esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dataset and Metrics
### Metrics  
In time series prediction tasks, the diversity of evaluation metrics can provide a multifaceted reference for assessing model performance. Considering that each method has its corresponding advantages and disadvantages, we chose five commonly employed evaluation metrics to evaluate the effectiveness of the time series prediction model constructed based on this study's journal metrics dataset in the dynamic journal evaluation task: MSE (mean squared error) [27], MAE (mean absolute error) [28], RMSE (root mean square error) [29], MAPE (mean absolute percentage error) [30], and NRMSE (normalized root mean square error) [31]. The smaller the value, the higher the accuracy of the model. We evaluated each method in our experiments using all of the above-mentioned metrics and analyzed the applicability of the methods by comparing them with one another.  
Among them, the mean absolute error (MAE) reflects the actual situation of the error of the predicted value but relies on a scale, while the mean absolute percentage error MAPE overcomes the disadvantage of relying on a scale, but we did not include a test sample with an actual value of 0 in the calculations during processing; the mean square error (MSE), root mean square error (RMSE), and standardized root mean square error (NRMSE) are all measures that reflect the degree of difference between the actual value and the predicted value, which are increasingly sensitive to outliers.  
The evaluation metrics are calculated in Equations (2)-(6), where \(n\) is the number of samples in the test set, \(y_{i}\) is the actual value of the target for the \(i\)-th sample, \(\hat{y}_{i}\) is the predicted value of the target given by the prediction model after receiving the input of the  
Figure 4: Deep learning method training for data processing.  
Figure 3: Machine learning method training for data processing.  
\(i\)-th sample, and \(y_{max}\)\(/\)\(y_{min}\) are the maximum and minimum values of the target columns in the test set samples, respectively.  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2} \tag{2}\]  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{3}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{4}\]  
\[\text{MAPE}=\frac{100\%}{n}\sum_{i=1}^{n}\left\lvert\frac{y_{i}-\hat{y}_{i}}{ y_{i}}\right\rvert \tag{5}\]  
\[\text{NRMSE}=\frac{\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}}{y _{max}-y_{min}} \tag{6}\]  
In addition, we designed an accuracy metric based on the idea of confidence in order to support the analysis and allowed the number of samples in the test set to be \(n\). For the x confidence level, when the absolute error range between the predicted value of \(i\)-th sample \(\hat{y}_{i}\) and the actual value \(y_{i}\) does not exceed \((1-x)\), it should be considered that the sample is correctly predicted, and vice versa. Finally, we calculated the accuracy metric according to Equation (7).  
\[\textit{score}=\frac{\sum_{i=1}^{n}\begin{cases}1,&\textit{if }\frac{\lvert\hat{y}_{i}-y_{i}\rvert}{y_{i}}<1-x\\ 0,&\textit{else}\end{cases}}{n} \tag{7}\]
## 3 Results
### Evaluation of Anti-Inflammatory Properties  
Different concentrations of _Fucus vesiculus_ extract were evaluated for their effect on the expression of certain pro-inflammatory cytokines (IL-1\(\upmu\)g, IL-6, and TNF-\(\upalpha\)) and enzymes (iNOS) (Figure 5). The relative expression values revealed that two main interleukins (IL-1\(\upmu\)g and IL-6), which are involved in the inflammatory process, were significantly downregulated following treatment with _F. vesiculus_ extract. Specifically, it was observed that for both IL-1\(\upmu\)g and IL-6, the greatest downregulation was achieved when the extract was added at a concentration of 50 \(\upmu\)g/mL, yielding fold-change values of 0.040 \(\pm\) 0.014 and 0.285 \(\pm\) 0.021, respectively, for IL-1\(\upmu\)g and IL-6. Instead, by increasing the concentration of the extract, the downregulatory effect decreases proportionally, reaching, at a 250 \(\upmu\)g/mL extract concentration, a fold-change value of 0.130 \(\pm\) 0.001 for IL-1, while for IL-6 at the highest concentration tested, there was a slight upregulation that led to a fold-changes value equal to 1.075 \(\pm\) 0.021 (\(p\) = 0.0321). On the contrary, the expression of TNF-\(\upalpha\) showed a downregulation once the macrophages were treated with the lowest concentration of extract (25 \(\upmu\)g/mL), with a fold-change equal to 0.280 \(\pm\) 0.028, while it was then significantly (\(p\) < 0.001) up-regulated once treated with the other concentration, reaching a fold-change value of 3.845 \(\pm\) 0.021 at a concentration of 250 \(\upmu\)g/mL. In addition to the cytokines mentioned above, the gene expression of a pro-inflammatory enzyme (iNOS) was also evaluated. The results showed that regardless of the concentration of the algal extract used, it underwent an upregulation with an up-regulatory peak at a concentration of 100 \(\upmu\)g/mL where the fold-change value was equal to 2.800 \(\pm\) 0.014.  
