esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiments
### Process Parameter Testing Points  
To optimize parameters to attain the minimum surface roughness, it is essential to establish an association between surface roughness and the process parameters. Additionally, the optimization process is constrained by the considerations of volumetric shrinkage, which represents another metric of product quality. Hence, it is crucial to establish an additional relationship between volumetric shrinkage and the process parameters. The relationships between surface roughness, volumetric shrinkage, and the injection molding process parameters are established based on experimental data collected from experiments carried out with predetermined combinations of process parameters. The preselected points are generated using the central composite design for a fractional factorial design (refer to [37] for a detailed discussion on this subject).  
The lower and upper limits of the process parameters determine the entire design space for the optimization. These limits are listed in Table 3. Recommendations from the producers of the plastic used (HDPE M80064 Series) [36] were the basis of the lower and upper limits for the melt temperature, mold temperature, and injection pressure. Conversely, the choice of boundaries for the packing time, packing pressure, cooling time, and injection speed was advised by experienced polymer engineers. Using the specified boundaries, a face-centered central composite design was employed to generate the test points, totaling 79 points [37].  
Figure 2: Figure showing details of mold used in this study: (**a**) picture of two halves of mold; (**b**) injection molding product dimensions (front and side views).
# Quantum Circuit Synthesis Using Fuzzy-Logic-Assisted Genetic Algorithms
## 4 Materials and Methods
### Fitness Evaluation  
Fitness functions are key to efficient GAs, as they guide the algorithm toward optimal solutions. While a naive fitness function may be used, which directly models the input problem, fitness functions can be enhanced with additional metrics to enable better search efficiency and faster convergence. Since efficient synthesis of quantum circuits for NISQ devices require simultaneous optimization of multiple objectives, the NSGA-II algorithm proves itself as a suitable candidate. Specifically, it allows the use of different objective metrics without needing to normalize them, assign weights to them or combine them into  
Figure 6: Three different crossover versions: (**a**) coarse-grained crossover, (**b**) fine-grained crossover, (**c**) mapping crossover.  
a single metric. This alleviates many of the issues which appear when trying to combine multiple metrics into a single one (e.g., using Euclidean distance). We discuss some of these issues in Appendix A for the interested reader.  
For a given chromosome, with circuit \(C_{syn}\) and unitary \(U_{syn}\), the approximation error \(\mathbf{E}(U_{in},C_{syn})\), shown in (3), is based on the Hilbert-Schmidt distance, and is adapted from [42]. This method is chosen over the more familiar Frobenius distance [73], \(\Delta_{F}=||U_{in}^{\dagger}U_{syn}-I||\), since the latter is not tolerant of global phase [42]. The chosen error function generates an error value between 0 and 1, where 0 indicates equivalence of two circuits/ unitaries and 1 indicates maximal error. The depth \(\mathbf{D}(U_{syn})\), and CNOT count \(\mathbf{G_{2}}(U_{syn})\) are obtained trivially by counting the number of layers and CNOT gates, respectively. The final fitness vector is therefore as shown in (6).  
\[\mathbf{E}(U_{in},C_{syn})=1-\frac{|\mathbf{Tr}(U_{target}^{\dagger}U_{syn})| }{N}\text{ where, }\ N=2^{n},\ n=\text{number of qubits} \tag{3}\]  
\[\mathbf{D}(C_{syn})=\text{Number of layers in the circuit} \tag{4}\]  
\[\mathbf{G_{2}}(C_{syn})=\text{Total number of CNOTs} \tag{5}\]  
\[\mathbf{F}(C_{syn})=\left[\mathbf{E}(U_{in},C_{syn}),\mathbf{D}(C_{syn}), \mathbf{G_{2}}(C_{syn})\right] \tag{6}\]
# Study of the Load-Bearing Characteristics of Bolts under Asymmetric Freezing Conditions
## 3 Results and Analyses
### Analysis of the Results of the Indoor Pulling Test of the Bolt
#### 3.1.2 Influence of Moisture Content on Ultimate Pull-Out Resistance  
Figure 8 shows that the ultimate pull-out resistance of anchors of the same anchorage diameter decreased with increasing water content of the soil. Taking 30 mm diameter anchors as an example, the ultimate pull-out resistances for 10, 12 and 14% water contents were 0.49, 0.451 and 0.4256 kN, and the increase in water content from 10 to 12 and 14% reduced the anchor pull-out resistance by 0.039 and 0.064 kN, respectively, reductions of 7.96 and 13.1%, respectively. The increase in water content from 12 to 14% reduced the ultimate pull-out resistance by 0.0  
Figure 8 shows that the displacement corresponding to the ultimate pull-out resistance reached by the same anchor decreased with increasing water content. Taking the 35 mm diameter anchor as an example, the displacements at 10, 12 and 14% of the ultimate anchorage force were 1.7, 2.2 and 2.9 mm, respectively. For each working condition, the 14% specimen was the first to reach the limit, followed by 12% and finally, 10%. This behaviour can be explained by the increase in water content that weakened the shear strength of the anchoring interface. When pulled by the same displacement, the soil with higher moisture content had less shear potential, and the contact interface was less capable of continuously exerting shear action, and thus, damage occurred earlier.  
