esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Virtual Sample Generation  
Although the performance of a mass ablation rate prediction model based on a small number of ablative samples may be unreliable, it uncovers some information concealed in the data of the ablative samples. When the model can fully reflect the relationship between the obtained small sample features and labels (the general standard is that the MAPE on the test set must be less than 10%), the hyperplane of input and output values deduced by the model is considered to be closer to the hyperplane of the real sample population. According to the distribution of the model and real data, virtual samples can be generated, and these virtual samples will have the same feature distribution as real small samples [34]. By mixing them with real small samples, the retrained prediction model will further approximate the real feature-label hyperplane compared with the initial estimate,  
Figure 3: Pool-based active learning.  
thus improving the performance of the model. Therefore, the application of virtual sample generation technology after active learning is expected to further improve the prediction model of ablation performance.  
Taking the labeled dataset finally generated by the pure exploration route as an example [34; 35], the generation of virtual samples first requires the extension of the range of features and labeled values of real samples using the global trend diffusion method. Then, the virtual sample generates eigenvalues in the expanded region according to the distribution of real data. The mass ablation rate of the virtual sample is assigned by the model obtained by the previous active learning process. The assigned value should be within the extended range of the label value, and the required virtual sample is finally obtained. However, it can be seen from Figure 4 that the lower bound of each feature and label is extended to a region less than zero. The contents of filler and the mass ablation rate cannot be negative, so the lower bound is 0 in this case. Virtual samples are then mixed with real samples to form a hybrid dataset. The reconstructed dataset is trained by each algorithm, and the prediction performance of each generated model is evaluated and compared by using the samples from the validation set.
# Removal of Chromium(VI) by Chitosan Beads Modified with Sodium Dodecyl Sulfate (SDS)
## 3 Results and Discussion
### Adsorption Experiment
#### 3.1.3 Effect of Contact Time on the Adsorption of Cr(VI)  
The effect of the contact time on the adsorption capacity of Cr(VI) by SDS-modified chitosan beads was explored. In this experiment, the concentration of Cr(VI) was set as 1 mg/L with the dose of 0.05 g at a temperature of 25 \({}^{\circ}\)C. The pH of the solution was kept at 4, in order to achieve the maximum removal of Cr(VI). Figure 5 demonstrates the adsorption of Cr(VI) with varying contact times from 1 to 96 h.  
The adsorption capacity of SDS-chitosan beads for Cr(VI) increased sharply within the first 24 h, which may be attributable to the availability of the sites on the surface of the adsorbent. It is suggested that a concentration gradient is present in both the adsorbent and adsorbate in the solution [48]. Then, it reached adsorption equilibrium at 72 h, and afterwards, there was no appreciable increase (Figure 5). Hence, the optimized contact time was taken to be 72 h for further studies. The surface modification of the chitosan beads by SDS facilitates the adsorption through an ion-exchange mechanism [38]. Usually, the complexation mechanisms involve slower kinetics than the ion-exchange and hydrogen bonding reaction mechanism [49; 50; 51].  
Figure 4: Species distribution curves of Cr(VI) in environmental water [32].  
Figure 5: Effect of the contact time on the adsorption of Cr(VI) on the SDS-modified chitosan beads.
## 2 Accuracy Metrics
### Percentage-Dependent Metrics  
To compare predictions with different units, unit-free measures are needed. Since there is no limitation on units, percentage-dependent metrics are preferred for this issue. The percentage-dependent metrics measure the size of errors in percentage terms and provide interpretable thinking regarding the quality of prediction [36]. Interpretable thinking should be expressed in percentage terms when the scale of data is unknown. For instance, a report saying that \"the prediction error is 5%\" is more meaningful than saying \"the prediction error is 50 cm (or other terms)\", if the reviewer does not know the scale of the data.  
The most commonly used percentage-dependent metrics are MAPE and SMAPE [39], and they are defined as [35]  
\[\text{MAPE}=\frac{100\%}{n}\sum_{i=1}^{n}\left|\frac{\text{D}_{\text{pre}}- \text{D}_{\text{act}}}{\text{D}_{\text{act}}}\right| \tag{7}\]  
\[\text{SMAPE}=\frac{100\%}{n}\sum_{i=1}^{n}\left|\frac{\text{D}_{\text{pre}}- \text{D}_{\text{act}}}{\left(\left|\text{D}_{\text{pre}}\right|+\left|\text{D }_{\text{act}}\right|\right)/2}\right| \tag{8}\]  
The MAPE calculates the average of the percentage error. It is a measure of prediction accuracy, especially in trend estimation. The MAPE is also abbreviated as MAPD (\"D\" for \"deviation\"). The MAPE is used as the loss function for regression models in machine learning, since it is very intuitive to explain the relative error. The disadvantage of the MAPE is that the MAPE is scale-sensitive; it will get extreme values if the actual value is quite small. Thus, the MAPE should be avoided as an evaluation metric for low-scale data.  
