esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Tree Policy with Attention-Based State Representation for Interactive Recommendation
## 1 Introduction
For example, DDPG-KNN [23] mapped the discrete action space to a low-dimensional continuous action space and selected actions based on their similarities. Such works are inefficient as the computation of action similarities is time-consuming. An alternative strategy is to design a specific neural network to facilitate learning the tasks in large action spaces. For instance, DRN [24] and DEERS [6] adopt a refined Deep Q-Networks (DQN) [25] to learn policies with large action spaces. However, learning the DQN-based methods always involves maximum operation over the actions, which is inefficient when the number of actions is large. Recently, TPGR (tree-structured policy gradient for recommendation) [5] was proposed, which organized the policy into a tree structure and is efficient in both  
Figure 1: A motivation example. learning and decision making. However, TPGR becomes inefficient during evaluation, especially when calculating the top-K ranked items, which is serious in recommender systems since the computation of each action choice needs to traverse the tree. To improve the ability and efficiency for dealing with long and noisy dependencies that occur in real-world applications, in this paper, an attention-based tree-policy recommendation (ATRec) method is proposed, which can effectively and efficiently capture user dynamics under long and noisy patterns. In addition, an efficient tree-structured policy network is devised, which can further improve the efficiency of TPGR with a refined tree policy model. Specifically, a complete tree to represent the policy and incorporate a parameter-sharing strategy is used to improve both the learning and evaluation efficiency. The proposed method is evaluated on three well-known benchmark datasets to illustrate the effectiveness and efficiency of the proposed methods. To summarize, the contributions of this paper are as follows:  
* An attention-based state representation model for IRS is proposed that can effectively capture the user dynamics even when the sequential data are long and noisy. * An efficient tree-structured policy is devised that can improve the learning and decision efficiency of TPGR through reinforcement learning. * The proposed model is evaluated with state-of-the-art methods and the results demonstrate that the proposed method is effective and efficient on three benchmark datasets.
# Evaluation of Short-Term Aging Protocol for Asphalt Mixtures
## 1 Introduction
### Background  
There are several mechanisms that cause asphalt mixture aging. Volatilization and oxidation are known as the biggest contributors among them [3; 10]. A lighter molecular weight can vaporize and escape into the atmosphere at high temperatures [11; 12]. Viscosity may increase from 150% up to 400% [13; 14] depending on the speed with which volatilization takes place. Due to volatilization, a significant increase in modulus and decrease in phase angle were also observed by [PERSON] et al. [15]. Binders are also subjected to aging due to oxidation, which causes significant changes in the physical and mechanical properties of asphalt.  
During short-term aging periods, the asphalt binders are exposed to elevated temperatures which enhance the aging mechanisms, especially volatilization. Moreover, a longer hauling distance between the plant and construction site, a delay, and/or increased paving time may cause excessive short-term aging. An asphalt mixture tends to become stiffer and its performance is affected when aged. Although stiffening of the asphalt mixture should be considered a benefit due to its enhanced resistance and load distribution to permanent deformation, it also causes embrittlement of the mixture, thus making it to more susceptible to thermal/fatigue cracking and moisture damage. A detailed work on asphalt aging; its mechanisms, effects, and performances; and methods to retard aging was provided by [PERSON] et al. [16].  
To simulate the short-term aging of an asphalt mixture, AASHTO R30 [17] recommends curing an asphalt mixture for 4 h at 135 \({}^{\circ}\)C for performance testing. However, the aging rate may vary depending on various factors such as binder type and source, aggregate gradation and absorption, plant type, mixing and discharge temperatures, and silo storage time [12; 18; 19; 20; 21; 22; 23]. [PERSON] et al., 1994 [3] conducted a comprehensive study to evaluate the aging of asphalt mixtures and concluded that the AASHTO short-term aging protocol simulates asphalt mixture aging adequately well except for a few conservative predictions. This protocol was also reported to be adequate by other researchers in the United States [24]. Another extensive investigation was conducted by [PERSON] and [PERSON] [18] in Colorado, United States. In this study, mixture samples were conditioned with different durations to develop a protocol which simulates short-term aging. It was recommended that laboratory-produced mixture samples should be conditioned for 2 h at the field compaction temperature so that asphalt aging and absorption which take place at the mixing plant can be simulated. Different aging protocols have also been studied by [PERSON] et al. [6]. They recommended conditioning laboratory mixtures at 135 \({}^{\circ}\)C for 2 h prior to compaction.