When the water content increased, a large amount of freely movable pore water was present between the soil particles, the mobility of the soil particles was enhanced, the stability became poor, and the shear strength of the unit soil at the anchor-soil interface decreased. In turn, the smoothness of the contact surface was enhanced, and the ultimate anchorage force was reduced. When the water content of the anchored soil rises due to rainfall and other factors, the anchoring capacity of the anchor decays to different degrees.
## 5 Discussion
### Model Interpretability Analysis  
This study uses Grad-CAM [46] to extract gradient information from the third upsampling layer, showing the model's attention focus at different image locations to verify the interpretability of the model for the cell semantic segmentation task. As shown in Figure 11, the red areas indicate significant attention, while the blue areas represent redundant feature regions. By transparently overlaying the obtained heatmap with the original image, the attention regions of UNet and this method on the images can be observed. In the rice leaf  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MoNuSeg**} & \multicolumn{2}{c}{**GlandCell**} & \multicolumn{2}{c}{**Nucleus**} \\ \cline{2-7}  & **IoU**\(\uparrow\) & **Dice**\(\uparrow\) & **IoU**\(\uparrow\) & **Dice**\(\uparrow\) & **IoU**\(\uparrow\) & **Dice**\(\uparrow\) \\ \hline DeepLabV3+ [14] & 45.80 & 61.82 & 78.41 & 87.55 & 63.20 & 77.02 \\ UNet++ [15] & 47.01 & 63.06 & 80.58 & 88.90 & 63.11 & 76.64 \\ ResUNet [16] & 40.98 & 57.94 & 80.49 & 88.99 & 62.65 & 76.57 \\ SwinUnet [37] & 62.28 & 76.66 & 79.29 & 87.95 & 85.40 & 91.81 \\ UTNet [45] & 18.34 & 30.41 & 83.97 & 91.09 & 65.90 & 79.00 \\
**CellGAN** & **63.72** & **77.84** & **83.72** & **91.14** & **86.10** & **92.53** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model Generalization Experimental Results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scheme** & **UNet** & **Transformer** & **Improved MHSA** & **GAN** & **IoU** & **Dice** \\ \hline
1 & \(\surd\) & & & & 89.69 & 94.43 \\
2 & & & & \(\surd\) & 70.26 & 82.53 \\
3 & \(\surd\) & \(\surd\) & & & 91.00 & 95.29 \\
4 & \(\surd\) & & & \(\surd\) & 89.94 & 94.71 \\
5 & \(\surd\) & \(\surd\) & & & 91.30 & 95.45 \\
6 & \(\surd\) & \(\surd\) & \(\surd\) & & 92.49 & 96.10 \\
7 & \(\surd\) & \(\surd\) & \(\surd\) & & 93.21 & 96.49 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation Results Comparison.  
cell and GlandCell datasets, UTNet can only focus on the approximate region of the cells, but cannot clearly distinguish between cells and cell boundaries. In contrast, this method can accurately capture cell locations and distinguish cells from boundaries in the three datasets, except for MoNuSeg, demonstrating the model's excellent modeling capability. The failure to focus on effective cell locations in the MoNuSeg dataset may be related to the small data size.
## 3 Optical Error Analysis
### Optical Errors of FPI
#### 3.1.2 Non-Parallelism Error of FPI
Notably, this effect does not influence the DC component \(a_{0}I_{0}(\Delta)\), as the DC term remains unaffected by the angular misalignment-induced broadening mechanism.