Another disadvantage of the MAPE is that it penalizes positive errors more heavily than negative errors [34; 35; 39]. For example, if the actual value of an original data point is 10, and its prediction value is 15, then the error is 5 (positive). The value of MAPE would be  
\[\text{MAPE}=100\%\times\left|\frac{15-10}{10}\right|=50\% \tag{9}\]  
However, when the actual value is 15, and the prediction value is 10, then the error is still 5 (negative). The value of MAPE is much lower:  
\[\text{MAPE}=100\%\times\left|\frac{10-15}{15}\right|=33.33\% \tag{10}\]  
The SMAPE is another commonly used percentage-dependent metric. It is the optimized version of MAPE. The SMAPE is regarded as the \"symmetric\" MAPE [34]. The SMAPE places the same penalty on both positive error and negative error.  
However, if the actual value is zero, and the prediction value is also zero or close to zero, the value of SMAPE is likely to be infinite [34]. Another disadvantage of SMAPE is that it is much more complex to calculate than other metrics.  
The range of both MAPE and SMAPE is (0%,+\(\infty\)); the smaller the value of MAPE and SMAPE, the better the accuracy of the prediction model. The perfect value of MAPE and SMAPE is 0%, indicating that the prediction model is perfect. If the value of MAPE and SMAPE is greater than 100%, this indicates that the prediction model is very poor.
## 2 Machine Topology and Finite Element Model  
The research object of this paper is a 130 kW, 1500 rpm IPMSM. The FEA model of the IPMSM and design specifications are given in Figure 1 and Table 1, respectively. The transient mathematical model of two-dimensional electromagnetic field calculation is presented in Equation (1). In the FEA model, the IPMSM machine is supplied by a current source [25].  
\[\left\{\begin{array}{c}\Omega:\ \frac{\partial}{\partial x}\left(\frac{1}{ \mu}\frac{\partial A_{\rm z}}{\partial x}\right)+\frac{\partial}{\partial y} \left(\frac{1}{\mu}\frac{\partial A_{\rm z}}{\partial y}\right)-\sigma\frac{ \partial A_{\rm z}}{\partial t}=-J_{z}\\ \Gamma_{1}:A_{z}=0\\ \Gamma_{2}:\frac{1}{\mu_{1}}\frac{\partial A_{\rm z}}{\partial n}-\frac{1}{ \mu_{2}}\frac{\partial A_{\rm z}}{\partial n}=J_{\rm s}\end{array}\right. \tag{1}\]  
where \(\Omega\) is the calculation region, \(A_{\rm z}\) and \(J_{\rm z}\) represent the magnetic vector potential and the source current density in the z-axial component respectively (in A/m\({}^{2}\)), \(J_{\rm s}\) is the equivalent face current density of the permanent magnet (in A/m\({}^{2}\)), and \(\sigma\) is the conductivity (in S/m). \(\Gamma_{1}\) indicates the parallel boundary conditions, \(\Gamma_{2}\) indicates the PM boundary condition, and \(\mu_{1}\) and \(\mu_{2}\) represent the relative permeability. Equation (1) presents the calculation basics of the IPMSM and indicates that the electromagnetic performances are directly related to the part of machine topology which is located near the airgap. Thus, more attention is paid to the rotor surface arc.  
In order to simplify the calculation and analysis, it is assumed that [26]:  
1. A displacement current and the skin effect in the stator windings are ignored.
2. Materials are isotropic. Permeability and conductivity of the materials are constant except the stator core and the rotor yoke.
3. The displacement current is ignored.
## 2 Methods  
### Data  
We analyzed the one-year PM2.5 sampling data from a previous study [14], in which a detailed description of the sampling measurements can be found. In order to determine PM2.5 total mass and ion concentrations, 24 h PM2.5 data were collected on pre-weighted 47 mm Teflon filters (ZefluorTM, Pall Corp., Port Washington, NY) using a low-volume (flow rate, 16.7 L min\({}^{-1}\)) sampler. This sampler was equipped with Teflon-coated aluminum cyclone on the rooftop of the Korea Institute of Science and Technology, located northeast of downtown Seoul, Korea (37.603\({}^{\circ}\) N, 127.047\({}^{\circ}\) E, at 58 m above sea level). The PM2.5 was measured daily during a one-year period from October 2012 to September 2013. The PM2.5 mass concentrations, inorganic ions, EC, OM (composed of WSOC, WISOC, and HULIS-C), NaCl, and crustal components were chemically analyzed.  
