esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Loss Function  
Three loss functions were evaluated to optimize network performance: Binary Cross Entropy (BCE), Focal Loss (FL), and Dice Loss. The selection of the loss function enables fine tuning the balance between precision and recall, preserving boundaries while maximizing IoU. BCE in combination with pixel-weight maps can effectively produce well-defined segmentation and individual mask for each hand. However, its equal treatment of foreground and background pixels can lead to class imbalance, as background pixels dominate in number. This imbalance, though partially mitigated by the high confidence typically assigned to background predictions, could be further optimized. FL extends BCE by introducing parameters \(\alpha\) and \(\gamma\) to emphasize harder-to-classify pixels and address class imbalance more effectively [43]. By adjusting these parameters, segmentation performance is enhanced, especially in challenging regions. Dice Loss, also designed for imbalanced  
Figure 3: U-Net architecture. Color used: ilic—input; light orange—convolutions; dark orange—ReLU activation; red—MaxPooling; green—dropout; dark blue—upsampling; gray—concatenation; yellow—concatenated block; purple—sigmoid activation.  
data, prioritizes foreground regions by directly optimizing the similarity between predicted and ground truth masks [44]. It calculates a differentiable Dice similarity coefficient [45] to emphasize true positives (\(TP\)) while minimizing the impact of class imbalance [46]. To counteract prediction noise near the decision threshold, predictions were polarized using a sigmoid function, yielding marginally improved results.
# Petri Net Modeling for Ising Model Formulation in Quantum Annealing
## 4 Binary Quadratic Net Construction from Problem Domain Petri Nets
### Firing-Based Construction
#### 4.3.2 Firing Count  
In some applications, we must specify the number of firing occurrences for each transition.  
\[\sum_{k=0}^{K}X_{k}(T_{i})=FC_{i},\forall i \tag{43}\]  
where \(FC_{i}\) is the specified number of firings of \(T_{i}\).  
We can convert the constraint into a binary quadratic net and represent it as the energy function under the one-hot constraint (32):  
\[H_{firings}(\mathbf{M})=\sum_{i=1}^{|T|}(\sum_{k=0}^{K}\sum_{n=0}^{N}nM(\hat{ p}_{i,n}^{k})-FC_{i})^{2} \tag{44}\]  
Note that \(M(\hat{p}_{i,n}^{k})\) in the binary quadratic net corresponds to \(X_{k}(T_{i})\) in the problem-domain Petri net.  
Assuming that each transition \(T_{i}\) should fire exactly once during \(\mathbf{X}_{0},\mathbf{X}_{1},\ldots,\mathbf{X}_{K}\), the function (43) can be represented as follows:  
\[H_{firings}(\mathbf{M})=\sum_{i=1}^{|T|}(\sum_{k=0}^{K}M(\hat{p}_{i}^{k})-1)^{2} \tag{45}\]  
In practical cases, this constraint is commonly used.
## 3 Discussion  
### Phylogenetic Position of Sect. Tubulose  
Floral characters have long been considered and taken as the most taxonomic features of _Clematis_[40]. Based on floral morphology, most of the previous classifications considered that sect. _Tubulose_ was closely related to _Clematis_ groups with bell-shaped (erect sepals) flowers and hairy anthers, such as sect. _Viorna_ and (or) sect. _Campanella_[40, 65, 66, 40]. However, this relationship has never been supported by phylogenetic analysis [47, 48, 49, 68], indicating that floral morphology may not be a good taxonomic character for subgeneric and sectional classification.  
In this study, the nuclear genome data provided more reliable estimates of phylogenetic relationships than the blastome sequences. Both nuclear datasets (nrDNA and SNPs) confirmed that sect. _Tubulose_ is a monophyletic group within _Clematis_ (Figure 2B,C), while the blastome phylogeny yielded confusing relationships (Figure 2A). Same as previous studies [43, 47, 49, 60], close relationships of sect. _Tubulose_ and sect. _Clematis_ are inferred in this study. Our SNPs analysis showed that North and South American dioecious species (sect. _Aspidanthen_ and sect. _Lasinthia_) and _C. williamsii_ (a Japanese species in sect. _Cheiopsis_) were nested together with sect. _Clematis_ as a larger clade (MLBS = 86, Figure 2C) sister to sect. _Tubulose_.  
