esa-sceva/satcom-chunk-collection huggingface dataset:
# Design Optimization of Underground Mining Vehicles Based on Regenerative Braking Energy Recovery
## 3 Analysis of Energy Consumption after Installing the Regenerative Braking Energy Recovery System
### Analysis of Forces on the Underground Mining Vehicle
It can be represented as:  
\[\rm F_{g}=\lambda m_{g}sin\alpha \tag{5}\]  
Calculation of Regenerative Braking Energy Recovery for Mining Vehicles  
The motion equation of the mining vehicle during braking is given by:  
\[\rm Ma=-\Sigma F_{r}-F \tag{6}\]  
where \(\rm F\) represents the braking force exerted on the vehicle, in units of \(\rm N\). The calculation formula for the braking force \(\rm F\) is:  
\[\rm F=\mu mg \tag{7}\]  
Figure 8: Force analysis diagram of the underground mining vehicle. where \(\mu\) is the coefficient of friction, generally taken as 0.3 in this paper, m is the mass of the underground mining vehicle, and g is the acceleration due to gravity. When the underground mining vehicle is traveling on uphill or downhill sections of the track, the recoverable energy during braking can be calculated as:  
\[\mathrm{E}=\frac{1}{2}\mathrm{m}\Big{(}\mathrm{V_{1}}^{2}-\mathrm{V_{2}}^{2} \Big{)}-\bigg{(}\mathrm{G_{r}}\mathrm{mgcos}\alpha-\frac{1}{2}\rho\mathrm{A_{f}} \mathrm{C_{D}}(\mathrm{V}-\mathrm{V_{w}})^{2}\bigg{)}\mathrm{S}-\lambda\mathrm{ M_{g}}\mathrm{h} \tag{8}\]  
where S represents the braking distance of the vehicle, h represents the height difference before and after braking, \(V_{1}\) represents the velocity of the vehicle before braking, and \(V_{2}\) represents the velocity of the vehicle after braking. When the underground mining vehicle is traveling on a smooth section of the track, the gravitational potential energy component of the vehicle's braking energy is nonexistent. Therefore, the recoverable energy during vehicle braking is given by:  
\[\text{F}_{r}=\text{G}_{r}\text{mgcos}\alpha \tag{2}\]0  
At this time, the energy converted from the inertia of the vehicle during braking is mainly consumed by the vehicle's friction losses and aerodynamic drag. The remaining energy can be recovered through energy storage devices. The braking distance of the vehicle can be calculated using the following basic formula:  
\[\text{F}_{r}=\text{G}_{r}\text{mgcos}\alpha \tag{2}\]1  
In the given context, a represents the deceleration of the braking system, F represents the braking force, and m represents the mass of the underground mining vehicle. According to relevant coal mine regulations, when the length of a slope is less than 300 m, the maximum speed for transporting personnel or materials using mining vehicles is 3.5 m/s. When the length of a slope is greater than 300 m, the maximum speed is 5 m/s. The acceleration or deceleration for transporting personnel is 0.5 m/s\({}^{2}\).
## 5 Hardware Detection Circuit Debugging and Result Analysis  
### Differential Amplifier Module Testing  
This section simulates the process of eliminating common mode signals by outputting the signal from the detection coil to the first module of the hardware circuit, which is the differential amplifier circuit module. The test circuit for the differential amplifier is shown in Figure 9a. The signal source produces an effective signal with an amplitude of 100 uV and a frequency of 1 MHz, while the noise source generates a white noise signal in the range of 0-1 MHz as common-mode noise. These signals simulate the output signals from the detection probe. Oscilloscope 1's left channel (Channel-A) detects the waveforms of the input signals, while the right channel (Channel-B) detects the waveforms of the output signals. Multimeters 1 and 2 measure the AC voltage amplitude and DC offset of the output signals, respectively. Finally, distortion analyzer 1 measures the total harmonic distortion rate after differential amplification.  
Figure 9b displays the results from oscilloscope 1. The blue waveform represents the input signal mixed with noise signal, while the red waveform represents the output signal after differential amplification. The period of the output signal is 996.5 ns, and the voltage amplitude is 9.945 mV. The total harmonic distortion is \(-\)62.159 dB, and the signal noise distortion is 51.746 dB. The DC bias voltage is \(-\)0.553 mV. Upon comparing the waveforms of the input and output signals, it is evident that the differential amplifier effectively amplifies the signal while filtering out the noise, thus satisfying a preamplifier's design requirements.
