esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Method Validation of the HPLC Assay for Quality Control of DSHT
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Analyte** & **Conc. (\(\mu\)g/mL)** & **Measured Conc. (\(\mu\)g/mL)** & **Precision** & **Accuracy (\%) \({}^{\circ}\)** & **Measured Conc. (\(\mu\)g/mL)** & **Precision** & **Accuracy (\%)** \\ \hline \multirow{3}{*}{Gallic acid} & 12.50 & 12.01 & 1.07 & 96.07 & 12.24 & 2.98 & 97.96 \\  & 25.00 & 24.13 & 0.30 & 96.50 & 24.12 & 2.06 & 96.50 \\  & 50.00 & 48.30 & 0.28 & 96.60 & 48.26 & 1.02 & 96.53 \\ \hline \multirow{3}{*}{Albiflorin} & 12.50 & 12.25 & 1.66 & 98.03 & 12.43 & 1.47 & 99.46 \\  & 25.00 & 25.19 & 0.28 & 100.78 & 25.03 & 1.25 & 100.13 \\  & 50.00 & 50.75 & 1.89 & 101.50 & 50.55 & 1.25 & 101.11 \\ \hline \multirow{3}{*}{Paeoniflorin} & 25.00 & 25.34 & 1.10 & 101.35 & 25.34 & 0.90 & 101.35 \\  & 50.00 & 52.47 & 1.09 & 104.95 & 51.27 & 2.01 & 102.58 \\  & 100.00 & 102.42 & 0.90 & 102.42 & 102.71 & 0.73 & 102.71 \\ \hline \multirow{3}{*}{Naringin} & 12.50 & 13.07 & 1.42 & 104.53 & 13.19 & 1.47 & 105.48 \\  & 25.00 & 25.32 & 0.78 & 101.29 & 25.37 & 1.15 & 101.47 \\  & 50.00 & 50.10 & 0.32 & 100.20 & 50.10 & 0.76 & 100.20 \\ \hline \multirow{3}{*}{Benzoic acid} & 12.50 & 12.80 & 0.35 & 102.40 & 12.94 & 1.30 & 103.55 \\  & 25.00 & 26.27 & 0.71 & 105.10 & 26.34 & 0.97 & 105.34 \\ \cline{1-1}  & 50.00 & 52.93 & 0.71 & 105.85 & 52.76 & 1.33 & 105.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Precision date of the 10 markers in the developed HPLC assay (\(n=5\)).
## 5 Experiments and Results
### Best Algorithms Comparison  
Figure 21 below compares the performance of the nine algorithms used to determine the best one for deployment. We observed that both the Decision Tree and SVM achieved the same accuracy of 0.92 for machine learning algorithms, while RNN secured the top position among deep learning algorithms. AraBERT demonstrated a significant accuracy of 0.89 for LLMs. However, both LSTM and DistilBERT exhibited overfitting, with accuracies exceeding 0.98. Ultimately, we decided to choose RNN as the best model for future deployment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criteria** & **NVIDIA Quadro K5000** & **Tesla T4** \\ \hline \multirow{2}{*}{Generation} & Kepler Architecture (2012) [46] & Turing Architecture (2018) \\ \hline Memory (VRAM) & 4 GB & 16 GB \\ \hline \multirow{2}{*}{\begin{tabular}{c} Computing \\ performance \\ \end{tabular} } & - & Tensor Cores \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Deep learning \\ performance \\ \end{tabular} } & Limited & Optimised for AI \\  & not optimized for AI &
\begin{tabular}{c} Optimised for AI \\ native support for modern \\ frameworks (TensorFlow, PyTorch) \\ \end{tabular} \\ \hline RAM & 32 GB & 12 GB \\ \hline Storage & 480 GB & 113 GB \\ \hline OS & Windows 10 & Ubuntu 22.04.4 LTS \\ \hline Python version & 3.8.10 & 3.11.11 \\ \hline PyTorch version & 2.2.2 + cpu & 2.6.0 + cu124 \\ \hline Tensorflow & 2.13.0 & 2.18.0 \\ \hline \multirow{2}{*}{Execution time} & \multirow{2}{*}{More than 10 h} & 5–10 min \\  & & Dependent on number of epochs \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparing Nvidia Quadro k5000 performances vs. tesla t4.  
