esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design and Configuration of DPMS  
### System Description  
The DPMS is shown in Figure 1 with rotating the platen and the swing-arm motion on a CMP tool. The monitoring mechanism is based on a concentric circle as shown in Figure 0(a). The surface topography is built by height information from a chromatic confocal sensor. This developed DPMS is divided into motion module and measuring module. The system is designed to ensure the movement does not have any interference with the space constraints of the polishing tool. Experimental set-up is shown in Figure 0(b). A chromatic confocal measurement sensor with STIL MG140/CL3 sensor is used in the measuring module. The chromatic confocal measurement is mainly done through the multi-wavelength white dispersion lens and spectrometer design. Different wavelengths of light are focused to different height positions, and through the pinhole design, unfocused wavelengths are blocked out, by avoiding entering inside the spectrometer, while controlling the hole size to control the measurement accuracy.  
With dynamic measurement by the swing-arm confocal system, the raw data contains surface non-uniformity, groove depth, surface height change or roughness [13]. The system disturbance from motor vibration or electrical noise needs to be considered for pre-processing of analysis. A filter based on FFT method is used and the filtered signals from specific frequencies are identified before the experiments [14; 15; 16]. Then, metrology data can be obtained from the signal measured from the system by reducing the disturbance of external environment.  
Figure 1: Dynamic swing-arm chromatic confocal system.
## 2 Data Collection
This feature renders RNNs particularly adept in applications where understanding dynamic temporal relationships in data is paramount [24]. In the realm of neural network development, traditional RNNs often face challenges in managing long-term dependencies during the training phase. This difficulty is primarily attributed to issues like escalating gradient accumulation errors and the phenomenon of vanishing gradients. To mitigate these challenges, advanced architectures such as LSTM and GRU networks have been introduced. These networks utilize multiple gates to significantly reduce error accumulation in extensive time-series analysis. Gradient accumulation, in contrast to the vanishing gradient, is a scenario where the gradient magnifies progressively, leading to a series of weight updates that can result in the divergence of gradient descent. GRUs present an advantageous alternative to LSTMs, offering similar functionality but with a more streamlined model architecture. This simplification enables GRUs to replicate LSTM-like behaviors while achieving faster convergence during training. GRUs are adept at inheriting and refining the patterns recognized by traditional RNNs, particularly in identifying dependencies in sequential data. They effectively address the limitations associated with traditional RNNs, such as increased gradient accumulation errors, vanishing gradients, prolonged processing times, and the tendency for overfitting in long sequence processing. Moreover, GRUs enhance the network's capabilities in local optimization and generalization [50]. Furthermore, the ConvGRU represents a significant evolution over standard GRUs, especially in handling two-dimensional time series data. Unlike the nearly fully connected architecture of LSTM and GRU networks, which can lead to redundancy issues and a neglect of spatial correlations in two-dimensional data, ConvGRUs integrate temporal and spatial features more effectively. ConvGRUs evolve the GRU framework by substituting the traditional dot-product operations with convolutional processes, thereby enriching the network's ability to process spatial information within time series data [51]. This research focuses on employing ConvGRU networks for the prediction of ionospheric brightness, highlighting their enhanced capabilities in handling complex spatiotemporal data. The subsequent sections will provide a detailed exploration of both GRU and ConvGRU networks, underscoring their structural nuances and applicative strengths.
