esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Validation
### Case 2: Bearing Fault Diagnosis Based on XJTU Bearing Datasets
#### 4.2.4 Comparison between Different Methods  
On the XJTU datasets, the proposed method was compared with Resnet, DCNN, ShuffleNet, MCCANN, and LightGBM. A total of ten comparison experiments were conducted, and comparative results can be seen in Figure 16. The average value and SD of classification results of all the methods are listed in Table 6. The average accuracy of the method proposed is 100%, followed by the MCCNN with an average accuracy of 95.95% and SD of 0.015. The accuracy of ShuffleNet and ResNet are 92.45% and 88.15%, with SD of 0.030 and 0.075, respectively. DCNN and LightGBM have the lowest accuracy, 58.45% and 64.70%, with SD of 0.037 and 0.106, respectively.
# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
## 3 FANET Routing Protocols, Mobility Models, and Reconnaissance Scenarios  
Section 3 describes the FANET protocols, mobility models, and reconnaissance scenarios. Section 3.1 describes three routing protocols: Ad Hoc On-demand Distance Vector (AODV), Optimized Link State Routing Protocol (OLSR), and Destination Sequenced Distance Vector (DSDV). Section 3.2 describes five mobility models: Smooth Random WayPoint (SRWP), Markov Process (MP), Enhanced Gauss-Markov (EGM), Distributed Pheromon Repel (DPR), and Random Destination with Partitioned Zone (RDPZ). Section 3.3 describes our reconnaissance scenario.
# Feature Mining: A Novel Training Strategy for Convolutional Neural Network
## 4 Results and Discussion
### Ablation Study  
**A. Influence of different choices of Feature Mining on network performance:** We explored different design choices for Feature Mining, and these choices were classified into two groups.  
As shown in Table 9, the first group discusses the number of layers using Feature Mining. ResNet-56 contains three layers, and we tested the Feature Mining on the last  
Figure 4: Accuracy(%) of ResNet-56 with and without Feature Mining under varying numbers of training samples per class in CIFAR-100.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Noise Type** & \(\epsilon\) & **Method** & **Accuracy (\%)** \\ \hline \multirow{4}{*}{Symmetric Flip} & \multirow{2}{*}{0.5} & ResNet-110 & 45.73 \\  & & ResNet-110+FM & 53.31 (+7.58) \\ \cline{2-4}  & & ResNet-110 & 61.16 \\ \cline{2-4}  & & ResNet-110+FM & 68.12 (+6.96) \\ \hline \multirow{4}{*}{Flip} & \multirow{2}{*}{0.5} & ResNet-110 & 35.73 \\  & & ResNet-110+FM & 38.78 (+3.05) \\ \cline{1-1} \cline{2-4}  & & ResNet-110 & 63.83 \\ \cline{1-1} \cline{2-4}  & & ResNet-110+FM & 71.22 (+7.39) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average test accuracy on noisy CIFAR-100 using ResNet-110 with and without Feature Mining.  
layers, the last two layers, and all three layers. All three models outperformed the baseline model from 74.55% to 75.35%, while the model with the last two layers using Feature Mining performed better than others. This shows that the penultimate layer's local feature is beneficial to the network training. While the feature of the first layer does not contain mature semantics, adding learning of the feature in the first layer is not beneficial for the network training.  
The second group focuses on the Feature Segmentation approach. The Feature Segmentation determines the additional feature pattern of the network learning, which plays a crucial role in the performance of our method. Dropout-FM denotes that the binary mask is set by random points as the Dropout. SpatialDropout-FM denotes that the feature is divided by channels as the SpatialDropout. The performance of these segmentation approaches could not compete with Feature Mining, which proves the segmentation feature based on the block region can ensure a larger difference between the two parts and make the network learn more feature information. Non-complementary FM denotes that we randomly chose two parts of the feature instead of the complementary ones; its performance was still not as good as the complementary way, which again verifies that the difference between the two parts of the feature is important.  
**B. Analysis of time and memory overhead:** Table 10 provides some complexity statistics of Feature Mining, which shows that the computation cost introduced by Feature Mining is negligible, especially when our method is only used in the last layer. Besides, this additional cost is introduced only in the training phase, and the inference cost is not increased.
