esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 3 Microbial Safety of Meat and Meat Products
### Pathogens in Meat
#### 3.2.3 Staphylococcus Aureus  
Other than _Salmonella_ and _Listeria_, meat and its by-products are broadly known to be a significant repository for _S. aureus_ and associated with various outbreaks. Therefore,  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Type of Micro-Organisms** & **Oxygen Need** & & **Symptoms** & **References** \\ \hline \multirow{6}{*}{Aerobic Bacteria} & & \(\bullet\) & Discoloration of meat & \\  & & \(\bullet\) & Development of surface slime & \\  & & \(\bullet\) & Variation in smell & [23] \\  & & \(\bullet\) & Decomposition of fat & \\  & & \(\bullet\) & Generation of gas & \\  & & & \(\bullet\) & Generation of gas & \\  & & \(\bullet\) & Sour taste of meat products & [23] \\  & & & \(\bullet\) & Foul smell and putrefaction & \\  & & & \(\bullet\) & Development of surface slime & \\  & & \(\bullet\) & Occurrence of discoloration & \\ Yeast & Present & \(\bullet\) & Variation in smell and taste & [20] \\  & & & \(\bullet\) & Fat decomposition & \\  & & & \(\bullet\) & Whiskery and sticky surface & \\  & & \(\bullet\) & Variation in smell & \\ Molds & Present & \(\bullet\) & Decomposition of fat & \\  & & & \(\bullet\) & Occurrence of discoloration & \\ \hline \end{tabular}
\end{table}
Table 1: Spoilage agents of meat and their impact on meat and meat product surfaces.  
the predominance and levels of _S. aureus_ from South to North China were examined in retail meat and meat items. In this study, an aggregate of 1850 samples was gathered in 39 Chinese urban areas. By and large, there were 647 examples showing coagulase positive and which were affirmed for _S. aureus_. The normal occurrence of _S. aureus_ was 35.0% (647/1850). The dissected food items were ordered into three classifications (raw form meat, fast frozen meat, and ready-to-eat meat), and the upsides of _S. aureus_ defilement in each sample were resolved. Among these classes, raw meat was the most often contaminated with _S. aureus_, with a pervasiveness that came to 51.0%, including 60.9% of positive samples in fast frozen meat and 12.2% of RTE tests [27; 28].
## 5 Performance Evaluation
### Comparison of BSMH and Other Benchmarks
#### 5.4.3 Control Overhead  
The total amount of control packets required by the protocols is presented in Figure 15. As the control overhead does not depend on the communication type, we plot the results regarding 1-n communication.  
As expected, GPSR achieves the best performance and its overhead is extremely smaller than those of the others. Although EDGR requires the sending of the beacons periodically, these beacons are quite lightweight, therefore the overhead caused by EDGR is still smaller than those of BSMH and LVGR. Both BSMH and LVGR need to determine the hole boundaries and dissem  
Figure 14: Average end-to-end delay of successfully delivered packets. (a) 1-n communication. (b) n-n communication.  
Figure 15: Total amount of control packets [38].  
Moreover, since the control packets used in these two phases are quite heavy, BSMH and LVGR result in the worst performance. However, note that the hole boundary determination and hole information dissemination phases of BSMH and LVGR are conducted only one time while the beaconing of EDGR is conducted periodically, the control overhead of EDGR may higher than those of BSMH and LVGR when increasing the simulation time. By using core polygon instead of the exact hole, BSMH reduces overhead significantly compared with LVGR. Specifically, the overhead caused by BSMH is always smaller than 53% that caused LVGR.  
Another observation is that the overhead of BSMH slightly decreases when increasing the \(\epsilon\). This is due to the fact that the increase of \(\epsilon\) will shrink the region for disseminating hole information.