Figure 20: Training and loss accuracy for DistilBERT model.  
Figure 22 compares the performance of the top models from each field--machine learning, deep learning, and LLMs--in terms of precision, recall, and F1-measure per class. Deep learning models consistently performed best for text classification. LLMs also exhibited significant strengths although they are primarily designed for text generation rather than classification. However, future research could further enhance LLMs' capabilities in classification tasks.
## 4 Experimental Result and Evaluation
### Evaluation  
To evaluate the overall performance of the proposed model, the performance was compared with the methods such as Mobile robot navigation (MRN) [4], BA*: An Online Complete Coverage Algorithm for Cleaning Robots (BA*) [9] and Complete Coverage Path Planning Algorithms based on A* Algorithms (A*-CPP) [21]. The repetition rate of methods is used to compare the performance of  
Figure 8: A sample of a vacuum robot faced with regular and irregular obstacles.  
Figure 9: A sample of a monitoring robot with multiple directions in regular and irregular obstacles at the CIST Lab.  
methods. It is indicated that the results in simulations is to consider the robot in traveling overlaps when it has completed a coverage path. Further testing these methods of a robot were evaluated in repetition rate in robot coverage path planning with moving obstacles.  
The robot coverage rate percentage can be calculated in Equation (2) [4]:  
\[\text{Percentage of Coverage rate}=\frac{AA_{p}}{A_{t}-A_{o}}.100\% \tag{4}\]  
where  
* \(A_{t}\) is the total nodes of the environment
* \(A_{o}\) is the total area of the obstacles and \(A_{p}\) is the total area covered by the robot's path.
## 2 InSAR Coseismic Deformation and Postseismic Time Series  
Due to the rugged topography (average elevation \(\sim\)4000 m) and sparse population in the seismic region, GPS measurements are challenging. Therefore, using archived InSAR observations, we analyzed the coseismic displacements and the evolution of postseismic displacements in both the temporal and spatial domains resulting from the 2021 Maduo earthquake. The archived Sentinel-1 A/B images, which are freely available to the public, can be accessed through the Alaska Satellite Facility's official website. We utilized archived Sentinel-1 A/B acquisitions from both before and after the mainshock to retrieve the coseismic deformation (Figure 1b). To investigate the postseismic process, we also collected the Sentinel-1 A/B observations from tracks 99 and 106 for a year and a half following the mainshock. In this paper, detailed information about the archived Sentinel-1 A/B observations is presented in Table 1.  
Figure 1: Tectonic setting of the study area. The blue vectors and black lines in (**a**) show the interseismic horizontal velocities [37] and block boundaries around the Tibetan Plateau, respectively. The red box in (**a**) indicates the survey region shown in (**b**). Red lines in (**b**) show the surface traces of the fault geometry [26]. The cyan and magenta rectangles in (**b**), respectively, show the ground coverage of tracks 99 and 106 images. The blue dots in (**b**) indicate the relocated aftershocks from 22 May 2021 to 30 May 2021, as referenced in [PERSON] et al. [4]. Beach balls in (**a**,**b**) represent the focal mechanism solutions (Mw \(\geq\) 6) from the Global Centroid Moment Tensor Project (GCMT, [[https://www.globalcmt.org/CMTsearch.html](https://www.globalcmt.org/CMTsearch.html)]([https://www.globalcmt.org/CMTsearch.html](https://www.globalcmt.org/CMTsearch.html)), accessed on 28 May 2024).