# Combustion and Emission Characteristics of a Diesel Engine with a Variable Injection Rate
## 1 Introduction
[14] used high-speed cameras to study the effects of ultrahigh injection pressure on the flame structure and soot generation characteristics of an impact diesel engine and employed a two-color flame measurement method to measure the soot discharge temperature and concentration. According to their findings, an ultrahigh injection pressure can increase the flame structure size and decrease the soot generation level. [PERSON] et al. [5] studied the specific influence of injection pressure on flash boiling spray breakup and its underlying mechanism and compared the impacts of injection pressure on spray shape and spray breakup. This study revealed that, for cold spray, the injection penetration distance can be reduced, and the spray width is not significantly increased if a relatively high injection pressure is used; for overheated spray, increased injection pressure dramatically decreases the spray width and slightly increases the injection penetration distance. [PERSON] et al. [15] analyzed the combustion characteristics of a high-speed diesel engine with different injection rates. A rectangular injection rate leads to the shortest ignition delay time, while a corresponding premixed combustion generates the highest heat release and the largest heat efficiency. According to their findings, a decrease in the natural gas injection rate significantly decreases the in-cylinder pressure, heat release rate, and average peak temperature. Nevertheless, most of the above studies have addressed the effects of injection pressure on the combustion and emission characteristics of a diesel engine but seldom focused on the mechanisms by which a varying injection rate can cause these influences. Some studies might have involved analyses of the influences of the injection rate, but only non-qualitatively. Therefore, there is an urgent need to explore the combustion and emission characteristics of a diesel engine with various injection rates and discover the regularity of variation to provide a basis for improving the overall performance. In this paper, a diesel engine was used to reveal the mechanisms by which its combustion and emission characteristics changed at different injection rates. An ultrahigh-pressure common rail system was, therefore, proposed, and a simulation model was developed to explore the effects of controlling the injection rate of this engine. Subsequently, fire software was employed to build a simulation model of the diesel engine combustion chamber. The accuracy of the model was then tested and verified. On this basis, the simulation model was used to analyze the mechanisms by which the combustion and emission characteristics of the diesel engine were affected by various injection rates and boot-shaped injection rates. Besides the related effect coupled with different main injection advance angles, pilot injection quantities and pilot injection advance angles are revealed.
## 5 Results
### Comparison between \"with\" and \"without\" Periods in BMI (Behavior Monitoring Indicator)  
This study also compared the \"with\" period with the \"without\" period based on the BMI calculated from Equation (1) to see if the driving behavioral difference due to the in-vehicle warning information is consistent over time. Figure 8 shows monthly box plots of BMI between July 2019 and October 2020 where both \"without\" and \"with\" periods are included. In each box plot, the top and bottom edges of a box show the first and third quartiles, and the horizontal line within the box shows the median value of the observations. A monthly average BMI is highlighted as a triangle shape, and the average BMI by period is represented as a dotted line.  
In Figure 8, we can see that the median BMI was between 55% and 59% during the \"without\" period and between 62% and 85% during the \"with\" period. Without exception, all median BMI during the \"with\" period was higher than \"without\". It appears that the variation in the height of the boxplot is larger in the \"with\" period. The difference in average BMI values between the two periods is about 13%. Figure 8 shows that more drivers responded to the warning when it was actually provided. Focusing on the trend of the monthly BMI over the \"with\" periods, the BMI tended to increase from July 2019 to September 2019, which was the initial period of providing FCWS warnings. Since that time,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{\(\mathbf{H_{0}}\): \(\mathbf{Spacing}_{before}\) = \(\mathbf{Spacing}_{after}\)} \\ \hline \multirow{3}{*}{**Spacing**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Standard Deviation**} & \multirow{3}{*}{**t**} & \multirow{3}{*}{**\(p\)-Value**} \\ \cline{2-2} \cline{4-6}  & **Before** & & & & **After** & \\ \hline ”without” & 56.81 & 55.52 & 28.77 & 28.24 & 2.2779 & 0.9886 \\ ”with” & 41.36 & 52.84 & 33.13 & 28.39 & \(-\)44.0336 & 0.000 \({}^{*}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: T-test result in spacing.  
Figure 8: Timeseries of monthly BMI over 13 months.  
the median BMI tended to decrease a little and then it stabilized at the average value, which was 69.3%. We suspect that some drivers selectively react to the in-vehicle warning after going through an adaptation phase. Perhaps the relatively high variation in BMI during the \"with\" period may also be explained by that reason.
# Extending Appearance Based Gait Recognition with Depth Data
## 5 Experimental Validation
### Experimental Setup
#### 5.1.4 Classification  
All experiments were performed on three different population sizes, a population with 32 subjects, here arbitrarily denoted as _small_; a population with 155 subjects, denoted as _medium_; and a population with 305 subjects, denoted as _large_. The rationale for choosing these particular values was that the number of subjects for the _large_ population size corresponds to the maximum number of subjects available in the dataset at the moment of writing, while the values for _small_ and _medium_ were chosen with comparability with the state-of-the-art [5] in mind. In the _large_ population case, the recognition was performed between 305 individuals, making it a 305-class classification task with a chance level of 0.33% for all experiments. In the _medium_ and _small_ cases, the recognition was performed between 155 and 32 individuals, making it a 155-class and 32-class classification task with chance levels of 0.65% and 3.1%, respectively, for all experiments.  
