esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Experimental Results Analysis
#### 4.5.4 Complexity Analysis  
The CJE-PCHF model performed exceptionally well in both ablation and comparative experiments. However, compared to the baseline model CasRel, the addition of pinyin and part-of-speech feature extraction modules, along with the progressive contrastive learning-based interactive fusion network, may increase training and inference times, potentially affecting practical deployment. To evaluate whether the CJE-PCHF model can maintain its performance advantage while controlling computational costs, a complexity analysis was conducted, focusing on comparing its parameter size, average training time per sample,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(\mathbf{\lambda}\) & **0.0** & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.8** & **0.9** & **1.0** \\ \hline F1 & 0.793 & 0.802 & 0.812 & 0.821 & 0.819 & 0.819 & 0.820 & 0.822 & 0.820 & 0.819 & 0.809 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The loss balance coefficient experiment results.  
Figure 4: The loss balance coefficient experiment results.  
and inference time with those of the baseline model. Table 7 presents the results of the complexity analysis. The average training time per sample refers to the time spent on training, while the inference time denotes the processing duration for each sample during inference. The analysis shows that the CJE-PCHF model's parameter size increased by 0.32%, training time rose by 40.11%, and inference time grew by 36.36%. Additionally, the runtime memory usage increased by 8.82%, and the GPU memory used expanded by 14.32%. These results indicate that although the model's complexity has slightly risen, leading to higher computational costs, the CJE-PCHF model delivered a significant 5.4% improvement in the F1 score compared to the CasRel model. Therefore, this increase in computational expense is acceptable and remains within reasonable bounds. In summary, the CJE-PCHF model enhances performance while keeping the computational cost increase within acceptable limits, demonstrating potential for practical deployment.
# An Overview of the Public Health Challenges in Diagnosing and Controlling Human Foodborne Pathogens
## 6 Control of Foodborne Microbial Pathogens
### Postbiotics  
The term postbiotics refers to preparations of inanimate microorganisms and their components that have been proven to benefit host health [290]. Several microbiota reside in the human intestine, and postbiotic substances may contribute to the maintenance of this microbiome [291]. With the premise that human health should not depend on bacterial survival, postbiotics have emerged as a potential way for addressing the possible dangers of live cell probiotics [292]. A great deal of interest has been shown in using postbiotics in farm animals instead of antibiotics. Due to the bans on antibiotics in many countries, alternative methods to provide antimicrobial effects have gained popularity [293]. The antibacterial properties of postbiotics appear to be one of their benefits [294], although the research on them is still in its early stages. Studies have revealed that postbiotic substances possess antibacterial (pathogenic and spoiler bacteria) properties, which prevent infectious diseases from spreading and food from spoiling. Consequently, these chemicals prevent pathogenic bacteria from colonizing the intestine, thereby preventing disorders of the intestine such as leaky gut syndrome [295]. Among the factors that affect postbiotic antibacterial capabilities are the type of target microbe, the concentration of postbiotics and the nature of the prebiotic source [293].  
Postbiotics affect the body's microbial flora and prevent invading bacteria from replacing the existing gut bacteria, producing antibacterial compounds and lowering ambient acidity [39]. The rate of intestinal infections and other gastrointestinal disorders is dramatically reduced when intestinal microbiota balance is preserved and fermentation and production of post-antibiotics are established [296; 297]. Through their attachment to bacterial receptors, postbiotics prevent pathogenic microorganisms, such as toxin-producing _Clostridia_ and _Esherichia coli_, from colonizing the gut lumen. In most countries, including Europe, _Bacillus_ strains have largely been used to produce postbiotics over the last few decades [298]. Among the most important products of postbiotics are short-chain volatile fatty acids and exopolysaccharide, which are synthesized in the laboratory [299]. A variety of bacterial and fungal strains produce postbiotics in fermented foods, including yogurt, sauerkraut, pickled vegetables and kombucha [300; 301]. They are most commonly found in _Lactobacillus_, _Bifidobacterium_, _Streptococcus_, _Eubacterium_, _Faecalibacterium_ and _Saccharomyces_.