## 3 Mathematical Formulation of the Problem
### Assumptions  
The following hypotheses are considered based on some industrial reality and constraints. Concerning the maintenance actions' duration, in the industrial reality, it is possible to consider that the repair and overhaul duration are negligible in the case where the production horizon as well as the production period are very lengthy. Hence, the duration of repair not does have a big impact on production and customer satisfaction, since the production system can recover the quantity lost during this small period of maintenance. Recall that our proposed strategy maintenance is characterized by a preventive maintenance with minimal repair. The minimal repair is applied for each corrective maintenance at each failure, which does not influence on the evolution of the failure rate, and hence, it does not require an important duration. In addition, this strategy proposes a forecasting maintenance planning characterized by the optimal number of preventive maintenance actions and the intervals between two successive preventive maintenance from which we have an idea to prepare the necessary tools, resources, and technicians to apply the repair and reduce the intervention durations. So, we can consider a negligible duration for each maintenance action in the proposed PMMR.  
On the other hand, we consider that a non-negligible duration for each action of perfect maintenance corresponds to each point of out of control due to assignable causes. The out-of-control points, which can alert at any moment in the process, therefore require a long duration to seek the origins of these assignable causes.  
The following hypotheses are considered:  
1. The random customer demand follows the normal law and every request unsatisfied in a period causes a delay penalty.
2. Each delivery time, \(\tau_{i}\), from \(S\) to each warehouse, \(w_{i}\), is constant and is a multiple of \(\Delta t\).
3. The maximal and minimal rates of production, respectively \((U_{min})\) and \((U_{max})\), are known and constant.
4. The durations of repair and preventive replacement are negligible for the maintenance strategy performed when the production process is under control.
5. The average time of perfect preventive maintenance performed for each detected assignable cause is \(t_{p}{}^{*}\), and a new maintenance cycle is started after this perfect maintenance.
6. The defective products are not inserted into the production process.
7. Defective product is due to the degradation process of the machine.  
All unit costs are constant and known.
* All resources to carry out maintenance actions are always available.
* The preventive maintenance activities are perfect and restore the equipment to \"as good as new\" condition.
## Nomenclature  
\begin{tabular}{l l} \multicolumn{2}{l}{**Nomenclature**} \\ \(\mu,\
u\) & Velocity along \(y\) and -coordinates (m s\({}^{-1}\)) \\ \(H_{x}\), \(H_{y}\) & Magnetic coordinates of velocities in \(x,y\) direction (Tesla) \\ \(\mu\) & Dynamicviscosity (kg m\({}^{-1}\) s\({}^{-1}\)) \\ \(\
u\) & Kinematicviscosity (m\({}^{2}\) s\({}^{-1}\)) \\ \(\rho\) & Fluid density (kg m\({}^{-3}\)) \\ \(g\) & Gravity acceleration (m s\({}^{-2}\)) \\ \(\beta\) & Coefficient of thermalexpansion (K\({}^{-1}\)) \\ \(\
u_{m}\) & Magneticpermeability (H m\({}^{-1}\)) \\ \(\alpha\) & Thermaldiffusivity (m\({}^{2}\) s\({}^{-1}\)) \\ T & Fluidtemperature (K) \\ \(C_{p}\) & Specific heat (J kg\({}^{-1}\) K\({}^{-1}\)) \\ \(T_{m}\) & Temperature at maximum density \\ \(\phi\) & Dimensionless magnetic field \\ \(\tau_{t}\) & Transient heat transfer \\ \(U\) & Dimensionless velocity \\ \(R_{g}\) & Reduced gravity parameter \\ \(\kappa\) & Thermal conductivity \\ MHD & Magnetohydrodynamic \\ \(T_{\infty}\) & Ambienttemperature (K) \\ \(R_{e_{t}}\) & Reynolds number \\ \(G_{\tau_{t}}\) & Grashof number \\ \(\tau\) & Shearing stress (P a) \\ \(\xi\) & Magnetic force parameter \\ \(\lambda\) & Mixedconvective number \\ \(\theta\) & Dimensionalized temperature \\ \(\gamma\) & Magnetic Prandtl parameter \\ Pr & Prandtl parameter \\ \(\xi\) & Electrical conductivity (s m\({}^{-1}\)) \\ \(\Delta T\) & Temperature difference \\ \(\tau_{s}\) & Transient skin friction \\ \(\tau_{m}\) & Transient current density \\ \(H_{o}\) & Magnetic field intensity \\ \(\rho_{m}\) & Maximum density \\ \(U_{\infty}\) & Free stream velocity \\ FDM & Finite difference method \\ \end{tabular}