Following the ideas presented in [5], a GAID dataset was split into training and test subsets. The training subset was comprised of the first four GEI silhouettes from _normal_ variation while the test subset contained all remaining GEI silhouettes, two from _normal_ variation, two from _backpack_ variation, and two from _shoes_ variation for each of the 305 subjects in the dataset. Test subset also contained six _time normal_, two _time backpack_, and two _time shoes_ variations for each of the 32 subjects in a subset recorded for a second time. The training and testing sets are balanced, i.e., the same number of samples from each class were used in both cases.  
Four different classification algorithms were used for classification and independently evaluated: k-nearest neighbors (kNN), Support Vector classification (SVC), Random forest classification (RFC), and Gaussian Naive Bayes (GNB). After training, each of the samples from the test subset was scored and results were split by the variation of the recorded sample (N, B, S, TN, TB, and TS) for each of the methods tested.  
Tested methods included a method based only on the height feature (Height), a method based on GEI feature (GEI), a method based on BGEI feature (BGEI), Height-GEI integration (HGEI-i), Height-GEI fusion (HGEI-f), Height-BGEI integration (HBGEI-i), and Height-BGEI fusion (HBGEI-f).  
For each sample, the classification based on GEI, BGEI, and Height features was done independently from each other, as well as the classification based on HGEI-i and HBGEI-i. HGEI-f and HBGEI-f methods used the results from GEI and Height classification.
## 4 Flexible Route Planning Method Combining Q-learning and Graph Search
### Outline of the Proposed Algorithm  
The flowchart of the proposed method for solving the route planning problem is shown in Figure 7. First, a Q-function is created offline such that AGVs can learn the actions avoiding collisions and interferences with each other by Q-learning. Then, route planning is conducted. The route planning is implemented by repeating the shortest path search, traveling, state observation, and action updated by the A* algorithm. The details of the route planning algorithm for each AGV are described as follows.  
Figure 7: Flowchart of the proposed algorithm. The flowchart on the right side shows the process of building a learning model for Q–learning. The flowchart on the left side shows the process of planning a route using the Q–learning training model that was created.  
Step 0 Q-learning  
This step generates a Q-function that enables AGVs to avoid collisions. The state space is created randomly, and the rewards are obtained from state observation. Then the action choice is conducted, and the next state is obtained by the action. The process of updating the Q-function using this data is repeated a certain number of times. The Q-function is created only at the first time of route planning. If the Q-function has already been created, the next step is performed.  
Step 1 Task assignment  
In route planning, a task is first assigned to each AGV. Tasks are assigned to the AGVs that have no task.  
Step 2 A* search  
Each AGV independently finds the shortest path using A*, regardless of each other's AGV position.  
Step 3 Traveling following the derived path  
All AGVs travel along the path explored by the A* algorithm. Simultaneously, all AGVs observe their own state space. If there is another AGV in front of its own state space, it travels to Step 4 due to the possibility of a collision between AGVs. Otherwise, this step is repeated.  
Step 4 Update actions  
If the possibility of collisions is recognized in the previous step, the action update is performed using the Q-function. In the action update, the state number is derived from the state space observed in the previous step and is referenced to the Q-function. The Q-function returns the action with the highest Q-value, and that action is chosen as the new action. After updating the action, it returns to Step 2 and repeats Step 2 to Step 4 until all AGVs have completed their tasks.
# Influence of MOSFET Parasitic Capacitance on the Operation of Interleaved ZVS Boost Converters
## 6 Influence of Output Capacitance  
A possible explanation for the differences shown in Figure 7 is the impact of output capacitance \(C_{\text{oss}}\) on the resonant frequency, as it is connected in parallel to the resonant capacitors, as shown in Figure 9. The total resonant capacitance is the sum of the used capacitor \(C\) and the output capacitance \(C_{\text{oss}}\). Therefore Equation (3) must be altered to:  
\[\omega_{0}=\frac{1}{\sqrt{L(C+C_{\text{oss}})}}. \tag{17}\]  
Output capacitance increases the resonant capacitance, therefore decreasing the resonant frequency which for a switching frequency value \(f_{\text{s}}\) results in a lower normalized frequency \(f_{\text{s}}\)/\(f_{0}\). However, because of the nonlinear character of \(C_{\text{oss}}\) its impact on the resonant frequency is variable.  