# Cooperative UAV Scheme for Enhancing Video Transmission and Global Network Energy Efficiency
## 2 Related Work
### VOEI Operation
#### 3.2.1 Route Establishment
The required energy to fly to such location depends on the Euclidean distance (\(dist_{L(X_{i},Y_{i}),L(X,Y)}\)) between both location and the flying speed \(s_{max}\), which is computed as follows:  
\[E_{fly}^{SN,RN}=E_{fly}(dist_{L(X_{i},Y_{i}),L(X,Y)},s_{max}). \tag{9}\]  
Any UAV \(v_{i}\) requires a given energy \(E_{replacement}\) to fly from their current location \(L(X_{i},Y_{i})\) to the battery replacement location \(L(X_{r},Y_{r})\). The required energy to fly to such location depends on the Euclidean distance (\(dist_{L(X_{i},Y_{i}),L(X_{r},Y_{r})}\)) between both location and the flying speed \(s_{i}\), which is computed as follows:  
\[E_{replacement}=E_{fly}(dist_{L(X_{i},Y_{i}),L(X_{r},Y_{r})},s_{i}). \tag{10}\]  
In this way, the \(CN\) computes a cost function \(C_{SN/RN}\) for all UAVs \(v_{i}\) based on Equation (11), and thus it selects the source node \(SN\) and a set of relay node \(RN_{i}\). Specifically, the \(CN\) selects the UAV \(v_{i}\) with the lowest value to become the \(SN\) and \(RN_{i}\), since these nodes fulfill the requirements mentioned above:  
\[C_{SN/RN}=\min=\forall\ v_{i}\in\mathrm{V}=\left\{ \begin{array}{cl}&1,& \text{if }t_{mov}>t_{max},\\ &1,&\text{if }RE_{t}>E_{min},\\ &w_{1}\times t_{move}+w_{2}\times E_{min},&\text{otherwise}.\end{array} \right. \tag{11}\]
## 3 Experiments and Analysis
### Training Results of Semantic Segmentation Model
In joint training experiment 2, it is necessary to first train the clear dataset and then train the foggy dataset. The training outcomes of the clear dataset, trained separately, are utilized as pre-weight inputs into the training process of the foggy dataset. The experiment converged around the 30 th epoch, where the training set loss function decreased from 0.113 to 0.085, and the validation set loss function decreased from 0.167 to 0.070. The training graph is depicted below (Figure 16):  
Figure 16: _Cont_. Figure 15: Joint training experiment 1. (**a**) Previous fog data set training experiment; (**b**) clear dataset training experiment with pre-trained weights from dehazing dataset. Combine the relevant indicators of the above training experiments and summarize them in Table 5:  
Except for mixed training, the performance of training the dehazing dataset alone and joint training 2 is superior on the hazy dataset compared to the haze-free dataset, while the performance of training the haze-free dataset alone and joint training 1 is superior on the haze-free dataset compared to the hazy dataset. This phenomenon arises due to the sensitivity of the semantic segmentation model to image features, which tends to be more inclined towards the characteristics of the latest training set. In transfer learning, the training set used for training the dehazing dataset alone and joint training 2 yields better performance on the hazy dataset compared to the haze-free dataset, while the training set used for training the haze-free dataset alone and joint training 1 demonstrates better performance on the haze-free dataset compared to the hazy dataset. This is attributed to the semantic segmentation model's sensitivity to image features, which tends to be more biased towards the characteristics of the latest training set. Figure 16: Joint training experiment 2. (**a**) Previous training experiment on clear dataset; (**b**) training experiment on dehazed dataset with pretrained clear weights. \begin{table}
\begin{tabular}{c c c c} \hline  & **mIoU (\%) on** & **mIoU (\%) on** & **Average mIoU (\%)** \\  & **Hazed Dataset** & **Clear Dataset** & **Average mIoU (\%)** \\ \hline Mixed training & 92.34 & 93.39 & 92.865 \\ \hline Training the dehazing dataset separately & 93.56 & 87.52 & 90.54 \\ \hline Training the haze-free dataset separately & 91.55 & **94.54** & 93.045 \\ \hline Joint training 1 & 91.02 & 91.43 & 91.225 \\ \hline Joint training 2 & **94.5** & 94.26 & **94.38** \\ \hline \end{tabular}
\end{table} 
Table 5: Experimental results.