# An Improved Chinese Pause Fillers Prediction Module Based on RoBERTa
## 5 Discussion  
Firstly, the experimental results demonstrate that creating speaker groups based on their pause fillers usage can enhance model performance, especially in predicting F-scores. This finding is significant as it highlights the potential of personalized models in speech recognition. It implies that by better understanding the individual habits and characteristics of speakers, we can improve the prediction of their pause fillers usage, thus enhancing the quality of speech synthesis.  
Secondly, we observe that the differences between different speakers have an impact on the model's performance. Some speakers lead to relatively poor predictive performance, which may be attributed to their distinct pause fillers usage patterns compared to other speakers. This finding suggests the challenges that personalized models may face in dealing with variations among different speakers. It also serves as a reminder that personalized models require more data and fine-tuning to adapt to various speaking styles.  
Additionally, we notice that the RoBERTa-trained pause fillers prediction module has a greater influence compared to the BERT-trained module. This indicates the critical role of model selection and training methods in the performance of personalized models. The RoBERTa model's natural language processing capabilities are more robust, leading to better performance in text comprehension and, consequently, improved speech synthesis quality when faced with extended textual inputs.  
Finally, the results of this study hold significant implications for the practical application of speech synthesis systems. It provides a method to enhance the performance of speech synthesis systems and offers valuable insights for future research. By improving the naturalness of synthesized speech, we can better meet user needs, making interactions between users and speech synthesis systems more comfortable and natural. In the future, further research and improvements in personalized models offer opportunities to enhance the performance and user experience of speech synthesis systems.
## 2 Materials and Methods  
### Literature Review  
The search timeline was set until August 2024 across the following databases: Scopus, PubMed, ScienceDirect, and Clarivate Web of Knowledge. Keywords such as \"3D printing\", \"additive manufacturing\", \"cage or truss\", and \"arthrodesis\" were used. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist was applied [13], as shown in Figure 1. Only the English literature was considered. A total of 119 papers were initially found (among which two were book chapters). After removing the duplicates with the Mendeley Reference Management Software 2.119.0 (Elsevier, Amsterdam, The Netherlands), 73 papers were kept for abstract and title screening. This step was conducted by three authors, and, in case of discrepancies, a forth author was consulted. The filtering criteria during this stage included limiting the studies to human research (5 exclusions), excluding those focused on the spine rather than the foot (32 exclusions), and eliminating reviews (9 exclusions). After this step, 27 papers were deemed eligible and considered for full-text review. New exclusion criteria (mentioned in Figure 1) were applied, resulting in 20 papers being retained for the quantitative synthesis. The risk of bias was linked to the lack of comparability among the included studies. Several records were excluded during the final filtering stage due to insufficient information relevant to our focus. For instance, the study by [PERSON] et al. discussed four cases of arthroplasty for bone loss and deformity [5], but it lacked details regarding the implants used, being focused primarily on the medical/surgical approach. Additionally, papers like that of [PERSON] et al. [14], which described the use of 3D-printed custom cages for post-traumatic femoral segmental defect reconstruction in five consecutive patients (3 males, 2 females), were excluded because they did not focus on the analyzed foot-related conditions. Although the study successfully addressed large defects with truss implants measuring 184 mm \(\times\) 64 mm \(\times\) 46 mm, with a total volume of 239 mL, its focus on bone reconstruction rendered it beyond the scope of this review.  
Data extraction was conducted by three authors and included both engineering and medical information. The extracted data were categorized as follows: study type, level of evidence, diagnosis, number of patients and demographics, implant material and manufacturing process, implant design, clinical outcomes, and follow-up.