## 3 Research Method
### Application and Cluster Management
The design of Mesos [333]-based frameworks, in particular DC/OS, allows that one cluster can be more easily deployed across multiple availability zones or regions because these CO frameworks have generic and automated support for setting up replicated masters (see Highly-Available Master/Manager architecture in Section 4.1). _Recovering from network partitions._ Mesos [236] provides good support for dealing and recovering from network partitions. Aurora v0.20.0 [103] has added an optional and experimental feature for using the Mesos partition-aware APIs in order to customize the job or service recovery strategy. Users of Aurora can set partition policies [334] per job whether or not to reschedule and how long to wait for the partition to heal. _Management of multiple clusters across multiple clouds._ Docker's Docker Cloud [335], Kubernetes' kubefed [336], and DC/OS' multi-cluster CLI [337] also offer CLI commands for managing multiple clusters across one or more cloud providers. _Federated authentication:_ Kubernetes's federated API [338] and DC/OS' single-sign-on across clusters [339] capability support federated authentication of users. _Multi-zone/multi-region workloads:_ All CO frameworks, except Docker Swarm stand-alone, allow controlling the availability of a service by spreading its containers across multiple fault domains (i.e., availability zones, regions or datacenters). Docker Swarm integrated mode [229], Mesos [340], Aurora [341], Marathon and DC/OS [342] require that nodes are in advance labeled with their zone, region or datacenter and offer a placement preference operator that ensures that containers of a service are spread across these different fault domains. Kubernetes [343] uses another approach: It uses its extensive support for federating multiple container clusters across different fault domains (see Section 5.1).
# A Neural Topic Modeling Study Integrating SBERT and Data Augmentation
###### Abstract  
Topic models can extract consistent themes from large corpora for research purposes. In recent years, the combination of pretrained language models and neural topic models has gained attention among scholars. However, this approach has some drawbacks: in short texts, the quality of the topics obtained by the models is low and incoherent, which is caused by the reduced word frequency (insufficient word co-occurrence) in short texts compared to long texts. To address these issues, we propose a neural topic model based on SBERT and data augmentation. First, our proposed easy data augmentation (EDA) method with keyword combination helps overcome the sparsity problem in short texts. Then, the attention mechanism is used to focus on keywords related to the topic and reduce the impact of noise words. Next, the SBERT model is trained on a large and diverse dataset, which can generate high-quality semantic information vectors for short texts. Finally, we perform feature fusion on the augmented data that have been weighted by an attention mechanism with the high-quality semantic information obtained. Then, the fused features are input into a neural topic model to obtain high-quality topics. The experimental results on an English public dataset show that our model generates high-quality topics, with the average scores improving by 2.5% for topic coherence and 1.2% for topic diversity compared to the baseline model.  
SBERT; topic models; data augmentation; feature fusion Article
# Anomaly Detection in Automotive Industry Using Clustering Methods--A Case Study
## 2 Clustering Algorithms
### Fuzzy C-Means (FCM)  
Fuzzy C-Means (FCM) has become an essential method with many grouping-clustering applications for real-world problems [37]. Among the diffuse clustering methods, FCM is one of the best known for its simplicity and efficiency. However, it shows some weaknesses, particularly its tendency to fall to local lows at minimum local points.  
This algorithm treats clusters as flexible groups in which each data object has presented a degree of association. These degrees are evaluated between 0 and 1, with a high value representing a high degree of similarity between the current object under study and the group [38].  
According to [39], FCM has shown important characteristics: the number of groups can be defined automatically; it works well with overlapping data and is robust for initialization, noise, and outliers.  
The FCM defines an association matrix \(\mathbf{U}=\left\{u_{ij}\right\}_{i,j}^{N,\ n_{k}}\), where \(u_{\bar{q}}\in[0,\,1]\) is the degree of membership, \(\sum_{j=1}^{n_{k}}u_{ij}\), \(\forall i\), and \(0<\sum_{i=1}^{N}u_{ij}<N\), being \(N\) the number of objects. Note that each pattern is named \(i\) for each cluster \(j\) and \(n_{k}\) is the number of objects in the respective cluster.  