Figure 4: Percentage of vitality of murine peritoneal macrophages after treatment with different concentration of _Fucus vesiculus_ extract. The data are shown as the means \(\pm\) standard deviations (SDs). a–d Means with different superscripts are significantly different (\(p\) < 0.05).
## 4 Experiments  
### Implementation Details  
Dataset: We conducted experiments on the large-scale detection benchmark COCO2017 [20], which contains more than 200,000 images and 80 object categories, including person, bicycle, car, motorbike, airplane, bus, train, truck, boat, traffic light, fire hydrant, bench, and so on. Some of the images are shown in Figure 6. Our models were trained on trainval35k (115k images) split and evaluated on minival split (5k images). Finally, we reported the COCO AP on test-dev split (20k images) by uploading detection results to the evaluation server. The dataset had the characteristics of rich categories, large number, diverse scenes, and large scale range, which made high detection performance very challenging.  
Evaluation Metrics: Following the evaluation protocol in MS COCO [8], we used the mean average precision(mAP); average precision of small, medium, and large objects (AP\({}_{\text{S}}\), AP\({}_{\text{M}}\), AP\({}_{\text{L}}\)); and average recall of small, medium, and large objects (AR\({}_{\text{S}}\), AR\({}_{\text{M}}\), AR\({}_{\text{L}}\)) metrics to evaluate the results. Specifically, mAP was computed by averaging over all 10 intersection-over-union (IoU) thresholds (i.e., in the range (0.50: 0.95) with the uniform step size 0.05) of all classes. In MS COCO, the small, medium, and large objects refer  
Figure 6: Some images in COCO2017 datasets. The dataset has the characteristics of rich categories, large number, diverse scenes, and large scale range, which makes high detection performance very challenging.  
to these area < 322, 322 < area < 962 and area > 962, respectively. Giga Floating-point Operations Per Second(GFLOPs) was used to measure the calculating consumption.  
Training Details: We used MMDetection [33], a deep learning object detection toolbox, to implement Dynamic Anchor. We used Resnet-50 [21] as the backbone network for all experiments, if not otherwise specified. Our network was trained with stochastic gradient descent (SGD) over 2 RXT 3090 GPUs with a total of 8 images per mini-batch for 12 epochs. Weight decay and momentum were set as 0.0001 and 0.9. The initial learning rate was 0.005 and reduced by a factor of 10 at epoch 9 and 11. We resized the input images so that the shorter side was 800 and the longer side less or equal to 1333. We used horizontal flipping as the only data augmentation method, and the weight pre-trained on ImageNet [34] was used to initialize the backbone.
# A Comprehensive Survey of Isocontouring Methods: Applications, Limitations and Perspectives
## 7 Discussion
### Ray Tracing Algorithms  
In the field of computer graphics, such as the visualization of simulation results, ray tracing algorithms offer the possibility of generating realistic light and shadow effects for three-dimensional scenes. However, this leads to great computational effort overall. The method also requires predominantly structured data, although approaches that can also process unstructured data have recently been pursued. It is found that the quality of the ray tracing method is mainly determined by visual inspections. However, due to the overall great computational effort of the method, the resulting computational cost of the algorithms is also a frequently used criterion.  
In summary, it can be stated that the methods were all developed from their respective areas of application and therefore require a specific form of data, often structured data. However, this frequently makes it difficult to transfer them to other areas of application that provide different data. Furthermore, the assessment of the algorithm quality largely relies on visual inspection, posing challenges in comparing different method families.
