esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology  
### Overall Framework  
Traditional fault detection methods for power batteries often rely on manually defined threshold rules to identify anomalies. However, such methods struggle to deliver satisfactory performance when faced with the complex, dynamic, and non-linear nature of real-world battery operation. To address these limitations, this study proposes a two-stage hybrid model based on deep learning, aiming to effectively detect faults in power battery operational data.  
Power battery data typically exhibit strong time-series characteristics and are subject to severe class imbalance between normal and faulty samples. To tackle these two key challenges, we design a detection framework that integrates a Generative Adversarial Network (GAN) for data augmentation and a Convolutional Long Short-Term Memory (CNN-LSTM) network for sequence modeling and classification.  
The overall architecture of the proposed model is illustrated in Figure 5.  
In the first stage, the GAN module--comprising a generator and a discriminator--operates through adversarial training, where the generator is iteratively optimized to synthesize new samples that closely resemble the distribution of real faulty data. Once trained, the generator is used to augment the minority fault class, producing a balanced dataset in which the number of fault and normal samples is approximately equal.  
In the second stage, the CNN-LSTM module is responsible for modeling and classifying the balanced multivariate time-series data. The CNN component extracts local spatial patterns from the battery operation sequences, while the LSTM network captures temporal dependencies and dynamic evolution across time steps. Through this joint architecture, the model achieves accurate identification of battery fault states.
## 4 Evaluation of Prototypes with Edge Damping
### Evaluation of the Sensitivity  
In addition to the DI, the impact on the sensitivity should be considered. If the damping minimizes the total sound pressure, then this compromises the maximum power handling and preference rating. If damping only occurs in the nonpreferred directions of radiation, then no trade-off arises. Table 3 shows the averaged SPL \(SPL_{AVG}\) in the frequency range of 1 kHz to 10 kHz for the LW and FH. The additional damping substantially impacts only the FH sensitivity and has a minimal impact on the LW sensitivity. The reduced sensitivity value in the LW is approximately 0.7 dB, which is within an acceptable range. The reduction in the FH is approximately 3 dB.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Frequency Range**} & \multicolumn{3}{c}{**Variants**} \\  & **Panel-Raw** & **Panel-BIF** & **Panel-BOF** \\ \hline
100 Hz–1.0 kHz & & & \\ Mean value & 0.44 dB & 0.19 dB & 0.10 dB \\ Standard deviation & 1.95 dB & 2.28 dB & 2.42 dB \\ \hline
1 kHz–7.5 kHz & & & \\ Mean value & \(-\)3.94 dB & \(-\)1.40 dB & \(-\)1.49 dB \\ Standard deviation & 3.03 dB & 1.92 dB & 2.92 dB \\ \hline
7.5 kHz–20 kHz & & & \\ Mean value & \(-\)1.90 dB & \(-\)0.92 dB & \(-\)1.47 dB \\ Standard deviation & 3.29 dB & 2.39 dB & 2.99 dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean value and standard deviation of the DI for three different frequency ranges calculated for all three variants.  
Figure 15: DI calculated as the difference in the LW (\(\pm\)30\({}^{\circ}\)) and the FH (\(\pm\)90\({}^{\circ}\)) of all three variants, namely, RAW, BIF, and BOF, measured at a 2 m distance. Both, BIF and BOF constructions have flatter and lesser negative DIs.  
This finding underlines that the additional damping has a greater impact on the FH and a lower impact on the LW response. Therefore, the results with additional damping can be interpreted as results with similar sensitivity but improved directivity. Any damping is assumed to be a loss of energy. In this case, the damping affects the radiation range exactly, which causes a lower listening preference rating. To further reduce the losses, more complex methods are necessary, e.g., the use of several exciters, which would result in higher complexity and higher production costs.
