esa-sceva/satcom-chunk-collection huggingface dataset:
# Open-Set Signal Recognition Based on Transformer and Wasserstein Distance
## 4 The Proposed Algorithm  
In the present work, we propose an efficient open-set signal recognition algorithm named the open-set signal recognition based on the transformer and Wasserstein distance (OSSG\({}_{TW}\)). The algorithm contains three key sub-modules: _the signal representation_ sub-module based on the ViT structure; _the set distance metric_ sub-module based on Wasserstein distance; and _the class space compression_ sub-module based on reciprocal point separation and central loss. In OSSG\({}_{TW}\), the representing features of the signals are established based on the transformer-based neural networks, i.e., ViT, in order to extract global information about time series-related data. The three sub-modules of OSSG\({}_{TW}\) are summarized below:  
* The signal representation sub-module: For signal recognition, the signal sequence is typically transformed into time-frequency matrices through energy detection and the short-time fast Fourier transform (FFT). However, although a sequential relationship structure must exist among the columns of the time-frequency matrix, the traditional convolution neural network-based (CNN-based) feature extraction technique lacks the information vision of the global feature. The transformer-based model with the ability to extract the global feature is more suitable for the feature extraction of signal sequences. We employed the transformer-based ViT model as the basic feature extraction scheme to obtain more efficient features of the original signal data.
* The set distance metric sub-module: The distance of the reciprocal point to the corresponding dataset was determined by combining the Euclidean distance and dot product in the classical ARPL. In order to calculate a better distance to a dataset, the Wasserstein distance could be employed, which is usually used to reflect the distance between distributions (a dataset can be considered as a uniform distribution defined in data samples). Furthermore, the use of the Wasserstein distance could be beneficial in determining the reciprocal point for each category.
* The class space compression sub-module: In order to realize the recognition of unknown electromagnetic signals by separating the known and unknown electromagnetic signal category feature spaces more accurately, we introduced the center loss to compress the feature space of the known electromagnetic signal target, and expanded the feature space of the unknown electromagnetic signal target based on the well-established feature center of known categories. The feature space range of unknown electromagnetic signal categories can be improved by constraining the inter-class dispersion and intra-class compactness, which could be beneficial in improving the classification performance.  
The overall algorithm framework is shown in Figure 1. The details of the above improvements will be discussed in the following sub-sections.
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 6 PVPBC Protocol: Cryptographic Considerations
### Post-Election Phase  
At the end of the voting phase, as in the Selene protocol, the front-end system starts the mix and decryption process. During this process, each encrypted vote and its corresponding encrypted tracking number are mixed, shuffled, and decrypted by the election tellers. At the end of this process, tracking numbers alongside plaintext votes are posted to the permissioned ledger.  
After the votes and tracking numbers have been published, the front-end sends \(\alpha_{i}\) and the corresponding VID to the EA, which, in turn, forwards it to the TTP. The TTP uses the VID to locate the identity of registered voters on the authentication blockchain in order to send their \(\alpha_{i}\) term via e-mail. In response to receiving the \(\alpha\) term, the voter can access their tracking number via a call to the front-end system to apply their trapdoor key to \((\alpha_{i},\beta_{i})\). This enables them to identify their vote on the ledger.  
After the election finishes, the front-end system carries out two steps to produce the final result.  
**Mix and decryption:** For each voter, \(V_{i}\), the encrypted tracking number and encrypted vote are extracted to give a pair of the form:  
\[(\{g^{n_{\pi(i)}}\}_{PK_{T}},\{vote_{i}\}_{PK_{T}}). \tag{7}\]  
This pair is put through a verifiable shuffle performed by the mix-nets [34], and then a threshold set of tellers perform a verifiable decryption of these shuffled pairs. Proof of shuffling and correct decryption are uploaded on the WBB. After decryption, the pairs, \((n_{\pi(i)},vote_{i})\), are published on the WBB.  