# Study on the Thermospheric Density Distribution Pattern during Geomagnetic Activity
## 5 Conclusions  
The thermospheric density distribution during the geomagnetic storm was analyzed in this paper based on the Swarm-C accelerometer's inversion results. First, three recent geomagnetic storms with more significant impacts were used as the research objects. Two geomagnetic indexes, Dst and Ap were selected to analyze the relationship between the geomagnetic index and the thermospheric density during the geomagnetic storm. After calculating the correlation between the geomagnetic index and the thermospheric density, it was found that the Ap index is advanced and can play a predictive role. Secondly, the measured atmospheric density data were separated according to the day and night regions. The empirical orthogonal analysis method decomposed the time distribution of the thermospheric density latitude and time during the three-field geomagnetic storm. The first-order time coefficient and the first-order EOF basis function were obtained to study the geomagnetic storm. Finally, we summarized temporal and spatial changes in atmospheric density during geomagnetic storms. We concluded that geomagnetic storms have a more significant impact on the temporal distribution of atmospheric density than on the spatial distribution. The spatial distribution of atmospheric density is relatively stable and changes slowly. Since this paper only takes three significant storms since 2015, s study using more storms would give a more robust result on the patterns of the thermosphere. There are also many factors related to the atmospheric density that this paper does not consider, such as thermospheric wind. For future studies, it is possible to consider more factors and use storms of different magnitudes to understand the thermosphere and geomagnetic activities better.  
Conceptualization, W.Z. and L.W.; methodology, L.Y. and J.T.; software, L.G. and J.T.; formal analysis, Z.Y., M.L. and L.G.; data curation, L.G. and J.T.; writing--original draft preparation, L.Y., M.L. and W.Z.; writing--review and editing, L.Y., L.W. and W.Z.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
All data used in this paper, including the Swarm accelerometer data and the Dst and ap indexes, are open-source data provided by the European Space Agency at [[https://earth.esa.int/eogateway/missions/swarm](https://earth.esa.int/eogateway/missions/swarm)]([https://earth.esa.int/eogateway/missions/swarm](https://earth.esa.int/eogateway/missions/swarm)) (accessed on 23 March 2023).  
The authors declare no conflict of interest.
# The Impact of Force Factors on the Benefits of Digital Transformation in Romania
## 2 Literature Review
### UTAUT and Development of Variables  
For the implementation of any type of technology, the study of human behavior is used as part of the information system and the most significant is the unified theory of acceptance and use of technology (UTAUT) [35] that helps predict technology acceptance in organizational implementations. Developed by reviewing and consolidating several models (motivated action theory, TAM 1, model of PC use (MPCU), planned behavior theory, motivational model, TAM and TPB combined, social cognitive theory and innovation diffusion theory), UTAUT is based on four key exogenous or independent variables that affect the behavioral intent to use the technology, namely: (a) performance expectancy, (b) effort expectation, (c) behavioral social influence and (d) facilitation conditions with a direct impact on use of technology [36]. Compared to the existing studies of the specialists [37; 38], we considered the following variables as representative for our Romanian study: Intention to Use Industry 4.0 processes (IUP) with the three influences (Perceived competitiveness (PC), Perceived opportunities (PO), Perceived risk (PR)), Intention to Use Industry 4.0 solutions (IUS) with three influences (Perceived vertical networking solutions (VS), Perceived horizontal integration solution (HS), Perceived integrated engineering solution (IS)) and Benefits of Digital Transformation (BDT). By creating a new conceptual model based on these variables, our study covers some existing gaps in the literature and opens new opportunities for future research in both academia and business. The relationship between the four exogenous variables of the UTAUT model and the three variables that inform the proposed model (IUP, IUS and BDT) are described below.
