esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Model Description
### Validation of the Simulation Model  
With the aim to guarantee the accuracy of the simulation results, validation was carried out using several relevant indicators of the system's behavior. The dynamics of these key indicators are presented in Figure 4. As an illustration, this validation was performed during a time interval where the parameters of the control rule were set at the following values: \(Z=20\), \(A_{sub}=130\), and \(A_{o}=175\). As presented in Figure 4, it is clear that at time \(t=0\), the production unit was in perfect condition and the inventory level increased steadily until it reached its optimal level (see arrow 1 in Figure 4g). Subsequently, the unit experienced some random failures, and at time \(t=37\), a decrease in the inventory level was observed because the machine suffered a failure (see arrow 2 in Figure 4g). The occurrence of random failures and the periods of inactivity of the machine are identified in Figure 4d (see arrow 3). At this point, the machine experienced progressive deterioration, and then, at time \(t=227\), the unit could no longer satisfy the product demand by itself; therefore, the subcontractor started to supply units to satisfy customer demand (see arrow 4 in Figure 4c). At time \(t=230\), the inventory level was maintained at its optimal level, where internal production and subcontractor were both needed (see arrow 5 in Figure 4g). Then, at time \(t\) = 250, the unit operated at the rate \(d/((1-\beta(a))\) to mitigate the increase in the defect rate (as indicated by arrow 6 in Figure 4e) because the level of deterioration was significantly high, as shown by arrow 7 in Figure 4b. Later, at time \(t=287\), the unit age surpassed the critical value \(A_{o}=175\), which initiated the conduction of major maintenance activities (as in arrow 8 in Figure 4f). Before the conduction of major maintenance, the maximum level of deterioration of the machine was reached (see arrow 9 in Figure 4a). Moreover, once the conduction of major maintenance was terminated, arrow 10 in Figure 4g shows a decrease in the inventory level due to the performance of such maintenance. It is evident  
Figure 3: Logic for the simulation.  
from the results of Figure 4 that after the end of the conduction of major maintenance, there was a significant decrease in the rate of defects (see arrow 11 in Figure 4a) since the major maintenance rejuvenated the system to its initial conditions and completely mitigated the harmful effects of deterioration. At this point, the inventory level reached its optimal level of \(Z=20\), indicating the beginning of a new deterioration cycle. Based on the analysis of the dynamics of Figure 4, the proposed control strategy was validated since it worked appropriately in the simulation model and it was observed that the role of the subcontractor was very important once the machine age surpassed the critical value \(A_{sub}\) and major maintenance was triggered if the age of the machine reached \(A_{o}\). Summing up, the operational validity of the simulation model is presented in Figure 4.
# Subpath Queries on Compressed Graphs: A Survey
## 3 Indexing Labeled Graphs and Regular Languages  
Interestingly enough, most techniques seen in the previous section extend to more structured data. In particular, in this section, we will work with _labeled graphs_, and discuss extensions of these results to _regular languages_ by interpreting finite automata as labeled graphs. We recall that a labeled graph is a quadruple \((V,E,\Sigma,\lambda)\) where \(V\) is a set of \(n=|V|\) nodes, \(E\subseteq V\times V\) is a set of \(e=|E|\) directed edges, \(\Sigma\) is the alphabet and \(\lambda:E\rightarrow\Sigma\) is a labeling function assigning a label to each edge. Let \(P=(u_{i_{1}},u_{i_{2}}),(u_{i_{2}},u_{i_{3}}),\ldots,(u_{i_{k}},u_{i_{k+1}})\) be a path of length \(k\). We extend function \(\lambda\) to paths as \(\lambda(P)=\lambda((u_{i_{1}},u_{i_{2}}))\cdot\lambda((u_{i_{2}},u_{i_{3}})) \cdots\lambda((u_{i_{k}},u_{i_{k+1}}))\), and say that \(\lambda(P)\) is the string labeling path \(P\). A node \(u\) is _reached by a path labeled \(\Pi\in\Sigma^{*}\)_ if there exists a path \(P=(u_{i_{1}},u_{i_{2}}),(u_{i_{2}},u_{i_{3}}),\ldots,(u_{i_{k}},u)\) ending in \(u\) such that \(\lambda(P)=\Pi\).  
The starting point is to observe that texts are nothing but labeled _path graphs_. As it turns out, there is nothing special about paths that we cannot generalize to more complex topologies. We adopt the following natural generalizations of count and locate queries:  
* _Count_: given a pattern (a string) \(\Pi\in\Sigma^{m}\), return the number of nodes reached by a path labeled with \(\Pi\).  
