esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Methodology  
In Figure 5, the research simulation integrates both traditional machine learning techniques and advanced deep learning architectures, which are elaborated upon in the following subsections.  
Figure 5 outlines the comprehensive workflow for human activity recognition using smartphone sensors, presenting the complete process from data acquisition to activity classification. Initially, raw data are collected from a variety of built-in sensors, including accelerometers, gyroscopes, linear accelerometers, and gravity sensors. These raw data undergo a crucial segmentation step to prepare them for detailed analysis, allowing for a more precise examination of different activity patterns.  
Key statistical features--such as mean, standard deviation, median absolute deviation, skewness, maximum, minimum, and kurtosis--are extracted from the segmented data. These features are critical as they capture important characteristics of the signal's distribution and variability. By analyzing these features, the system can better differentiate between various physical activities, such as running, walking, or stationary positions. The extraction of these features enables the capture of subtle differences in movement patterns and ensures that the activity classification is based on robust and informative data metrics.  
The extracted features are then used as inputs for a range of machine learning models. This study incorporates traditional classifiers, including DT, KNN, and SVC, as well as ensemble models such as RF and XGBoost. These models were selected for their interpretability and effectiveness in structured data classification.  
\begin{table}
\begin{tabular}{c|c|c} \hline
**Actual V Predicted** & **Positive** & **Negative** \\ \hline Positive & TP & FN \\ \hline Negative & FP & TN \\ \hline \end{tabular}
\end{table}
Table 6: Confusion Matrix for Binary Classification.  
Figure 5: Methodological framework for assessing machine learning and deep learning techniques.  
Additionally, advanced deep learning architectures, including LSTM networks, TCN, GRU, and Transformers, are integrated to capture sequential dependencies and complex temporal patterns in sensor data. The combination of traditional and deep learning models allows for a thorough exploration of both conventional and contemporary approaches to activity recognition.  
The models are trained to classify activities into distinct categories, including running, stationary, and walking. Their performance is evaluated across two different scenarios, providing valuable insights into their ability to generalize and perform effectively in varied contexts. This dual approach--integrating traditional machine learning techniques with cutting-edge deep learning methods--significantly improves the accuracy and reliability of the human activity recognition system. By harnessing the strengths of both methodologies, the research strives to achieve superior performance in differentiating between various physical activities, thereby advancing the capabilities of activity recognition technologies.
## 6 Experiment  
In this section, to verify the effectiveness of the whole proposed method, the quadrotor UAV manipulator is assigned to conduct a hovering task beside an electrical fan, which could generate several level gear wind as torque disturbance acting on the UAV system. BAC+FASTESO is adopted for UAV disturbance rejection test, meanwhile a single BAC without any observer is also conducted for comparison. The experimental scene is shown in Figure 17. The whole proposed control scheme is built in Pixhawk [35]. The parameters are chosen as follows. For BAC+FASTESO: \(\alpha_{1}=diag(0.2,0.2,0.3)\); \(\alpha_{2}=diag(0.3,0.3,0.4)\ e=0.001\); \(k_{e}=0.1\); \(K_{b1}=diag(3,3,5)\); \(K_{b2}=diag(2,2,2.5)\). For BAC: \(K_{b1}=diag(6,6,9)\); \(K_{b2}=diag(3.5,3.5,5)\).  
The fan is placed to mainly apply the external torque in roll channel. The results of UAV hovering under first and second gear wind are shown in Figures 18-23. We found that the attitude of hovering UAV changes following the variation of wind gear. Moreover, the attitude vibration offset is reduced by help of FASTESO compared to the situation with single BAC. Meanwhile, the angular rate fluctuation is also reduced. They intuitively verify the effectiveness of FASTESO. Additionally, Figures 22 and 23 show the control output generated only from BAC in the control situations single BAC and BAC+FASTESO, respectively. We could find that the control signal is around zero with FASTESO; however, the control signal is impacted so much owing to external disturbance in situation without any observer. Moreover, the controller gain in BAC+FASTESO could keep smaller to make the whole system stable thanks to the disturbance estimation and compensation from FASTESO, which also contributes to the better performance of BAC+FASTESO. Meanwhile, the estimation of the lumped disturbance including external wind and model uncertainties is shown in Figure 20. Although we do not know the ground truth of the torque disturbance generated from the fan and model uncertainties to verify if the estimation result in Figure 20 is right or not, according to Figures 20 and 22, we could see that their results are almost the same, which also demonstrates the effectiveness of FASTESO to an extent.  