## 3 Results and Analysis
### Results and Analysis of Basic Performance Test of Modified Bitumen
#### 3.1.2 Softening Point Test  
The results are shown in Figure 2.  
Figure 1: Penetration test results.  
Figure 2 shows that the softening point of bitumen increased to different degrees when Sasobit and XT-W3 modifiers were incorporated. Sasobit increased by 36.1%, 65.1%, and 80.1%, respectively; XT-W3 increased by 2.2 \({}^{\circ}\)C, 4.2 \({}^{\circ}\)C, and 6.1 \({}^{\circ}\)C, respectively, while the incorporation of Evotherm modifier decreased by 0.6 \({}^{\circ}\)C, 0.9 \({}^{\circ}\)C, and 1.8 \({}^{\circ}\)C. This shows that the Sasobit modifier can improve the high-temperature stability of the bitumen material as well as its high-temperature deformation resistance; after mixing with the XT-W3 modifier, the improvement in the softening point of bitumen is not significant; after mixing with the Evotherm modifier, the softening point is reduced, and overall, the bitumen becomes softer. When the Sasobit dose was greater than 3%, its softening point was higher than that of the SBS-modified bitumen, while the softening points of the XT-W3 and Evotherm-modified bitumens were consistently lower than those of the SBS-modified bitumen, indicating that under a specific Sasobit dose, the high-temperature performance of the modified bitumen was better than that of the SBS-modified bitumen, while the high-temperature stability of XT-W3 and Evotherm-modified bitumens were lower than that of the SBS-modified bitumen.
# Brazing Ti-48 Al-2 Nb-2 Cr Alloys with Cu-Based Amorphous Alloy Filler
## 3 Results and Discussion
### Mechanical Properties of the Brazed Joint  
Figure 8 shows the room temperature shear strength of the joints brazed at different temperatures for 600 s. It can be seen that the shear strength of the joint is not satisfactory at low brazing temperatures. When brazed at 1183 K, the shear strength is very low: about 160 MPa. The main cause is the low amount of atomic diffusion and the insufficient dissolution reaction between the TiAl base alloy and the molten Cu-based filler metal. With the increasing brazing temperature, the joint strength changed correspondingly. When the brazing temperature is up to 1213 K, the maximum strength achieved 266 MPa. Two aspects could contribute to the maximum shear strength. Firstly, the increased brazing temperature can improve dissolution and diffusion, leading to the formation of the appropriate interfacial reaction layers in the interface [23; 41]. In the present work, the brazing temperature changes the thickness of the reaction layers, as shown in Figure 6, and thus affects the joint strength directly. At the lower brazing temperature, the main cause was the low amount of atomic diffusion and the insufficient dissolution reaction between the TiAl base alloy and the molten Cu-based filler metal, which resulted in lower joint strength. Contrarily, the higher brazing temperature promoted the growth of reaction layers. It is reported that the thickness of the reaction layer dramatically influences the strength of the joint, and there is an optimized thickness of the reaction layers for brazed joints [42]. So, the strength of the reaction layers could be weakened when its thickness exceeds the best value. Secondly, a certain amount of AlCuTi phase homogeneously distributed in the interface, which made the reaction layers a metal matrix reinforced by AlCuTi particles. Further increasing the brazing temperature led to a dramatic deterioration of its bonding strength, and the shear strength was decreased to 197 MPa at 1273 K for 600 s. It is suggests that residual stresses might be yielded in the brazed joint due to the mismatch of the thermal expansion coefficient and Young's modulus between the TiAl base material and the filler metal during the cooling process. These residual stresses can induce microcracks in the interface and deteriorate the mechanical properties [43]. Moreover, further increasing the brazing temperature leads to a larger amount and size of brittle intermetallics (e.g., Ti\({}_{2}\)Al and AlCuTi) at the interface, as shown in Figure 6. The existence of these intermetallics weakens the plastic deformation of the joint, which is detrimental to the relaxation of the residual stress in the joint. This consequentially leads to the decrease of shear strength.  
Figure 7: Formation model of joint: (**a**) dissolution and diffusion of elements; (**b**) formation of reaction layers; (**c**) formation of AlCuTi and growth of reaction layers; and (**d**) formation of \(\alpha\)-Ti.