Figure 6: (**Top**) Parsed text according to the LZ78 factorization. Text indices are shown in gray. (**Bottom left**) four-sided geometric data structure storing one labeled point \((x,y,\ell)\) per phrase \(y\), where \(x\) is the reversed text prefix preceding phrase \(y\) and \(\ell\) is the first position of the phrase. (**Bottom right**) given a pattern occurrence \(\Pi=\mathcal{T}[i,j]\) (green dot), we can locate all phrases that completely copy it. For each phrase \(\mathcal{T}[t,t+\ell-1]\) for which source is \(\mathcal{T}[i^{\prime},i^{\prime}+\ell-1]\), a labeled point \((i^{\prime},i^{\prime}+\ell-1,t)\) is inserted in the data structure. In the example: phrase \(Z=\mathcal{T}[t,t^{\prime}]\), red dot, copies \(\mathcal{T}[i^{\prime},i^{\prime}]\) which completely contains \(\Pi\). Note that \(\Pi=\mathcal{T}[i,j]\) defines the query, while each phrase generates a point that is stored permanently in the geometric structure.  
* _Locate_: given a pattern \(\Pi\), return a representation of all nodes reached by a path labeled with \(\Pi\).  
We will refer to the set of these two queries with the term _subpath queries_. The node representation returned by locate queries could be an arbitrary numbering. This solution, however, has the disadvantage of requiring at least \(n\log n\) bits of space just for the labels, \(n\) being the number of nodes. In the following subsections, we will discuss more space-efficient solutions based on the idea of returning a more regular labeling (e.g., the DFS order of the nodes).
## 4 Results and Discussion
### Discussion  
A seed is both the source/starter of agricultural production and the propagation matter of many crops. Access to high-quality seeds is important for the agriculture and food sectors. In the literature, research has been conducted on the quality status of chickpea seeds, the determination of their purity from foreign substances, and the classification of their varieties using artificial intelligence techniques. In studies targeting chickpea variety classification, machine learning algorithms, deep learning approaches, and optimization-based hybrid methods are used. In [35], the ANN method was used, and four types of chickpeas were classified with 79% accuracy. In [1], the ANN and PSO methods were used together and the classification of five chickpea varieties was performed with 98% accuracy. In [32, 36], using pre-trained models, an accuracy of 94.21% was achieved by the VGG16 model in [32] and 100% by the ResNet50 model in [36]. In a classification of five chickpea varieties [38], the ResNet-50, DenseNet-201, and EfficientNet models presented training accuracies of 93.9%, 97.2%, and 92.3%, respectively. Their test accuracies were 95.7%, 93%, and 90%, respectively.  
In studies using hybrid methods based on pre-trained CNN models, it was stated that an F1-score of 86.2% was obtained for seven types of chickpeas with the SVM-based model [70], accuracy of 92.97% for six types of chickpeas with the LSTM-based model [33], and accuracy of 99.10% for three types of chickpeas with the ensemble-learning-based model [71]. The hybrid models proposed in this research, especially including the SVM and LDA machine learning algorithms, can be utilized for the classification problem of eight chickpea varieties with an accuracy of 94% and over. As a result, a fundamental requirement for increasing the production and use of high-quality chickpea seeds was satisfied.  
For more accurately classifying chickpea seeds or improving the efficiency of transferring information (extracting discriminative features), a method can be applied to block the convolution-based progressive training proposed by [72]. On the other hand, testing the proposed hybrid models on different datasets in future work may increase their applicability. This could further increase the value of this study and contribute to the field.