Figure 17: Experimental scene.  
Figure 19: Experimental results of angular rate with BAC and BAC+FASTESO under external wind.  
Figure 20: Disturbance estimation result from FASTESO.  
Figure 18: Experimental results of attitude with BAC and BAC+FASTESO under external wind.  
Figure 21: Observer gain of FASTESO during adaptation process.  
Figure 19: Experimental results of angular rate with BAC and BAC+FASTESO under external wind.
# Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network
## 2 Related Work
### Wavelet Packet Transform  
The wavelet transform (WT) is a common method to obtain the local time-frequency information from signals. The wavelet transform is performed on the signal \(x(t)\) by analyzing the mother wavelet function \(\psi(t)\) and convolving \(x(t)\) with the scaling and conjugate wavelet function. This process can be described as:  
\[W_{f}(a,s)\ =\ \int_{-\infty}^{+\infty}x(t)\,\frac{1}{\sqrt{a}}\psi*(\frac{t-s}{a})dt \tag{5}\]  
where \(\psi(t)\) denoted the Morlet wavelet function. \(a\) is the dilation and \(s\) is the translation. The factor \(\frac{1}{\sqrt{a}}\) is used to preserve energy. The \(a\) and \(s\) could be changed for obtaining different time-frequency segmentation.  
WPT is a generalization of WT, which is used to approximate wavelet coefficients of different frequency bands. The node values for each level can be described as:  
\[W_{j+1}^{2k}\ =\ W_{j}^{\ k}(n)\ *\ h(-2n) \tag{6}\]  
\[W_{j+1}^{2k+1}\ =\ W_{j}^{\ k}(n)\ *\ g(-2n) \tag{7}\]  
where \(W_{j+1}^{2k}\) is the coefficient value of \(j\)-th decomposed level at the frequency band of \(2k\). The \(h(-2n)\) and \(g(-2n)\) are the low-pass filter and the high-pass filter, respectively, which depend on the selected scaling function \(\phi(x)\) and mother wavelet function \(\psi(x)\). The relationship of the two functions can be described as:  
\[\phi_{j}(t)\ =\ \sum_{k}h(k)2^{\frac{j+1}{2}}\phi\big{(}2^{j+1}\ \times\ t-k \big{)} \tag{8}\]  
\[\psi_{j}(t)\ =\ \sum_{k}g(k)2^{\frac{j+1}{2}}\phi\big{(}2^{j+1}\ \times\ t-k \big{)} \tag{9}\]  
Admittedly, the fault features of the bearing are related to the low-frequency band of the condition signals. The low-frequency information of the condition signals can be obtained by using the WPT, which is useful for bearing fault diagnosis. In general, a 1-D time-frequency coefficients vector with length L could obtain through the WPT.
# Safety, Security and Privacy in Machine Learning Based Internet of Things
## 4 Machine Learning for IoT Security
### Unsupervised ML
#### 4.4.1 K-Means Clustering (KMC)  
The KMC is an unsupervised machine learning approach, which is used to discover the clusters in any dataset. Clustering applies an iterative process to generate an accurate output. The input clusters consist of a set of dataset features. In the first step, the k centroid is calculated to assign the closest centroid cluster by calculating the distances between the points using Euclidean distance. Then, the cluster centroids are recalculated using the mean of samples assigned to one particular cluster. The same method is repeated until no clusters exist in the dataset [57]. The KMC approach is applied to the network detection of anomalies by classifying abnormal behavior [58]. In [59], k-means clustering is used to detect anomalies in an IoT system. Unsupervised algorithms have numerous applications in the safety and security of IoT systems. A study in [60] showed that KMC method has the ability to protect private data anonymization in an IoT environment. This clustering method is used to develop an algorithm of data anonymization for advance data-exchange security. The limitation of this approach is the selection value of k, assuming the clusters have approximately the same numbers in the dataset.