## 4 Results
### Experimental Result
#### 4.3.2 Comparison of Resource Scheduling Policies
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Scenario**} & \multicolumn{8}{c}{**Simulation Result**} \\ \cline{2-9}  & **FIFO** & **FIFO** & **SRPT** & **SRPT** & **CR** & **CR** & **Integrated** \\  & **(Random)** & **(Centroid)** & **(General First)** & **(Senior First)** & **(General First)** & **(Senior First)** & **ML** \\ \hline A & 328.57 & 325.75 & 328.31 & 323.69 & 327.77 & 323.97 & 321.13 \\ B & 395.42 & 393.43 & 397.14 & 393.45 & 394.41 & 392.35 & 388.65 \\ C & 262.85 & 246.01 & 264.04 & 263.40 & 265.57 & 261.72 & 258.96 \\ Average & 328.95 & 321.73 & 329.83 & 326.85 & 329.25 & 326.01 & 322.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Compares of average LoS between resource scheduling policies (min). of 500.69 min, the integrated ML strategy reduced the LoS to 497.03 min, showcasing its efficiency in managing patient flow even during peak conditions. Similarly, in Scenario C for KTAS3, the LoS was lowest for the CR (General First) strategy at 308.36 min compared to 318.06 min by SRPT (General First), highlighting the potential of targeted strategies in specific patient severity contexts. Moreover, the integrated ML strategy consistently outperformed or was competitive with traditional methods across all scenarios. For example, in KTAS4 + 5 of Scenario C, while the LoS for CR (Senior First) peaked at 192.98 min, integrated ML managed to keep it lower at 186.44 min. These findings highlight the potential of integrating machine learning with traditional scheduling approaches to create more resilient and efficient emergency care environments, which is essential for improving patient outcomes and operational efficiency in high-stakes healthcare settings.
# Internet Traffic Prediction with Distributed Multi-Agent Learning
## 5 Experiment and Analysis
In other words, the GRU-based scheme is more stable, with less extreme predicted values than LSTM and a better overall prediction performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Agent Number** & **RMSE (Mbps)** & **MAE (Mbps)** \\ \hline TST [42] & N/A & 16.28 & 9.71 \\ \hline MLP [43] & N/A & 14.81 & 8.51 \\ \hline TCN [44] & N/A & 13.39 & 9.31 \\ \hline FCN [45] & N/A & 12.79 & 8.10 \\ \hline ResNet [45] & N/A & 11.68 & 7.16 \\ \hline InceptionTime [46] & N/A & 11.65 & 7.07 \\ \hline \multirow{4}{*}{LSTM} & 1 & 12.57 & 7.10 \\ \cline{2-4}  & 2 & 12.11 & 6.86 \\ \cline{2-4}  & 3 & 11.93 & 6.77 \\ \cline{2-4}  & 4 & 11.83 & 6.69 \\ \cline{2-4}  & 5 & 11.43 & 6.54 \\ \hline \multirow{4}{*}{GRU} & 1 & 12.11 & 6.84 \\ \cline{2-4}  & 2 & 11.84 & 6.71 \\ \cline{1-1} \cline{2-4}  & 3 & 11.56 & 6.55 \\ \cline{1-1} \cline{2-4}  & 4 & 11.42 & 6.50 \\ \cline{1-1} \cline{2-4}  & 5 & 11.22 & 6.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The experimental results.
###### Abstract  
Predicting the remaining useful life (RUL) of wind turbine generator rolling bearings can effectively prevent damage to the transmission chain and significant economic losses resulting from sudden failures. However, the working conditions of generator bearings are variable, and the collected run-to-failure data combine multiple working conditions, which significantly impacts the accuracy of model predictions. To solve the problem, a local enhancement temporal convolutional network with multistage degenerate distribution matching based on domain adaptation (MDA-LETCN) is proposed, extracting degradation features of wind turbine generator bearings and predicting their remaining service life in composite working conditions. This method first utilizes the local enhancement temporal convolutional network (LETCN) to extract time series features and used the K-means method for unsupervised division of the degradation status of rolling bearings. Secondly, the multistage degradation stage distribution matching (MDSDM) module is proposed to learn domain-invariant temporal features at different stages of bearing degradation under composite working conditions. Finally, the model is transferred to the target bearing using some health data that are easily available from the target bearing to solve the problem of individual differences in the degradation of generator bearings in different wind turbines. Comparative experiments were conducted using actual wind farm data, and the results showed that MDA-LETCN has high prediction accuracy.  
rolling bearings; deep learning; remaining useful life prediction; wind turbine; domain adaptation
# Pruning Optimization over Threshold-Based Historical Continuous Query
## 5 Experiment
### Performance of Different Data Scales  
In this part of the experiments, datasets AIS01, AIS02, AIS03, AIS04, and AIS05 were used as experimental data to evaluate the query performance of the three methods under different scales, as shown in Figure 8.  