The objective of the method is to minimize the cost function \(\mathbf{J}_{m}\) described in Equation (4):  
\[\mathbf{J}_{m}=\sum_{i=1}^{N}\sum_{j=1}^{n_{k}}u_{ij}^{m}\|\mathbf{x}_{i}-\mathbf{ c}_{j}\|^{2} \tag{4}\]  
in which \(\|.\|\) it is usually the Euclidean norm, and \(m\) is the inaccuracy coefficient provided by the user.  
After that, the degree of relevance and the central positions are calculated by Equations (5) and (6):  
\[u_{ij}^{m}=\frac{1}{\sum_{k=1}^{C}\frac{\|\mathbf{x}_{i}-\mathbf{c}_{j}\|^{ \frac{2}{m-1}}}{\|\mathbf{x}_{i}-\mathbf{c}_{k}\|}} \tag{5}\]\[\mathbf{c}_{j}=\frac{\sum_{i=1}^{N}u_{ij}^{m}\cdot\mathbf{x}_{i}}{\sum_{i=1}^{N}u_{ ij}^{m}} \tag{6}\]
# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
###### Abstract  
The TransT object tracking algorithm, built on Transformer architecture, effectively integrates deep feature extraction with attention mechanisms, thereby enhancing the stability and accuracy of the algorithm. However, this algorithm exhibits insufficient tracking accuracy and boundary box drift when dealing with similar background clutter, which directly affects the subsequent tracking process. To overcome this problem, this paper constructs a semantic enhancement model, which utilizes multi-layer feature representations extracted from deep networks, and correlates and fuses shallow features with deep features by using cross-attention. At the same time, in order to adapt to the changes in the surrounding environment of the object and establish good discrimination with similar objects, this paper proposes a dynamic mask strategy to optimize the attention allocation mechanism and finally employs an object template update mechanism to improve the adaptability of the model by comparing the spatio-temporal information of successive frames to update the object template in time, further enhancing its tracking performance in complex scenes. Experimental comparison results demonstrate that the algorithm proposed in this paper can effectively handle similar background clutter, leading to a significant improvement in the overall performance of the tracking model.  
dynamic mask; multi-layer depth features; object tracking; similar background clutter; template update; transformer Article
# Interleaved Boost Converter with ZVT-ZCT for the Main Switches and ZCS for the Auxiliary Switch
## 2 Two-Phase Converter with Proposed Soft Switching  
In Figure 1, the proposed inductor resonant circuit applied to the two-phase interleaved converter is built up by only one resonant \(L_{r}\), two resonant capacitors \(C_{r1}\) and \(C_{r2}\), two resonant diodes \(D_{r1}\) and \(D_{r2}\), one auxiliary switch \(S_{a}\), and one auxiliary diode \(D_{a}\).
## 3 Satellite-to-Ground QKD Simulator
### Protocol Assumptions and Finite Key Size Analysis
\[SKR\geq f_{rep}\times q\bigg{\{}Q_{1}[1-H_{2}(e_{1})]-Q_{\mu}f\big{(}E_{\mu} \big{)}H_{2}\big{(}E_{\mu}\big{)}-\frac{\Delta}{\mathrm{N}_{s}}\ \bigg{\}}\eta_{\mathrm{dead}}, \tag{20}\]  
where \(\eta_{\mathrm{dead}}\) corresponds to the dead time efficiency, which accounts for the limitations in maximum count rates due to the detectors' dead time [77]; \(f\big{(}E_{\mu}\big{)}\) is the non-ideal error correction efficiency; and \(N_{s}\) is the total number of transmitted signal states. The value of \(\Delta\) was calculated to be the following [75]:  
\[\Delta=7\times\sqrt{\mathrm{N}_{\mathrm{n}}\times\log_{2}\bigg{(}\frac{2}{ \varepsilon_{s}-\varepsilon_{pe}}\bigg{)}}+2\times\log_{2}\bigg{(}\frac{1}{2 \times(\varepsilon-\varepsilon_{s}-\varepsilon_{ec})}\bigg{)}, \tag{21}\]  
where \(\varepsilon_{s}\) corresponds to the smoothing parameter, \(\varepsilon_{pe}\) to the estimation parameter, and \(\varepsilon_{ec}\) to the error correction parameter, while \(\varepsilon\) denotes the overall security parameter of the final key, which is the sum of the aforementioned parameters. The finite key size effects also play a vital role in satellite-based QKD since the communication time windows during which the satellite is visible by an OGS vary for different orbits and remain limited for specific cases.