On the basis of PM2.5 chemical species, composed of inorganic matter (ammonium, sulfate, and nitrate), carbonaceous organic matter, NaCl, and crustal components, we applied the SMP model. The PM2.5 source apportionment results reported in [PERSON] et al. [14] indicate the source-based mass concentration and their mass contributions.  
Nine sources from both transported and local sources were identified: (1) transported secondary sources (TS), (2) local secondary sulfates (LS), (3) local secondary nitrates (LN), (4) gasoline (G), (5) diesel (D), (6) transported biomass burning (TBB), (7) local biomass burning (LBB), (8) fugitive soil dust (FSD), and (9) waste burning (WB). TS is closely related to NH\({}_{4}\)\({}^{+}\), NO\({}_{3}\)\({}^{-}\), SO\({}_{4}\)\({}^{2-}\), and OM; LS to NH\({}_{4}\)\({}^{+}\) and SO\({}_{4}\)\({}^{2-}\); LN to NH\({}_{4}\)\({}^{+}\), NO\({}_{3}\)\({}^{-}\); G to OM (water-soluble) and EC; D to OM (water-insoluble) and EC; TBB to OM and EC; LBB to OM (HULIS, water-insoluble); and FSD to the crustal component and OM [14]. Supplementary Figure S1 shows a comparison of the measured PM2.5 mass concentration with the SMP-estimated PM2.5 mass concentration, which was also described in [PERSON] et al. [14]. As Figure S1a shows, the PM2.5 measurement data and SMP estimated data have good agreement without significant loss of accuracy, which confirms that the data are reliable. Supplementary Figure S1b also shows the comparison of the PM2.5 mass concentration with the extinction coefficient with geometric mean diameter (d\({}_{\text{g}}\)) of 0.1 \(\upmu\)m at geometric standard deviation (GSD) of 1.5. Generally, also these data are in good agreement, as shown in Supplementary Figure S1. A more detailed explanation and comparison of source-based mass contribution with source-based extinction coefficient (\(b_{ext}\)) contribution will be given at Section 3.
# A Parallel Open-World Object Detection Framework with Uncertainty Mitigation for Campus Monitoring
## 2 Related Work
### Open-World Object Detection  
Compared with open-set tasks, open-world tasks are more demanding since these tasks require not only identifying unknown classes, but also learning incrementally based on newly obtained category data. Open-world tasks go beyond open-set by requiring incremental learning based on newly obtained category data. [PERSON] et al. [45] proposed the first open-world classification model, and [PERSON] et al. [46] introduced a meta-learningmethod for identifying unknown classes. Previous works [36; 47; 48] explored more complex scenarios, e.g., long-tail distribution [49], few-shot learning [50] and zero-shot learning [51] on the open-world classification, respectively.  
ORE, the first OWOD method proposed by [PERSON] et al. [14] designed a specific RPN, selecting top-k region proposals sorted by the objectness of the RPN from the background to discover unknown objects. SA proposed by [PERSON] et al. [52] defined semantic centroids in feature space to exploit embedded semantic topology. [PERSON] et al. [53] designed a two-stage detector distinguishing diverse classes on the basis of clustering and similarity to handle the raised Unknown-Classified OWOD problem. [PERSON] et al. [15] proposed a model coupling an auxiliary proposal advisor and a class-specific expelling classifier to improve the detection performance of unknown classes. Inspired by the powerful presentation ability of transformer [54], [PERSON] et al. [17] first extended transformer to OWOD problem and proposed a transformer-based open-world detector OW-DETR, which combines an attention-driven pseudo-labeling scheme for selecting unknown query boxes and an objectness branch to effectively separate foreground objects and background.  
Previous methods used complex strategies to generate pseudo-labels for unknown classes but introduced excessive uncertainties, harming the learning of unknown objects and influencing the known classification as well. In contrast, our method only explores the unknown information from the known instances through a reasonable decoupling framework, which improves the detection performance of the unknown objects while maintaining that of the known objects.