# Development of Alfa Fiber-Based Mortar with Improved Thermo-Mechanical Properties
## 2 Materials and Methods
### Thermal Conductivity Prediction Models  
Many researchers have developed theoretical models to calculate the equivalent thermal conductivity value of composite materials. These models combine in different ways the thermal conductivity of two phases in composite materials at dry state: solid phase (solid matrixes of mortar and fibers) and air phase. These models were rearranged in this study to be applied for three phases: solid matrix of mortar, solid matrix of alfa fiber and air. Table 2 provides the rearrangement expressions of seven models presented in the literature (fourth column of Table 2). These models were applied to calculate the thermal conductivity of AFM composites for different volume ratios of AF's solid matrix \(\varphi\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Equation** & **Comment** & **Reference** \\ \hline \multirow{7}{*}{Parallel} & k\({}_{//}=\varphi(1-\text{P}_{\text{t}})\text{k}_{\text{i}-\text{f}}+(1-\text{P}_{ \text{t}})(1-\varphi)\text{k}_{\text{i}-\text{M}}+\text{P}_{\text{t}}\text{k} _{\text{a}}\) & (4) & \\  & \(\varphi=\frac{\partial\text{k}_{\text{i}-\text{k}_{\text{i}-\text{M}}}\partial \text{k}_{\text{i}-\text{M}}}{\partial\text{k}_{\text{i}-\text{M}}}\) & (5) & \\  & \(\rho_{\text{abs EMA}}=\frac{1}{\omega\chi\left(\frac{1}{2\pi\text{d}_{\text{abs EMA}}}\right)^{\frac{1}{2\pi\text{d}_{\text{abs EMA}}}}}\) & (6) & \(\varphi\): Volume ratio of solid matrix of AF per absolute volume of sample. \\  & \(\rho_{\text{abs EMA}}=\frac{\rho_{\text{abs EMA}}}{\rho_{\text{abs EMA}}}\): Absolute density of composite. & \(\rho_{\text{t}}\): Total porosity. \\  & \(\rho_{\text{t}}=\left(1-\frac{\rho_{\text{abs EMA}}}{\rho_{\text{abs EMA}}}\right) \times 100\) & (7) & \(\rho_{\text{t}}\): Intrinsic thermal conductivities of AF and reference \\  & Serial & k\({}_{\text{i}-\text{f}}\) (8) & mortar (RM) respectively. \\  & Beck’s & k\({}_{\text{i}}\): Thermal conductivity of air. \\  & & \(\omega\): Weight content of AF per weight of mixture. \\  & & \(\rho_{\text{b}}\): Bulk density of composite. \\  & & \(\rho_{\text{abs EMA}}\) and \(\rho_{\text{abs EMA}}\): Absolute densities of reference mortar and AF. \\The goal is to determine the effect of the three components (mortar, air and AF) distribution on the predicted thermal conductivity and to compare them to the measurement data.
# Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier
## 2 Study Materials and Method
### Automatic Classifier Using the Random Forest Algorithm
#### 2.2.3 Frequency-Domain Attributes
The formulas can be written as follows:  
\[\text{F\_high}=\text{maxF(PSD(F)}<0.2\times\text{max(PSD))}, \tag{6}\]  
\[\text{F\_low}=\text{minF(PSD(F)}<0.2\times\text{max(PSD))}, \tag{7}\]  
where PSD(F) is the spectral value of the frequency F.  
In summary, the 24 attributes listed as Table 1 were considered for the signal characteristics of different classes of seismic events, including 9 attributes in the time domain and 15 in the frequency domain. Those attributes were calculated from all 642 training data, and a classifier was built using the attributes. Then the trained classifier could automatically identify the class of a new seismic signal through its calculated attributes.
# A Hybrid Hidden Markov Model for Pipeline Leakage Detection
## 1 Introduction
DNN computes the emission probabilities of states for the HMM, which offers strong ability of feature learning and provides a better recognition result [24]. In recent research, [PERSON] et al. presented an early-warning model of equipment chain in gas pipeline based on DNN-HMM which demonstrated preferable generalization accuracy [25]. [PERSON] et al. conducted comparison study between GMM-HMM and DNN-HMM for acoustic event detection and demonstrated that the performance may varied by using different features [26]. In this study, the DNN-HMM hybrid model is proposed to detect pipeline leakage locations as different states from lead zirconate titanate (PZT) transducer signals generated by negative pressure wave. In the proposed DNN-HMM hybrid model, DNN consists of the unsupervised deep belief network (DBN) that computes the emission probabilities of leakage states for the HMM instead of GMM. First, the DBN pre-training is used to make sure that training would be effective. The DBN is built by stacking top-down restricted Boltzmann machines (RBMs). The RBM is bipartite connectivity structured and has an unobserved subset of the variables. The cyclic process of serve inferred the result of one RBM as training data for another RBM generated a multilayer feature detector. Based on the pre-trained network, the DNN will outperform the random initialization for more complex statistical structure extracted from the PZT signal. Then, the posterior probabilities of leakage state as the output of the DNN will serve as the input parameter for HMM. Differing from [PERSON] et al. [13], who implemented the GMM-HMM based pattern classification to monitor the pipeline integrity, the DNN-HMM is implemented in our study to detect the pipeline leakage location. Thus far, there is only one work, presented by [PERSON] et al. [25], that applied DNN-HMM for classifying the generalized damage causation in equipment chain. Different from the existing work, we extract one time domain index and one frequency domain index from noisy negative pressure wave signals collected by PZT transducers when leakage occurs as observations for the proposed hybrid HMM method. To illustrate the effectiveness of proposed method, two groups of experiments that contain different states are conducted. The comparison results of DMM-HMM and GMM-HMM are also presented in the paper. The rest of paper is organized as follows. The hybrid HMM method is presented in Section 2. The experimental results and DNN-HMM leakage location detection results are presented in Section 3. The conclusions are drawn and future work is discussed in Section 4.