# Multilevel Bipolar Electroforming-Free Resistive Switching Memory Based on Silicon Oxynitride
###### Abstract  
Resistive random-access memory (RRAM) devices are fabricated by utilizing silicon oxynitride (SiO\({}_{x}\)N\({}_{y}\)) thin film as a resistive switching layer. A SiO\({}_{x}\)N\({}_{y}\) layer is deposited on a p+Si substrate and capped with a top electrode consisting of Au/Ni. The SiO\({}_{x}\)N\({}_{y}\)-based memory device demonstrates bipolar multilevel operation. It can switch interchangeably between all resistance states, including direct SET switching from a high-resistance state (HRS) to an intermediate-resistance state (IRS) or low-resistance state (LRS), direct RESET switching process from LRS to IRS or HRS, and SET/RESET switching from IRS to LRS or HRS by controlling the magnitude of the applied write voltage signal. The device also shows electroforming-free ternary nonvolatile resistive switching characteristics having R\({}_{\text{HRS}}\)/R\({}_{\text{IRS}}\)\(>\) 10, R\({}_{\text{HRS}}\)/R\({}_{\text{LRS}}\)\(>\) 5, R\({}_{\text{HRS}}\)/R\({}_{\text{LRS}}\)\(>\) 10\({}^{3}\), and retention over 1.8 \(\times\) 10\({}^{4}\) s. The resistive switching mechanism in the devices is found to be combinatory processes of hopping conduction by charge trapping/detrapping in the bulk SiO\({}_{x}\)N\({}_{y}\) layer and filamentary switching mode at the interface between the SiO\({}_{x}\)N\({}_{y}\) and Ni layers.  
b  
b
## 5 Conclusions  
Building exposure data is the basis for structural vulnerability calculations, rate determination of earthquake insurance, and earthquake loss estimation. However, establishing a complete database of building information with high accuracy and rich semantics is a huge challenge worldwide. This study takes an urban area as a research object; integrates multi-source spatial data such as remote sensing data, land cover data, crowdsourcing data, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Construction Age**} & \multicolumn{6}{c}{**Number of Real Data**} & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**Accuracy Rate**} \\ \cline{3-3} \cline{5-8}  & & **Before 1990** & **1991–2000** & **2001–2010** & & **After 2011** & & **(\%)** \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Number of \\ validation \\ data \\ \end{tabular} } & Before 1990 & 81 & 1 & 0 & 0 & 82 & 98.78 \\  & 1991–2000 & 3 & 151 & 23 & 1 & 178 & 84.83 \\  & 2001–2010 & 0 & 7 & 171 & 36 & 214 & 79.91 \\  & After 2011 & 0 & 1 & 23 & 161 & 185 & 87.03 \\  & Total & 84 & 160 & 217 & 198 & 659 & \\  & Accuracy rate (\%) & 96.43 & 94.38 & 78.80 & 81.31 & & \\  & Overall accuracy (\%) & & & & 85.58 & & & \\  & Kappa coefficient & & & & 0.802 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix of construction age of the buildings. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Structure Type**} & \multicolumn{6}{c}{**Number of Real Data**} & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**Accuracy Rate (\%)**} \\ \cline{3-3} \cline{5-8}  & & **Industry** & & **Industry** & & **Masony** & **Brick and** & **Reinforcement** & & **(\%)** \\  & & **Single-** & **Multi-** & **Structure-** & **Concrete** & **Concrete** & **Concrete** & **Concrete** & **Rate (\%)** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Number of \\ validation \\ data \\ \end{tabular} } & Industry & 42 & 2 & 0 & 0 & 0 & 0 & 44 & 95.45 \\  & \begin{tabular}{c} Industry \\ multi-story \\ data \\ \end{tabular} & 1 & 15 & 0 & 0 & 0 & 0 & 16 & 93.75 \\  & \begin{tabular}{c} Masorry \\ structure \\ \end{tabular} & 5 & 0 & 58 & 7 & 2 & 0 & 72 & 80.56 \\  & \begin{tabular}{c} Brick and \\ concrete structure \\ \end{tabular} & 0 & 0 & 9 & 224 & 35 & 0 & 268 & 83.58 \\  & \begin{tabular}{c} Reinforcement \\ concrete structure \\ \end{tabular} & 0 & 0 & 1 & 19 & 212 & 1 & 233 & 90.99 \\  & \
