esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks
## 4 Experimental Results
### Performance Result of the Classification Algorithm
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Num** & **Model** & **F1-Score** & **AUC** & **Recall** & **Precision** \\ \hline
1 & Logistic Regression & 0.8849 & 0.8849 & 0.89 & 0.87 \\
2 & Naive Bayes & 0.6942 & 0.7168 & 0.45 & 0.97 \\
3 & Support Vector Machine & 0.8185 & 0.8210 & 0.71 & 0.92 \\
4 & Random Forest & **0.9961** & 0.9961 & **1** & **1** \\
5 & ANN & 0.9820 & 0.9817 & **1** & **1** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: F-1 Score, AUC, Recall, and Precision of Machine Learning Classifiers. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Num** & **Model** & **Accuracy** & **MSE** \\ \hline
1 & Logistic Regression & 88\% & 0.1150 \\
2 & Naive Bayes & 71\% & 0.2835 \\
3 & Support Vector Machine & 82\% & 0.1791 \\
4 & Random Forest & **99\%** & 0.0038 \\
5 & ANN & **98\%** & **0.0017** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Machine Learning Classifiers. Table 1 described the features of the UE; using feature creation, one of the techniques in feature engineering, also using the haversine formula, we created a new feature and named it a distance. It is the distance between a UE and its cell center. The feature importance helps us to understand the solved problem and sometimes leads to model improvements. Figure 12 describes which features are relevant to the model, and the feature distance comes at the first position. In addition, the feature importance section explains how features like distance, latitude, and longitude are significant in demonstrating why the location (UE and Satellite) triggering [35] is one of the most crucial conditional handover triggerings. Besides the machine learning classifiers, we also built an artificial neural network using the sequential keras model. Figure 13 shows us the training process of the model. This sequential model API is a way of creating deep learning models where an instance of the sequential class is created and model layers are created and added to it. Therefore, we built a simple classification network that has 21 neurons in the input layer, 2 hidden layers with 4 neurons each, and an output layer with 1 output. Rectified linear activation functions and random-normal as kernel-initializer are used in each hidden layer and a sigmoid activation function is used in the output layer, for binary classification; then, we compiled the model with binary cross-entropy as the loss and adam as the optimizer to minimize the loss and adjust input weights. We then fitted the model to the training and validation data set. Figure 13 shows us that the accuracy depends on the number of epochs, with 75 epochs we obtain 98% accuracy. The recall and precision of the ANN are described in Table 4. In addition, the loss functions are used to determine the amount that a model should try to minimize during training, and as shown in Figure 14, the ANN has a lower loss of 0.0017 than all the models. Figure 12: Feature Importance.
## 2 Materials and Methods--Implementation, Integration, and Testing of the IDEASSat Flight Model
### Payload: Compact Ionospheric Probe (CIP)  
CIP is an in-situ plasma sensor which was developed by the National Central University Space Payload Laboratory. Its predecessor was the scientific payload of FORMOSAT-5: the Advanced Ionospheric Probe (AIP). CIP reduces the volume of the instrument to 1 U and retains all the functions of ionospheric observation available with AIP. The sensor needs to point to the ram direction of the spacecraft in order to perform scientific measurements.  
CIP integrates a number of technologies for measuring the ionosphere. Table 5 shows the operational modes included in CIP in a time-sharing manner, as well as the plasma parameters that can be derived.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Protocol** & **Implementation** & **Number** \\ \hline I2C & RTC/S-band/EPS & 3 \\ SPI & Micro SD card/S-band & 3 \\ UART & GSE/ADCS/UHF/CIP & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: IDEASSat data interface.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Instrument** & **Plasma Parameters** \\ \hline Planar Langmuir Probe (PLP) & Electron temperature \\ Ion Trap (IT) & Ion density \\ Light/heavy ion mass ratio \\ Retarding Potential Analyzer (RPA) & Ion temperature \\ Ion ram speed \\ Ion Drift Meter (IDM) & Ion arrival angles \\ \hline \hline \end{tabular}
\end{table}
Table 5: CIP built-in observation modes and corresponding plasma parameters.  