It can be seen from Figure 8a,b that as the data scale increases, the number of accessed nodes increases in the pruning step. However, the increase rate of the pruning results is smaller than the change of data scales, so the selectivity is gradually decreasing. Among the three methods, CDQLTD has the fewest number of the accessed nodes, because it uses a single MBR of the whole query trajectory as the pruning reference, and the spatiotemporal index of the dataset only needs to be traversed once. However, CDQLTD has the lowest pruning precision, and the pruning results contain some irrelevant data, so CDQLTD has the worst selectivity. While TDS uses each trajectory segment of the query trajectory as the reference, it is necessary to repeatedly traverse the spatiotemporal index of the dataset, and the number of the accessed nodes is one order of magnitude larger than the two methods. However, it also benefits from the segment-based pruning way, the pruning results of TDS is closer to the final query results, so its selectivity performance is better than that of CDQLTD. BFT also uses each query trajectory segment as the pruning reference, but thanks to the E3 DR-tree structure and the best-first traversal algorithm, the number of the accessed node is much smaller than that of TDS.  
Moreover, the pruning strategy adopted by BFT combines MBR overlap and Mindist calculation, so the selectivity performance of BFT is better than that of TDS which only consider MBR overlap.  
Figure 8c shows the performance of pruning latency. Since BFT accesses fewer nodes and its pruning strategy based on extended MBR overlap effectively reduce the time overhead of a single pruning check, so BFT takes less time than the two methods. Although CDQLTD visits the fewest nodes when pruning, the calculation process of pruning check based on Mindist is complex and time-consuming, resulting in a large time overhead for the entire pruning process. TDS has the lowest  
Figure 8: Performance of different scales: (**a**) Number of the accessed nodes, (**b**) selectivity, (**c**) pruning latency, (**d**) query latency.  
time-consuming pruning check method, but the number of the accessed nodes is much more than the two methods, resulting in the most time-consuming pruning.  
The performance of query latency is shown in Figure 8d, where BFT performs best, not only because BFT takes the least time to complete the pruning step, but also generates the highest precision of pruning results, so the process of refinement also takes the least time. The pruning results of CDQLTD have the lowest precision, which makes it necessary to spend a lot of time on refinement processing, the time difference between it and the two methods will be further amplified as the data scale increases.
## 4 Results
### AZON in Numbers  
The adopted solutions allowed the dissemination of 44,278 data records with 391,926 original, unprocessed data files. These data files occupy 172.29 TB, and 11,197 of them represent multimedia (not including a large number of scanned documents: big size, high-quality tiff images). The size of serialized metadata for all deposited data records is 221.70 MB. These numbers were valid on 18 November 2020 (and continuously increase). The statistic below gives more information (again the types of data records holding multimedia by default are bolded, the numbers in parentheses indicate the number of data records).  
* _Data_ (total: 19,381): Catalogue (0); Chemical analysis (763); Dataset, database (13,582); **Dendrological collection (356)**; Flow (NetFlow) from a firewall device (886); **Herbarium sheet (1018); Histological preparation--human (1277); Histopathological preparation--animal**; Source code (65); Threat log (1224);
* _Documents_ (total: 14,619): Article, chapter (2682); Book (1933); Journal (9475); Legislation (1); Legislation collection (0); Map (1); Map collection, atlas (0); Synopsis (154); Thesis;
* _Materials_: Archival material (0); Educational material (408); Manuscript (0); Note (1); Other document (965);
* _Multimedia_ (total 8327): **3D, foto360** (1953); **Artistic, architectural work** (245); **Audio (418); Photo (1956); Presentation (107); Video (3648);
* _Portfolio_ (577): Expert (176); Research equipment (241); Research laboratory (45); Research offer (115);The details are as follows. Thumbnails of resources generated automatically occupy around 7.83 GB. Audio/video uploaded and processed in OZONE take 1.27 TB, and processed with ffmpeg: 70.67 GB. Automatically generated subtitles (VTT, SRT): 38.47 MB and transcripts (MD): 15.51 MB (the transcription process took 656 h). The additional volume of images processed for better rendering: 913.13 GB; and foto 360: 71.00 GB. The books conversion results into mobi format: 12.00 GB, and into epub format: 7.20 GB. The total number of downloads of files and metadata views reached 4,577,301 level. Finally, 100% of the deposited and published data records can be rated with 4 stars and 99% with 5 stars according to the 5-star rating system ([https://5 stardata.info/en/](https://5 stardata.info/en/)).