###### Abstract  
Several methods for corrosion monitoring of reinforced concrete structures (RCS) have been proposed in the last few decades. These systems may be used either in new, existing or repaired structures. The corrosion monitoring can be performed by different methodologies. These may or may not be destructive, use different degrees of complexity and cost, and provide information on the progression and kinetics of the corrosion phenomena. The destructive methods are limited to sampling. Therefore, these may not be representative of the whole structure, which is extremely important in RCS with large heterogeneities both in terms of materials used and in terms of the exposure environment. Within this context, non-destructive methods have been widely developed, which are intended to provide quick information about the entire structure. Ideally, these systems should be able to detect the corrosion state of the steel inside the concrete, the main causes of corrosion and the evolution of corrosion phenomena over time. This manuscript reviews and summarizes the actual state of the art and the main achievements in the field of electrochemical sensors based on non-destructive methods for corrosion monitoring of RCS in the last few years. The challenges and perspectives in this field will also be discussed.  
electrochemical sensors; reinforcing steel; corrosion rate; concrete +
Footnote †: journal: Applied Sciences
## 7 Limitations and Future Work  
### Limitations  
Because only predetermined choreographies were evaluated in this study, it was not possible to evaluate choreographies that were modified by the dancers. In addition, because the system is designed for self-training at home, it cannot cover a wide area and does not support practice for more extensive movements on stage. Another limitation is that the system is limited to a single user and cannot track multiple people. Furthermore, the current feedback mode does not adequately take into account the level and characteristics of individual users, leaving room for further personalization. Although the current study is limited to hip-hop, the proposed evaluation framework could be applied to other dance genres. The annotations are based on six abstracted items, but for a more detailed analysis, segmented labels are needed.
## 3 Results and Discussion
### Soil Water Change in Slopes under Natural Evaporation Conditions
#### 3.3.4 Soil Water Dissipation Model of a Slope under Natural Evaporation Conditions  
This study quantitatively analyzed the soil water dissipation of bare slopes and vegetated slopes with varying slope ratios over 14 days after rainfall. The results of the comparative analysis revealed that the soil water dissipation of vegetated slopes was significantly higher than that of bare slopes at the same slope ratio. This difference can be attributed to the water absorption capacity of plant roots. Vegetation can reduce the soil water content of slopes through transpiration, which leads to a decrease in soil bulk density and an increase in slope stability. Furthermore, the impact of vegetation on slope water content becomes more pronounced as the slope ratio decreases.  
To investigate the quantitative impact of vegetation on dynamic soil water changes, this study conducted a fitting analysis of soil water dissipation on both bare slopes and vegetated slopes over 14 days. The dissipation model shown in Table 2 was subsequently developed. The R\({}^{2}\) values of the logarithmic function \(\Delta w=a\times\ln(\Delta t)+b\) ranged from 0.914 to 0.996. This formula can effectively fit the change in soil water dissipation over time, characterizing the pattern of faster to slower soil water dissipation rates under natural evaporation. The parameter '\(a^{\prime}\) indicates the rate of change in soil water dissipation, with higher values suggesting faster rates. Similarly, the parameter '\(b^{\prime}\) represents the overall magnitude of soil water dissipation, with larger values indicating greater dissipation. The highest values of parameters '\(a^{\prime}\) and '\(b^{\prime}\)' were observed in the vegetated slope with a slope ratio of 1:1.5 at a depth of 20 cm (2.162, 3.065). This indicates that the soil water dissipation rate is significantly lower in bare slopes compared to vegetated slopes. Additionally, the soil water dissipation rate decreases with increasing soil depth.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Types of Slope**} & \multirow{2}{*}{**Ratio of Slope**} & \multirow{2}{*}{**Soil Depth/cm**} & \multicolumn{3}{c}{\(\Delta w=a\times\ln(\Delta t)+b\)**} \\ \cline{3-6}  & & & \(a\) & \(b\) & \(\mathbf{R}^{2}\) \\ \hline \multirow{8}{*}{Bare Slope} & \multirow{2}{*}{1:1.5} & 20 & 1.073 & 1.401 & 0.914 \\  & & 40 & 0.886 & 1.198 & 0.953 \\  & & 60 & 0.664 & 0.964 & 0.979 \\ \cline{1-1} \cline{2-6}  & \multirow{2}{*}{1:1.75} & 20 & 1.046 & 1.828 & 0.991