Figure 8: Waveforms of the gate-source voltage, drain-source voltage and phase current of phase 1 with a single turn-on in zero-voltage conditions highlighted 6. Influence of output capacitance  
Figure 9: Topology of the examined converter with nonlinear parasitic capacitance \(C_{\text{oss}}\).  
Discharging of resonant capacitance begins when its voltage is equal to \(V_{\rm o}\). Therefore \(C_{\rm oss}\) is a function of \(V_{\rm o}\), thus making it variable with \(V_{\rm in}\) and \(f_{\rm s}\). Output capacitance especially impacts the resonant frequency at low voltages, as \(C_{\rm oss}\) usually gets lower at higher voltages. To consider \(C_{\rm oss}\) in calculations using Equation (13) the plot given in the transistors' datasheet was digitized and the resonant frequency and characteristic impedance value were updated at each calculated point. The analysis shown in Figure 7 was redone and plotted, as shown in Figure 10. The same experimental results were used to compare them with redone calculations. As it can be seen both curves are nearly identical, with the highest difference in \(G\) being below 0.05. Therefore including \(C_{\rm oss}\) in calculations is necessary to obtain results similar to the physical converter. The results can also explain why the difference between the analytical results and experimental measurements is larger at higher values of normalized switching frequency. As the switching frequency rises the output voltage gets lower (Figure 4), thus resulting in the growth of the output capacitance \(C_{\rm oss}\). Therefore the difference between the additional capacitance \(C\) and its sum with the output capacitance rises thus increasing the discrepancy between both curves. The analysis of output capacitance influence on the voltage ratio could also lead to an interesting deduction. Namely the analyzed converter topology would achieve different voltage ratios for the same values of normalized switching frequency and normalized load resistance, if the input voltage varied. Because of the variation of input voltage, the output voltage would change, thus changing the output capacitance, which would in turn impact the resonant frequency.
# Event-Sampled Adaptive Neural Course Keeping Control for USVs Using Intermittent Course Data
## 4 Control Design
### Stability Analysis of Control Law
That is, \(\forall t\in[t_{\kappa},t_{\kappa+1})\) and \(\forall t\in[t_{\ell},t_{\ell+1})\), \(\dot{\dot{\psi}}(t)=0\) and \(\dot{\tau}(t)\). Taking the time derivative of \(e_{\varphi}\) and \(z\), one can obtain  
\[\left\{ \begin{aligned} \frac{d|e_{\varphi}|}{dt}& \leq|\dot{\psi}-\dot{\dot{\psi}}|\leq|\dot{\psi}|\\ \frac{d|z|}{dt}&\leq|\dot{\tau}-\dot{\dot{\tau}}| \leq|\dot{\tau}|\end{aligned} \right. \tag{49}\]  
According to (1) and (2), one can obtain \(\dot{\psi}=\chi_{2}\). One can determine the boundedness of \(\chi_{2}\), which implies that \(\dot{\psi}\) is bounded by \(|\dot{\psi}|\leq\sigma\dot{\psi}\) with \(\dot{\psi}\) being a positive constant. In addition, from (38) and (39), one has  
\[\dot{\tau}=\left(\frac{\partial\dot{\tau}}{\partial s_{2}}+\frac{\partial\dot {\tau}}{\partial\tanh(*)}\frac{\partial\tanh(*)}{\partial s_{2}}\right)\dot{ s}_{2}+\frac{\partial\dot{\tau}}{\partial\dot{\theta}}\dot{\theta}^{2}+ \frac{\partial\dot{\tau}}{\partial\dot{\phi}(x)}\left(\frac{\partial\phi(x)} {\partial\chi_{2}}\dot{\chi}_{2}+\frac{\partial\phi(x)}{\partial\dot{\alpha}} \dot{\alpha}\right) \tag{50}\]  
