esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Linking Early Robust Design and Optimal Parameter and Tolerance Design
### Effects of Robust Concept Design on Parameter and Tolerance Optimization
This entails a reduced dimensionality of the search space as well as a lower complexity of the transfer functions. However, the level of complexity reduction induced by the _Information Axiom_ differs significantly from the simplification described in the previous paragraph. Instead of eliminating the interactions between different KCs and transfer functions, Axiom 2 yields a reduction of the interactions of the parameters within a transfer function. However, the resulting reduced interactions in the parameter and tolerance definition also lead to basically better optimization results. Thus, there is a significant effect of the axiomatic conceptual design changes on robust parameter optimization as well as the tolerance-cost optimization usually applied in the subsequent stages. While both concept changes associated with the axioms have a positive effect on the optimization, the application of the _Independence Axiom_ is to be prioritized higher at first, since the complexity reduction caused by uncoupling may include a reduced information content in the then separated optimization. This is consistent with the Axiomatic Design philosophy according to [PERSON] [10]. Figure 4: Effects of concept design changes according to _Independence and Information Axiom_ on robust parameter and tolerance-cost optimization.
## 6 Application and Discussion of the Proposed Method
### Evaluation and Control of Tunnel Displacement  
The technical code CJJ/T 202-2013 [39] gives the early warning value of 10 mm and the control value of 20 mm for the horizontal and vertical displacements of the tunnel, respectively. The technical code GB 50911-2013 [40] gives the control range of 3-10 mm for the settlement of the tunnel and 3-5 mm for the heave and horizontal displacement of the tunnel. The above prediction results show that, excluding the existing tunnel displacement before construction, the excavation-induced tunnel displacement exceeds 10 mm in the podium building area and exceeds 20 mm in the tower building area, which has exceeded the thresholds in technical codes. To protect the tunnel from damage during basement excavation, some measures such as ground improvement, zoned excavation, and micro-disturbance grouting were taken, and field monitoring was adopted to monitor the tunnel and retaining wall displacement. [PERSON] et al. [44] give more details about the construction and monitoring schemes.  
The monitoring shows the maximum retaining wall deflection in the tower building area is 59.33 mm, which is about 0.17%\(H_{\mathrm{e}}\), and the maximum retaining wall deflection in the podium building area is 45.18 mm, which is about 0.20%\(H_{\mathrm{e}}\). The monitoring results of the retaining wall are close to the above assumption that the retaining wall deflection is \(\delta_{\mathrm{hm}}=0.18\%H_{\mathrm{e}}\). Therefore, the field monitoring results of tunnel displacement were used to verify the predicted results under the condition of \(\delta_{\mathrm{hm}}=0.18\%H_{\mathrm{e}}\).  
Figure 18 shows the horizontal displacement evolution curves of the up-track tunnel. Y20-Y29 are the monitoring sections along the longitudinal direction of the tunnel. If the tunnel is regarded as an initial state with no additional displacement before basement excavation, the maximum horizontal displacement of the tunnel induced by basement excavation is about 18 mm, which appears in the monitoring section Y20 corresponding to the tower building area.  
Comparing the predicted and monitored results, it is observed that the monitored horizontal displacement of the up-track tunnel is slightly smaller than the predicted results, which indicates that the adopted control measures effectively reduced the scope of the influenced zone and the adjacent tunnel displacement.
## 4 Numerical Simulation
### Influence of the Initial Velocity of the Vehicle
#### 4.2.2 Influence on the Load Characteristics of the Vehicle  
As shown in Figure 33, the maximum stress at the vehicle head increases with the initial velocity of the vehicle. When the initial velocity is 20-80 m/s, the maximum stress at the vehicle head is 4.93 \(\times\) 10\({}^{7}\) Pa, 5.61 \(\times\) 10\({}^{7}\) Pa, 9.48 \(\times\) 10\({}^{7}\) Pa, 1.09 \(\times\) 10\({}^{8}\) Pa, 1.20 \(\times\) 10\({}^{8}\) Pa, 1.55 \(\times\) 10\({}^{8}\) Pa, and 1.70 \(\times\) 10\({}^{8}\) Pa, respectively. This phenomenon is caused by the high strain rate when the ice collides with the vehicle moving at a high velocity. In this case, the strength of the ice increases, which causes higher resistance to the vehicle. In the process of breaking ice, the vehicle generates a great amount of broken ice, which might splash and hit the vehicle head. For this reason, a number of small peaks still occur after the maximum stress occurred.  