###### Abstract  
In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved gray wolf optimization (GWO) technique by fully integrating and embedding within it the powerful importance sampling (IS) concept. This new approach, referred to hereafter as GWOEIS (for \"GWO embedding IS\"), guarantees global optimality, and offers higher resolution capabilities over orthogonal frequency division multiplex (OFDM) (i.e., multi-carrier and multi-path) single-input multiple-output (SIMO) channels. The traditional GWO randomly initializes the wolfs' positions (angles and delays) and, hence, requires larger packs and longer hunting (iterations) to catch the prey, i.e., find the correct angles of arrival (AoAs) and time delays (TDs), thereby affecting its search efficiency, whereas GWOEIS ensures faster convergence by providing reliable initial estimates based on a simplified importance function. More importantly, and beyond simple initialization of GWO with IS (coined as IS-GWO hereafter), we modify and dynamically update the conventional simple expression for the convergence factor of the GWO algorithm that entirely drives its hunting and tracking mechanisms by accounting for new cumulative distribution functions (CDFs) derived from the IS technique. Simulations unequivocally confirm these significant benefits in terms of increased accuracy and speed Moreover, GWOEIS reaches the Cramer-Rao lower bound (CRLB), even at low SNR levels.  
Keywords:importance sampling (IS); gray wolf optimization (GWO); data-aided (DA); joint angle and delay estimation (JADE); maximum likelihood (ML); multi-carrier; orthogonal frequency division multiplex (OFDM); multi-path; single-input multiple-output (SIMO) +
Footnote †: journal: _Sensors_**2024**, 24, 5821. [[https://doi.org/10.3390/s24175821](https://doi.org/10.3390/s24175821)]([https://doi.org/10.3390/s24175821](https://doi.org/10.3390/s24175821))
## 1 Introduction
The inherent strain method involves a heat transfer analysis performed according to the welding conditions to predict the size of the heat-affected zone of the material, and then applies the inherent strain value to the corresponding area by substituting an elastic equivalent force to calculate the deformation of a structure. Here, inherent strain refers to permanent strains including plastic strain, thermal strain, and phase transformation strain. [PERSON] et al. proposed a disk-spring model that used the strain-based equivalent load method for the analysis of three-dimensional structures [7]. [PERSON] et al. calculated an inherent strain considering a temperature \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Period** & **Research Method** & **Researcher** \\ \hline
**Elasticity method** & 1950 & Inherent strain theory & [PERSON] et al. [1] \\ \hline
**Numerical method** & 1960– & Finite element method, boundary element method & [PERSON] et al. \\ \hline
**Experimental-based** & 1980– &
\begin{tabular}{c} Calculates an equivalent loading using \\ experimental data, elastic analysis \\ \end{tabular} & [PERSON] et al. [3] \\ \hline
**Equivalent** & & & \\
**load method** & **Inherent** & 1990– & Calculates an equivalent loading using \\  & **strain-based** & 1990– & inherent strain, elastic analysis \\  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Methodologies of welding deformation analysis. gradient [8]. [PERSON] et al. developed a model based on the inherent strain concept and iterative substructure method to calculate a large-scale welding problem [9]. [PERSON] et al. calculated the weld strain of the vacuum vessel using the inherent strain method [10]. In accordance with the thermal load conditions, a modified inherent strain method was applied to the 3D printing field [11] or friction stir welding field [12]. In this study, we propose an inherent strain method to efficiently calculate the welding deformation and shrinkage, considering the welding sequences, phase transformation, and natural convection with regard to the stiffened plates of the hull structure [13,14]. We calculated the welding deformation and residual stress of the stiffened curved plate by applying the method proposed in this study. This study also verified the validity of the proposed method by comparing the calculated welding deformation and residual stress with the experimental results under the same conditions. It is expected that the method proposed in this study can be applied to predicting the thermal deformation of plate forming by line heating [7], and line heating with the weaving motions [15] of curved plates.