In the recently published phylogenomic study on _Clematis_[60], similar results were observed. Sect. _Tubulose_ and sect. _Clematis_ were nested together in the blastome tree,  
Figure 5: Ancestral morphological character reconstruction of _Clematis_ sect. _Tubulose_ using Mesquite onto the phylogeny inferred by the SNPs sequences. (**A**) Pollen. (**B**) Sexuality. (**C**) Flower number. (**D**) Pedicel. (**E**) Calyx. (**F**) Sepal. (**G**) Persistent style. (**H**) Leaflet margin.  
whereas they were clearly separated by the nuclear genome data. Their simulation analysis showed that cytonuclear discordance might have been caused by intersectional hybridization, which has been confirmed by other pieces of evidence [46; 61]. In this study, the plastome phylogeny yielded a mess of relationships of sect. _Tubulosae_ and sect. _Clematis_. However, the plastome phylogeny hints at a history of hybridization among species of these two sections. It seemed that _C. brevicaudata_ and _C. gratopsis_ (sect. _Clematis_), and _C. tubulosa_ var. _ichangensis_ (sect. _Tubulosae_) may have undergone ancient intersectional hybridization and subsequent introgression, which has been reported in many angiosperm taxa [69; 70; 71] because some or all of their plastome sequences clustered with those from the opposite sections, whereas in the nuclear phylogeny, all those samples were grouped in the right position with the relatives from their own sections (Figure 2).
## 1 Introduction
In addition, it has been reported that application of the fusion sputtering technique promotes a rough surface and significantly enhances the zirconia-resin microshear bond strength [14; 15]. However, the influence of fusion sputtering technology on the crystal phase change and mechanical properties of zirconia, as well as the long-term bond strength remains unclear. [PERSON] et al. [16] found that the application of laser treatment could potentially replace traditional surface treatment by APA, due to the high speed and precise control of the laser. A more regular micro-texture and a reduction in contamination were obtained through the application of a femtosecond laser (FSL), compared with CO2 and Nd-YAG lasers, for zirconia surface micromachining [12]. [PERSON] et al. [13] evaluated the use of an ultrashort-pulse laser to irradiate the zirconia ceramic surface, so as to improve adhesive properties in the resin-zirconia interface. The results showed that the topography of the zirconia ceramic surface was regularly roughened and wettability was increased, while an improvement in microtensile bond strength was promoted without a significant tetragonal-monoclinic phase transformation [13]. This may be attributed to the fact that the energy of the laser was absorbed by the surface of the zirconia, and the thermal induction process produced shell-like ruptures on the surface [17]. Through the use of FSLs, the surfaces of zirconia ceramics could be effectively modified without inducing thermal or mechanical damage [17; 18]. While limited research has been conducted on the surface pre-treatment of zirconia ceramics using laser technology, the ideal effect of zirconia modification by laser, in order to improve the bond strength, has not yet been achieved [19]. Therefore, the changes in surface morphology and flexural strength upon applying the FSL technique require further exploration. This study aims to evaluate the effect of zirconia surface modification using the FSL method. The null hypothesis was that the application of the FSL would not affect the surface morphology and flexural strength of translucent monolithic zirconia ceramic material.
# Reinforcement Learning for Semi-Active Vertical Dynamics Control with Real-World Tests
## 3 Modeling and Parameter Optimization of the Training Model
### Selection of the Training Model Structure  
The model used for the training of the RL-based controller should fulfill two objectives: First, it should represent the real-world dynamics of the plant reasonably well. Second, a fast execution time of the model is desirable. This can reduce the time for the controller training significantly. Usually, the training model realizes a tradeoff between these two objectives.  