# A Model Based Framework for IoT-Aware Business Process Management
## 3 Background
### Model-Driven Engineering and Model-Driven Architecture  
_Model-driven development (MDE)_ is an approach to software systems engineering that addresses the increasing complexity of execution platforms by focusing on the use of formal models [29]. The founding pillars of MDE are constituted by model transformations. An appropriate chain of _model-to-model_ and _model-to-text_ transformations is specified and executed in order to progressively translate abstract models into more refined models until executable artifacts or models that meet the desired level of abstraction are generated.  
One of the most important initiatives driven by MDE is the _Model Driven Architecture (MDA)_, the Object Management Group (OMG) incarnation of MDD principles [3].  
The Model-Driven Architecture prescribes that various models have to be specified throughout the software development lifecycle. Such models, which provide different views of the system, are specified from viewpoints focusing on particular system concerns. The following main standards have been introduced as part of the MDA effort:  
* _Meta Object Facility (MOF)_: for specifying technology neutral metamodels (i.e., models used to describe other models) [30];
* _XML Metadata Interchange (XMI)_: for serializing MOF metamodels/models into XML-based schemas/documents [31];
* _Query/View/Transformation (QVT)_ and _MOF Model To Text (MOFM2T)_: for specifying _model-to-model_ and _model-to-text_ transformations, respectively [32; 33].  
In a general model-driven engineering process, an input model can be used for generating an output model or a textual document (e.g., executable code, text documents, configuration files, etc.)  
Specifically, let us consider an input model Model M\({}_{A}\), which is an instance of source metamodel MM\({}_{A}\). Let us assume that such a model has to be mapped to the output model M\({}_{B}\), which, in turn, is an instance of target metamodel MM\({}_{B}\).  
Both MM\({}_{A}\) and MM\({}_{B}\) have to be specified in terms of _MOF Model_ constructs.  
The _model-to-model_ transformation that generates the output model is specified by use of QVT, the declarative language for specifying model-to-model transformations. In order to be handled by a QVT transformation engine, the input and output models have to be serialized as XML-based documents which are obtained by applying the rules specified by the XMI standard.  
Finally, the required textual document is generated throughout the execution of an appropriate _model-to-text_ transformation specified by use of the _MOFM2T metamodel_ standard.
# State-of-the-Art and Opportunities for Bioactive Pentacyclic Triterpenes from Native Mexican Plants
## 2 Pentacyclic Triterpenes from Mexican Plants
### Biological Activity Survey of PCTs Found in Mexican Plants
For the carrageenan model, the mice received carrageenan at 2% to induce paw edema. After 1 h of application, the animals received each mixture separately at a dose of 150 mg/kg. After 5 h, fraction one showed inhibitory activity of 11.31% and 19.06% for male and female mice, respectively. Finally, fraction two generated an inhibition of 3.70% and 19.84% in both respective sexes [47]. Another example of the evaluated extracts is the research performed by [PERSON] et al., who prepared dichloromethane-soluble extracts from multiple Mexican _Bursera_ species (_Bursera lancifolia_, _B. fagaroides_, _B. grandifolia_, _B. longipes_, _B. morelensis_, _B. bicolor_, and _B. submoleiformis_) and tested them on seven cell lines [48]. Regarding the results, the _Bursera bicolor_ species caused high growth inhibition in colorectal (HCT-15) and lung (SK-LU1) cancer cell lines, while _B. fagaroides_ was active against human glioblastoma astrocytoma (U-251), prostate adenocarcinoma (PC-3), chronic myelogenous leukemia (K-562), colorectal adenocarcinoma, and lung adenocarcinoma (SK-LU1). However, it also showed action against normal fibroblasts, whereas _B. bicolor_ extracts did not. In general, the lung cell line was the most susceptible to the evaluated extracts, even with those of _B. grandifolia_, _B. morelensis_, and _B. lianoe_. In this case, the triterpenes determined were lupenone **(8)**, \(\alpha\)-amyrin **(16)**, 3-epitaped **(27)**, and sitosterol in these dichloromethane-soluble bark extracts of all _Bursera_ tested species [48]. It is important to clarify that the authors attributed the observed activity in the extracts to the PCTs; however, the extracts also contain lignans (deoxypodophilotoxin and acetylpodophilotoxin), podophyllotoxin-typelignans with proven cytotoxic activity, and some fatty acids according to [PERSON] et al. [49]. Hence, the anticancerogenic activity may be a synergy of all components in the prepared extracts. Additional experiments are necessary to identify the individual contributions of the compounds to the cytotoxicity of all tested cancer cell lines. Additionally, methanolic extract of flowers from _Crataegets gracilior_ presented vasorelaxant effects on rat aortic rings [50]. The extract had an EC\({}_{50}\) of 1.83 \(\pm\) 1.39 \(\mu\)g/mL and an E\({}_{\text{max}}\) of 100 \(\pm\) 3.4%, being 5-fold more potent than the positive control acetylcholine (ACh) (EC\({}_{50}\) of 9.77 \(\pm\) 0.81 \(\mu\)g/mL and an E\({}_{\text{max}}\) of 66.72 \(\pm\) 1.07%). In this study, the team identified consolic **(37)** and euscapic **(45)** acids as part of the main components in the extract and established the hypothesis of these two components as being responsible for the vasorelaxant effects.
## 3 Experiment and Results  
In this section, we validated the effectiveness of the proposed method through comparative experiments and ablation studies. First, we conducted a comprehensive comparison between our method and previously published approaches, and the experimental results thoroughly demonstrate the significant advantages of our method in terms of performance. Next, through systematic ablation experiments, we further analyzed the contribution of each module to the model's performance, thereby validating the rationality of the model design. Additionally, to explore the impact of the pretraining strategy on refractive error screening tasks, we conducted large-scale transfer learning comparative experiments. These experiments validated the effectiveness and superiority of our method from multiple perspectives.
###### Abstract  
Although many attempts have been made, it has remained a challenge to retrieve the aerosol optical depth (AOD) at 550 nm from moderate to high spatial-resolution (MHSR) optical remotely sensed imagery in arid areas with bright surfaces, such as deserts and bare ground. Atmospheric correction for remote-sensing images in these areas has not been good. In this paper, we proposed a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from moderate to high spatial-resolution imagery in arid areas with bright surfaces. Land surface in arid areas is usually bright and stable and the variation of atmosphere in these areas is also very small; consequently, the land-surface characteristics, specifically the bidirectional reflectance distribution factor (BRDF), can be retrieved easily and accurately using time series of satellite images with relatively lower spatial resolution like the Moderate-resolution Imaging Spectroradiometer (MODIS) with 500 m resolution and the retrieved BRDF is then used to retrieve the AOD from MHSR images. This algorithm has three advantages: (i) it is well suited to arid areas with bright surfaces; (ii) it is very efficient because of employed lower resolution BRDF; and (iii) it is completely automatic. The derived AODs from the Multispectral Instrument (MSI) on board Sentinel-2, Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Gao Fen 1 Wide Field Viewer (GF-1/WFV), Gao Fen 6 Wide Field Viewer (GF-6/WFV), and Huan Jing 1 CCD (HJ-1/CCD) data are validated using ground measurements from 4 stations of the AErosol Robotic NETwork (AERONET) around the world.  
AOD; moderate to high spatial resolution; atmospheric correction; AERONET; automatic; validation 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol. 2, No. 1, pp. 1-10, 2008.
# Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing
## 5 Experimental Results  
To verify the performance of the proposed QMTSF, we simulated task scheduling in cloud computing on the CloudSim simulation platform. In the simulation experiment, we validated scenarios with \(100,300,500,800\), and \(1000\) tasks and with randomly generated task lengths between \(500\) and \(3000\). There were four servers (i.e., task categories) in the cloud environment, and each server had five VMs with processing speeds randomly generated between \(500\) and \(1000\). To ensure the accuracy of the experiment, we took the average of multiple experimental results as the final experimental result. The average number of experiments conducted in this paper was ten times. We found that when the number of experiments exceeds ten, increasing the number of experiments further had a minimal impact on the average results.  