From the composition of the CIP structure, it can be understood that the CIP is a laminated structure integrating multiple PCBs. Figure 2 shows the overall architecture of CIP, which is composed of the front-end aperture and meshes module (AMM), analog preprocessing unit (APU), digital control unit (DCU), and power management unit (PMU). The corresponding functions are:  
* [noitemsep,topsep=0 pt]
* AMM: A sensor for measuring plasma, which converts the measured variable into a voltage/current signal.
* APU: Converts the analog voltage/current of the sensor into a digital signal.
* DCU: Controls all of the analog to digital converters (ADCs) and digital to analog converters (DACs) of the APU. It monitors the temperature of CIP circuit boards and communicates with CDH.
## 2 Materials and Methods
### Performance Evaluation of SM-OGFC
#### 2.4.2 Low-Temperature Mechanical Performance Test  
The low-temperature properties of SM-OGFC is observed via Low-temperature splitting test and Low-temperature beam bending test. The splitting strength (R\({}_{\rm T}\)) of mixtures is obtained by \(-15\)\({}^{\circ}\)C splitting test according to Chinese standards GB/T0716-2011. The \(-15\)\({}^{\circ}\)C splitting strength is an important factor for assessing the capacity of SM-OGFC to bear dynamic loads at low temperature. The bending-tensile strength (R\({}_{\rm B}\)), maximum bending strain (\(\epsilon_{\rm B}\)) and the bending stiffness modulus (S\({}_{\rm B}\)) of mixture is acquired through \(-10\)\({}^{\circ}\)C beam bending test according to Chinese standards GB/T0715-2011.
# Performance of the Dual-Chamber Fungal Fuel Cell in Treating Tannery Wastewater
## 2 Materials and Methods
### Adsorption Isotherm  
The most common models representing the isotherm behavior for hexavalent chromium sorption in both the trained and control strains were examined, as shown in Figure 6. The findings showed that the Freundlich and Koble-Corrigan models, respectively, can adequately describe the hexavalent chromium sorption in both the trained and control strains. The Freundlich adsorption model can be used to explain heterogeneous adsorption surfaces. The Freundlich isotherm model can also be used to describe multilayer adsorption since it is not limited to monolayer creation. The total amount of materials adsorbed at each particular active site equals the number of molecules that have been adsorbed. The mechanism of the adsorption process depends on the energy of binding between the sorbent and the adsorbed molecules. The adsorption energy progressively dropped until it disappeared after the adsorption process was finished. The three-parameter Koble-Corrigan model, where A, B, and n are Koble-Corrigan constants, combines the Langmuir, Freundlich, and pure hydrocarbon adsorption isotherm models with other adsorption isotherm models [44; 45].  
Figure 4: Control and trained _A. niger_ strains growth behavior under different hexavalent chromium initial concentrations.  
Figure 5: Hexavalent chromium removal efficiency by both the control and trained _A. niger_.  
According to the Freundlich model, the maximum uptake of the trained sample was K\({}_{f}\) was 14.81 mg/g and n was 1.694. According to the Koble-Corrigan model for the control samples, the maximum uptake was 7.388 mg/g, B was 0.037, and D was 0.755, which showed greater decreases in the uptake values than in the trained sample. The obtained results indicated that the maximum uptake of the trained samples is more efficient than the control samples. Table 3 indicates the variations between experimental and calculated Q\({}_{e}\) during isotherm studies for all concentrations.