# Cooperative Data Collection Mechanism Using Multiple Mobile Sinks in Wireless Sensor Networks
## 4 The Proposed CDCA Algorithm
### Speed Control Phase  
This phase first constructs a global path which passes through each root of \(T_{i}\) for global mobile sink \(m_{0}\) by applying the Hamiltonian algorithm. Let \(T\ =\ \left\{T_{i}|1\leq i\leq k\right\}\) denote the set of \(k\) subtrees. The global mobile sink \(m_{0}\) will leave from the static sink, move along the constructed path and collect all data stored in root \(T_{i}\). Then the global mobile sink \(m_{0}\) goes back to the static sink. Assume \(\left\{\pi_{0},\pi_{1},\pi_{2},\ldots,\pi_{k}\right\}\) denote the paths of the mobile sink \(m_{0}\), \(m_{1}\),..., \(m_{k}\), respectively. Let \(l_{i}\) denote the root location of \(T_{i}\) which is also the rendezvous location of global mobile sink \(m_{0}\) and mobile sink \(m_{i}\). Assume \(\left\{\hat{l}_{0},\hat{l}_{1},\ \hat{l}_{2},\ldots,\hat{l}_{k}\right\}\) denote the order of rendezvous locations that the global mobile sink \(m_{0}\) travel in a counter clockwise direction, where \(\hat{l}_{i}\) be the rendezvous location of global mobile sink \(m_{0}\) and local mobile sink \(m_{i}\). Let \(\hat{t}_{i}\) denote the rendezvous time point of every mobile sink \(m_{i}\) and global mobile sink \(m_{0}\). Assume \(L_{i}\) denote the length from \(\hat{l}_{i}\) to \(\hat{l}_{i+1}\). The following presents how each \(m_{i}\) adjusts its velocity such that it can meet \(m_{0}\) at the rendezvous time point \(\hat{t}_{i}\). Let \(v\) denote the velocity of the mobile sink \(m_{0}\). The time required for mobile sink \(m_{0}\) traverses path \(L_{i}\) can be derived by applying Equation (18).  
\[t_{i}^{move}=\frac{L_{i}}{v} \tag{18}\]Let \(t_{i}^{collect}\) denote the time of the mobile sink \(m_{0}\) collecting data in rendezvous point \(\hat{t}_{i}\). The total time, denoted by \(t_{i}^{arrive}\), required for global mobile sink \(m_{0}\) traversing from static sink to the rendezvous point \(\hat{l}_{i}\) can be measured by Equation (19).  
\[t_{i}^{arrive}=t_{0}^{move}+t_{1}^{collect}+t_{1}^{move}+t_{2}^{collect}+\cdots t _{i-1}^{move}=\sum_{i=0}^{i-1}t_{i}^{move}+\sum_{i=1}^{j-1}t_{i}^{collect} \tag{19}\]  
The total time, denoted by \(t^{cycle}\), required for mobile sink \(m_{0}\) touring a cycle can be measured by Equation (20).  
\[t^{cycle}=\ \frac{\sum_{i=0}^{k}L_{i}}{v} \tag{20}\]  
The velocity of the mobile sink \(m_{i}\) can be measured by Equation (21).  
\[v_{i}=\frac{\left|R_{j}\right|}{t_{j}^{arrive}} \tag{21}\]  
where \(\left|R_{i}\right|\) denote the path length of subtree \(T_{j}\) passes through all collection point.