## 3 Case Study and Parametric Analysis
### Calculation and Analysis of Critical State  
#### 3.2.1 Critical Magnification and Critical Percolation Channel Height  
The magnification in which the critical percolation channel appears is called the critical magnification. When the critical magnification increases, the height of the critical percolation channel decreases, and the sealing performance is enhanced. The squeezing pressure determines the waterproof ability by affecting the critical magnification and the height of the critical percolation channel. The larger the squeezing pressure, the larger the critical observation scale, the smaller the critical percolation channel's height, and the stronger the waterproof performance.  
As shown in Figure 8, at the initial stage when the nominal contact stress increases, the height of the critical leakage channel decreases rapidly. This is because, when the contact stress is very small, asperity on the contact surface does not deform sufficiently, and there are a large number of connected non-contact regions that can be found at the sealing interface, with no need to increase the observation scale; as a result, the height of the critical percolation channel mainly depends on the deformation of the larger asperity. As the nominal contact stress continues to increase, the decreasing trend of the critical leakage channel height becomes gentle, while the critical magnification increases rapidly. This is caused by that, with the increase in contact stress, the larger asperity of the contact surface is flattened, and the actual contact regions are mostly found among smaller asperities; this means that the critical percolation channel only could be found by increasing the observation scale.
# Potential of Deep Learning Methods for Deep Level Particle Characterization in Crystallization
## 3 Material and Methods
### Experimental Set-Up and Procedure  
The batch cooling crystallization experiments were carried out in a 1.2 L double jacket crystallizer with the model system L-alanine (Evonik Industries, purity \(\geq\) 99.7%) and ultrapure water (Milli-Q Integral System, 0.05 \(\upmu\)Scm\({}^{-1}\)). Experiments with different saturation temperatures (\(\theta_{sat}=40\)\({}^{\circ}\)C/50 \({}^{\circ}\)C) were performed in triplicate, respectively. In accordance to the solubility data [49]:  
\[c^{sat}(\theta)[g_{Ala}\cdot g_{sol}^{-1}]=0.11238\cdot exp(9.0849\cdot 10^{-3} \cdot\theta[^{\circ}C]) \tag{1}\]  
the components were weighed and transferred to the crystallizer. Then the crystallizer was heated up to 10 K over the saturation temperature of the system and kept at the same temperature for 1 h to ensure identical thermal history for each experiment. Afterwards it was cooled down to 20 \({}^{\circ}\)C with a cooling rate of 0.3 Kmin\({}^{-1}\). During the whole experiment the stirring rate was set to 200 rpm.  
Images were taken after crystallization, when the end temperature of 20 \({}^{\circ}\)C is reached, to determine the product quality. To reduce the influence of downstream processing the suspension was harvested and transferred to a self-constructed filter tower [30]. By starting the vacuum pump (Laborport N 840 G) the mother liquor is removed and the crystals are further humidified for 1 min. After the filtration step, the crystals were resuspended in saturated solution and fractionated in a sample divider (Fritsch, Laborette 27) to obtain representative subsamples. For \(\theta_{sat}=40\)\({}^{\circ}\)C and for \(\theta_{sat}=50\)\({}^{\circ}\)C, amounts of \(\frac{1}{8}\) and \(\frac{1}{32}\) of the crystal mass, respectively, are transferred to a collecting vessel. The sample was further diluted to approximately 1 L to obtain an optimal optical density on the image of 2-5% for dynamic image acquisition using the QICPIC equipped with the LIXELL wet dispersing system (Sympact, QICPIC R02). In this work, grayscale images (camera resolution: 2048 \(\times\) 2048 pixels; camera pixel size: 5.5 \(\upmu\)m \(\times\) 5.5 \(\upmu\)m; measurement module M6: 2:1 magnification) were recorded in a flow cell with cell gap depth 2 mm. An exemplary image is shown in Figure 1. The background is constantly grey and the crystals appear dark with a decisive contrast to the background.