# Delivering Extended Cellular Coverage and Capacity Using High-Altitude Platforms
## 5 Performance Evaluation
### Beam-Pointing performance
The results show that the user allocation probability increases with increasing overlap ratio up to a point beyond which it starts decreasing. This is expected as increasing overlap plugs coverage holes until there are no longer holes within the system coverage. Introducing more overlap at this point starts creating coverage holes, resulting in a decrease in the user allocation probability. Furthermore, the 95 th percentile user throughput expectedly decreases with increasing overlap as a result of the increasing ICI, which affects user throughputs. The best value for \(\varepsilon\) can be derived as an optimisation problem such that both user allocation probability and user throughput are maximised. Figure 16: CINR distribution of the proposed scheme and schemes in [14]. Figure 17: Platform altitude vs. 50 th percentile user CINR. Figure 19 presents the CDFs of the achievable throughput per user for the different cell-pointing schemes, showing the probability that a user achieves a throughput greater than \(T\). It highlights improvements of between 40 and 70% by the proposed scheme compared with the other schemes, with more than 80% of users achieving throughput greater than 1 bit/s/Hz using the proposed scheme compared with about 40% for the state-of-the-art. The improvement is profound because the other algorithms were not developed for extended coverage scenarios; therefore, they do not consider beam broadening, which worsens ICI and results in the poor throughput performance. In Figure 20, the average capacity and CINR per user are shown for the different beam-pointing schemes. Capacity is evaluated using the Shannon equation, as discussed in Section 5.1. Clearly, the figure further highlights the superior performance of the proposed scheme in comparison with the other schemes. The proposed scheme offers an average CINR of over 5 dB and capacity of over 2 bit/s/Hz. Figure 19: User throughput distribution of different cell-pointing schemes. Figure 18: Overlap ratio vs. user allocation probability and 95 th percentile user throughput.
# _Applied_
## 3 Methodology
### Classifiers  
There are several classical ML techniques, which enable effective sound classifiers. For this evaluation, supervised ML techniques, which use correctly labelled data during the classifiers' training, are used. They are known to deliver a good performance when classifying acoustic events. These are the selected supervised ML techniques:  
* **k-Nearest Neighbour (k-NN)**: The key idea behind this classifier is that, given a set of patterns (unknown feature vector), X, its k-nearest neighbors in the training set are detected and the count of those that belong to each class is calculated. The feature vector is assigned to the class which has the highest number of neighbors.
* **Naive Bayes**: The goal of this classifier is to decide which class a novel instance belongs to, by calculating the posterior probability of each class, given the feature values present in the instance. The instance is assigned to the class with the highest probability.
* **Artificial Neural Network (ANN)**: An ANN consists of a set of processing elements, also known as neurons or nodes, which are interconnected. It can be described as a directed graph in which each node \(i\) performs a transfer function \(fi\).
* **Support Vector Machine (SVM)**: Given a set of points which belong to either of two classes, a SVM finds a hyper plane leaving the largest possible fraction of points of the same class on the same side, while maximizing the distance of either class from the hyper plane. SVM performs pattern recognition between two classes by finding a decision surface that has maximum distance to the closest points in the training set which are termed support vectors.
* **Decision Tree**: The decision tree algorithm tries to solve the problem by using tree representation. Each internal node of the tree corresponds to an attribute, and each leaf node corresponds to a class label.