## 3 Results
### Alpha Particle Irradiation  
Alpha particle irradiation experiments were conducted at the University of Saskatchewan using a custom-made source consisting of Amercium-241 with 2.5 \(\mu\)Ci activity and 4.61 \(\times\) 10\({}^{7}\) per hour emissivity. The alpha source was placed above the die of the chip at a distance of less than 8 mm. The voltage of the chains was kept at the nominal voltage of 0.8 V, and testing was conducted for over 400 cumulative hours. During this time, no upsets were recorded on any of the chains, indicating excellent SER performance of low-LET particles at nominal voltage.  
Additional alpha particle tests were conducted at a lower core voltage of 0.6 V for 150 h. During this time, no upsets were observed. These results show that all flip-flops, including the reference design, had strong resistance to upsets due to low-LET particles. This demonstrates the excellent inherent SEU resilience of this 22 nm FD SOI technology.
## 3 Results
### Clinical and Radiographic Outcomes Following NSRME and SARME
#### 3.3.2 Clinical Crown Height (CCH)  
Five studies measured the change in the CCH after maxillary expansion using stone casts [29; 37; 40; 41; 43] and one study used cross-sections from CBCT [38]. Studies using NSRME [29; 40; 41] reported an increased CCH in posterior teeth ranging between 0.30 and 1.30 mm, while the range for SARME was from -0.15 to 0.77 mm [37; 38; 41; 43]. Among the observed studies, the increase in the CCH was less pronounced in SARME compared to NSRME. It can be said that an increase in the CCH is equivalent to an increase in REC and/or a decrease in buccal bone height (BBH).
## 3 Method
### Sinc-LSTM Network Structure
Each memory cell has a self-connected recurrent unit that maintains a hidden state and an input gate, output gate, and forget gate that regulate the flow of information into and out of the cell. The input gate controls the amount of new input that is added to the current hidden state, while the output gate determines how much of the cell's current state is exposed to the rest of the network. The forget gate decides how much of the previous state should be forgotten. Figure 3: The internal structure of Sinc filter. The proposed LSTM network has three layers, with each layer comprising 288 LSTM cells. The output of each layer is fed as input to the next layer. The input to the network is a sequence of data points, and the output is a reconstructed sequence. LN modules are added to each LSTM layer to normalize the inputs to the next layer, reduce the effect of the vanishing gradient problem, and improve the stability and effectiveness of the network for processing sequential data. A combination of dense layers and dropout layers are used after the LSTM layers to reduce overfitting and improve the generalization of the model. The network is trained by a variant of the backpropagation algorithm called the Adam optimizer, which is well-suited for training recurrent neural networks. To prevent overfitting and select the best-performing model based on validation accuracy, early stopping is also utilized. At last, full connection layers added to the sequence to sequence LSTM network can help to learn more complex mappings between sequences. The proposed Sinc-LSTM network utilizes a sinc filter layer to extract relevant features from time series data, which are subsequently fed into the LSTM network for sequence modeling. By leveraging the feature extraction capabilities of SincNet with the temporal modeling capabilities of LSTM networks, the proposed approach enhanced the performance of CMG's telemetry data time series anomaly detection. The Sinc-LSTM network obtains the mapping \(f_{j}:S(j)_{l-p}^{t-1}\xrightarrow{W}\mathcal{S}^{R}(j)_{l}^{t+l-1}\) by training, and predicts and generates the \(l\) time steps of the reconstructed data according to the previous \(p\) time steps of the input data at the current time. The training set \(\Psi\) input for Sinc-LSTM training is organized as follows \(\Psi=\left\{S(j)_{l-p}^{t-1},S^{R}(j)_{l}^{t+l-1}:1\leq j\leq m,p\leq t\leq n \right\}\), where m is the number of reconstructed data channels and n is the length of reconstructed data. The fitting reconstruction method is to model and analyze the data of each channel of the control torque gyroscope separately. The control moment gyroscope telemetry data were made into 16 data files, each containing the data from the remaining 15 channels as input and the data from a single channel as supervision. In the training process of the Sinc-LSTM network, the information of the remaining 15 channels is used as the input training data. After the training, the model parameters of the 16 channels are stored in different files. Figure 4: The internal structure of LSTM unit.