\begin{tabular}{c} Other \\ \end{tabular} & 2 & 1 & 0 & 1 & 3 & 19 & 26 & 73.08 \\  &
\begin{tabular}{c} Total \\ Accuracy rate (\%) \\ \end{tabular} & 84.00 & 83.33 & 85.29 & 89.24 & 84.13 & 95.00 & & \\  & Overall accuracy (\%) & & & & & 86.49 & & & \\  & Kappa coefficient & & & & & 0.805 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix of structure type of the buildings.
# EC-GAN: Emotion-Controllable GAN for Face Image Completion
## 2 Related Work
### Image Completion with Diverse Results  
Image completion can be viewed as an ill-posed inverse problem with no determined solution, and for face images, it seems more reasonable to obtain multiple results. Diverse image completion methods have been studied in several recent studies. PIC-Net [13] develops a probabilistic principled framework with two parallel paths, utilizing prior conditional lower bound coupling to provide multiple diverse results with reasonable content for a single masked input. By learning the image mapping between the instance image space and the conditional completion image space, UCTGAN [21] yields a variety of completion results. PD-GAN [20] samples random latent vectors from the standard Gaussian distribution to generate different images. By separating the structural and textural information, VQ-VAE [33] constructs an autoregressive model for discrete distributions over the structural features, thus obtaining samples with different structural characteristics. ICT [34] and PUT [22] introduce the transformer [35] to their autoencoder pipelines to reduce the loss of information during the encoding process. Even though these methods have successfully generated multiple results, they are incapable of producing images that are semantically specific or have controllable expressions since they are based on random sampling from the latent space.  
Our work is mainly inspired by PIC-Net [13], and we have made significant improvements. In contrast to the methods mentioned above that generate multiple images at random, our method is intended to complete images with the specified expression semantics or correctly inferred emotions.
## 3 Results
### The Effect of Chaperone Treatment on FVII Variant Biosynthesis and Secretion  
FVII:Ag levels were measured in conditioned medium and in cell lysates from cells treated with chemical chaperones. In cells treated with 4-PBA, the secreted amounts of rFVII-354V-464 Hs increased \(\sim\)2.5-fold, while intracellular levels of the rFVII-354V-464 Hs were increased \(\sim\)2-fold compared to untreated cells (Figure 3A). Treatment with BIX had a modest although statistically significant effect on secretion of the rFVII-354V-464 Hs (approximately 1.3-fold at the highest concentration), whereas the intracellular levels of the variant were increased up to \(\sim\)2.3-fold at the highest concentration (Figure 3B). Because we observed that treatment with the chemical chaperones also increased the FVII:Ag levels in cell lysates, a ratio between secreted (conditioned medium) and intracellular (lysates) FVII was calculated. The secreted/intracellular ratio in cells treated with 4-PBA was increased up to 2-fold compared to untreated cells (Figure 3C). However, the ratio was decreased 0.7-fold and 0.6-fold in cells treated with 25 or 50 \(\upmu\)M BIX, respectively, compared to untreated cells (Figure 3D). We found no significant effect of BIX on rFVIIwt secreted to the conditioned medium, but a small increase of rFVIIwt was measured in cell lysates at the highest BIX concentration (Figure S2).  