## 2 Results and Discussion
### Results
#### 2.1.3 GO (Gene Ontology) Functional Classification  
GO assignments were used to classify the predicted functions of _P. ginseng_ adventitious root genes. In our work, the Blast2 GO program [28] was used first to obtain GO annotation of assembled unigenes annotated by Nr and then the WEGO software [29] was utilized to perform GO functional classification of the unigenes. In total, 43,051 unigenes with Blast matches to known proteins were assigned to the three GO ontologies that were further sub-divided into 46 categories (Table S1). As shown in Figure 3, assignments to the cellular component ranked the highest (34,946; 81.17%), followed by biological process (33,708; 78%) and molecular function (32,970; 76.58%). Under biological process, cellular process (64.44%) and metabolic process (61.34%) were prominently represented  
Figure 1: Comparison of unigene length between hit and no-hit unigenes in Nr database.  
indicating that some important cellular processes and metabolic activities occurred in _P. ginseng_ adventitious roots. Under the category of cellular components, cell (76.67%), cell part (76.67%) and organelle (61.10%) represented the majorities of the category. Catalytic activity (50.70%) and binding (50.34%) were the most abundant among various molecular functions. However, we found only a small number of genes in the categories of cell killing (1 unigene), channel regulator activity (1 unigene), protein tag (3 unigenes) and metallochaperone activity (10 unigenes).  
Figure 2: Searching characterization of the assembled unigenes against Nr and Swiss-prot databases. (**A**) _E_-value distribution of Blast hits in Nr database; (**B**) _E_-value distribution of Blast hits in Swiss-prot database; (**C**) Distribution of assembled unigene similarities based on the top Blast hits in Nr database; (**D**) Distribution of assembled unigene similarities based on the top Blast hits in Swiss-prot database; and (**E**) Homology distribution among other plant species based on the top Blast hits in Nr database.
# Stiffness Estimation and Equivalence of Boundary Conditions in FEM Models
## 4 Overview of Possible Applications of the Presented Methods  
Many examples can be found in engineering practice, where instead of the whole model, only one of its components or subassemblies is analyzed. In such component tests, only ideal constraints are often used. This can, in some cases, lead to errors (e.g., in life prediction). In study [32] it was shown how problematic it is to calibrate a numerical model with ideal constraints so that the results of the fatigue analysis match the experimental data. The subject of the analysis was a car horn holder (Figure 11).  
If the analysis is performed on a partial model, the neglected geometry is usually replaced by a point mass with inertial effects corresponding to the removed part. However, stiffness properties are ignored. A good example is the crash management system (CMS) of passenger cars, which is subjected to an impact test [33; 34; 35; 36]. The CMS is a combination of conventional multi-chamber crashboxes and a bumper  
Figure 11: Car horn holder and its FE model [32].  
Figure 10: Mode shapes of the final equivalent FE model.  
are attached to the so-called longitudinal members by means of connector plates. The longitudinal member establishes the main load path in the frontal crash scenarios. In the case of slow-speed impacts, they are usually not included to the model and the connector plates are coupled to a point mass replacing the rest of the vehicle, as shown in Figure 12.  
Similar examples for the application of equivalence methods could be found in aviation (e.g., in analysis of wings) [37; 38; 39; 40]. These include static wing deformation, the analysis of vibration behavior, or the determination of aero-elastic stability limits of wings with regard to flutter.
# Improved YOLOv7 Algorithm for Small Object Detection in Unmanned Aerial Vehicle Image Scenarios
## 3 Methodology
### Extra Prediction Head (P2)  
In the complex background of UAV images, objects on the ground are difficult to detect accurately due to their small size or the dense object environment. Therefore, we add an extra detection head based on the traditional YOLOv7 network to better detect small objects in UAV images. The prediction result of the new prediction head is generated from the shallow feature maps, providing richer feature information on small objects than the deep-level feature maps.  