## 2 Literature Review
### GSK and Its Variants
#### 2.2.1 Population Enhancement Techniques  
To boost GSK's convergence and diversity, [PERSON] et al. [34] integrated opposition-based learning and introduced Cauchy mutation to diversify the search. [PERSON] et al. [35] developed an enhanced version of the GSK algorithm, named mGSK-DPMO, which incorporates a dual-population framework and multiple operators to adaptively improve the algorithm's performance. [PERSON] et al. [36] introduced FDBAGSK--an adaptive variant that balances fitness and distance from the best solution to enhance search capabilities. In another development, [PERSON] et al. [37] proposed the Parallel Opposition-based GSK (POGSK), segmenting the population into multiple groups and employing the Taguchi method for cross-group knowledge sharing. Similarly, [PERSON] et al. [38] proposed the Adaptive Population GSK-Improved Multi-Operator DE (APGSK-IMODE) algorithm to identify the optimal locations and operational states of switches for these enhancements. Furthermore, [PERSON] and [PERSON] introduced the EPD-GSK framework [39], an enhanced version of the GSK algorithm designed to prevent premature convergence due to loss of population diversity. EPD-GSK improves diversity through three key strategies: using Sobol sequences for diverse initialization, applying Cauchy mutation to the junior stage to broaden the search space, and employing reverse learning in the senior phase to help escape local optima.
## 2 Distributed PV System and Proposed MPPT Algorithm
### Distributed PV System
#### 2.1.1 PV Module  
The equivalent model of a photovoltaic cell is shown in Figure 2. The photovoltaic module consists of an ideal current source (Iph), two internal resistors (Rsh, Rs), and a diode (D).  
The equivalent circuit of photovoltaic cells and Kirchhoff's law, as well as the output current equation can be written as shown in in Equation (1):  
\[I=I_{ph}-I_{D}-I_{sh} \tag{1}\]  
The current flowing through the diode can be written as shown in Equation (2):  
\[I_{D}=I_{0}[\exp(\frac{q(U+IR_{s})}{AKT})-1] \tag{2}\]  
The total current generated by the PV module can be estimated by applying KCL to the circuit, and the final current equation can be written as shown in Equation (3):  
\[I=I_{ph}-I_{0}[\exp(\frac{q(U+IR_{s})}{AKT})-1]-\frac{U+IR_{s}}{R_{sh}} \tag{3}\]  
In order to further calculate the total current of the PV module under partial shading, the formula in Equation (3) can be rewritten as [24]:  
\[I=I_{ph}N_{pp}-I_{0}N_{pp}\Bigg{[}\exp(\frac{\left(U+IR_{s}\big{(}N_{ss}/N_{pp} \big{)}\right)}{a})-1\Bigg{]}-\frac{\left(U+IR_{s}\big{(}N_{ss}/N_{pp}\big{)} \right)}{R_{sh}\big{(}N_{ss}/N_{pp}\big{)}} \tag{4}\]  
\[a=\frac{N_{ss}AKT}{q} \tag{5}\]  
Figure 1: MATLAB Simulink Photovoltaic system model.  
Figure 2: Equivalent circuit of a photovoltaic cell.  
where \(N_{pp}\) and \(N_{ss}\) represent the number of parallel and series-connected PV modules, respectively.  
\(I\) is the output current of the photovoltaic cell, \(I_{ph}\) is the photocurrent, \(I_{0}\) is the reverse saturation current, \(q\) is the unit charge, \(IRs\) is the equivalent series resistance, \(Rsh\) is parallel resistance, \(K\) is the Boltzmann constant, \(A\) is the quality factor of the diode, and \(T\) is the absolute temperature [25].  
The characteristic curves of the photovoltaic module drawn according to Equation (4) are shown in Figure 3. These curves have several variables worthy of attention, including The short-circuit current (ISC), open-circuit voltage (VOC), maximum power point (MPP), voltage at MPP (VMPP), current at MPP (IMPP), and power at MPP.
# EGSDK-Net: Edge-Guided Stepwise Dual Kernel Update Network for Panoptic Segmentation
## 4 Experiments
### Implementation Details  
EGSDK-Net is implemented in the Detectron2 framework using PyTorch [30]. The base variant of RTFormer is utilized, initialized with a pre-trained model from ImageNet [31]. The hyperparameters associated with training are as follows: batch size (32), initial learning rate (0.0002), weight decay (0.05), maximum iterations (90 k), optimizer (AdamW), scheduler ('poly'), \(\omega_{mask}\) (1.0), \(\omega_{dic}\) (4.0), \(\omega_{rank}\) (0.1), \(\omega_{cls}\) (2.0), and \(\omega_{seg}\) (1.0). We incorporate warm-up in the training process, raising the learning rate from 0.000002 to 0.0002 in the first 1000 iterations. The number of prediction masks (convolutional kernels) \(N\) is set to 100. Our kernel update frequency matches that of RT-K-Net [5]. All experiments are conducted on a single V100 GPU.