## 1 Introduction
\[\stackrel{{\rightharpoonup}}{{\overrightarrow{t}}}= \stackrel{{\rightharpoonup}}{{\overrightarrow{a}}}_{g}+ \stackrel{{\rightharpoonup}}{{\overrightarrow{a}}}_{ng}=\left(- \frac{GM_{b}}{t^{3}}\stackrel{{\rightharpoonup}}{{ \overrightarrow{t}}}+\frac{\stackrel{{\rightharpoonup}}{{ \overrightarrow{F}}}_{pot}+\stackrel{{\rightharpoonup}}{{ \overrightarrow{F}}}_{slb}+\stackrel{{\rightharpoonup}}{{ \overrightarrow{F}}}_{dt}+\stackrel{{\rightharpoonup}}{{ \overrightarrow{F}}}_{ot}}{m}\right)+\frac{\stackrel{{ \rightharpoonup}}{{\overrightarrow{F}}}_{sp}+\stackrel{{ \rightharpoonup}}{{\overrightarrow{F}}}_{dp}}{m} \tag{1}\]  
Figure 1: The orbital perturbation model of satellites under the factors of third-body gravity, non-uniform gravity, and light pressure. Figure 2: The magnitude of the perturbation force on satellites in different orbits. Among them, \(\overrightarrow{t}\) is the position of the satellite in the inertial coordinate system, \(\overrightarrow{t}=(x,y,z)^{T}\); \(\overrightarrow{a}_{g}\) is the sum of the conservative forces acting on the satellite; \(\overrightarrow{a}_{g}\) is the sum of the non-conservative forces acting on the satellite surface; \(GM_{e}\) is the gravitational constant of the Earth; \(\overrightarrow{F}_{pot}\) is the perturbation force of the Earth's gravitational field; \(\overrightarrow{F}_{stb}\) is the gravitational perturbation of the sun, moon, and other planets; \(\overrightarrow{F}_{dt}\) is the solid tide perturbation; \(\overrightarrow{F}_{ot}\) is the ocean tide perturbation; \(\overrightarrow{F}_{sp}\) is the solar light pressure perturbation; \(\overrightarrow{F}_{dp}\) is the Earth's atmospheric resistance perturbation. For the convenience of solving, the equation of motion is usually transformed into a first-order differential equation, and then the variational equation is solved by numerical integration method according to the initial parameters of the satellite orbit. In this paper, the initial parameters of the satellite orbit are obtained by the two-line element (TLE), and the initial position and velocity of the satellite can be obtained by solving the TLE data into six Keplerian numbers \(\sigma=(a,e,l,\Omega,\omega,\
u)\). Where \(a\) is the semi-major axis of the orbit, \(e\) is the orbital eccentricity, \(l\) is the orbital inclination, \(\Omega\) is the ascending node right ascension, \(\omega\) is the argument of perigee, and \(\
u\) is the true perigee angle. Figure 3 illustrates the specific meaning of the six elements of the orbit and their relationship to the Earth's inertial coordinate system. The mechanical models used for various perturbation forces are shown in Table 1.  
The extracted initial parameters of the satellite orbit and the corresponding time are taken as the initial conditions of the orbit integration. The Runge-Kutta method [14] in the finite difference calculation method is used in reference [15] to
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 6 Numerical Results and Discussions
### Modeling of LoRa/LoRaWAN
#### 6.2.1 Effect of _SF_ and _CR_ on Consumed Energy  
Using Equation (15), Figure 8 displays the time on air as a function of payload size at different _SF_ and _CR_ values. The bandwidth channel is set to 500 kHz. When the _SF_ is high, the time on air increases  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Useful bits to transmit & 32 bits \\ Battery capacity & 950 mAh \\ Self-discharge current of the battery & \(7.5\times 10^{-3}\) mA \\ Transmission power & 13 dBm \\ Supply voltage (MCU, transceiver) & 3.3 V \\ Supply voltage (Sensor unit) & 2 V \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the application.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Modes** & **Characteristics** & **Explanations** \\ \hline Mode 1 & \(BW=125\) kHz, \(SF=12\), \(CR=\frac{4}{5}\) & \begin{tabular}{c} \(\text{Largest distance mode (max range and slow data rate)}\) \\ Intermediate mode \\ Minimum range, high data rate and minimum battery impact \\ \end{tabular} \\ Mode 2 & \(BW=250\) kHz, \(SF=10\), \(CR=\frac{4}{5}\) & \
\begin{tabular}{c} \(\text{Largest distance mode (max range and slow data rate)}\) \\ Intermediate mode \\ Minimum range, high data rate and minimum battery impact \\ \end{tabular} \\ Mode 3 & \(BW=500\) kHz, \(SF=7\), \(CR=\frac{4}{5}\) &
\begin{tabular}{c} \(\text{Minimum range, high data rate and minimum battery impact}\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Different LoRaWAN modes [31].  
Figure 7: Hardware of the sensor node.  
(Figure 8a), which means that the sensor node consumes more power to transmit data. The effect of \(CR\) on the time on air is depicted in Figure 8b. We note that increasing the number of encoding bits causes an increase of packet transmission, which allows consuming more power by the radio module.  