Our experimental process consisted of two scenarios. In the first scenario, we used QMTSF priority task scheduling and compared it with RR and random task scheduling in the first stage of task allocation. In the second stage, we used the UQRL algorithm for scheduling to verify the effectiveness of QMTSF priority task scheduling. In the second scenario, we used QMTSF task scheduling in the first stage and compared the UQRL algorithm with the PSO heuristic algorithm and \(\varepsilon\)-Greedy Q-Learning algorithm in the second stage to verify the advantages of UQRL algorithm compared with other task scheduling algorithms. Our simulation results showed that the UQRL algorithm can significantly reduce the makespan and average processing time. Finally, we compared the energy consumption of scheduling methods such as QMTSF, Q-Learning, PSO, RR, and random. Our simulation results demonstrate that QMTSF achieves the highest energy efficiency.
# The Effects of Functional Ankle Taping on Postural Stability in Elite Judo Players
###### Abstract
To conclude, ankle taping that supports ankle joint stiffness may not always be beneficial when attempting to functionally stabilize in static or dynamic movements. There has been scarce research on the biomechanical effects of ankle taping on postural stability. Most studies have used the center of pressure (COP) to assess postural stability, and COP was derived from sway area and velocity of the whole body [14, 15]. For example, ankle taping can provide about 20% smaller COP sway area and 10% slower COP velocity compared with non-taping in people with chronic ankle instability [14]. However, it may be overly simplistic to conclude that ankle taping brings an increased stiffness to the ankle that reduces both the COP sway area and velocity, thereby improving the whole-body balance. Postural stability is achieved through complex interactions between muscular-skeletal systems and nervous systems controlled by the postural control system. These complex interactions entail processing and interpreting movement information, integrating sensorimotor information, and executing appropriate movement to achieve and modulate an erect posture [16]. In summary, a non-linear approach to COP movement patterns and structural analysis that can show postural stability reflective of the complex interactive processes is necessary, in addition to the linear approaches such as COP velocity and sway area analyses. The objective of this study is to examine the effectiveness of ankle taping on postural stability that reflects COP movement quality in elite Judo players. Both linear and nonlinear analyses were performed to assess postural stability during static postures including three different types of static standings (i.e., narrow, normal, and wide).
# A Robust Sharding-Enabled Blockchain with Efficient Hashgraph Mechanism for MANETs
## 8 Conclusions  
In this paper, we have presented the behavior-based sharding hashgraph scheme for MANETs. Dishonest behaviors of nodes are recorded in the decentralized blacklist. With the assistance of the blacklist, the reputation values of candidate receivers for gossip information, candidate senders for gossip information, the requester for joining a shard, and a shard are evaluated. In this way, gossip information is sent to a reliable receiver, and gossip information from another reliable sender is received. It improves the efficiency of the hashgraph mechanism. In addition, the tree-assisted inter-sharding consensus is proposed to prevent Sybil attacks. Only the nodes with enough reputation values are allowed to participate in activities in a shard and without waiting. It also improves the efficiency of the node joining a shard. Moreover, the combination of shard recovery and reconfiguration based on shard reputation is proposed to prevent targeted attacks. The reputation values of a shard not only resist DDoS (Distributed Denial of Service) attacks of shard recovery but also improve the efficiency of shard reconfiguration through a dynamic epoch. Finally, we have shown that our proposed scheme can achieve some security properties, i.e., accountability, adaptability and decentralization. A lot of experiments are conducted to simulate the communication of consensus and disclose the effects of mobility on consensus. The results demonstrate that the proposed scheme is efficient.  
In our future work, some real Epuck2.0 robots and an electronic sand table are used to implement a MANET-based blockchain in practical scenarios. Due to scarce wireless resources, robots need to compete for transmission channels. The distance between the communication parties and the interference from nearby robots also weaken the signal, increasing the latency of events to become famous witnesses. Hence, the hashgraph mechanism and gossip about gossip need to be improved further.  
