation (6)**} & \multicolumn{3}{c}{**Equation (7)**} & \multicolumn{3}{c}{**Equation (8)**} & \multicolumn{3}{c}{**Equation (9)**} \\ \cline{2-10}  & \(AE\) & \(RE\) & \(AE\) & \(RE\) & \(AE\) & \(RE\) & \(AE\) & \(RE\) & \(AE\) & \(RE\) \\ K1 & 0.22 & 4.7 & 4.51 & 93.3 & 1.54 & 31.4 & 2.03 & 42.9 & 3.39 & 70.8 \\ K2 & 0.26 & 7.7 & 2.45 & 85.0 & 0.56 & 20.4 & 0.25 & 18.6 & 1.19 & 48.7 \\ K3 & 0.46 & 7.3 & 5.53 & 96.7 & 2.34 & 39.8 & 2.69 & 48.7 & 4.15 & 74.2 \\ K4 & 0.68 & 10.5 & 5.96 & 97.1 & 2.42 & 39.8 & 2.33 & 43.2 & 4.03 & 68.3 \\ K5 & 0.17 & 4.6 & 3.07 & 76.4 & 0.00 & 3.8 & 0.25 & 20.8 & 1.15 & 42.3 \\ K6 & 0.23 & 5.0 & 4.09 & 93.9 & 1.00 & 21.8 & 1.43 & 36.4 & 2.91 & 69.2 \\ K7 & 0.21 & 7.4 & 2.18 & 77.9 & 0.80 & 36.0 & 0.63 & 21.4 & 0.83 & 40.6 \\ K8 & 0.33 & 11.8 & 1.24 & 66.8 & 1.42 & 78.8 & 0.87 & 38.8 & 0.39 & 34.6 \\ K9 & 0.49 & 7.7 & 3.20 & 60.6 & 0.02 & 22.5 & 1.05 & 25.8 & 0.38 & 25.4 \\ K10 & 0.57 & 11.1 & 2.00 & 64.4 & 0.86 & 21.4 & 0.83 & 31.5 & 0.10 & 58.5 \\ K11 & 0.52 & 6.7 & 7.18 & 93.2 & 2.35 & 30.9 & 1.14 & 28.3 & 3.05 & 49.7 \\ K12 & 0.77 & 18.4 & 2.78 & 59.1 & 0.04 & 31.9 & 0.88 & 19.0 & 0.61 & 22.1 \\ K13 & 0.70 & 13.3 & 1.93 & 46.9 & 0.18 & 10.9 & 0.38 & 30.7 & 0.78 & 39.8 \\ K14 & 0.61 & 14.2 & 2.83 & 64.4 & 0.83 & 18.3 & 0.92 & 32.0 & 2.16 & 51.5 \\ K15 & 0.46 & 8.9 & 5.46 & 84.2 & 0.96 & 15.5 & 0.18 & 20.3 & 1.42 & 35.2 \\ _Avr._ & 0.45 & 9.3 & 3.63 & 77.3 & 1.02 & 28.2 & 1.06 & 30.6 & 1.77 & 48.7 \\ _R2_ & 0.96 & & 0.21 & & 0.79 & & 0.78 & & 0.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The comparison of absolute error (\(AE\)), relative error (\(RE\) (%)), average values of these errors (\(Avr.\)), and coefficient of determination (\(R^{2}\)) for the analyzed models.