As shown in Figure 4, the new shallow feature map has more feature information. The C2, C3, C4, and C5 feature maps are extracted by the backbone network with 4, 8, 16, and 32 times down-sampling, respectively. The deepest feature map obtained by multiple convolutions corresponds to the P5 prediction head. Thus, P5 has the largest anchor box size and receptive field, which make it difficult to fit the prediction box to the ground-truth box and extract the feature information of small objects. In addition, multiple operations of down-sampling of the input images will affect the features of small objects in UAV images with many small objects. The prediction head P2 from the shallow feature map corresponds to the small anchor box size and receptive field, which can be effective in extracting the feature information of small objects. In order to enhance small object detection, the P2 prediction head is used by the YOLOv7-UAV network in place of the P5 prediction head. Meanwhile, we cut off the part of the network units containing the P5 detection head. In addition, the extra P2 prediction head increases the parameters in the network, but the removed deep-level P5 prediction head can reduce the number of parameters even more. If we adopt the lower-level prediction head P1 with a resolution of \(320\times 320\), the shallower feature map C1 will be used as the basic feature map to fuse other feature maps. C1 undergoes only one stage of feature extraction, resulting in inadequate feature extraction and an inability to accurately predict objects. Therefore, we chose the extra prediction head P2 instead of the lower-level prediction head P1.  
An object detector that is based on anchors is sensitive to the size of the anchor box. Therefore, the K-means++ algorithm is used to adjust the size of the anchor box for small objects [32]. Table 1 shows the correspondence between the three prediction heads and the  
Figure 4: Feature extraction model of YOLOv7-UAV.  
anchor box sizes for the three scales when the image size is 640 \(\times\) 640 on the VisDrone2019 dataset [33].
# Seismic Performance Evaluation of Highway Bridges under Scour and Chloride Ion Corrosion
## 3 Effect of Cl\({}^{-}\) Corrosion on the Seismic Performance of Bridge Column
### Comparison of Quasi-Static Test Results of Columns before and after Cl\({}^{-}\) Corrosion
#### 3.3.5 Equivalent Viscous Damping Coefficient of Specimens  
Equivalent viscous damping coefficients reflect the level of energy dissipation in bridge columns at different displacements. In pseudo-static tests for bridge columns with reciprocating loading and unloading processes, absorption and release of energy in components were alternately carried out so that the seismic performance of each specimen before and after corrosion could be measured by the equivalent viscous damping coefficient. It can be observed from the plot of equivalent viscous damping coefficient versus displacement for each group of specimens in Figure 30 that the slope of the curve decreased or even changed from positive to negative at ultimate displacement. The equivalent viscous damping coefficient of specimens corroed by Cl\({}^{-}\) was always greater than those of the corresponding un-corroded specimens before reaching ultimate displacement. At the same stage, plasticity development in the plastic hinge region of the corroded columns was larger, which indicates that corroded columns are more prone to damage.
## 6 Results Analysis and Discussion  
In the first part of our research, we compared multiple AQM algorithms with L4S and tested them in two different scenarios: Scenario A--10 Mbps and Scenario B--1 Mbps.  
We deployed multiple instances of iperf3 under FreeBSD to generate four TCP NewReno flows with staggered start and end times (starting at t = 0, 10, 20, and 30 s and each flow lasting 60 s).  
Each instance of FQ-CoDel was configured for target 5 ms, interval 100 ms, quantum 1514 bytes and with 10,240 packets of bottleneck buffering shared by 1024 FQ-CoDel sub-queues.  
Each instance of FQ-PIE was configured for target 15 ms, tupdate 15 ms, max_burst 100 ms, quantum 1514 bytes, and with 10,240 packets of bottleneck buffering shared by six FQ-PIE sub-queues. We have data for both cases when ECN was enabled and disabled. We use six sub-queues because our L4S also uses six sub-queues, which helps us compare both algorithms under similar conditions.  
Each instance of L4S was configured for target 15 ms, tupdate 15 ms, max_burst 100 ms, quantum 1514 bytes, and with 10,240 packets of bottleneck buffering shared by three pairs of L4S sub-queues.  
We have data for both cases when ECN was enabled and disabled.  
The main difference in scenarios is the bandwidth. Scenario A's bandwidth is 10 Mbps; in Scenario B, the bandwidth is 1 Mbps.