## 2 Materials and Methods
### Environmental Covariates  
Based on the SCORPAN (S: soil, C: climate, O: organism, R: relief, P: parent, A: age, N: position) model, five categories with 141 environmental covariates related to SOC formation were selected [24]. The initial and selected environmental covariate databases from multiple data sources are shown in Tables S1 and Table 1, including Landsat 8 Operational Land Imager (OL), Digital Elevation Model (DEM), and data products. All available Landsat 8 Collection 2 Tier1 Level 2 images at 30 m spatial resolution covering the study area were collected from 2015 to 2017. These images were processed with the Land Surface Reflectance Code (LaSRC) and operated using the processes of radiometric correction, geometric correction, and atmospheric correction ([[https://www.usgs.gov/](https://www.usgs.gov/)]([https://www.usgs.gov/](https://www.usgs.gov/)), accessed on 28 September 2023) [25]. Cloudless composited images were used to calculate indices after removing cirrus, clouds, and cloud shadows with CFmask [26]. The detailed descriptions of the environmental covariates are as follows.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Abbreviation** & **Scale** & **Covariate \({}^{\text{a)}}\)** & **Type \({}^{\text{b)}}\)** & **Period** & **Reference** \\ \hline Soil erosion & SE & 1000 m & S\&P & Q & 2002–2016 & [27] \\ Silt content & Silt & 250 m & S\&P & Q & 1960–2020 & [10; 28] \\ Sand content & Sand & 250 m & S\&P & Q & 1960–2020 & [10; 28] \\ Brightness index & BI & 30 m & S\&P & Q & 2015–2017 & [25] \\ Bare soil index & BSI & 30 m & S\&P & Q & 2015–2017 & [25] \\ Carbonate index & CarI & 30 m & S\&P & Q & 2015–2017 & [25] \\ Gypsum index & GI & 30 m & S\&P & Q & 2015–2017 & [25] \\ Isothermality & BIO03 & 1000 m & C & Q & 1970–2000 & [29] \\ Temperature seasonality & BIO04 & 1000 m & C & Q & 1970–2000 & [29] \\ Temperature annual range & BIO07 & 1000 m & C & Q & 1970–2000 & [29] \\ Mean temperature of wettest quarter & BIO08 & 1000 m & C & Q & 1970–2000 & [29] \\ Mean temperature of driest quarter & BIO09 & 1000 m & C & Q & 1970–2000 & [29] \\ Mean temperature of warmest quarter & BIO10 & 1000 m & C & Q & 1970–2000 & [29] \\ Mean temperature of coldest quarter & BIO11 & 1000 m & C & Q & 1970–2000 & [29] \\ Precipitation of wettest month & BIO13 & 1000 m & C & Q & 1970–2000 & [29] \\ Daytime land surface temperature & LSTD & 1000 m & C & Q & 2002–2017 & [30] \\ Nighttime land surface temperature & LSTN & 1000 m & C & Q & 2002–2017 & [30] \\ Solar radiation & Sol & 1000 m & C & Q & 1970–2000 & [29] \\ Average temperature & Tavg & 1000 m & C & Q & 1970–2000 & [29] \\ Maximum temperature & Tmax & 1000 m & C & Q & 1970–2000 & [29] \\ Minimum temperature & Tmin & 1000 m & C & Q & 1970–2000 & [29] \\ Vapor pressure & VP & 1000 m & C & Q & 1970–2000 & [29] \\ The average of CANI in the crop period & CroCANIa & 30 m & O & Q & 2015–2017 & [25] \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of environmental covariates in the selected database.
# Recent Development of Aptamer Sensors for the Quantification of Aflatoxin B1
###### Abstract  
Aflatoxin B1 (AFB1) is one of the most frequently-found mycotoxins in contaminated food. As the content of mycotoxins is particularly low in food, the development of probes to detect AFB1 in foods with high sensitivity and selectivity is an urgent social need for the evaluation of food quality. Numerous techniques have been developed to monitor AFB1. Nevertheless, most of them require cumbersome, labor-consuming, and sophisticated instruments, which have limited their application. An aptamer is a single, short nucleic acid sequence that is capable of recognizing different targets. Owing to their unique properties, aptamers have been considered as alternatives to antibodies. Aptasensors are considered to be an emerging strategy for the quantification of aflatoxin B1 with high selectivity and sensitivity. In this review, we summarize recent developments in colormetric, electrochemical, SERS, and fluorescent aptasensors for the quantification of AFB1. Finally, the perspectives and current challenges of aptasensors for AFB1 are outlined.  
afflatoxin B1; aptamer; detection +
Footnote †: journal: Applied Sciences
### Simulation Techniques Utilized in PVA Studies  
Studies evaluating patient-ventilator interactions (e.g., triggering functions) often utilize simulation techniques to represent the patient. There are various approaches to test and calibrate a mechanical ventilator. Several approaches target to simulate a passive lung with singular interchangeable properties, such as resistance and compliance. Some testing devices allow to also target patient ventilator interaction at specific boundary conditions (e.g., chosen ventilation modes, patient specific characteristics) [36; 37; 38].  
