esa-sceva/satcom-chunk-collection huggingface dataset:
# Trajectory Tracking Algorithm Study of Coal Mine Water Detector Drilling Bar Installation
###### Abstract
In response to the need for the automatic injection of intelligent water detectors in coal mines, the supplied four-degrees-of-freedom material device which is the object of study here demonstrates a sliding-model control strategy based on PSO to optimize the trajectory tracking controller for the purpose of improving its joint trajectory tracking accuracy. First, we established a dynamic model of the supplied material device. Then, we designed a sliding mold controller based on the exponential approximation law and used the saturation function to replace the symbol function in the reaching law to weaken the vibration. Finally, using PSO to optimize the above controller parameters, the controller's optimal combination of parameters is obtained. The simulation results showed that the controller optimized by PSO was better. Figure 1: Drilling bar installation and dismantling module. Figure 2: Artificial drilling operations scenario.
## 2 Materials and Methods
### Hardware  
A Raspberry Pi 3 B+ single-board computer was employed as the main component of the barometric chamber circuitry (Figure 4). Analog signals from the barometric pressure sensor were acquired using an ADS1115, which is an analog-to-digital converter with 16-bit resolution that incorporates a programmable gain amplifier (PGA). The I2C interface of the Raspberry Pi was enabled to interface with the ADS1115 and the gain of the PGA was set to 1. Air humidity and temperature inside the chamber were monitored using a DHT22, which is a low-cost module connected to the Raspberry Pi over the 1-Wire protocol interface. A relay circuit was used to switch the air pump. A Raspberry GPIO (i.e., general purpose input/output) pin was connected to the base of an NPN transistor (model BC548); this pin switches an electromechanical 12-V relay (127 V/10 A) that enables the pump. In addition, a resistor-capacitor snubber circuit was installed in parallel with the load (i.e., the pump) to suppress voltage surges while switching the air pump on/off. The two proportional solenoid valves were controlled by PWM at 300 Hz, as recommended by the valve manufacturer. PWM signals were generated by software (10-bit resolution) using two GPIOs of the Raspberry. NPN transistors (model TIP122) were used to power the valves. 1N4007 diodes were used as flyback diodes connected in parallel with the valves and with the relay inductor to suppress the sudden voltage spike that might occur while switching the current across these components. Finally, a 10.1\" LCD touch screen was connected to the HDMI port of the Raspberry Pi to enable the user to operate the pressure chamber over a graphical user interface (GUI).
# Cognitive Electronic Unit for AI-Guided Real-Time Echocardiographic Imaging
## 3 Results  
### Evaluation of CNN Models  
The performance of the first CNN is shown in Table 2, while the confusion matrix on the test set is reported in Figure 5.  
For training and evaluation of the second CNN, only 500 images were used, each labeled at the frame level to capture variations in image quality. Because labels in the CAMUS dataset are defined on individual frames rather than full sequences, the model was trained using a stratified K-fold validation strategy, with a 70-10-20 split for training, validation, and testing, respectively. Consequently, 20% of the dataset was reserved exclusively for testing, while the remaining part was allocated to the training and validation phases. The overall accuracy of the model reached 66%. Considering that the dataset was originally designed for purposes different from ours, this result is satisfactory, although further data collection remains necessary. The confusion matrix is reported in Figure 6.  
\begin{table}
\begin{tabular}{l c c c} \hline  & **Precision** & **Recall** & **F1-Score** \\ \hline
2 CH & 0.86 & 0.79 & 0.82 \\
4 CH & 0.81 & 0.88 & 0.85 \\ Unknown & 1.00 & 0.97 & 0.99 \\ \hline Accuracy & 0.85 & – & – \\ Macro Average & 0.89 & 0.88 & 0.89 \\ Weighted Average & 0.85 & 0.85 & 0.85 \\ \hline \end{tabular}
\end{table}
Table 2: Classification report on the test set.  
Figure 5: Confusion matrix for cardiac projection classification on the test set; 1040 images were used.  
To provide a robust evaluation of classifier performance, the balanced accuracy and Matthews correlation coefficient (MCC) were computed. The results are reported in Table 3. These metrics offer complementary insights, with the balanced accuracy mitigating the effects of class imbalance and the MCC providing a comprehensive measure of overall prediction quality.  