For the vertical dynamics control, several types of vehicle models can be considered for training. The most accurate model types are full-vehicle models. These types of models can usually represent the horizontal vehicle dynamics, the induced pitch, and roll dynamics, as well as the vertical dynamics induced by road excitations. In contrast to that, half-vehicle models cover either pitch or roll dynamics and also represent vertical dynamics induced by road excitation. QVMs are only able to represent road-induced dynamics.  
In this work, the aim of the RL controller was to compensate for the vertical road-induced disturbances. Since we assumed that the pitch and roll dynamics were handled by feed-forward control, we selected the QVM as the training model. The QVM model structure was able to represent the desired dynamics and had favorable simulation complexity. In order to obtain a sufficiently accurate simulation model, we used an extended model structure instead of a linear QVM. We considered complex valve behavior, nonlinear kinematics, and elastic bearings in the extended model structure.
## 3 Results
### Validation of the Proposed DBIM-FISTA Algorithm
#### 3.1.1 Reconstructions with CST Data
Moreover, the models in FDTD and CST have discrepancies in the Debye material properties due to their different computational environments. This includes errors in the head boundary between the CST (or experimental model) and the FDTD solver, due to discretization errors and the coarser resolution used in FDTD. To assess whether a fixed number of 20 iterations for both 2-D and 3-D algorithms leads to fair comparison, we have run Case I with a large number of iterations which can ensure that the residual error \(RE\) converges to almost a fixed value (see plots (a) and (b) in Figure 8). The numbers of iterations and errors for these cases are given in Table 3, and the resulting images are shown in Figure 8.  
Relative to the reconstruction results with 20 DBIM iterations, reconstructions after a much greater number of iterations (120 in 3-D, 200 in 2-D) lead to rather modest reductions in the errors in Table 3, despite a significant decrease in the data residual \(RE\). Importantly, the ratio between the 3-D and 2-D \(RMSE\) errors for the increased iterations is similar to the 20 iterations case, thereby allowing us to draw conclusions for 2-D vs. 3-D results using this limited fixed number. These observations and the results in Figure 8 suggest that using a fixed number of 20 iterations is sufficient for our comparison, although it does not represent complete convergence of the algorithms. Figure 7: 2-D reconstructions of: (**a**) relative permittivity \(\Re(\varepsilon_{r})\) and, (**b**) contrast \(\Re(\delta_{\varepsilon})\) for “Case I” of Figure 2 at 1.0 GHz.
## 3 Research Data and Methods
### Research Methodology  
Figure 2 shows the flow chart of this research. In this research, a 12.5 m DEM of the Lengqu River section was first visualized and enhanced using the RRIM method, and the GALs in the Lengqu River section were identified based on the obtained RRIM map. The mean slopes of the sub-basins of the Lengqu River section were then extracted and analyzed to obtain the threshold hillslope of the whole Lengqu River section. Excess topography was extracted by using Matlab based on threshold hillslope; then, the relationships between GALs, resurgent GALs, the deposition of inactive GALs and excess topography were quantitatively analyzed separately, which enabled the identification of GALs for the indication of GAL risk locations.
## 2 Related Work
### Motivation  
In the literature, most of the CSS schemes are based on the assumption that the local sensing results are independent and identically distributed. However, the assumption is unrealistic in practical applications. As a result, the independent and non-identically distributed CSS, namely, HCRN, is discussed in this paper. For the state-of-the-art research on the HCRN, the N-out-of-K rule is widely considered due to its superb performance, compared with the AND rule and the OR rule. However, the closed-form expression for the optimal sensing threshold of the N-out-of-K rule-based HRCN, namely the optimal K, is difficult to obtain at present, according to the discussion in [24].  
The sensing threshold at the FC directly determines the sensing performance of the N-out-of-K rule-based HRCN. How to obtain a closed-form expression for the optimal sensing threshold of the N-out-of-K rule-based HRCN seems significant for the further development of the HCRN and serves as the motivation for this paper.