In Figure 34, the cloud diagrams of ice damage are presented for the vehicle's initial velocities of 20 m/s, 40 m/s, 60 m/s, and 80 m/s in the process of breaking ice. Evidently, in the early stage, the vehicle head intrudes into the ice and causes a small amount of broken ice in the vicinity of the collision point. When the vehicle constantly moves upwards, the damaged area of the ice expands, and the central part of the ice is \"pushed upwards\". After the vehicle head passes through the ice, the cylindrical section of the vehicle has friction against the ice, and a large amount of broken ice is scattered around the vehicle head. The time taken by the vehicle to pass through the ice at different initial velocities varies significantly. When the initial velocity is 20 m/s, 40 m/s, 60 m/s, and 80 m/s, it takes 0.76 ms, 0.44 ms, 0.27 ms, and 0.20 ms for the vehicle to pass through the ice, respectively.  
The relationship of the vehicle's maximum stress with axial distance and velocity is illustrated  
Figure 34: Cloud diagrams of ice damage at different initial velocities.  
Figure 35: Relationship of the vehicle’s maximum stress with axial distance and initial velocity.
## 5 Simulation and Experimental Results
### Experimental Result 2: Sensing Failover
The maximum packet length is 993 bytes, which requires only 4.4 ms to receive the packet, and we have 21 ms processing delay for detection. It takes 0.85 ms on average for V2 to perform decision-making including the estimation of future trajectory of V1. Moreover, V2 decides that the target object is a car. In total, only 29.36 ms is required until V2 begins decelerating, which is much shorter than human reaction time. An environment modeling for this scenario is shown in Figure 17. As shown in Figure 17b, it is predicted that the accident will happen after 0.7 s. However, under the proposed system, V2 can stop as soon as recognizing movement of the V1 as in Figure 17c. Figure 15: V2’s combined measurement and estimated trajectory results about V1: (**a**) Position comparison. (**b**) Measured and estimated velocities with respect to x and y axes with and without compensation. (**c**) Calculated acceleration comparisons. Figure 16: Confidence comparison: (**a**) Changes of the confidences based on the proposed data fusion scheme. (**b**) Changes of the confidences under the weighted data fusion scheme. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **V2** & **V3** & **V4** & **V5** & **Output(V2)** \\ \hline \multirow{2}{*}{Existence} & Existence & 0.1 & 0.1 & 0.7 & 0.9 & 0.58 \\  & Non-existence & 0.8 & 0.75 & 0.1 & 0.05 & 0.42 \\  & Uncertainty & 0.1 & 0.15 & 0.2 & 0.05 & 0.0 \\ \hline \multirow{2}{*}{Class} & Car & - & - & 0.153 & 0.153 & 0.329 \\  & Other 15 classes (person, bus, \(\cdots\)) & - & - & 5.64 \(\times 10^{-2}\) & 5.64 \(\times 10^{-2}\) & 4.47 \(\times 10^{-2}\) \\ \hline \multirow{4}{*}{Latency} & Detection time [ms] & 21.53 & 23.44 & 22.41 & 24.36 & \\  & Communication delay [ms] & - & 2.28 & 3.88 & 4.15 & 29.39 \\  & (message size [bytes]) & (-) & (513) & (873) & (933) & (totally) \\ \cline{1-1}  & Estimation+Decision [ms] & 0.85 & - & - & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results for the sensing failover case. Figure 17: Environment modeling: (**a**) Traces at \(t=0\) s. (**b**) Traces at \(t=0.7\) s without the anti-collision system. (**c**) Traces at \(t=0.7\) s with the proposed system.