To verify the hardware performance, we acquired the ultrasound video stream during an echocardiography performed on a volunteer. Using the first CNN, we classified the cardiac projection contained in each frame in real time. The output of the network was printed on a monitor and superimposed on the ultrasound image. During the test, the CPU temperature remained stable at around 55.5 \({}^{\circ}\)C and the RAM usage was approximately 70%. The average inference time for a single frame was measured at 13.74 \(\pm\) 2.48 ms, confirming the hardware's capability for the intended application. It should be noted that until now it was not necessary to use the GPU to perform the tests.
# Resilience in the Internet of Medical Things: A Review and Case Study
## 5 Analysis of Resilience in Existing Works
### Control  
Access control is another required requirement for resilient data transmission in the IoMT networks. It provides security and privacy in the IoMT network. Several authorization protocols can be used to provide access control in the IoMT networks, such as OAuth [86]. [PERSON] et al. [64] studied trustworthy authentication and unassailable communication between patients, healthcare experts, and nursing and medical staff in IoMT networks. They used a machine learning algorithm along with a parameter-based cryptographic technique called the CPBED algorithm. Although decentralized verification and authentication was used to reduce the authentication time, computation cost, and communication overhead, how this model performed in a real IoMT-based CPS network was not discussed.  
Ref. [77] provides a structure that enhances data traffic control by leveraging the SDN control plane that reroutes traffic to a pre-configured backup edge server in the situation of a system failure. Ref. [73] again used the control plane through an SDN controller to adjust the control parameters. They used another technique, Network Function Virtualization (NFV), along with the SDN controller to enhance the control feature of the data traffic for next-generation offshore wind farms. Ref. [18] describes how microservices architecture under the fog computing paradigm provides security-aware placement to obtain security and control among these microservices. Ref. [61] investigates the combination of unsupervised methods (K-means method) with SDN to enhance the control of the data traffic of distributed nodes.
## Appendix B  
For a non-singular matrix \(E\), if the first column and the first row are removed from the matrix and get \(C\):  
\[E=\left[\begin{array}{cc}a&b^{T}\\ b&C\end{array}\right]. \tag{13}\]  
Suppose the inversion of \(E\) is formulated as:  
\[E^{-1}=\left[\begin{array}{cc}d&e^{T}\\ e&F\end{array}\right]. \tag{14}\]  
Then the \(C^{-1}\) can be calculated by solving:  
\[\begin{cases}bd+Ce=0\\ be^{T}+CF=1^{\prime}\end{cases} \tag{15}\]  
where \(C^{-1}\) can be obtained as \(C^{-1}=F-ee^{T}/d\).
## 3 Experimental Data Set and Analysis  
The PPP results based on raw observations are evaluated with 78 global IGS stations for the whole month of September 2017 to verify the improved troposphere- and ionosphere-constrained for GPS SF PPP performance. For every station, the 24-h observations were divided into eight 3-h sessions to evaluate the performance of PPP. Therefore, a total of 18720 experiments were tested and compared. Figure 3 shows the distribution of these stations in the world. Figure 4 show a flowchart of our study procedure, while Table 1 lists the models and strategies that were used in this study.  
The PPP performance in terms of convergence time in the horizontal and vertical components is evaluated at a different confidence level (the 68% and 95%) in two modes (static and kinematic). The criterion for judging convergence is when the positioning error in the horizontal and vertical components is less than 0.5 m (95% level) and 0.3 m (68% level), respectively [11].  
Figure 3: Geographical distribution of the selected 78 global IGS tracking stations.  
Figure 2: Schematic illustrating Bicubic interpolation.
# Passive Sampling and Analysis of Naphthalene
## 4 Results and Discussion
### Effect of Sampling Time (t)  
RSDs associated with mass extracted for all studied extraction times varied from 4 to 10%. The use of longer extraction time (\(t\geq 1440\) min) led to the saturation of SPME fiber coating (Figure 5). Thus, the use of longer than \(t\geq 1440\) min is not recommended.  