**Tracking-number retrieval:** Once the Grace period has ended, each voter, \(V_{i}\), is sent the \(\alpha_{i}\) described in Section 6.1. The received \(\alpha_{i}\) can be combined with the \(\beta_{i}\) term to reconstruct the tracking number encrypted under the voter's public key \(PK_{i}\).  
\[(\alpha_{i},\beta_{i})=\{g^{n_{\pi(i)}}\}_{PK_{i}} \tag{8}\]  
The voter, \(V_{i}\), can request that the front-end system use their secret key to decrypt and retrieve the \(g^{n_{\pi(i)}}\), and thus obtains the tracker \(n_{\pi(i)}\), which they can then use to look up their vote on the ledger.
## 3 Results and Discussion
### Microstructure Analysis  
The changes in the microstructure according to the substitution elements and their amounts are shown in Figure 4a-l. The average grain size is shown in the lower-right corner of each figure. Most samples exhibited a porous microstructure because they were sintered without sintering additives. In Figure 4a-d, the microstructure of the Si-Mg-substituted SrM is shown with increasing substitution levels (\(x\)). When \(x\) = 0.05 and 0.1, the average grain size (D\({}_{\text{ave}}\)) decreased to 0.78 and 0.65 \(\upmu\)m, respectively. It increased to D\({}_{\text{ave}}\) = 0.98 \(\upmu\)m at \(x\) = 0.2. The microstructures of the Si-K-substituted SrM samples are shown in Figure 4e-g, and those of Si-Li are shown in Figure 4h-l. In the case of Si-K doping, the grain size was finely controlled when the substitution amount was \(y\)\(\leq\) 1, whereas in the case of Si-Li substitution, a fine microstructure could be obtained when the substitution amount was \(z\)\(\geq\) 0.2.
## 3 Results
### Serum Protein Concentrations of Anti- and Proinflammatory Cytokines and Chemokines  
After the consumption of Nutri4 eggs, a significant decrease in serum TGF-1\(\beta\) and IL-17A concentrations was observed, while INF-\(\gamma\), IL-6, IL-10, IL-23, TNF-\(\alpha\), and MCP-1 concentrations remained unchanged compared to baseline measurements (Table 5). After regular chicken egg consumption, no significant changes in the serum concentrations of cytokines and chemokines compared to initial measurements within the control group  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Group** & \multicolumn{2}{c}{**Control (N = 15)**} & \multicolumn{2}{c}{**Nutri4 (N = 15)**} \\ \hline
**Parameter** & **before** & **after** & **before** & **after** \\ \hline
**Oxidative stress biomarkers** & & & & \\ TBARS (mM/L TE) & 0.029 \(\pm\) 0.007 & 0.032 \(\pm\) 0.008 & 0.033 \(\pm\) 0.008 & 0.490 \(\pm\) 0.007 \\
8-iso-PGF2\(\alpha\) (pg/mL) & 140 \(\pm\) 58 & 129 \(\pm\) 48 & 223 \(\pm\) 266 & 227 \(\pm\) 282 \\ AOPPs (\(\mu\)mol/L) & 0.056 \(\pm\) 0.003 & 0.055 \(\pm\) 0.003 & 0.056 \(\pm\) 0.004 & 0.055 \(\pm\) 0.003 \\ OxLDL (pg/mL) & 103 \(\pm\) 10 & 102 \(\pm\) 9 & 111 \(\pm\) 7 & 110 \(\pm\) 5 \\ Biomarkers of antioxidant defense & & & & \\ FRAP (\(\mu\)m/MDA) & 0.250 \(\pm\) 0.021 & 0.246 \(\pm\) 0.034 & 0.397 \(\pm\) 0.166 & 0.355 \(\pm\) 0.128 \\ GPx (U/mg protein) & 0.021 \(\pm\) 0.003 & 0.022 \(\pm\) 0.002 & 0.017 \(\pm\) 0.003 & 0.017 \(\pm\) 0.003 \\ CAT (U/mg protein) & 4.716 \(\pm\) 1.464 & 4.022 \(\pm\) 1.054 & 5.138 \(\pm\) 1.462 & 4.507 \(\pm\) 1.873 \\ SOD (U/mg protein) & 5.073 \(\pm\) 0.400 & 5.287 \(\pm\) 0.259 & 5.400 \(\pm\) 0.343 & 5.387 \(\pm\) 0.233 \\ \hline \end{tabular} Data are presented as mean \(\pm\) standard deviation (SD). TBARS—thiobarbituric acid-reactive substances; 8-iso-PGF2\(\alpha\)—8-iso prostaglandin FA2; AOPPs—advanced oxidation protein products; OxLDL—oxidized low-density lipoprotein; FRAP—ferric-reducing ability of plasma; GPx—glutathione peroxidase; CAT—catalase; SOD—superoxide dismutase.  