## 2 Feature Extraction Method--Dual Time-Frequency Images Fusion  
### Data Preprocessing  
The time-domain signal of the compressor shows less characteristic information because the coupling between the fluid flow and the mechanical structure of the hermetic refrigeration compressor causes the vibration signal to have complex characteristics. It is necessary that the vibration signal is transformed in the time-frequency domain to obtain richer feature information. Time-frequency transform methods, such as Wigner-Ville distribution (WVD) [19], short-time Fourier transform (STFT) [20], continuous wavelet transform (CWT) [18] and Hilbert-Huang transform (HHT) [21], have been widely used to extract feature from vibration signal. However, WVD and STFT are hindered by low time-frequency concentration and cross-term interference, and they also need additional expertise and prior knowledge to identify the fault features [22; 23]. The most widely used methods are CWT and HHT. In the following, the principles of the two time-frequency transformations are introduced in detail.
# Pulsed Nanoelectrospray Ionization Boosts Ion Signal in Whole Protein Mass Spectrometry
## 1 Introduction
Here, 10 to 350 kHz externally pulsed nanoelectrospray ionisation (pulsed nESI) with nanoscale ion emitters is demonstrated for use in whole protein MS. During the course of this project, [PERSON] and [PERSON] reported the use of a high frequency pulsed nESI source with microscale ion emitters (4 \(\upmu\)m i.d. ), in which a DC voltage of up to \(\sim\)1500 V was superimposed onto a pulsed waveform of up to \(\sim\)4000 V to initiate and maintain nESI [55]. However, a direct comparison between the analytical performance of such a source to conventional direct current nESI was not reported. Here, we report the use of high frequency pulsed nESI with nanoscale ion emitters can be used to efficiently ionise molecules by rapidly increasing the voltage from 0 to \(\sim\)1.0 kV with pulse widths that range from 2.85 to 100 \(\upmu\)s (duty cycles ranging from 10 to 90%) and frequencies from 10 to 350 kHz. As a proof of concept, four prototypical test proteins were selected as test analytes of relevance to top-down MS (ubiquitin, Ubq; cytochrome C, Cyt C; myoglobin, Myo; and carbonic anhydrase II, CAII). By the use of pulsed, high frequency nESI with nanoscale ion emitters, the performance of MS for the detection of protein ions can be improved in terms of an enhanced sensitivity and decreased background chemical noise.
# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 2 Related Works  
### Deep Learning with Image Editing  
In recent years, deep learning technology has achieved significant breakthroughs in the field of image editing. Early deep-learning-based image editing methods mainly focused on using convolutional neural networks (CNNs) to extract and classify image features [26; 27], in order to achieve basic image operations such as image segmentation and edge detection [28; 29]. Subsequently, with the development of technology, the emergence of generative adversarial networks (GANs) has introduced new possibilities for image editing. GANs are capable of generating realistic image content and have been widely applied in tasks such as image style transfer [14] and super-resolution reconstruction. DragGAN [30] allows for arbitrary dragging of images for editing, enabling precise control over aspects such as the shape, layout, pose, expression, position, and size of objects. However, GANs are plagued by instability during the training process, and the controllability of the generated results is relatively weak [31].
# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 3 Renewable Generation and Load Probabilistic Forecasting
### Load Probabilistic Forecasting
mixture distribution model (MCM) was employed for the purpose of very short-term load forecasting considering residential households in Australia. The model forecasts on a half-hour-ahead resolution basis. The authors then proved the high computational speed as well as the acceptable competitive performance of their proposed model. Regarding interval forecasting on a day-ahead basis, the probabilistic forecasting NWP models can be adopted. For instance, the forecasting model can be constructed according to the weather ensemble prediction taking into account the consumption under different weather scenarios [63] or be built according to the quantiles of forecasting errors of the historical data [64]. In addition, a partially linear additive quantile regression model was proposed by [65] to develop a probabilistic forecast of day-ahead hourly electricity loads with a focus on the demand of peak hours in South Africa. As an example of data-driven models, the long short-term memory (LSTM) model was used to forecast the quantiles of electricity loads aiming to minimize the quantile pinball loss function [66]. Finally, ref. [67] assessed the benefits of using probabilistic methods to estimate the required day-ahead reserves. In this regard, the authors developed two probabilistic methods to forecast the system net loads. The results of probabilistic forecasts are then utilized to quantify reserves that are required to compensate for the intermittency and uncertainties of renewable generation.