# A Domain-Independent Classification Model for Sentiment Analysis Using Neural Models
## 2 Related Works  
Although linguistics and natural language processing (NLP) have a long history of research, few works were published concerning sentiments before the year 2000 [6]. After 2000, the field has attracted the attention of researchers and many research groups to work on. To provide an example, [7] studies the prediction of every review for being negative or positive in the aspect-oriented opinion in the opinion mining domain at the sentence level. The authors of this work propose groups of selected models based on conditional random fields (CRFs) with an added multi-label presentation that not only models the opinion in a review, but also models set of opinions in a single review. In [8], authors suggest a sentiment analysis system that is able to identify and relate the sentiment to every rated product or item in the reviews. They present a probabilistic model to investigate the structure of each review and to which cluster each of them is related to, where it represents a specific sentiment. In [9], the researchers offer a flexible automated classification system that uses supervised machine learning techniques using Markov Logic for sentiment classification on a sub sentence level and incorporates polarity differentiations from different origins. Furthermore, in [10], enhanced latent aspect rating analysis model is presented. This model does not require predefined keywords that are associated with specific aspects. This work investigates the reviews in order to define the topical aspects, the ratings of the individual aspect and assigning weights that differentiate depending on the aspects from a reviewer point of view. [11] proposes a simple hierarchical clustering approach (unsupervised model) for product aspects extraction, clustering, and also defining the relations between aspects (relevant and irrelevant). In [12], the authors introduce a novel supervised approach for joint topic aspects for choosing specific reviews that are considered to be helpful among a set of reviews. Moreover, in [13], an employee dataset is created and a novel ensemble model for sentiment analysis is proposed on aspects level. In [14], a sentiment analysis is conducted on movie reviews. New features are extracted that have influence on determining the polarity scores of the opinion more accurately. Natural language processing approaches are applied using the impact of the unique extracted features. In [15], supervised and semi-supervised approaches are investigated for text classification. Additionally, deep learning is also used for sentiment analysis. Authors of convolutional neural networks for sentence classification use CNN to classify users' reviews for movies. Others, as in [15], use bidirectional long-short term memory models which is applied to the IMDb dataset. Table 1 shows a summary of the state-of-the-art approaches for sentiment analysis. More information regarding the performance of different approaches can be found in Section 6.
# Estimation of Aircraft-Dependent Bumpiness Severity in Turbulent Flight
## 4 Conclusions  
To estimate and prevent severe aircraft bumpiness in advance is beneficial to the flight safety of civil aviation aircraft. This paper put forward a new severity estimation method of aircraft bumpiness based on in-situ EDR indicator. Some innovations are as follows:  
1. Based on in-situ EDR indicator, the aircraft-dependent bumpiness severity is estimated. A turbulence time series according to the von Karman model is generated. Based on the assembly of wing and horizontal tail, the unsteady vortex lattice method is proposed to obtain the aerodynamic performance induced by turbulence. After obtaining the time series of vertical acceleration, the bumpiness severity is estimated by Welch spectrum estimation.
2. To improve the computing accuracy, there are several improvements on traditional UVLM. Vortex rings are assigned on the mean camber surface of lifting body, and the lattices neighboring to the wingtip, wing root, and leading edge are refined by semi-circle division method. In addition, when it comes to the control surfaces, the divided lattices are assigned as close as possible to the structural boundary. By the comparison of aircraft modeling data and recorded QAR flight data, it shows that the aerodynamic force change in turbulent flight can be computed with better accuracy by UVLM.
3. By separating turbulence-induced and aircraft maneuvers-induced aerodynamic force change, the adverse effects of control surface deflections on bumpiness severity estimation can be effectively removed. It is also beneficial to estimate in-situ aircraft-dependent bumpiness.  
Figure 14: The bumpiness severity of different aircraft types.  
This paper is the first attempt to apply the UVLM on the estimation of aircraft bumpiness severity in turbulence. With the proposed method, the bumpiness severity of the next aircraft flying through the same turbulence field can be predicted. In the future, the vortex ring model and related UVLM will be refined to further improve the computing accuracy of vertical acceleration. In addition, by extending and applying the method in Chinese airlines, the practicability of the method can be improved by closed-loop verification with flight data.  
Conceptualization, H.W. and Z.G.; methodology, H.G. and H.W.; software, Z.G. and H.W.; validation, H.W. and Z.G.; formal analysis, Z.G.; investigation, H.W. and K.Q.; resources, H.W.; data curation, K.Q.; writing--original draft preparation, H.W.; writing--review and editing, Z.G. and H.W.; visualization, Z.G.; supervision, H.G.; project administration, H.G.; funding acquisition, Z.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of China, grant number [NSFC: U1733122].  
Not applicable.  
Data Availability Statement: Data is not publicly available.  
The authors declare no conflict of interest.