\end{table}
Table 4: The effect of functionally enriched (Nutri4) and regular (Control) chicken egg consumption on oxidative stress biomarkers and antioxidant capacity in participants with chronic coronary syndrome.  
Figure 4: The effect of functionally enriched (Nutri4) and regular (control) chicken egg consumption on serum concentrations of three nitric oxide synthase isoforms. (**A**) Endothelial NOS (eNOS), (**B**) neuronal NOS (nNOS), (**C**) inducible NOS (iNOS). Results are shown as mean \(\pm\) standard deviation (SD); \({}^{\star}\)\(p\) \(<\) 0.05 before vs. after within the group (control (N = 15) or Nutri4 (N = 15))—paired _t_-test.  
were recorded. There were no statistically significant differences in the concentrations of measured cytokines and interleukins between groups.
## 3 The Inverse Problem
### Artificial Bee Colony Algorithm  
In ABCA optimization, each food source represents one possible solution to the optimization problem, and the fitness values represent the solution quality. In detail, the ABCA can be listed in the form of the following steps.  
1. The algorithm is initialized: \(SN\) is the number of employed bees (=the number of onlooker bees); \(D\) is the dimension of the solution; _limit_ is the number of corrective flights around \(\lambda_{i}\); and _MCN_ is the maximum number of iterations.  
2. Initial solutions of thermal conductivity \(\lambda_{i}\) (\(i\) = 1,..., \(SN\)) are randomly generated using Equation (5):  
\[\lambda_{i}=lb+(ub-lb)\cdot rand(0,1)\quad i=1,2,\ldots,SN \tag{5}\]  
where \(ub\), \(lb\) are the upper and lower limit of \(\lambda_{i}\), respectively.  
3. The FDM is used to solve the positive forward problem of Equations (1)-(3) with \(\lambda_{i}\), then \(J(\lambda_{i})\) is calculated. If the stop criterion is satisfied, the inversion of iteration will be finished, otherwise, go to step 4.  
4. Each employed bee searching for a new solution according to Equation (6):  
\[\lambda_{i+1,i}=\lambda_{i,j}+r_{i}\left(\lambda_{i,i}-\lambda_{i,i}\right) \tag{6}\]  
where \(it\) is iteration number, \(r_{i}\)\(\in\)[-1,1] is random data, and \(i\)\(\
eq\)\(i\) and belongs to \(I\)-\(SN\).  
If \(J(\lambda_{i+1,i})<J(\lambda_{i,i})\) then \(\lambda_{i}=\lambda_{i+1,i}\) (greedy algorithm).  
In the ABCA, after _limit_ times of cycling, if one solution \(\lambda_{i}\) has not been improved, it will be given up and the employed bee will become a scout bee, and generate a new solution according to:  
\[\lambda_{i}=\lambda_{min}+(\lambda_{max}-\lambda_{min})\cdot rand(0,1) \tag{7}\]  
x\({}_{\min}\) is the current minimum solution, x\({}_{\max}\) is the maximum solution.  