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 5 Analysis of Node Transmission Capacity
### Calculation of Capacity of Nodes at Different Values of Power  
Suppose there are four randomly distributed nodes in a sea area of 20 \(\times\) 20 square nautical miles. \(B=100\), \(N_{0}=1\). The experimental parameters are shown in Table 3. We select one of the nodes as OS, and the other three nodes are the TSs. When own ship is interacting with a target ship, the signals of the other two ships interfere. Suppose that the transmission power of OS has a total of ten kinds of power, from one to ten, and three other ships set three groups of power according to the difference of signal power between them, namely, 1, 2, 3; 3, 6, 9; 1, 5, 10. The calculation is based on Formula (19).  
According to these three groups of experiments, as shown in Figure 10, for the fixed signal interference of other ships, the ship increases the transmission power, which makes the channel capacity of the ship continue to increase. When the ship transmits signals at a certain power, it is not that the smaller the power of the interference signal, the larger the channel capacity. This means that the power of our ship MANET equipment is not always large or small, but the optimal value should be selected within a certain range according to the experimental results. Notably, a linear functional relationship between number of nodes and radius of OS does not exist.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Parameters** & **Value** \\ \hline Sea area & A circular with a radius of 20 \\ The position of OS & (0, 0) \\ Transmission step & 1 \\ The number of TSs & 10 \\ Number of experiments & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental parameters and value of Section 5.1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Parameters** & **Value** \\ \hline Sea area & A circular with a radius of 20 \\ The position of OS & Random \\ The power of OS & 1–10 \\ The number of TSs & 3 \\ The position of TSs & Random \\ The power of TSs & 1, 2, 3; 3, 6, 9; 1, 5, 10 \\ Number of experiments & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental parameters and value of Section 5.2.  
Figure 9: Radius vs. distance & number of nodes.  
Figure 10: The capacity of nodes at different values of power.
## 3 Results and Discussion
### Lagoon Storage Results  
A total of 75% of the environmental impacts associated with Lagoon Storage are in the climate change category, as illustrated in Figure 3. The climate change and environmental impacts in other damage categories associated with dewatering, Lagoon Storage, Air Drying, and transportation operations are presented in Figure 4. As noted for Air Drying as a standalone treatment process, most of the climate change and resources impacts are associated with the Air-Drying step itself, although the Lagoon Storage treatment phase also contributes substantially to climate change impacts. In contrast, transportation contributes the most to the human health and ecosystem quality impact categories.  
The sources of the environmental impacts associated with Lagoon Storage are shown in Figure 5B. Biogenic emissions account for the majority of impacts, followed distantly by 'utilities/fuel', i.e., the electricity for dewatering and diesel for moving and turning biosolids.
## 2 Related Works
### Deep State-Space Model  
The deep state-space model builds upon the foundation of the state-space model by incorporating stochastic latent variables from variational autoencoders (VAE) [13; 14], thereby enabling scalable training through stochastic backpropagation and inference networks. The SSM model allows for modeling the temporal relationships of stochastic latent variables, extending the time dimension of VAEs. The integration of SSM and VAE permits the creation of flexible conditional distributions using neural networks, offering a viable approach to modeling uncertainty in latent variables. Deep state-space model primarily focus on unsupervised learning of complex temporal data probability distributions [9; 15], with wide applications in areas such as speech, music, video, and text generation [16; 17; 18]. These applications face similar challenges, characterized by complex high-dimensional temporal distributions with uncertainty and variability. For instance, video generation necessitates sophisticated architectures capable of modeling high-dimensional observation data at each time step while capturing long-term temporal dependencies. Similarly, engine sensor data exhibit characteristics such as nonlinearity, high dimensionality, dynamics, and strong correlations. Modeling sensor data in high dimensions at each time step serves the purpose of fault detection and diagnosis. Hence, the deep state-space model aligns well with the requirements of liquid rocket engine fault diagnosis.
# Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite
## 1 Introduction
Its successful operation has been demonstrated on-board HYPSO-1 during its first year in orbit. This paper is organized as follows. After providing a brief background in Section 2, the On-board Processing Unit (OPU) implementation, Minimal On-Board Image Processing (MOBIP) pipeline, and on-board processing pipeline architecture are described in Section 3. Testing and benchmarking of the system, as well as presenting in-orbit results are done in Section 4. Lastly, discussions and concluding remarks are provided in Section 5.
# Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction
## 2 Materials and Methods
### Activation Functions  
Activation functions are used to add the non-linearity behavior of the ANN [53; 54; 55; 56]. Without the activation function, the output of each layer of the ANN would just be the output of a linear model with number of parameters equal to the number of the neurons in each layer [54; 55]. Consequently, activation functions increase the overall performance of the ANN and add a nonlinear behavior to it, depending on the behavior of the activation function itself. Thus, if activation functions are not applied on the ANN, the ANN usually has limited performance and acts as a linear regression model [54; 55; 57; 59].  
Figure 5 shows the basic structure of the activation function, where \(x\) = inputs, \(w\) = weights, \(f(\Sigma)\) = activation functions, and \(y\) = outputs [54; 55].  
Figure 4: Long-short-term memory (LSTM) at time \(t\).  
The most common activation functions are hyperbolic tangent function, sigmoid function, linear function, ReLU (rectified linear unit) function, leaky-ReLU function, softmax function, and swish (a self-gated) function [55, 58, 59].  
In this work, we use the hyperbolic tangent function (_tanh_) as the activation function for our proposed ANN and DNN models. The tanh function is used for the input and hidden layers.  
In hyperbolic tangent function (\(-1\), \(1\))  
\[f(x)=tanh(x)=\frac{(e^{x}-e^{-x}\ )}{(e^{x}+e^{-x}\ )}\, \tag{11}\]  
However, the rectified linear unit (ReLU) activation function is utilized in the output layer to provide a non-negative solar radiation predictive value [54, 55, 56, 57, 58, 59].  
ReLU (rectified linear unit) function [\(0\), \(\infty\))  
\[f(x)=\left\{\begin{array}{l}0\ for\ x<0\\ x\ for\ x\geq 0\end{array}\right., \tag{12}\]
# PCB Defect Images Super-Resolution Reconstruction Based on Improved SRGAN
## 4 Simulation Experiments and Results Analysis
### Evaluation Indicators  
In this paper, the training of the experimental model will be HR circuit board defect images in accordance with the random cropping method for the image block, each patch was 320 \(\times\) 320 size, LR circuit board defect images using a dual three-time interpolation algorithm for downsampling pre-processing to obtain the patch size of 80 \(\times\) 80, each batch was sent to the model for training circuit board defect images for four, the choice of Adam optimizer to minimize the loss function, the parameter setting is A = 0.9. The learning rate of the network training is 0.001, and a total of 100 epochs are trained.  
The objective evaluation indices selected in this paper are Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measurement (SSIM), which is an image quality criterion based on the pixel level and reflects the degree of image degradation. The PSNR is a pixel-level image quality criterion that reflects the degree of image degradation, as shown in Equation (8):  
\[PSNR=10*\log_{10}\frac{MAX^{2}}{MSE} \tag{8}\]  
where \(MAX\) is the maximum value that can be obtained from the image pixels and \(MSE\) is the mean square error of the two images. A larger \(PSNR\) represents a lower degree of image distortion and a higher quality of reconstruction. The \(SSIM\) evaluates the image quality from three perspectives: brightness, contrast, and structure of the image, respectively, which are defined by Equation (9):  
\[SSIM(x,y)=[L(x,y)]^{\alpha}*[C(x,y)]^{\beta}*[S(x,y)]^{\gamma} \tag{9}\]  
where \(x\) is the set reference image; \(y\) is the reconstructed image; \(L\), \(C\), and \(S\) represent the brightness, contrast, and structure contrast functions, respectively; and \(\alpha\), \(\beta\), and \(\gamma\) are all numbers greater than 0, which are used to adjust the importance between each module. The subjective evaluation method is selected to visually demonstrate the effect of the leaky hole reconstruction effect image in the test set images, and the MOS evaluation method is used to discriminate the different scores of the same reconstructed PCB defect images according to the multiple evaluation values to take the mean value, the scoring criteria are between 1-5, and the MOS value of the original HD image is 5. The experiments in this paper also compare the improved super scoring algorithm in terms of the number of model parameters and the size of the model space with the SRGAN network is compared with the SRGAN network in terms of the number of model parameters and model space size.