Figure 9a presents the consumed energy per useful bit as a function of the payload at different spreading factors (\(SF\)). Using Equation (21), it is noted that this energy decreases with the increase of the number of useful bits. This result is shown in Figure 9b, which depicts the evolution of the energy per useful bit as a function of \(SF\) for constant payload size (equal to 10 bits). As mentioned before, the greater value of \(SF\), the more time is taken to send a packet, so the more consumed energy is needed to transmit data.  
The effect of the coding rate (\(CR\)) on the energy per useful bit is depicted in Figure 10. When the coding rate decreases, the time on air and the consumed energy are increased.  
The results presented in Figures 8-10 show that optimizing the LoRa parameters such as \(SF\), \(CR\) and payload size is a key element to reduce the consumed energy by the sensor node.  
Figure 8: (**a**) Time on air vs. payload at different \(SF\); and (**b**) time on air vs. payload at different \(CR\).  
Figure 9: (**a**) Effect of \(SF\) on the consumed energy, \(CR=\frac{4}{5}\); and (**b**) energy per useful bit evolution as a function of \(SF\).
## 2 Algorithm Principle
### State Equation
#### 3.1.1 The State Equation of INS  
The error state equation of INS is as follows [19]:  
\[\dot{\mathbf{X_{S}}}(\mathbf{t})\mathbf{=}\mathbf{F_{IS}}(\mathbf{t})\mathbf{ X_{S}}(\mathbf{t})\mathbf{+}\mathbf{G_{S}}(\mathbf{t})\mathbf{W_{S}}(\mathbf{t}) \tag{6}\]  
where the state variable \(\mathbf{X_{S}}(\mathbf{t})\) is shown in Equation (5), and the expression of the process noise vector is as follows:  
\[\mathbf{W_{S}}(\mathbf{t})=[\mathbf{\varphi_{bx}},\mathbf{\varphi_{by}}, \mathbf{\varphi_{bz}},\mathbf{\sigma_{ax}},\mathbf{\sigma_{ay}},\mathbf{ \sigma_{az}}]^{T} \tag{7}\]  
where \(\mathbf{\sigma_{ax}}\), \(\mathbf{\sigma_{ay}}\), and \(\mathbf{\sigma_{az}}\) are the random offset noise of the accelerometer in the \(\mathbf{E}\), \(\mathbf{N}\) and \(\mathbf{U}\) direction, and \(\mathbf{\sigma_{bx}}\), \(\mathbf{\sigma_{by}}\), and \(\mathbf{\sigma_{bz}}\) are the random drift noise of the gyroscope in the \(\mathbf{E}\), \(\mathbf{N}\) and \(\mathbf{U}\) direction.  
\(\mathbf{F_{S}}(\mathbf{t})\in\Re^{15\times 15}\) is the state transition matrix. \(\mathbf{G_{S}}(\mathbf{t})\in\Re^{15\times 6}\) is the noise driving matrix, and the expression is as follows:  
\[\mathbf{G_{S}}(\mathbf{t})=\left[\begin{array}{cc}\mathbf{C_{b}^{n}}& \mathbf{O_{3\times 3}}\\ \mathbf{O_{3\times 3}}&\mathbf{C_{b}^{n}}\\ \mathbf{I_{3\times 3}}&\mathbf{O_{3\times 3}}\\ \mathbf{I_{3\times 3}}&\mathbf{O_{3\times 3}}\\ \mathbf{O_{3\times 3}}&\mathbf{I_{3\times 3}}\\ \end{array}\right]_{15\times 6} \tag{8}\]  
where \(\mathbf{C_{b}^{n}}\) is the coordinate conversion matrix, with its specific expression given later; \(\mathbf{X=X_{S}}\); \(\mathbf{F=F_{S}}\); \(\mathbf{G=G_{S}}\); \(\mathbf{W=W_{S}}\).