Examples for the passive test systems are the IMT test lung (imtmedical, Buchs, Switzerland) ), representing a single compartment solution as well as the TTL lung simulator (Michigan Instruments, Kentwood, USA), allowing a two compartment simulation. The ASLS00 breathing simulator (ImgMar Medical, Pittsburgh, USA) is an example of a more sophisticated test system, targeting also the comparison of ventilation modes, which has been shown in several studies [39; 40; 41; 42]. The fundamental disadvantage of such methods are the unchangeable maximum volume of the test lung, restricted possibility of simulating lung behavior and limited simulation of expiratory efforts [41].
# A Malware Detection Framework Based on Semantic Information of Behavioral Features
## 4 Dataset and Evaluation Methods
### Evaluation Metrics  
In our experiments, we select 80% data from the dataset for training, while keeping the remaining 20% for testing. We assess the performance of our proposed model depending on the following metrics, including accuracy, precision, recall, F1-Score, and Receiver Operating Characteristic (ROC) curves. The assessment indicators are calculated as shown in Equations (9)-(12).  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{9}\]  
\[Precision=\frac{TP}{TP+FP} \tag{10}\]  
\[Recall=\frac{TP}{TP+FN} \tag{11}\]  
\[F1-Score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{12}\]  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Hyper-Parameter** & **Value** \\ \hline Window sizes in Skip-Gram model & 5 \\ The minimum count in Skip-Gram model & 3 \\ Units of embedding layer in Sequence-based model & 200 \\ Units of embedding layer in Name-based model & 10 \\ Activation function of convolution layers & Relu \\ Bi-GRU layer & 1 \\ Units of Bi-GRU layer & 100 \\ Units of the first dense layer & 64 \\ Units of the second dense layer & 32 \\ Activation function of dense layers & Relu \\ Dropout Ratio & 0.2 \\ Learning rate & \(10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyper-parameters in experiments.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Embedding Model** & **Embedding Vector Size** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{3}{*}{CBOW} & 100 & 0.9770 & 0.9793 & 0.9743 & 0.9780 \\  & 128 & 0.9720 & 0.9724 & 0.9715 & 0.9719 \\  & 200 & 0.9715 & 0.9709 & 0.9719 & 0.9714 \\ \hline \multirow{3}{*}{Skip-Gram}
## 3 Empirical Gramians
### Parameter-Space Empirical Gramians
#### 3.2.2 Empirical Identifiability Gramian  
For an observability-based parameter identification, the parameters are interpreted as additional states of the system [14; 19]. This approach leads to the **augmented system**, in which the system's state \(x\) is appended by the parameter \(\theta\) and, since the parameters are (assumed) constant over time, the components of the vector-field associated to the _parameter-states_ are zero:  
\[\begin{pmatrix}\dot{x}(t)\\ \dot{\theta}(t)\end{pmatrix} =\begin{pmatrix}f(t,x(t),u(t),\theta(t))\\ 0\end{pmatrix}, \tag{5}\] \[y(t) =g(t,x(t),u(t),\theta(t)),\] \[\begin{pmatrix}x(0)\\ \theta(0)\end{pmatrix} =\begin{pmatrix}x_{0}\\ \theta\end{pmatrix},\]  
leaving the parameter-state's initial value for testing parameter perturbations. The observability Gramian to this augmented system, the _augmented observability Gramian_, has the block structure:  
\[\widehat{W}_{O}=\begin{pmatrix}W_{O}&W_{M}\\ W_{M}^{\intercal}&W_{P}\end{pmatrix},\]  
with the state-space observability Gramian \(W_{O}\), the parameter-space observability Gramian \(W_{I}\) and the mixed state and parameter block \(W_{M}=W_{M}^{\intercal}\). To isolate the parameter identifiability information, the state-space block is eliminated.  
**Definition 7** (Empirical Identifiability Gramian).: _The **empirical identifiability Gramian** is given by the Schur-complement of the empirical augmented observability Gramian for the lower right block:_  
\[W_{I}=W_{P}-W_{M}^{\intercal}W_{O}^{-1}W_{M}.\]Often, it is sufficient to approximate the empirical identifiability Gramian by the lower right block of the augmented observability Gramian \(W_{P}\):  
\[W_{I}\approx W_{P}.\]  
Apart from the relation of the identifiability Gramian to the Fischer information matrix [19], also the connection of the (parameter) observability Gramian to the (parameter) Hessian matrix [65] is noted here.