5. The fitness value and probabilities are derived as follows. The fitness value of the food source corresponds to its richness (quality) and its calculation form is:  
\[fit\lambda_{i}=\left\{\begin{array}{ll}\frac{1}{1+J(\lambda_{i})}&J( \lambda_{i})\geq 0\\ 1+abs(J(\lambda_{i}))&J(\lambda_{i})<0\end{array}\right. \tag{8}\]  
The probabilities \(P_{i}\) are defined as:  
\[P_{i}=prob(\lambda_{i})=\frac{fit\lambda_{i}}{\sum_{n=1}^{SN}fit\lambda_{n}} \tag{9}\]  
6. Each onlooker chooses one of the sources \(\lambda_{i}\), \(i\) = 1,..., \(SN\), with probability \(P_{i}\). One source can be chosen by a group of onlooker bees. Then, each onlooker bee explores the chosen \(\lambda_{i}\) and modifies it according to the procedure described in step 4.  
7. The choice of \(\Delta T\left(\lambda_{i+1}^{best}\right)\) of \(\lambda_{i+1,i}\) is determined by the bee colony. If \(\Delta T\left(\lambda_{i+1}^{best}\right)\) is better than the choice from the previous iteration, it is accepted as \(\lambda^{best}\).  
8. If \(J(\lambda^{best})\)\(<\varepsilon\) or if \(i\)+1==\(MCN\) then iterations are complete, else \(i\)=\(it\)+1, and steps 4-8 are repeated until the stop criterion is satisfied.
# SemABC: Semantic-Guided Adaptive Bias Calibration for Generative Zero-Shot Point Cloud Segmentation
## 4 Experiments  
### Settings  
**Datasets.** We conduct experiments on three public 3D semantic segmentation datasets: ScanNet [39], 53 DIS [40], and SemanticKITTI [41], which follow the setting of SV-Seg [19]. We select four unseen categories for each dataset. For ScanNet, which includes 20 categories, the unseen classes are \"desk\", \"bookshelf\", \"sofa\", and \"toilet\". For 53 DIS, with 13 categories, the unseen classes are \"beam\", \"column\", \"window\", and \"sofa\". For SemanticKITTI, with 19 categories, we set \"motorcycle\", \"truck\", \"bicyclist\", and \"traffic sign\" as unseen classes.  
**Metrics.** We employ the mean Intersection-over-Union (mIoU) to measure the accuracy of segmentation results and the Harmonic mean of mIoU (HmIoU) to report the overall performance on the combined seen and unseen classes. The metrics are defined as follows:  
\[mIoU=\frac{1}{k}\sum_{i=1}^{k}\frac{TP_{i}}{FN_{i}+FP_{i}+TP_{i}}, \tag{15}\]  
\[HmIoU=\frac{2\times mIoU_{seen}\times mIoU_{unseen}}{mIoU_{seen}+mIoU_{unseen }}, \tag{16}\]  
where \(TP_{i}\), \(FP_{i}\), and \(FN_{i}\) represent the counts of true positives, false positives, and false negatives for category \(c_{i}\), respectively. \(k\) is the total number of categories.  
**Baselines.** We compare the proposed approach with four baselines. Following 3 DGenZ [18], we evaluated ZSLPC-Seg [32] and DeViSe-3 DSeg [42], which are developed for ZSL classification and 2D segmentation, and are then adapted to solve the zero-shot point cloud segmentation problem. In addition, we compare with 3 DGenZ [18] and SV-Seg [19], which are the state-of-the-art generative point cloud segmentation methods.
## 3 Materials and Methods
### Data Description  
Thanks to its capability for real-time dissemination and interactivity, social media has become a crucial medium for analyzing the public perception of disasters [44; 45]. Given the extensive active user base of the Sina Weibo platform, it offers data resources for tracking typhoon cascading disasters and user concern patterns. Therefore, this study utilized social media text data as a proxy for public opinion, serving as the basis for our experimental analysis.  