Figure 2: FVII protein forms in patients’ plasma and upon recombinant expression. (**A**) Schematic representation of the FVII p.A354V-p.P464 Hs variant. (**B**) Western blot analysis (image adjusted for brightness/contrast) of FVII in plasma from the four patients (Pt 01–04), a healthy volunteer (normal plasma, NP) or cell lysates from cells with stable expression of rFVIIwt or rFVII-354V-464 Hs.
## 5 Simulation-Based Performance Evaluation
### Multi-Objective Quantitative Indicator Comparison  
To systematically evaluate the control performance across key objectives, four standardized evaluation metrics are defined, using fixed-MPC as the baseline. These cost-type indicators are designed such that lower values indicate better performance.  
The energy consumption ratio reflects the efficiency of energy usage:  
\[\frac{E}{E_{\text{ref}}}=\frac{E_{\text{ctrl}}}{E_{\text{ref}}}. \tag{24}\]  
The jerk-based comfort index evaluates ride comfort based on the average longitudinal jerk:  
\[\frac{J}{J_{\text{ref}}}=\frac{J_{\text{ctrl}}}{J_{\text{ref}}}. \tag{25}\]  
The following distance error ratio quantifies the accuracy in spacing control:  
\[\frac{FDE}{FDE_{\text{ref}}}=\frac{FDE_{\text{ctrl}}}{FDE_{\text{ref}}}. \tag{26}\]  
The response time ratio measures the speed at which the controller responds to sudden events:  
\[\frac{RT}{RT_{\text{ref}}}=\frac{RT_{\text{ctrl}}}{RT_{\text{ref}}} \tag{27}\]  
An overall composite score, referred to as the performance evaluation index (\(PEI\)), is computed as the arithmetic mean of the four normalized metrics:  
Figure 7: Distribution of longitudinal acceleration under WLTC conditions for DWMPC, fixed-MPC, and PI controllers. The histograms represent the empirical acceleration frequency, while the overlaid curves indicate fitted normal distributions. The inset summarizes the statistical mean (\(\mu\)) and standard deviation (\(\sigma\)) for each method. DWMPC achieves the narrowest distribution, reflecting enhanced ride comfort.  
\[PEI=\frac{1}{4}\bigg{(}\frac{E}{E_{\text{ref}}}+\frac{J}{J_{\text{ref}}}+\frac{FDE}{ FDE_{\text{ref}}}+\frac{RT}{RT_{\text{ref}}}\bigg{)}. \tag{28}\]  
Table 5 summarizes the indicator values for each controller. Among them, DWMPC achieves the lowest values in energy consumption (0.985) and jerk (0.571), indicating high efficiency and comfort. It also excels in tracking precision (\(FDE=0.660\)) and emergency response time (\(RT=0.582\)), outperforming both fixed-MPC and PI across all dimensions.  
As a result, DWMPC attains the lowest composite performance evaluation index (\(PEI=0.700\)), showing a 22.9% improvement over fixed-MPC (\(PEI=0.909\)) and a 32.8% gain over PI (\(PEI=1.041\)). These improvements stem from its dynamic weighting strategy, which enables the controller to continuously prioritize the most critical objective depending on real-time driving conditions.  
This comparative performance is further visualized in Figure 8, where each axis represents one evaluation dimension. The radar plot reveals that DWMPC encloses the smallest performance region, implying the most balanced and efficient trade-off among energy efficiency, comfort, tracking accuracy, and responsiveness. In contrast, PI forms the widest area, suggesting significant compromise in multiple objectives.