###### Abstract  
Assessment of human locomotion using wearable sensors is an efficient way of getting useful information about human health status, and determining human locomotion abnormalities. Wearable sensors do not only provide the opportunity to assess the behavior of patients as it happens in their daily life activities, but also provide quantitative, meaningful feedback data of patients to their therapists. This can pinpoint the cause of problems and help in maximizing their recovery rates. The popularity of using wearable sensors has received attention from a number of researchers from both the academic and industrial fields in the past few years. The different types of wearable sensors have given birth to the realization of a standard measurement model that can support different types of applications. Wireless body area networks (WBANs) are starting to replace traditional healthcare systems by enabling long-term monitoring of patients and tele-rehabilitation, especially those who suffer from chronic diseases. This paper investigates using wearable accelerometers and surface electromyography (EMG) in human locomotion monitoring for tele-rehabilitation. It proposes and investigates new positions for the proposed sensors, and compares the measured signals to similar techniques proposed in the literature. Realistic measurements show that the proposed positions of surface EMG sensors (on the forearm muscles) provide more reliable results in the classification of motion abnormality as compared to the sensor positions proposed in the literature (biecps muscles). Seven statistical features were extracted from accelerometer signals, and four time domain (TD) features are extracted from EMG signals. These features are used to construct six machine learning classifiers for automatic classification of [PERSON]'s tremor. These models include; decision tree (DT), linear discriminant analysis analysis (LDA), k-nearest-neighbor (kNN), support vector machine (SVM), boosted tree and bagged tree classifiers. The performance of the applied classifiers is analyzed using accuracy, confusion matrix, and area under ROC (AUC) curve. The results are also compared to corresponding findings in the literature. The experimental results show that the highest classification accuracy is achieved when using the proposed measurement set and bagged tree classifier with a value of 99.6%.  
accelerometer; body sensor networks (BSNs); electromyography (EMG); Parkinson's disease (PD); wearable sensors; tremor
## 5 Existing Cybersecurity Solutions for Smart Manufacturing
### Cybersecurity Countermeasures  
#### 5.1.1 Cryptographic Techniques  
One of the most common and widely used cybersecurity countermeasures is encryption which is available at multiple levels. A smart manufacturing environment consists of multiple devices and equipment. Hence, there are also networking and software protocols for communication among them and with the human operator. Cryptographic techniques such as symmetric encryption, hash functions, digital signature, key agreement, and distribution protocols are used in these types of communications to achieve confidentiality and integrity of the data which also ensures that only authorized entities can have access to it. As discussed in [18], there are several key management techniques. The key distribution center (KDC) is one of the key management solutions where the KDC server will generate and distribute the session keys to the communication entities. Another solution for key management is using point-to-point architecture, where the two communicating parties will either agree on a key exchanging random nonce, or one of the entities will generate the key and distribute it to the other entity encrypting it with a long-term symmetric key. In the scenario of asymmetric keys, a standard PKI can be one of the key management solutions which are responsible for certifying the public keys of each entity by digital signature. They can also verify and revoke the certificates if necessary.
## 5 Experiments and Results  
The evaluation of the proposal was carried out using a 19 dual-core processors workstation. The processor used is an Intel copyright Xeon copyright CPU E5-2670 v3 2.30 GHz, and 128 GB of RAM.  