## 1 Introduction
measured the hydrogen diffusion and accumulation in corroded steel wires and, combined with an electron microscopic analysis of fracture surfaces in actual bridge wires, concluded that the hydrogen penetrating the steel had not reached the critical concentration required to induce brittle fracture [27]. In summary, by correlating descriptions of the corrosion state with fatigue life, it is possible to assess the remaining service life of steel wires. However, the decrease in fatigue life due to corroded steel wires exhibits strong variability, necessitating further research into representative defects on the steel wire surface. This study employs numerical simulation methods based on Paris' law to establish numerical models for coplanar dual cracks and dual cracks with corrosion pits in steel wires. It investigates the effects of initial crack morphology and crack interactions on fatigue crack propagation and fatigue life. By combining the results of the fracture cross-sections from fatigue tensile tests with numerical simulations, it was found that fatigue crack propagation in coplanar double cracks can be divided into three stages: independent propagation, crack coalescence, and post-coalescence propagation. The interaction of multiple cracks is studied by comparing stress intensity factor amplification factors. In this paper, the double-crack region is combined into an equivalent single crack with the same depth to jointly assess the lifespan of the steel wire.
## 2 Chirp Rate Estimation  
### Theory Background  
The pioneer of CR estimation in the TF domain using the complex phase of STFT was [PERSON], who proposed a method for determining the instantaneous frequency rate a two-dimensional signal distribution [16; 17]. In general, the signal described by the following model will be considered:  
\[x(t)=A_{x}\exp(j\Phi_{x}(t)), \tag{4}\]  
for the amplitude \(A_{x},j=\sqrt{-1}\) and phase described as:  
\[\Phi_{x}(t)=\phi_  
In Reference [19] it was proposed that the \(\mathfrak{K}\) estimator can be calculated more efficiently utilizing the modified analysis window. Additionally, two new estimators were revealed. The uncertainty effect occurring in the \(\mathfrak{K}\) estimator has been reduced by the differentiation of the numerator and the denominator with respect to time (giving the \(\mathfrak{D}\) estimator) and frequency (giving the \(\mathfrak{F}\) estimator) giving the following relationships:  
\[\mathfrak{D}(t,\omega)=-\left(\frac{\partial^{2}\Lambda_{x}^{h}(t,\omega)}{ \partial t^{2}}\Big{/}\frac{\partial^{2}\Lambda_{x}^{h}(t,\omega)}{\partial \omega\partial t}\right), \tag{11}\]  
\[\mathfrak{F}(t,\omega)=-\left(\frac{\partial^{2}\Lambda_{x}^{h}(t,\omega)}{ \partial\omega\partial t}\Big{/}\frac{\partial^{2}\Lambda_{x}^{h}(t,\omega)}{ \partial\omega^{2}}\right). \tag{12}\]  
This method was tested using different types of signals and is described in the literature. Acoustic signals were processed using this method, which can be found in References [17; 20]. Radar applications utilizing this approach are presented in References [21; 22; 23]. In this paper, the PFSR application is proposed in order to verify the possibility af assessing the motion parameters of a target. Because the estimators given by Equations (10)-(12) are actually equivalent, further considerations are made using one of them to present the correctness of the concept. This is an example of using the idea in PFSR applications, and each of the estimators should give similar results. The comparison of estimators as well as their limitations and computational complexity have been made in the literature [19; 21]. Due to the smaller variance in comparison to the estimator \(\mathfrak{K}\) (see Section 2.2) and the lower sensitivity to noise (see Reference [21]), the authors decided to perform the tests using the estimator \(\mathfrak{F}\); however, the estimators \(\mathfrak{K}\) and \(\mathfrak{D}\) can be used in the same way.