## 3 Hybrid Topology Architecture
### Implementation
#### 3.2.2 Gateway  
The LoRaWAN gateway used for the implementation is Lorix One from Wifx [37]. This gateway is compact and includes an AMR Cortex-A5 600 MHz CPU, a 125 MB RAM, and an internal memory of 512 MB. It is also equipped with its own Operating System (OS), called Lorix OS, which is fully compatible with Chirpstack servers and other popular LoRaWAN server implementations. The gateway predominantly maintains its standard configurations. The only exception pertains to the MQTT broker.  
In our proposal, the nodes that are configured in mesh topology send their data to the hybrid nodes. The data is combined into one message, which the hybrid node sends to the Gateway. Gateways are configured to post the received data in a specific MQTT topic. The default topic for Chirpstack LoRaWAN implementations is _us915_0/gateway/l.GatewayID 1)/event/l.EventType 1)_, where the _us915\(0\) is changed according to the employed band. To extract the information from the combined message so that it can be re-directed to its respective topics, an intermediate MQTT topic was created. This was done by accessing the configuration file of the gateway and changing the default topic to _csc_tsc/gateway/l.GatewayID 1)/event/l.EventType 1)_.
## 3 Results and Discussion
### SEM Analysis  
To prove the presence of the above-mentioned cavities, SEM analysis was carried out. The results of the SEM analysis are presented in Figures 7 and 8. As can be seen from Figures 7 and 8, the surface of the MIPs is not smooth, and they have some irregularities that look like cavities. Also, it can be seen that the structure of the MIPs is heterogeneous and dense, having in some places cavities due to the harsh formation of the monomeric structure, and these cavities are the molecular imprints, which were obtained in various forms and types. As the structure is heterogeneous, it can be concluded that the bond between the template and the monomer is not stable because the silver is ionizing, and perhaps it is present in an ionic form, which is not a stable bond. Also, the diameter of the cavities was measured using the program ImageJ (version 1.46r). As a result, the average cavity size for MIP(DEGDMA) is 0.81 \(\pm\) 0.20 \(\upmu\)m and for MIP(DVB) is 0.68 \(\pm\) 0.23 \(\upmu\)m in diameter. The difference in sizes of cavities could be explained by the difference in the structures of the crosslinkers: the DEGDMA molecule is more flexible due to its long chain structure, and DVB has an aromatic ring that can affect the steric factor.  
Figure 6: Proposed reaction mechanism for obtaining MIP(DVB).
# Non-Ionizing Radiation Measurements for Trajectory Radars
## 3 Materials and Methods
### Measurement Setup  
A spectrum analyzer and a directional antenna (22 dBi of gain) are used to restrict signals from other sources into the frequency and space domains, respectively. The spectrum analyzer allows the average power measurement of a specific bandwidth centered on the carrier frequency of the radar signal, excluding emissions from sources in other frequency bands. The directional antenna allows the signal reception to be mainly from the direction of the radar's antenna. Furthermore, the use of a receiving antenna with such a gain ensures a high dynamic range for the setup.  
An isotropic probe-a quasi-isotropic antenna-is the most used on NIR measurements, because it can capture signals from all directions and in the three polarization axes, which will eventually compose the total human exposure. In the specific case of this work, the possible drawback of not measuring multipath signals due to the high receiving antenna directivity, its fixed linear polarization and cross-polarization discrimination, is avoided since radar half power beam width is extremely narrow (about 0.92\({}^{\circ}\)) and there are no significant features causing reflections in the scenario. Therefore, it is possible to use a directional antenna to measure the NIR levels exclusively from a trajectography radar, as proposed. Anatel establishes the possibility of using either an isotropic or a directional antenna [4].  