## 3 Results and Discussion  
### Genome-Wide Identification and Chromosomal Distribution of GpOSC Family  
With the rapid advancement of genome sequencing technology, bioinformatics analysis methods have facilitated the identification of an increasing number of gene families. Previous studies have demonstrated that OSC enzymes play a crucial role in the biosynthesis pathway of triterpen saponins, with numerous members of the _OSC_ gene family identified and isolated in various plant species. For instance, 13 _OSC_ genes have been discovered in _A. thaliana_, 9 of which can be heterologously expressed in yeast [39]. Additionally, 12 _OSC_ genes were identified and modified in rice to enhance its triterpen content [40]. As more members of the _OSC_ gene family are identified, researchers have shifted their focus toward understanding the structure, regulation, and evolution of _OSC_ genes [41; 42].  
Current research has largely elucidated the biosynthetic pathways for ginsosides. The enzyme-catalyzed conversion of 2,3-oxidized squalene by _DSII_ and _PNY_ (\(\beta\)-_AS_) genes leads to the production of dammarenediol and \(\beta\)-amyrin, ultimately resulting in the formation of dammarane-type and oleanane-type ginsosides [43]. Proteins encoded by _OSC_ genes represent key initial enzymes involved in gypenoside biosynthesis. Therefore, identifying _OSC_ synthesis genes implicated in this pathway is essential for a comprehensive pathway analysis. To identify _GpOSCs_, we conducted BLAST and HMM searches that revealed a total of 11 _GpOSC_ genes, which were sequentially designated as _GpOSC1_ to _GpOSC11_. Based on annotation data, it was found that these _OSC_ genes were located on six chromosomes (Figure S1). Five _GpOSCs_ (_GpOSC1_, _GpOSC5_, _GpOSC9_, _GpOSC10_, and _GpOSC11_) were located within telomeric regions on their respective chromosomes; conversely, six other _GpOSCs_ (_GpOSC2_, _GpOSC3_, _GpOSC4_, _GpOSC6_, _GpOSC7_, and _GpOSC8_) were clustered in the chromosome arm regions. These results are consistent with previous studies on the _OSC_ gene families of _Rosa_ and _M. charantia_[17; 18]. Chromosomes 2 and 7 each contained three _GpOSC_ genes, while chromosomes 4, 8, 9, and 10 each contained one or two _GpOSC_ genes.
# A Study of Stray Neutron Field Measurements for the Neutron Scattering Instruments at CSNS
## 3 Experiments and Discussion
### Experimental Setup  
BL06 is a temporary test beam line without any neutron optical devices, which was constructed to perform neutron detector tests and prototype neutron experiment scheme research and validation. It extracted neutrons from a decoupled water moderator [5] located at a distance of 8.5 m. As illustrated in Figure 2, the experimental area of BL06 is around 2.3 m (W) \(\times\) 2.5 m (L) \(\times\) 2.8 m (H). The diameter of the beam spot is 6 cm. A beam dump made of boron-containing PE and carbon steel with dimensions of 0.2 m (W) \(\times\) 2.0 m (D) \(\times\) 0.2 m (H) was added to reduce the scattered neutrons. A labyrinth structure, composed of 1 m thick concrete blocks and a 0.6 m (W) \(\times\) 2.0 m (H) corridor, was designed to attenuate the neutron intensity.  
Figure 1: Response functions of the developed ERBSS.  
Figure 2: Geometry of BL06. A and B indicate two measurement locations. (Dark blue—carbon steel; green—heavy concrete; light blue—ordinary concrete; orange—experimental area).  
Considering the main hazards of stray neutrons described in Section 1, two locations were selected inside the experimental area: location A was at the entrance to the labyrinth, perpendicular to the beam line from the center with a distance of around 1.8 m, while location B was near the exit of the labyrinth. The plate samples used were 10 cm \(\times\) 10 cm PE plate samples with thicknesses of 1 cm and 2 cm and located at a distance of 50 cm from the terminal.