## 2 Methods and Materials
### Materials
#### 2.2.3 LST Retrieval  
In this step, LSTs were estimated to determine HICS effectiveness. The LSTs were determined based on values measured via TIR sensors installed on a UAV. The TIR sensors measured the temperature of the target object based on the fixed emissivity of all objects. Accordingly, the temperature of the target object was overestimated or underestimated. For this reason, in the present study, bright temperature values acquired via TIR sensors were calculated as corrected LSTs that reflected the emissivity of each object. LSTs were estimated using a formula based on TIR remote sensing, as suggested by [PERSON] and [PERSON] [44]. This estimation requires bright temperature values of the study area (acquired via TIR sensors), along with the land surface emissivity and background temperature (T\({}_{\text{bkg}}\)) of the study area.  
The bright temperature values in this study were acquired using an FLIR Vue Pro R TIR sensor. The values observed via the TIR sensor are recorded in degrees Celsius. The land surface emissivity estimation algorithm utilized in this study was developed by [PERSON] et al. [45] using UAVs, with data obtained from the red and near-IR range bands of MicaSense RedEdge-MX [45]. The distribution of surface emissivity throughout the study area was determined. The T\({}_{\text{bkg}}\), a value required for atmospheric correction, was estimated based on downward longwave radiation according to the Stefan-Boltzmann law [43]. The estimation algorithm proposed by [PERSON] et al. [46] was used to obtain downward longwave radiation [46]. Atmospheric temperature and relative humidity are input data required for value estimation; measurements were made via automatic weather station sensors installed in the study area (Figure 8).  
Figure 7: Sky view factor map. The spatial extent of the study is inside the red line.
# Challenges and Future Trends in the Treatment of Psoriasis [PERSON] and [PERSON]
## 4 Future Aspects in the Treatment of Psoriasis
### Biologic Agents and Small Molecule Inhibitors under Development
#### 4.1.2 JAK Inhibitors  
Type 1 and type 2 cytokine receptors rely heavily on the Janus kinase (JAK) and signal transducer and activator of transcription (STAT) pathways. The JAK family consists of intracellular protein tyrosine kinases, including JAK1, JAK2, JAK3, and tyrosine kinase 2 (TYK2), while the STAT family comprises STAT1, STAT2, STAT3, STAT4, STAT5 (STAT5A and STAT5B), and STAT6. In psoriasis, the crucial IL-23 receptor is associated with JAK2, TYK2, and STAT3 [54]. While JAK 1-3 inhibitors have demonstrated efficacy in moderate-to-severe psoriasis patients, safety concerns have persisted, and no JAK inhibitor has received regulatory approval for the treatment of psoriasis [55]. Meanwhile, deucravacitinib is a selective TYK2 inhibitor that targets key inflammatory pathways involved in psoriasis pathogenesis. Clinical trials have demonstrated its effectiveness in reducing psoriasis symptoms and improving patients' quality of life [56].
## 7 Evaluation and Discussion
### Experimental Results  
#### 7.2.1 Runtime Overhead  
In order to accurately calculate the execution time of the TPIs of different lengths, we tested these applications four times separately using the execution time calculator mentioned earlier.  
Figure 6 shows the test results. Due to different applications, different key code segment lengths, and different difficulty levels of key code segments, the execution times are obviously different from each other. Figure 6a represents all the test results, and we can see that the results of HViewer, Calculator, and Csnake are not clear. Therefore, we draw another four subfigures. Figure 6b shows the test results of Compress, and Figure 6c to HViewer, Figure 6d to Calculator and Figure 6e to Csnake. Table 3 shows the test results in detail. Take IpMsg.exe as an example, when TPI length is 0, that is to say, we don't insert any TPI Handler into the protection area, the execution time corresponds to the original time. To be clear, we collected 20 different execution times and calculated the average value, so when TPI length is 32 bytes (which means that the protection range is 32 bytes), the average execution time of the key code segment is 4462 \(\upmu\)s. Similarly, when TPI length is 16 bytes and 8 bytes, the average execution time is 4557 \(\upmu\)s and 4761 \(\upmu\)s, respectively.  
In order to see the execution time increment transparently, we defined:  
\[T\_Incre_{rate}=\Delta Time/O\_Time. \tag{1}\]  
We used the formula to calculate an increment for different TPI lengths. From Figure 7, we still took IpMsg.exe as the example, when TPI length is 32 bytes, the increment is 7.8%, and when TPI lengths are 16 bytes and 8 bytes, the increments are 10.1% and 15.02% separately. Please note that the unit of time is microsecond (us). You have not even \"felt\" that the application has finished executing.