The current methodology was tested on three natural scenes databases. The metric used was Euclidean.  
* [PERSON] and Shiele (V_S) [14] comprised of 700 classified scenes: 144 coast, 103 forest, 179 mountain, 131 field, 111 river/lake, and 32 sky/cloud.
* [PERSON] and Torralba (O _T) [15] 1472 Images classified as: 360 coast, 328 forest, 374 mountain, and 410 field.
* [PERSON] et al. (Sh) [16] with 35,000 classified videos and images captured using 35 different portable cellphones. We have selected only 3000 natural scenes: 500 coast, 500 forest, 500 mountain, 500 field, 500 river/lake, and 500 sky/cloud.
# Adaptive Tolerance Dehazing Algorithm Based on Dark Channel Prior
## 2 Related Work  
### Atmospheric Scattering Model  
To describe the formation of hazy images, [PERSON] [5] proposed an atmospheric scattering model in 1976. Later, [PERSON] and [PERSON] [26; 27] further derived the model, which describes the degradation process of the hazy images and is widely used in the dehazing of hazy images. Mathematically, the atmospheric scattering model can be described as:  
\[I(x)=J(x)t(x)+A(1-t(x)), \tag{1}\]  
where \(I(x)\) is a hazy image, \(J(x)\) is a clear image, A is the atmospheric light, and \(t(x)\) is the transmission. The process of dehazing is actually that of calculating A and \(t(x)\) from hazy image \(I(x)\) to restore \(J(x)\) through the atmospheric scattering model. The transmission \(t(x)\) can be expressed as:  
\[t(x)=e^{-\beta d(x)}, \tag{2}\]  
where \(\beta\) is the atmospheric scattering coefficient, which is related to the wavelength of visible light, \(d(x)\) is the depth of the scene, which is the distance between the imaging device and the scene.
## 5 Experimental Result
### Event-Driven vs. Current-Driven
Even with this extra execution time, the total time for computing the network matches the real-time requirements of the SHM application. Due to the lower power cost of the SPI transfer (w. DMA), however, the energy-consumption for the eLSNN flavours ( event-driven and current-driven inputs) is comparable and in the range of 46-49 \(\upmu\)J. In future works, we will explore temporal coding techniques and migrate the coding task directly in the MEMS sensor and in the time domain. These on-sensor coding techniques will remove the FFT cost and the amount of data to be read from the MEMS sensors, achieving further energy reduction in the sensor node. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{5}{c}{**Current-Driven**} \\ \cline{2-6}
**Stage** & **Time** & **Cycles** & **Current** & **Power** & **Energy** \\  & [\(\upmu\)s] & **[\#]** & **[mA]** & **[mW]** & **[\(\upmu\)J]** \\ \hline SPI & 542 & - & 5.01 & 16.53 & 8.96 \\ FFT & 41 & 6971 & 38.76 & 127.91 & 5.24 \\ ABS & 26 & 4284 & 36.58 & 120.71 & 3.14 \\ Encoding & - & - & - & - & - \\ eLSNN & 215 & 36,036 & 40.26 & 132.86 & 28.56 \\ \hline Total & 824 & 47,291 & - & - & 45.90 \\ Mean & - & - & 16.88 & 55.70 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Power report of the SHM application featuring the best eLSNNs tested. The SPI master transfers data with a serial clock of 8 MHz. The SPI stage includes: (i) The SPI data transfer, (ii) the time required by the system-on-chip to reconfigure the clock to 168 MHz when it wakes-up from SLEEP mode and (iii) the conversion of sensor data from raw integers to floating-point. Figure 12: SHM application current consumption patterns for the best eLSNN networks working with current (**top**) and event (**bottom**) inputs. To better highlight the different stages of the application, the waveforms were obtained with the MCU clock slowed down to 16 MHz, while the SPI clock was 2 MHz.