Methodology, Y.H.; Software, Z.H.; Writing--original draft, R.L.; Writing--review & editing, G.Z. All authors have read and agreed to the published version of the manuscript.  
This work is funded by the National Key-Area Research and Development Program of China (2018 YFB1404402), Key-Area Research and Development Program of Guangdong Province(No. 2019B010137003), Guangdong Science & Technology Fund (No. 2016B030305006, No. 2018A07071702, No. 201804010314), and Guangzhou Science & Technology Fund (No. 201804010314), VeChain Foundation (No. SCNU2018-01).  
The research data are uploaded as an attachment along with the article.  
I would like to give my heartfelt thanks to all the people who have ever helped me in this paper.  
The authors declare no conflict of interest.  
Figure 12: The delay of cross-shard transactions in the number of different involved shards.
# Stripe-Assisted Global Transformer and Spatial-Temporal Enhancement for Vehicle Re-Identification
## 4 Experiment
### Dataset
#### 4.1.2 VehicleID  
The VehicleID [35] dataset captured 221,763 images of 26,267 vehicles from multiple non-overlapping surveillance cameras in real-world scenarios. Each image was also annotated with vehicle ID, color, and model information. Unlike VeRi with multiple different viewpoints, images in VehicleID were taken exclusively from the front or back, with fewer but more drastic changes in viewing angle. Similarly, the dataset was divided into training and testing sets. The training set contained 110,178 images of 13,134 vehicles, while the testing set retained 111,585 images associated with 13,133 vehicles. Three subsets were further extracted from the testing set, i.e., a small subset with 7332 images of 800 vehicles, a medium subset with 12,995 images of 1600 vehicles, and a large subset with 20,038 images of 2400 vehicles. More specifically, for each subset, the gallery set was formed by randomly extracting one image for each vehicle, and the remaining images were used for query set construction. It is important to note that we iteratively constructed each subset and evaluate it 10 times, taking the average result as the final performance. Unfortunately, it provided no timestamp and camera location information to evaluate our STPro method.
## 4 Proposed Approach and Experiments
### Results and Evaluation
This result shows the effect of the contextual enrichment provided by the attention mechanism of GAT on Node2 Vec and GraphSAGE embeddings. The graphs in Figure 7 show the performances of Node2 Vec + GAT + CNN-GRU-3, GraphSAGE + CNN-GRU-3, Node2 Vec + CNN-GRU-3, and GraphSAGE + GAT + CNN-GRU-3 models during the training and validation processes. The performance evaluation of the models shows that the proposed Node2 Vec + GAT + CNN-GRU-3 model outperforms other approaches in terms of accuracy and stability. Achieving an accuracy of 0.9961, this model benefits from the effective use of Node2 Vec embeddings, which better understand structural relationships, and GAT's attention mechanism, which improves the contextual relevance of these embeddings. When combined with the sequential modeling capabilities of the CNN-GRU-3 classifier, the proposed framework achieves a better generalization ability compared to alternatives such as GraphSAGE + GAT + CNN-GRU-3 and Node2 Vec + CNN-GRU-3, which show slightly lower accuracy and more fluctuating validation loss. When we examine different GAT scenarios, the scenarios where GAT is used as an embedding enhancement method have shown superior results, especially in performance metrics such as accuracy, precision, recall, and MAE, compared to those used as a classifier. The main reason for this difference is that the initial embeddings created with Node2 Vec and GraphSAGE are contextually enriched with attention coefficients by GAT. This improvement of the embeddings has increased the generalization ability of the classification models (CNN-GRU-3, ensemble learning) by making the complex relationships in API calls and DLL information more meaningful. In cases where GAT is used directly as a classifier, the contextual information is only processed during the classification. The initial embeddings are not improved, so MAE is higher, and recall is relatively low. The results show that GAT offers a more effective approach to malware detection when used as an embedding enhancement method.