It is also important to consider the contribution of mass extracted by metallic surfaces of SPME assembly to the total mass extracted (Figure 6). Shorter \(t\) (\(<\)540 min) were characterized by higher % contribution of metal assembly ranging from 26% to 43%. For extractions longer than 540 min, the % contribution was virtually constant, at 16% (Figure 6). It was caused by \"saturation\" of metallic surfaces. This effect was reproducible, i.e., RSDs for the % contribution were lower at longer \(t\) (540-2880 min) ranging from 17 to 23%. For shorter extraction times (\(<\)540 min), RSDs were higher (20-29%). Thus, an optimal sampling time for naphthalene using retracted TWA-SPME is between \(\sim\)600 and \(\sim\)1000 min considering minimization of (a) the effects of extraction time and (b) the variability associated with the effect of metallic parts of SPME assembly. In the \(\sim\)8 to \(\sim\)48 h sampling time, the contribution of extraction by metallic parts of SPME assembly can be considered constant (16%) and accounted for (i.e., subtracted from the total mass extracted) and used for quantification with Equation (1).  
Figure 5: Effects of sampling time on the extracted naphthalene mass. \(t\) = 30, 60, 180, 300, 540, 1440, 2880 min. \(Z\) = 10 mm, \(T\) = 313 K, \(C_{g}\) = 2.0 ppm.  
Figure 6: Effect of sampling time \(t\) on the % of naphthalene mass extracted onto metallic surfaces of SPME assembly.  
Selection of TWA-SPME sampling time needs to be informed by the range of naphthalene concentrations, and thus, it needs to be considered for specific applications. Fiber sorption capacity is the ultimate limitation to assure that TWA-SPME extractions follow the Fick's Law model (Equation (1)). Thus, only shorter sampling times can be used for very high \(C_{g}\) (>2 ppm) for naphthalene (e.g., diesel exhaust from low-quality fuel and out-of-tune or cold engine). On the other hand, much longer sampling times are feasible for low \(C_{g}\) (\(\leq\)2 ppm) for naphthalene (e.g., diesel exhaust from high-quality fuel and well-performing engine, or ambient air). The method development was on a relative clean system and in many areas the system is not as clean (0.06-0.147 mg\(\cdot\)m\({}^{-3}\)) [44] as those in US (0.008 mg\(\cdot\)m\({}^{-3}\)) [45]. Thus, the sampling time of the proposed method has to be optimized to a specific application. Many practical applications such as ambient air quality measurements, tunnels, and long-term operating diesel engines (e.g., small power stations, trains, oil rigs, cold-climate machinery) can be considered for the TWA-SPME method.
# A Systematic Analysis of Narrowband IoT Quality of Service
## 2 Narrowband IoT Fundamentals  
Narrowband IoT is a clean-slate LPWAN standard specified by the 3 GPP standardization organization. It was introduced in LTE Release 13 [5], with significant improvements made in Releases 14 and 15 [12]. NB-IoT is designated as a 5G mMTC enabler in Release 15 [6] and will thus stay relevant in future mobile networks. In this section, only the fundamental aspects of the NB-IoT Rel-13 standard are introduced, which are of relevance for the analysis in this paper. Additional details can be found in [1; 13].
## 2 Model and Method
### Track Irregularity Simulation Method  
Track irregularities are stochastic in nature, and the track irregularity power spectrum density (PSD) is generally used to reflect their statistical properties. The PSD can only be directly used in calculation when linear stochastic vibration frequency domain analysis is performed, but in nonlinear stochastic vibration problems, a track stochastic irregularity time-domain sample is also necessary. The commonly used methods for the numerical simulation of a track stochastic irregularity time-domain sample are the triangular series method and the Fourier inversion method [53; 54].  
Figure 1: Side view of the nonlinear vehicle–track–bridge model [8].  