###### Abstract
These off-shore investments currently result in a typical levelised cost of (produced) electricity (LCoE) in the range between USD 70 to USD 210 per MWh [2], with a mean around USD 90 per MWh, which is currently still higher than that of most onshore wind turbines estimates (USD 47-60 per MWh\({}_{produced}\)) [6]. These costs are mainly composed of three items: capital expenditures (CAPEX) for power production, conversion and transmission installations; operational expenses (OPEX) and the weighted average cost of capital (WACCs) for financing related costs, such as the cost of depth and risk premiums. This market growth and shift of deployment area does have consequences: Weather conditions and corrosion effects are more severe in offshore and saline environments, making structural stability more critical, while decreasing maintenance time windows. This leads to increased costs for operation and maintenance (O&M) compared to onshore installations, signalling an economic opportunity for (data-driven or model based) optimisation of maintenance scheduling. Due to the unpredictable nature of corrosion, both for installations that are approaching the end of design lifetime and more recent deployments (which may be engineered with lower tolerances), regular inspection and preventive maintenance is crucial to monitor, track and predict the individual status of structural health of individual wind turbines. Moreover, near the end of the planned turbine lifetime, decommissioning should be scheduled and performed. The scheduling of these maintenance tasks is a complex planning problem [7], wherein the two key bottlenecks of failure mode analysis are: the availability of suitable measurement data and expert insights to interpret the data correctly. In order to bridge this gap, further research is warranted. The contributions of this paper are threefold:  
* The shifting needs for the wind farm sector with regard to structural corrosion monitoring are identified and listed;
* A market analysis is performed and presented for existing, commercially available software solutions that combine these needs;
* A solution for the identified knowledge domain gap is presented, in the form of an open-source platform based software tool that can combine available SCADA data and web-based datastreams. In the following sections, the state-of-the-art of corrosion monitoring is presented (Section 2), together with a market analysis (Section 3.1) and a presentation of our custom software (SW) tool (Section 3.3.2) developed to tackle these specific needs.
# Research on the Auto-Exposure Method of an Aerial TDI Camera Based on Scene Prediction
## 3 Auto-Exposure Effect Evaluation
### Image Histogram Evaluation  
An image histogram is an important statistical feature which can intuitively show the distribution of gray level in the image. The histogram of a digital image, whose grayscale range is [0, L - 1], can be viewed as a discrete function:  
\[Hist(x)= n_{x}, \tag{21}\]  
where \(x\) is the gray value of level \(i\) and \(n_{x}\) is the number of pixels in the image whose gray value is \(i\), where \(x=\)\(0,1,2,\cdots,L-1\). Usually, the normalized histogram can be obtained by the total number of pixels in the image, dividing by each component in the histogram. The normalized histogram is defined as follows:  
\[P(x)= \frac{n_{x}}{M\times N}, \tag{22}\]  
where \(M\) and \(N\) represent the rows and columns of the image, respectively, and \(P(x)\) refers to the frequency of pixels with gray level \(x\) in the image. The sum of all components of the normalized histogram is 1.  
The image histogram can directly reflect the brightness distribution and overall exposure of the image, as shown in Figure 5. Figure 5(a1-a3) are the three images taken of the same scene, and from left to right are the under-exposed, moderately exposed, and over-exposed images, respectively. Below, Figure 5(b1-b3) are the gray histograms of Figure 5(a1-a3). The abscissa represents the gray value, and the ordinate represents the number of pixels per gray value in the image.  
As can be seen in Figure 5, when the image is under-exposed, its gray value mainly concentrates on the lower gray level part, and can not cover the whole gray level range. When the image is over-exposed, the number of pixels near the gray value 0 is very small, while the number of pixels near the gray value 255 is very large. Especially, when the gray value is 255, there is an obvious overflow phenomenon, and part of the image information is lost, while when the image is moderately exposed, its gray value almost covers the entire gray level range and the distribution is uniform, whether the histogram is evenly distributed or cannot reflect the exposure condition of the image. When the histogram is  
Figure 5: Comparison of images and histograms of different exposures in the same scene: (**a1–a3**) are the three images taken of the same scene, and from left to right are the under-exposed, moderately exposed, and over-exposed images, respectively; (**b1–b3**) are the gray histograms of (**a1–a3**).  
evenly distributed, it reflects that the exposure condition is good, while, when the histogram is concentrated in a small range of gray levels, it reflects that the exposure condition is poor.
# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 2 Related Work
### Supervised Learning Methods for Multiclass Imbalanced Data Streams
The improved online ensemble (IOE) [23] employs a recall rate for online resampling of instances, where oversampling is conducted if the recall rate is lower than the average level; otherwise, undersampling is performed, thereby allowing IOE to pay more attention to minority class instances in the data stream. However, the undersampling-based approach may result in partial information loss. The cost-sensitive adaptive random forest (CSARF) [24] adopts two cost-sensitive strategies (local and global), employing the Matthew's correlation coefficient (MCC) [25] instead of accuracy to assign weights to each internal tree, and oversamples minority classes during the learning process. A comparative analysis with ARF and ARFre suggests the effectiveness of the cost-based strategy. The dynamic queue algorithm (DynaQ) [26] utilizes a batch-based sampling approach, creating queues for each class to store recent instances, and evaluates the effects of using four different rate parameters for online resampling of minority classes. In summary, most existing online learning algorithms determine the resampling rate based on class proportions to provide more learning opportunities for minority classes. However, significant information may also exist in instances of other classes, such as those situated at decision boundaries. Thus, determining the sampling level solely based on class proportions is not comprehensive. Moreover, instance labels in real data streams are not immediately available, and in situations with limited label budgets, oversampling strategies should also be adjusted.
## 5 Validation of Surrogated MIMO-NARX Model
### Comparison of Peak Story Responses  
The safety and serviceability requirements for structural design mainly depend on the maximum peak responses. Figure 17a compares the peak inter-story velocity responses between Kriging/PCE/APC-NARX model prediction and MCS. It can be found that peak values from Kriging/PCE/APC-NARX models align well with actual values from MCS  
Figure 16: Fitness for the inter-story responses (a typical sample). (**a**) Velocity responses; (**b**) Displacement responses.  
Figure 15: Time history of the inter-story responses (a typical sample). (**a**) Velocity responses; (**b**) Displacement responses.  
validation cases. The peak values of inter-story displacement responses are further compared in Figure 17b, where good agreement again can be observed. It demonstrates that the maximum peak displacements are generally close to each other and are not significantly affected by the accumulation errors from integrating the time history of velocity responses. Correlation coefficients between the actual and predicted maximum peak values of velocity and displacement responses are further compared in Figure 18. It should be noticed that the correlation coefficients are relatively low due to some extreme data points commonly referred to as outliers. Figure 18 implies that the correlation coefficients for peak displacement are lower than those of peak velocity due to the existence of more outliers. For peak responses, it can be further observed that PCE and APC meta-models have more stable performance than the Kriging meta-models.  
Figure 17: Peak values of velocity responses (300 samples). (**a**) Peak velocity; (**b**) Peak displacement.
# An Energy Balance Clustering Routing Protocol for Intra-Body Wireless Nanosensor Networks
## 2 System Model
### Energy Consumption Model  
The energy consumption mainly focuses on the energy consumed by the nano-nodes to receive and transmit data. The energy consumption of the nano-nodes in receiving and transmitting data in the TS-OOK based terahertz channel can be expressed as [19]:  
\[E_{tx}(N_{bit})=\omega N_{bit}E_{ptr} \tag{9}\]  
\[E_{rx}(N_{bit})=N_{bit}E_{prx}=\frac{1}{10}N_{bit}E_{ptrx} \tag{10}\]where \(E_{tx}(N_{bit})\) and \(E_{rx}(N_{bit})\) represent the energy consumed by the nano-nodes to transmit and receive the \(N_{bit}\) bits data packet respectively and \(E_{ptx}\) and \(E_{ptx}\) represent the energy consumed to transmit and receive the single pulse respectively. The value of \(\omega\) is related to the coding weight, indicating the probability that the symbol \"1\" appears in the \(N_{bit}\) bits data. \(\omega\) is usually set to 0.5 in order to make the probability of the symbol \"1\" and the symbol \"0\" the same. In a terahertz communication system based on TS-OOK modulation, the received energy consumption is usually set to 1/10 of the transmitted energy consumption [19]. The energy consumption of transmitting unit-bit data can be expressed as:  
\[E_{bit}=\omega E_{ptx}+E_{prx} \tag{11}\]  
where \(E_{bit}\) represents the energy consumed by a node in the transmission link \(p\) to transmit and receive unit bit data. If the node is a source node which means the information the node senses needs to transmit to the control node, it is considered \(E_{prx}=0\), if the node is a destination node, it is considered \(E_{ptx}=0\). The energy consumption for transmitting and receiving a single pulse \(E_{ptx}\) and \(E_{prx}\) are set according to the terahertz channel parameter setting based on TS-OOK modulation in [21]. When the nano-nodes' communication range is 2 mm, the values of the energy consumed by transmitting and receiving a single pulse in equation (11) are fixed at 1 and 0.1 pJ.