## 4 Results and Discussion
### PlanetQi Observations Spatial and Temporal Distribution
The total number of observations for GN02 and GN04 on DOY 3023-342 is 689 ROE and 1143 ROE, respectively. Notably, the observations from the GPS constellation account for 455 ROE, GLONASS for 440 ROE, Galileo for 444 ROE, and BeiDou for 493 ROE. The analysis further explores hourly observations, revealing distinctive patterns throughout the day. The maximum observations occur at 20:00 h, totaling 131, while the minimum observations are recorded at 08:00 h, amounting to 31. This temporal analysis provides valuable insights into the dynamic nature of observations during the specified day. Additionally, the spatial distribution analysis reveils intriguing details. The maximum 1\({}^{\circ}\) latitude with 21 observations is found at \(-25.0^{\circ}\), while the minimum is observed at \(-90.0^{\circ}\) with a single observation. In terms of longitude, the maximum longitude is 131\({}^{\circ}\) with 14 observations, contrasting with the minimum at 130\({}^{\circ}\) with only one observation.
## 2 Low-Offset Operational Amplifier
### Chopping  
Chopping stabilization technology is a modulation technique used to mitigate the impact of defects in operational amplifiers, including noise and offset voltage. With advancements in integrated circuit technology, choppers can be efficiently implemented on chips. Chopping operates by modulating the input signal, noise, and offset voltage to different frequencies, followed by low-pass filtering to produce a signal with reduced nonlinearity.  
The simplified structure of the chopper amplifier is shown in Figure 2. The typical chopper amplifier consists of a chopper, amplifier, and demodulator. Among them, the input signal is modulated by the first chopper to the odd harmonic frequency of the chopping frequency. The amplifier amplifies the offset voltage, noise, and input signal, and then modulates it back to the initial frequency by the second chopper. The offset voltage Vos and noise Vn of the operational amplifier are only modulated to the chopping frequency by the output chopper and filtered by subsequent filters, thereby achieving the separation of the input signal Vn from the offset voltage and noise.  
Figure 2: The structure of the chopper amplifier.  
However, this method may introduce significant ripple. Several techniques are commonly employed to mitigate this ripple. One approach is to add a low-pass filter at the amplifier's output; however, this reduces the available signal bandwidth. A second method involves using a multi-path amplifier topology, known as chopper stabilization, although this technique increases low-frequency noise. The third method incorporates a notch filter into the amplifier. A switched-capacitor notch filter can be synchronized with the chopper clock to filter out the ripple. This type of notch filter creates notches in the amplifier's transfer function at the chopping frequency and its odd harmonics, which can be effectively mitigated through a multi-path topology.  
The notch filter depicted in Figure 3 suppresses ripple by incorporating a switched-capacitor notch filter (SC-NF) after the output chopper. This amplifier builds upon the classic chopper amplifier design by adding a notch filter and two amplifiers, thereby enhancing its ability to resist interference, particularly in suppressing noise and common-mode disturbances. Unlike low-pass filters, the SC-NF selectively filters out ripples near the chopping frequency, leaving low-frequency signals unaffected. As a result, this method does not impact the overall bandwidth of the operational amplifier. The use of a notch filter is particularly efficient, as the switched-capacitor network does not consume any bias current.
## 3 Description of the Tool
### Input/Output Structure  
ODREC accepts a set of input parameters, as shown in the flowchart given in Figure 3. These parameters include the name of the oxidizer and the fuel, the thrust, the oxidizer-to-fuel mixture ratio, the combustion chamber pressure, the ambient pressure, the fuel and oxidizer temperatures entering the combustion chamber, the engine wall roughness, the thermal conductivity, whether the fuel or the oxidizer is the coolant, the coolant inlet temperature and pressure, the number, width, and depth of the cooling channels, the wall thickness at channel base, the characteristic length of the combustion chamber, the nozzle inflection and exit angles, and the number of discretization at each thrust chamber part. In addition, coolant thermophysical properties for liquid, saturated, and vapor phases should be given as inputs, besides the pressure values for which vapor property tables are provided. The required coolant thermophysical properties are dynamic viscosity, thermal conductivity, constant pressure specific heat, and density. Properties of the coolant at a certain pressure and temperature are obtained by interpolation, which is explained in Section 3.3. Thermophysical properties could be obtained from the NIST database [33].  