The experimental dataset comprised two components: (1) Data 1: These data were extracted from the publicly available Weibo_Senti_100k dataset, which consists of Sina Weibo user reviews. As one of the largest social media platforms in China, Weibo's user-generated comments are rich and diverse, covering various topics and viewpoints, and can better reflect emotional expressions in the real world; this large-scale dataset provides a rich sample for the training of sentiment analysis models, which helps improve their generalization ability. After preprocessing, the dataset contained 119,820 sentiment-labeled data, including 59,910 positive (labeled \"1\") and 59,910 negative samples. (2) Data 2:  
Figure 1: Overview of the study area.  
These data were collected via the Scrapy framework using Sina Weibo's API. Between 13 September and 18 September 2024, public sentiment text related to keywords such as \"Typhoon Bebinca\" and \"Shanghai Typhoon\" was systematically scraped. These data cover four stages of public opinion: latency, occurrence, development, and dissipation. Therefore, the user sentiment and corresponding user concern information extracted from these textual data represent the public opinion trends during the landfall of Bebinca in Shanghai.  
However, the collected text data require preprocessing before experimental use. This study standardized the data through three key steps: data cleaning, filtering, classification, and systematic organization. During the cleaning phase, irrelevant content such as noise symbols was removed, resulting in a refined dataset of 27,056 text entries. These entries were then filtered, categorized, and archived to ensure consistency and usability for subsequent analysis.
# Systemic Evaluation of PSS in the Early Concept Phase Using Graph-Based Reasoning
## 4 Solution Approach
### PSS Abstraction Framework  
The purpose of the _PSS abstraction framework_ is to provide a symbolically structured representation of the a priori knowledge of PSS developers about a PSS concept and its anticipated behavior as a probabilistic graph (i.e., a Bayesian network). To this end,  
Figure 2: Macro perspective of the solution approach with two solution elements: (1) the PSS abstraction framework and (2) the GEAR method.  
the PSS abstraction framework defines the structure of the graph model capturing the PSS concept's semantics across three layers: the _PSS Concept Variation Layer_ (_CVL_), the _Evaluation Logic Layer_ (_EVL_), and the _Knowledge Gap Layer_ (_KGL_) which are schematically illustrated in Figure 3. In the following, each layer of the Bayesian decision network (a Bayesian network enhanced by utility and decision nodes, as depicted in Figure 3) is explained briefly. In the CVL and KGL nodes are random variables representing the PSS concept's variations and knowledge gaps, respectively, with directed edges indicating causal dependencies influencing the EVL nodes. In the EVL, random variables represent PSS behavior relevant for EVC evaluation, with reward nodes used to represent EVCs in a utility-theoretic context. Decision nodes allow targeted changes to be grouped in the graph as individual decisions, enabling a direct comparison of the expected values of individual decisions. While in the proposed approach, alternative configurations of the network are explored during reasoning analysis, the configuration of graph is configured manually in order to make the process understandable. Hence, the presence of a decision node in the EVL is not required for the solution approach itself but primarily supports the functioning of reasoning algorithms, which require the existence of such a decision node in the graph in order to be executable. The structure and function of the layers within the PSS abstraction framework are explained in more detail in the following.
# Learning Accurate Pseudo-Labels via Feature Similarity in the Presence of Label Noise
## 4 Experiments and Discussion
### Experimental Setup
#### 4.1.3 Baselines  
This paper utilizes the original code version and default configurations; this study reproduces the state-of-the-art technique and subsequently contrasts its performance against FPL.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **MNIST** & **SVHN** & **CIFAR10** & **CIFAR100** & **Clothing1M** \\ \hline classes & 10 & 10 & 10 & 100 & 14 \\ Train & 60k & 7325 & 50k & 50k & 50k \\ Test & 10k & 26,032 & 10k & 10k & 14k \\ Size & 28 \(\times\) 28 & 32 \(\times\) 32 & 32 \(\times\) 32 & 32 \(\times\) 32 & 224 \(\times\) 224 \\ \hline Classifier & 9-CNN & & Resnet34 & & Resnet18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset statistics and classifier architectures used.  