## 3 Research on BPL Decoding Algorithm for Polar Codes Based on Information Geometry
### Path Selection Criterion for BPL Decoding of Polar Codes Based on Geometric Distance  
Based on the above analysis, this paper proposes a weight selection method based on geometric distance. In the Belief Propagation (BP) decoding of polar codes, the magnitude of the absolute value of soft information represents the reliability of the decision: the larger the absolute value of the soft information, the greater the reliability of the decoding result. Within the mathematical framework of information geometry, points farther from the origin represent more reliable decoding results. Therefore, the distance from the origin to \(\lambda_{i}\) can be used as the weight for the soft information corresponding to that point. According to Equations (14) and (15), the distance from a point to the origin can be expressed as:  
\[\begin{array  
This scheme uses the statistical manifold formed by the joint probability density distribution of the Bernoulli distribution as the research platform, replaces Euclidean distance with geometric distance, and simultaneously considers the soft information from the decoding results of \(L\) decoders. It provides a new scheme for the path selection criterion in the BPL decoding algorithm for polar codes. The primary advantage of this scheme lies in its elimination of the need for a decision \(\hat{x}=\hat{u}\)Gat each iteration, thereby enhancing the error correction performance of BP decoding for polar codes with a reduced number of iterations and BP decoders.
## 4 Discussion
### Critical Examination of the Methodology
report in their study that navigation would have a direct influence on surgery in 25% of the cases they examined; without navigation, one would have to switch to transfacial access in 14% of cases, and the operations would not have been performed with the required thoroughness in 11% of cases [1]. In their feasibility study, [PERSON] et al. observed that, following primary navigated sinus surgery, there was a tendency to have fewer missed sinus compartments, meaning sinus compartments that would have required surgical treatment but were inadvertently not operated upon [19]. In further studies, representative surveys show that the majority of surgeons see no major added benefit to navigation, or its use is viewed as not essential in nearly three-quarters of cases [1,20]. [PERSON] et al. viewed the navigation tool as a hindrance to process steps, especially in initial operations for chronic sinusitis [21]. Consequently, the results of this study support the results of numerous studies that conclude that navigation systems result in longer operating times and higher expenditure [19,22,23,24]. However, a distinction must be made, because older papers still assume an additional surgical preparation time of up to 15 min, which is included in their calculations [25,26]. A system setup, taking place at the same time as regular preparations and patient registrations, which takes longer than 5 min is a rarity with modern navigation systems [2,17,27]. [PERSON] et al. conclude that computer-assisted endoscopic sinus surgery carries higher costs, but they express the opinion that not all benefits of computer-assisted endoscopic sinus surgery are quantifiable, and the immaterial benefits outweigh the higher costs. In their view, these benefits include the surgeon's added self-confidence and verification during surgery [24]. In their meta-analysis of the use of navigation devices in sinus surgery, [PERSON] et al. report that the properly supervised use of navigation by trainees is an effective learning tool. They quote, among others, the study by [PERSON] and [PERSON], showing that navigation systems have a positive impact on surgical accuracy and efficiency in sinus operations during the training of junior doctors (equivalent to \"residency\" in the American system) [28,29]. A study by [PERSON] et al. was published in 2018; in it, they developed surgical process models that enable surgeons, by means of navigation, to identify the differences between their own surgical technique and that of other, more experienced colleagues, thereby improving their surgical performance [30]. As early as 2002, [PERSON] et al. reported in their study that computer-assisted surgery for sinus operations contributes to a reduction in complications and allows more thorough surgery to be performed [31]. In their 2013 meta-analysis, [PERSON] et al. conclude that major complications, as well as complications overall, are less likely when navigation systems are used in sinus surgery [32].
# Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review
## 4 Conventional Traffic Signal Control Strategies
### Adaptive Traffic Signal Control  
#### 4.3.1 Conventional Adaptive Traffic Signal Control Strategies  
ATSC strategies respond to fluctuations in daily traffic patterns by adjusting signal program timing according to current traffic demand, reducing traffic congestion, delays, and travel time [29]. ATSC was first proposed as early as 1960s [30]. Early studies and implementations include SCOOT [31], SCATS [32], UTOPIA [33], and RHODES. SCOOT and SCATS are conventional systems that rely on loop detectors and communication infrastructure to collect traffic data. The main philosophy of these systems is a fast response to the change in traffic demand generating green signal time. They are based on solving an optimization problem during operation. Emphasize of adaptation can be changed by setting the appropriate optimization criteria (intersection throughput maximization, delay minimization, giving priority to public transportation). Recently developed systems such as RHODES can realize also proactive control by predicting traffic demands at a downstream intersection and optimizing lost times on a global scale [34].