To measure the higher NIR levels possible, by capturing the main lobe of the radar emission, both radar and receiving antennas were precisely directed towards each other for all measurement points, except at point P01, due to a characteristic of the radar design. Its antenna could not be exactly positioned toward the receiving antenna, as shown in Figure 3. There is a difference of 16.43\({}^{\circ}\) in elevation between the desired position and the lowest possible radar antenna position. Thus, for the point P01, the NIR levels were measured from a side lobe of the radar antenna. Since P01 corresponds to the position of the optical designation instrument, this means an operator cannot receive radiation from the antenna's main lobe.  
The receiving antenna was connected to the spectrum analyzer and was placed at 1.60 m from the ground, assuming to be approximately the mean height of the Brazilian people. Furthermore, the radar's antenna and the receiving antenna were set to vertical polarization, and the measurements were performed for 6 min, resulting in a mean value of power. This measurement period is in accordance with Anatel regulations [4].  
The Channel Power function of the spectrum analyzer was used to perform the power measurement of the radar signal. It is designed to measure the average power across a given frequency band. So, it was set to 5800 MHz of center frequency and to 6 MHz of integrated bandwidth (IBW), encompassing at least the main and the first adjacent sidelobes of the signal spectrum [20]. The average power measurement was performed for all points, using the setup shown in Figures 3-6.
# Effects of Illuminance Level of Light Source on White Appearance of a Tablet Display
## 5 Modeling  
In applications, a predictive model can be developed to evaluate changes in perceived whiteness, serving as a reference for adjustments to provide optimal ambient lighting and display white point settings when using a display. This study employs an artificial neural network (ANN) to propose a perceived whiteness prediction model. Determining the input parameters is the initial step in training the machine learning model. This experiment used variables including CCT, illuminance levels, and the CIE 1931 (x, y) chromaticity coordinates of display stimuli as inputs to the model. The output was the whiteness response values from psychophysical experiments, ranging from 0 to 100. During the data preprocessing stage, the illuminance levels were first log-transformed. Subsequently, all inputs are standardized using z-score normalization, transforming the data with a mean of 0 and a standard deviation of 1. This ensures a standard normal distribution, speeding up convergence during model training. The dataset consisted of 608 records, split into training and test sets using the holdout method at an 80-20% ratio, resulting in 486 training and 122 test samples. Furthermore, 10% of the training set is used as a validation set during model training.  
The model architecture, as shown in Figure 9, included an input layer with four neurons, two hidden layers with fifty neurons each, and an output layer with a single neuron. Batch normalization was applied before the tanh activation function in each layer to make the training process faster and more stable. The loss function used is a mean square error (MSE). Additionally, a 25% dropout is added at the end of each layer to prevent overfitting. For hyperparameter settings, the Adam optimizer was employed  
Figure 8: The u’ and v’ of the whitest stimulus judged by the observers under 150 k and 1200 k at two CCTS (labeled with a cross-blue for the light source of 6500 K and orange for the light source of 3500 K).  
with a learning rate of 0.001. The model was trained over 3000 epochs with a batch size of 128. Figure 10 shows the training history of the mean absolute error (MAE) and loss of the ANN model for each epoch, respectively.  
For regression analysis, the coefficient of determination (R-squared) and root mean square error (RMSE) are primarily used to evaluate the machine learning models. The average test results for RMSE, MAE, R-squared, and Pearson's correlation coefficient are 7.128, 5.162, 0.892, and 0.956, respectively. Based on the evaluation metrics, the results demonstrate that the model can accurately predict subjective perceived whiteness, as indicated by the high R-squared value and acceptable RMSE. Figure 11 shows a scatter plot of the predicted values and the ground truth, where the distribution of the data points is closely aligned with the 45-degree line, representing a perfect prediction.  
Figure 11: Scatter plot of prediction.  
Figure 9: Architecture of whiteness prediction model.  
Figure 10: The history of training (**Left**: MAE; **Right**: loss).