In order to avoid the challenging 3-D combustion analyses in rocket engines, a 1-D analysis conducted by the NASA CEA program [34] is used. To improve the robustness of the tool, an open-source MATLAB implementation of the program developed in [35] is integrated into the tool. Therefore, the notation in defining the name and the temperatures of the propellants is the same as that on the CEA website [34]. In addition, another MATLAB package that allows using dimensional variables is also required to use the tool [36]. The properties of the combustion products at a certain temperature are obtained through interpolation or extrapolation using the values at the combustion chamber and at the nozzle throat extracted from the MATLAB implementation of CEA.  
A built-in function in ODREC generates a thrust chamber profile based on a bell-shaped nozzle [37; 38] and a cylindrical combustion chamber. However, if desired, a user may provide the thrust chamber profile externally. Also, channel depth and width may be given as variables or constants along the thrust chamber. The program outputs the specific impulse, the thrust coefficient, the characteristic velocity, the gas Mach number, the thrust chamber profile, the wall temperature, the wall heat flux, the mass flow rate, the vapor fraction, and the pressure and temperature of the coolant.
## 5 Implementation
### Platform  
A crucial part of IoT environments is a platform that collects, analyses and orchestrates data. Data in this context describes physical environmental conditions, such as temperatures or actuators, such as light switches. This data from distributed entities is gathered and combined to create a meaningful result. For the SBT framework, Node-RED [58] was chosen. It acts as a middleware to connect entities and perform computations. Its application to industrial systems has been, for example, discussed by [PERSON] and [PERSON] [59] as well as [PERSON] et al. [60]. The benefits of Node-RED are its easy to use interface, its flexibility, and its potential to be extended with custom functionality. It is lightweight and supports several hardware platforms. It provides a large set of supported functions and communication protocols. Additionally, implementing a semantic representation of the data points by integrating the Protocol Buffer descriptions was a simple task. Node-RED allows the creation of flows that connect inputs from distributed devices to outputs. An exemplary flow is shown in Figure 4.  
Node-RED furthermore provides means to extract data and store it persistently, for later analysis and visualisation. For the SBT, InfluxDB in combination with Grafana were used, as they both are tailored for time series and the serial values in IoT environment can easily be represented as a time series. Due to the versatile nature of Node-RED, sending time series data into InfluxDB is possible independent of the manner of hosting the database. In our case, the InfluxDB database was located on the same system as Node-RED. However, as a web service, it can be hosted on premise as well as the cloud. Since Node-RED provides connectors for commonly used protocols, it was easy to connect SBT devices to the Node-RED platform. Communication of the SBT framework is discussed in the next subsection. This platform is currently hosted on a Raspberry Pi 3. In the context of the SBT-project, Node-RED modules for collecting and working on the data generated by the embedded devices were implemented. Furthermore, logic to compute output signals that could be translated to actions by the IoT devices was developed. The concept of a Node-RED platform was taken and extended with custom functionality, namely additional function blocks for reading, decoding, encoding, and writing data points to and from the embedded devices. This feature enabled the IoT platform to orchestrate the embedded devices.  
Figure 4: Exemplary flow in Node-RED.
## 3 Material and Methods
### Dimensionality Reduction  
Dimensionality reduction techniques are employed to reduce the number of variables, reduce the computational complexity of high-dimensional data, improve the model's accuracy, improve the visualization, and understand the process that generated the data [6].  
[PERSON] and [PERSON] [32] reported that dimensionality reduction transforms high dimensional data into low dimensions where newly transformed data is a meaningful representation of original data. Two main approaches to dimensionality reduction are feature selection and feature extraction.  
Feature selection is the process of selecting the most valuable features from the feature space so that the feature space can be reduced; in comparison, feature extraction creates new features from the original data space using functional mapping [33]. The feature extraction process creates a low-dimensional representation of the feature space that preserves the most valuable information. [PERSON] et al. [6] identified two approaches to feature selection: the filter method and the wrapper method. These researchers identified three approaches for feature extraction: the performance measure, transformation, and the generation of new features.