Although the number theoretic point selection method can be used to select low-deviation sequences in the stochastic variable selection to reduce the computational convergence time, the low-deviation sequences of stochastic variables selected by the number theoretic point selection method will inevitably be clustered when the stochastic variable dimension is high. In order to reduce the number of stochastic variables required for generating track stochastic irregularities, this paper used the spectral representation random-function method (SRRM) [55] to generate track irregularities time samples, by simulating the generation of track irregularities time samples with a large number of frequency components using two stochastic variables. The track irregularities samples can be expressed as  
\[z(x)=\sum_{i=1}^{N}\sqrt{2S_{Z}(\omega_{i})\Delta\omega}\text{-}cos(2\pi \omega_{i}x)X_{i}+sin(2\pi\omega_{i}x)Y_{i} \tag{1}\]  
where \(x\) is the distance in m; \(z\) is the track irregularity sample amplitude in mm; \(\omega_{i}\) is the spatial frequency component in \(1/m\); \(S_{z}\) is the track irregularity power spectrum density function, which, in this paper, adopts the Chinese high-speed railway ballastless track spectrum, measured as mm\({}^{2}/(1/\text{m})\); \(X_{i}\) and \(Y_{i}\) are a set of orthogonal stochastic variables, satisfying, respectively:  
\[\left\{\begin{array}{l}E[X_{i}]\text{= }E[Y_{i}]=0\\ E[X_{i}Y_{j}]=0\\ E\left[X_{i}X_{j}\right]\text{= }E[Y_{i}Y_{j}]=\delta_{ij}\end{array}\right. \tag{2}\]  
\(\delta_{ij}\) is the Kronecker Delta function.  
The Hartley orthogonal basis function was used to construct the orthogonal stochastic variables:  
\[\overline{X_{i}}=cas(i\Theta_{1}),\overline{Y_{i}}=cas(i\Theta_{2}),i=1,2,3, \cdots N \tag{3}\]  
where \(cas(x)=\cos(x)+\sin(x)\); the stochastic variables \(\Theta_{1}\) and \(\Theta_{2}\) obey a uniform distribution on \([0,\,2\pi]\). The calculation of track irregularity also requires the mapping of the stochastic variable \(\{X_{i},Y_{i}\}\) to \(\{\overline{X_{i}},\overline{Y_{i}}\}\). Referring to the high-speed railway ballastless track irregularity power spectrum calculation method in [56], the frequency sampling points were taken as 4096.
## 4 Results and Discussion
Table 8 shows the percentage of GHUVE ANN models resulting from combinations of input variables out of the total of possible combinations for each case, whose nRMSE was below 15%. For the combination of five variables, with two to five neurons in the hidden layer, nRMSE \(<\) 15% was obtained for 5%, 25%, 28%, and 30% of the generated ANNs, respectively. For the combination of six variables, from two neurons to six neurons, nRMSE \(<\) 15% was obtained for the range 22 - 57% of the generated ANN models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**All-Skies** & **nRMSE(\%)** & \\ \hline \hline \multirow{2}{*}{\(\mathrm{GHI},\theta_{x}\)} & One neuron & Two neurons \\  & 18.65 & 17.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: nRMSE for ANN model \(\mathrm{(GHI,\theta_{x})}\) calculated with one and two neurons. Figure 9: Comparison between calculated \(\mathrm{(GHUVE_{mod})}\) from ANN models and experimental \(\mathrm{GHUVE_{exp}}\) data under different sky conditions: (**a**) all skies, (**b**) overcast, (**c**) partial, and (**d**) clear skies. The green line represents the fitting line; the red line represents the x = y line. Table 9 shows the combinations of five and six input variables ranging from two to six neurons in the ANN hidden layer, which estimated the lowest nRMSE. Among the various ANN models considered, despite its slightly higher error, Model 2 was considered the optimal model due to the use as input data variables that are commonly measured at ground radiometric stations. The practical advantage of this model lies in the accessibility and availability of GHI, \(\theta_{\mathrm{z}}\), T, k\({}_{\mathrm{t}}\), and \(\psi\) data, making it easier to implement in real-world scenarios without the need for additional specialized measurements. In contrast, the other models are based on meteorological variables that are not frequently recorded at meteorological stations. ANN Models 3, 7, 8, and 9 depend on CC, which requires the use of a sky camera. Models 1, 4, 5, and 6 rely on DHI, thus requiring the use of a pyrheliometer and a solar tracker, elements of high cost and complex maintenance, and, therefore, scarce in meteorological ground facilities.
# Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling
## 3 Methodology and Data
### Hierarchical Bayesian Modeling  
The hierarchical Bayesian model can be structured as three levels of probabilistic models [18]: the data model, the process model, and the parameters model. These three levels (or stages) can be represented as follows:  
\[First\ level\ [data\ |\ process,parameter_{data}]\] \[Second\ level\ [\ process\ |\ parameter_{process}]\] \[Third\ level\ [\ parameter_{data},\ parameter_{ process}]\]  
In the first level, we obtain the data model according to a certain process \(Y_{(s_{ij};t)}\) and some errors \(\epsilon_{l(s_{ij};t)}\) that are assumed to be independently normally-distributed (\(\epsilon_{l}\sim N(0,\sigma^{2})\)). The data model is described as shown below:  
\[Z_{l}(s_{ij};t)=\ Y_{l}(s_{ij};t)+\epsilon_{l}(s_{ij};t) \tag{1}\]  
The process model in Equation (2) captures the relationship of the underlying nature expressed by the data at location \(s\) at time \(t\). The process model can be one of three; Gaussian process (GP), autoregressive (AR), or Gaussian predictive process (GPP). In Equation (2), the process \(Y_{l(s_{ij};t)}\) is expressed by a Gaussian process \(\mu_{(s_{ij};t)}\) in addition to some errors \(\eta_{(s_{ij};t)}\):  
\[Y_{l}(s_{ij};t)=\mu_{(s_{ij};t)}+\eta_{(s_{ij};t)} \tag{2}\]  
The third level of hierarchical modeling defines the model parameters. According to Equations (1) and (2), these parameters are the variance of \(\epsilon_{l(s_{ij};t)}\), the variance of \(\eta_{(s_{ij};t)}\), the coefficients of the GP, and \(\phi\), which defines the spatial correlation.  
Generically, Equation (3) represents the structure of estimating the model parameters of the hierarchical model, using Bayesian inference. Starting with the Gaussian process, \(Y(s_{ij};t)\), which follows a normal distribution conditioning on the model parameters \(\theta_{i}\), where \(\theta_{i}\) is a vector containing \(\sigma_{y}\), \(\beta\), and \(\phi\). The prior distribution of \(\theta_{i}\) conditioning on \(\phi\) where \(\phi\) will have a prior distribution that follows a gamma or uniform distribution.  
\[y_{i} \sim p(y|\theta_{i})\] \[\theta_{i} \sim p(\theta_{i}|\phi) \tag{3}\] \[\phi \sim p(\phi)\]  
\[p(\theta_{i},\phi|y)\propto p(y|\theta_{i},\phi) \tag{4}\]  
From Equation (4), the posterior distribution is proportional to the likelihood being conditional on both \(\theta_{i}\), and \(\phi\) multiplied by the prior for \(\theta_{i}\) and \(\phi\). Based on the conditional independence rules and knowing that our data are independent of \(\phi\), if we know \(\theta_{i}\), we can take the joint distribution and break it down into conditional distribution \(p(\theta_{i}|\phi)\) and multiply it by the distribution of \(p(\phi)\). Equation (5) formalizes these steps, which is derived from Equation (4).  
\[p(\theta_{i},\phi|y)\propto p(y|\theta_{i},\phi)=p(y|\theta_{i})p(\theta_{i}| \phi)p(\phi) \tag{5}\]
# nmODE-Unet: A Novel Network for Semantic Segmentation of Medical Images
## 4 Experiments and Results  
### Dataset  
The experiments were conducted using the IDRiD dataset [27], the e-ophtha dataset [28], and the LGG segmentation dataset [29; 30], all of which contain ground truth annotations by medical professionals. In the IDRiD dataset, 81 fundus images were meticulously annotated at the pixel level. Following the data split outlined in the IDRiD competition, we used 54 images for training and 27 images for validation. The e-ophtha dataset comprises two types of lesions: EX and MA, with 47 images of EX lesions and 148 images of MA lesions. We utilized 31 images of EX lesions and 98 images of MA lesions for training, and 16 images of EX lesions and 50 images of MA lesions for validation. The LGG segmentation dataset includes 1319 brain MRI images. Out of these, 1000 images were used for training, and the remaining 319 images were used for testing.  
To mitigate the risk of overfitting caused by the small dataset size, we employed diverse data augmentation techniques. In practice, we applied horizontal flips, vertical flips, and rotations of 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\)to augment the data. Furthermore, to reduce  
Figure 1: Intuitive diagram of the nmODE.  
Figure 2: The architecture of the network.  
computational overhead, all images were resized to a standardized size of 448 \(\times\) 448, which served as the input to the network.