Furthermore, from (27), (35), (39), and the boundedness of \(\dot{\alpha}\), one can understand that \(\dot{\theta}\), \(\dot{\chi}_{2}\), and \(\ddot{\alpha}\) are bounded, which implies that \(\dot{\tau}\) is bounded by \(\tau_{z}\). Therefore, there exist two constants satisfying \(\lim_{t\to\kappa+1}\lvert e_{\dot{\varphi}}\rvert=\bar{e}_{\dot{\varphi}}\) and \(\lim_{t\to\ell+1}\lvert z\rvert=z\), showing that the inter-execution intervals \(\sigma t_{\kappa}=t_{\kappa+1}-t_{\kappa}\) and \(\sigma t_{\ell}=t_{\ell+1}-t_{\ell}\) are bounded by \(\bar{\kappa}\) and \(\bar{\ell}\) for \(\forall\kappa,\ell\in N\), respectively. Therefore, one has \(\sigma t_{\bar{k}}\geq t_{k}^{\prime}\geq\frac{\bar{e}_{\varphi}}{\bar{k}}\) and \(\sigma t_{\ell}\geq t_{\ell}^{\prime}\geq\frac{\bar{\tau}}{\bar{\ell}}\), which indicates that the Zeno behavior [25] can be avoided, i.e., the infinite triggering issue cannot occur. **Remark 10**. : _Compared with references [23; 24; 25; 26], we proposed a dual-channel ETM to reduce the transmission of course data and control command. In references [23; 24; 25; 26], the transmission of control commands is reduced by construing an ETM on a C-A channel to achieve the requirement of saving communication resources. To do this, the method proposed in references [23; 24; 25; 26] is effective. However, the authors of references [23; 24; 25; 26] only considered a single channel, and did not consider the data transmission from sensors to controllers under the network environment. Therefore, we not only drew on their experience, but also established an ESM in S-C channel, which is only used to transmit intermittent course data. Moreover, it cannot only ensure the intermittent heading data required by the control design requirements, but also reduce the data transmission to a greater extent and save communication resources._
## 3 Trajectory Optimization Design
### Adaptive Genetic Algorithm
#### 3.3.4 Operators  
The genetic operator operation consists of three major operations: selection, crossover, and mutation. To deal with the time optimization of the pickup manipulator, they simulate the biological genetic evolution mechanism in different periods.  
(1) Select Operation  
The selection operation is an operation of evaluating and selecting individuals in the time population according to the fitness function of the time-optimal trajectory planning of the pickup manipulator. The commonly used selection algorithm is the fitness proportional method, which is also called the roulette method [24] or Monte Carlo selection method. So, this work takes the roulette method to select individuals in the time population. The solution is to calculate the individual fitness in the population according to the fitness function. An individual with big fitness will be selected in the next generation because the probability of an individual in the population being selected is proportional to the fitness. The specific steps are as follows:  
1. Calculate the fitness \(f(x_{i})\) of individuals in the time population of the pickup manipulator and \(i=1,2,\cdots,N\), \(x_{i}\) is the initial population, \(N\) is the number of the initial population.
2. Calculate the probability that an individual will be selected: \[p_{i}=\frac{f(x_{i})}{\sum\limits_{u=1}^{N}f(x_{u})},i=1,2,3,\cdots,N,\] (19)
3. Calculate the cumulative probability of the individual: \[q_{i}=\sum\limits_{u=1}^{i}p(x_{u}),i=1,2,3,\cdots,N,\] (20)
4. Generate a uniformly distributed random number \(r\) within [0, 1];
5. If \(r\leq q[1]\), select individual 1, otherwise, select individual \(k\) such that \(q[k-1]\leq q[k]\) is valid;
6. Repeat the process e for N times.  
A visual depiction of the selection operation for the time individual of the pickup manipulator is shown in Figure 4. According to the probability \(p_{i}\) in the time population, the individuals are sequentially assigned to the corresponding areas in the roulette and form a complete roulette. The position corresponding to each rotation of the turntable is uniform, and the rotational position is generated by a random function.  
The random number \(r\) represents the cumulative probability \(q_{i}\). When the wheel stops rotating, the area pointed by the pointer will be selected into the next generation population.