# Self-Powered, Hybrid, Multifunctional Sensor for a Human Biomechanical Monitoring Device
## 2 Materials and Methods  
Figure 1a shows a schematic diagram of the application with the entire self-powered multifunctional sensor (SPMS). Figure 1b illustrates the SPMS' piezoelectric actuators and the zigzag structure of the triboelectric nanogenerator (TENG). The material used is an electrostatic generator composed of polytetrafluoroethylene (PTFE) and aluminum (Al). On top of the SPMS is a piezoelectric nanogenerator (PENG). The size of the SPMS is 5 cm \(\times\) 4 cm \(\times\) 1 cm. In addition, the size of the PTFE film is 4 cm \(\times\) 4 cm \(\times\) 0.3 mm and the Al film is 4 cm \(\times\) 4 cm \(\times\) 0.16 mm. The zigzag-shaped electrostatic generator's operation schematic diagram is presented in Supplementary Information Figure S1. Figure 1c illustrates a schematic diagram of the multilayered TENG with three basic contact-separation units in the amplified view. A 0.3 mm thick, 4 cm wide PTFE film and a 0.16 mm thick, 4 cm wide Al film are formed in a zigzag configuration and adhesively bonded with a copper tape electrode. Figure 1d is a schematic diagram of the structure inside the piezoelectric actuation of the PENG. The entire structure is beneath 304 stainless steel actuators, which also serve as the spacers in the SPMS for dynamic recovery. Lead zirconate titanate film (PZT) is the material of the piezoelectric layer in the middle of the structure with a thickness of 0.2 mm. In addition, the device uses a material called Thunder(r) (Face International Corporation, Norfolk, VA, USA), which consists of a layer of polyimide adhesive on the upper and lower parts with a thickness of 25.4 um. The outermost layer is coated with a layer of aluminum foil with a thickness of 25.4 um, which is formed when the composite laminate is heated under pressure to temperatures that allow the adhesive to bond and then cooled to room temperature [37].  
In this study, the PTFE layer inside the TENG was ground to produce a microstructure on the surface to increase the contact area. Figure 1e is the scanning electron microscope (SEM) image of the surface microstructure of the PTFE after grinding with #1000 grit sandpaper. In addition, a layer of polyamide adhesive is on the upper PTFE surface, producing a microstructure on the surface. Many abrasive traces with widths of less than 20 um can be found on the PTFE surface. The method of grinding the PTFE surface is shown in Figure S2. Figure 1f shows the surface structure of #1000 grit sandpaper under SEM. In the following experiments, we showed that this microstructure can improve the overall performance of the TENG and increase the output. The external wire connects to the SPMS and oscilloscope to measure the output, then integrates the piezoelectric and electrostatic generators for a better output performance.
# Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals
## 2 Proposed Method
### Importance-Weight Extraction  
Figure 6 illustrates the flowchart of Grad-CAM with an example of CNN structure. Each convolutional layer consisted of several filters with trainable filter coefficients. CNN applies these filters to the input data for extracting the informative features from the data. In Grad-CAM, the outputs of final convolutional layers were used to calculate the importance-weight for each characteristic frequency in _NBCC_. To obtain the importance-weight, a partial derivative of the score for class \(c\) was calculated of the \(k\)-th activation map. The following equation represents the definition of \(p_{k}^{c}\)[17]:  
\[p_{k}^{c}=\frac{1}{N}\sum\
olimits_{i=1}^{N}\frac{\partial y_{c}}{\partial A_{ i}^{k}}, \tag{10}\]  
where \({p_{k}}^{c}\) indicates the importance-weight of the \(k\)-th filter for class \(c\), \(y_{c}\) is a classification score of class \(c\), and \(A_{i}^{k}\) is \(i\)-th element in \(k\)-th activation map. When CNN was being trained, \(\frac{\partial y_{c}}{\partial A_{i}^{k}}\) was calculated in the back-propagation step. Finally, the importance-weight of class \(c\) was calculated as follows [17]:  
\[M_{c}=\sum\
olimits_{k}p_{k}^{c}A^{k}. \tag{11}\]
## 4 Experiments And Results
### Performance Evaluation  
We used four popular indicators in medical image segmentation tasks to evaluate organ segmentation quality, including IoU [18], dice [31] and recall [39]. In addition, we also use Hausdorff distance (HD) [35] as an indicator to measure the performance of boundary segmentation. The above metrics are formulated as follows:  
\[IoU=\frac{TP}{TP+FP+FN} \tag{8}\]  
\[Dice=\frac{2\times TP}{2\times TP+FP+FN} \tag{9}\]  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
\[HD(Y,\bar{Y})=\max\left\{d_{Y\bar{Y}},d_{\bar{Y}Y}\right\}=\max\left\{\max \limits_{y_{1}\in\bar{Y}}\min\limits_{y_{2}\in\bar{Y}}d\left(y_{1},y_{2} \right),\max\limits_{y_{2}\in\bar{Y}}\min\limits_{y_{1}\in Y}d\left(y_{1},y_{ 2}\right)\right\} \tag{11}\]  
where \(TP,TN,FP\), and \(FN\) are the true positive, true negative, false positive, and false negative respectively. \(Y\) means the segmentation result when \(\bar{Y}\) is the annotated result. \(d()\) means thecalculation of the Manhattan distance. As the values of the first four indicators rise, the similarity increases and the accuracy of segmentation improves. The Hausdorff distance is added for evaluation of the segmentation on boundary.