1. GCE [12]: A novel GCE loss function integrates the robustness to noise characteristic of the mean absolute error (MAE) loss function with the training expediency inherent in the conventional cross-entropy loss function.
2. APL [15]: A framework is suggested for crafting a robust loss function, termed active passive loss (APL). APL synergizes two interdependent robust loss functions to enhance both robustness and performance.
3. ELR [34]: Takes advantage of early learning through regularization. Firstly, the semi-supervised learning technique is used to generate the target probability according to the model output. Secondly, a regularization term is designed to guide the model to these targets, implicitly preventing the memory of false labels.
4. CDR [35]: This method categorizes parameters into critical and non-critical, employing distinct update regulations for various parameter types.
5. MSLC: Introduces a meta-learning model designed to predict soft labels by implementing a meta-gradient descent procedure guided by clean metadata, thereby efficiently allocating pseudo-labels to noisy ones.
6. PES [36]: Trains the network into different parts to counteract the effects of label noise.
7. MLC: The label correction process is considered a meta-process, where the label correction network functions as a meta-model to generate corrected pseudo-labels for noisy labels. Concurrently, the main model is trained to learn from these pseudo-labels instead of the noisy labels. Introduces a fresh contrast regularization function for acquiring representations in the presence of noisy data, mitigating the influence of label noise.
8. CTRR [37]: Proposes a new contrast regularization function for acquiring representations in the presence of noisy data, relieving the influence of label noise.
## 3 Results and Discussion  
### RC Column Experimental Validation  
Figure 3 compares experimental hysteresis loops with those re-simulated using the BWBN-VP and BWBN models for the three RC column cyclic-loading tests in Table 2. Results show a good match between the experimental and re-simulated hysteresis loops using the BWBN-VP and BWBN models with an average correlation coefficient of 0.98 and 0.85 for all tests, respectively. As seen in Figure 3, there is a considerable pinching behavior in the shear failure (SF) mode, while pinching effects in flexural-shear (FSF) and flexural (FF) failure modes are slight and negligible, respectively. The BWBN-VP model significantly outperforms the BWBN model in all failure scenarios, with especially notable improvements in flexural and shear failure modes. Therefore, the proposed BWBN-VP model successfully captures the highly non-linear behavior of RC columns with different failure modes showing completely different hysteresis loop shapes.  
Figure 3: Comparison between experimental force-deformation hysteresis loops and those of identified using (**a**) BWBN-VP and (**b**) BWBN hysteresis models for Test 1–3 results.  
Figure 4 presents parity plots that compare the performance of the BWBN and BWBN-VP hysteresis models in simulating forces based on results from column Tests 1-3. In these plots, lines with slopes closer to 1 indicate better model accuracy. The comparison demonstrates that the BWBN-VP model improves upon the BWBN model's performance by 64%, 113%, and 20% for Flexural, Shear, and Shear-Flexural failure modes, respectively. These results confirm that the BWBN-VP model performs best, particularly in scenarios involving Shear-Flexural failures.
# A Novel Hybrid Model for the Prediction and Classification of Rolling Bearing Condition
## 4 Experimental Analysis
### Experimental Results and Analysis
#### 4.2.1 Decomposing the Vibration Signal Using VMD  
According to the steps in Section 3, a group of IF data as an example was analyzed. Table 2 shows the VMD relevant parameters according to [19]. The time-waveform of the original signal and the modes obtained by VMD are presented in the left of Figure 3, and the corresponding frequency spectrums are presented in the right of this figure.