## 4 Results and Discussions  
### Experimental Measurement Results  
Using the measuring device shown in Figure 3, the velocity and transmission loss of ultrasonic waves passing through the graphite rod at four different frequencies, 25 kHz, 47 kHz, 60 kHz, and 100 kHz, respectively, were measured. The sample diameter was 50 mm, and the length was 200 mm and 400 mm, respectively. The two ends of the sample were processed flat and placed between the transmitting transducer and the receiving transducer. We applied butter between the transducer and the sample to realize the coupling between the transducer and the sample. We used Equation (6) to calculate the ultrasonic propagation velocity in the graphite electrode according to the first wave arrival time in the received signals of the two samples. Using the ratio of length and time difference to calculate the ultrasonic propagation velocity can reduce the influence of measuring equipment error on measuring results.  
\[c=\frac{l_{2}-l_{1}}{t_{2}-t_{1}} \tag{6}\]  
where, \(l_{1}\) = 200 mm, \(t_{1}\) is the first wave arrival time of the 200 mm sample, \(l_{2}\) = 400 mm, and \(t_{2}\) is the first wave arrival time of the 400 mm sample.  
Figure 8 shows the time and frequency response of the received signal in a 400 mm sample. The ultrasonic velocity in the graphite rod was 2275 m/s when the working frequency of the transducer was 25 kHz, 47 kHz, 60 kHz, and 100 kHz, respectively; the ultrasonic velocity in the graphite rod changed by only 2%, which is within the measurement error.  
Figure 7: Mesh settings for the model.  
When ultrasonic waves pass through the medium, the lattice structure will absorb part of the energy and reduce the wave amplitude. This attenuation is caused by the type of material, small holes, and small cracks. For long-distance measurements, attenuation is a major problem. Therefore, it is necessary to study the transmission loss of ultrasonic waves in graphite rods. This paper used Equation (7) to calculate the transmission loss. Figure 9 shows the time and frequency response of the received signal with a frequency of 47 kHz in different long test samples.  
\[a(\omega)=\frac{20\text{lg}A_{1}(\omega)-20\text{lg}A_{2}(\omega)}{l_{2}-l_{1}} \tag{7}\]  
where, \(A_{1}(\omega)\) and \(A_{2}(\omega)\) are the amplitude spectrum of the received signals in two samples with different lengths, and \(l_{1}\) and \(l_{2}\) are the length of the samples, respectively.
# An Evaluation of Mine Water Inrush Based on Data Expansion and Machine Learning
## 1 Introduction  
The mining environment in North China coalfields is complex. As coal mines continue to be mined deeper, the pressure on coal seam floors has also increased. Under the combined influence of various unfavorable factors, such as high pressure, high water content, and confining layer thinning, the risk of water inrush increases significantly. Through studies on the water inrush mechanism, many scholars have applied data analysis algorithms, such as regression analysis [1, 2, 3, 4], classification [5], support vector machine [6, 7, 8, 9], neural network [10, 11, 12], and random forest algorithms [13], to the prediction of water inrush in coal mines.  
Due to the small amount of water inrush data in coal mines, the data features required for the prediction model cannot be provided, which reduces the accuracy of the prediction model. This paper presents a new oversampling method. Different from the traditional SMOTE [14] method of randomly selecting sampling points and linear interpolation, this paper introduces the natural neighbor theory [15, 16, 17] to determine the neighbor relationships between samples. The outliers and noise values existing in the water inrush datasetare removed to reduce the risk of overfitting of the prediction model, and the water inrush data samples are divided into center minority class samples and borderline minority class samples [18; 19; 20]. Since the nearest neighbor of a borderline minority sample contains majority class samples, it is easier to generate new noise samples as sampling points. To address this problem, this paper presents a natural neighbor interpolation method to expand the amount of data. At the same time, for center minority samples, a spatial interpolation method is proposed to generate new synthetic water inrush samples.  
At present, the method of expanding the small sample data to achieve data balance is mainly the SMOTE algorithm, which functions by increasing the number of required category samples to achieve the purpose of expanding the dataset. Although the algorithm can effectively expand the water inrush dataset, due to the use of a few quasi-random linear interpolation methods, the diversity of synthesized samples is poor, and it is easier to synthesize samples with similar features to the selected samples, resulting in the overfitting phenomenon of too many similar features learned by the model. At the same time, because of the lack of a SMOTE classification screening mechanism, it is easier to synthesize noise samples in the face of complex sample spatial distribution. This paper presents an improved SMOTE (_ISMOTE_) algorithm based on natural neighbors and spatial interpolation to expand a water inrush sample set and uses the mutual information dropout sparse autoencoder (_MISAE_) to extract the characteristics of coal mine water inrush data and evaluate the risk of coal mine water inrush. At present, few studies have focused on water inrush prediction methods based on data augmentation deep learning.