###### Abstract  
Strain measurements are vital in engineering trials, testing, and scientific research. In the process of signal acquisition, baseline drift has a significant impact on the accuracy and validity of data. Traditional solutions, such as discrete wavelet transform and empirical mode decomposition, cannot be used in real-time systems. To solve this problem, this paper proposes a Transformer-based model to eliminate the drift in the signal. A self-attentive mechanism is utilized in the encoder of the model to learn the interrelationships between the components of the input signal, and captures the key features. Then, the decoder generates a corrected signal. Meanwhile, a high-precision strain acquisition system is constructed. The experiments tested the model's ability to remove drift from simulated voltage signals with and without Gaussian noise. The results demonstrated that the transformer model excels at eliminating signal baseline drift. Additionally, the performance of the model was investigated under different temperature conditions and with different levels of force applied by the electronic universal testing machine to produce strain. The experimental results indicate that the Transformer model can largely eliminate drift in dynamic signals l and has great potential for practical applications.  
transformer model; strain measurement; drift signal; baseline drift mitigation Article
## 3 Dataset
### Data Pre-Processing  
To provide a dataset suitable for the DNN and clustering algorithm based on association analysis, as much as possible, the pre-processing steps should be standardised. The detailed pre-processing process in this study is as follows:  
1. **Data integration.** The data obtained from the school's educational administration system is a single piece of score data containing several attributes of the course, which is first stored in the MySQL server of the student integrated management system independently developed by the college, and the current 21,350 scores are integrated and exported to the table using database statements.
2. **Data cleaning.** Data cleaning refers to screening and reviewing the original data, as well as addressing the inconsistency of data, missing data, wrong data, and duplicate data. It should be considered that as the training scheme evolves, the curriculum system in different grades will change, and some courses will be replaced or deleted from the perspective of professional personnel.
3. **Data conversion and processing.** Using the Python Pandas framework, the String type is converted to the double type, and the excellent (A), good (B), moderate (C), pass (D), and fail (E) types are converted to numerical values of 95, 85, 75, 65, and 55, respectively. Then, missed exams are converted to 0 points, and the missing values are converted into the average of the student's average score and the average score of the course using the horizontal and vertical categories for the student's grade data below 10 points. The mask and number start with student number 0.
4. **Training data division.** After the cleaned data are randomly disrupted, the original data are randomly divided into a training set and a verification set in a 3:1 ratio. The training set is used to train the DNN model, and the verification set is used to test the generalisation ability of the model after each iteration to prevent overfitting.
## 2 UAV-Enabled ITS Framework
### Second Layer: Communication Networks for Traffic Monitoring  
The second layer of the UAV-enabled ITS application focuses on using advanced communication technologies to ensure that data from sensors and UAVs are transmitted and processed in real time. This aims to improve traffic management by enhancing connectivity, reducing latency, and enabling timely responses to traffic conditions.
## 2 Literature Review
### SNSAs Use in Relation to Students' Social Trust  
Trust has been defined as a positive emotional bias in decisions about others [34], or as a type of anticipation of behaviors of other people [35]. There appear to be two types of trust, which need to be identified: social trust or generalized trust, and particularistic trust or assurance [36]. Social trust is a predominant belief of trustworthiness as a natural aspect of human beings. Assurance is trust that emerges from secure relationships with specific people [37] and is established on secure feelings stemming from solid knowledge about a particular person [38]. In terms of social capital, social trust represents an essential component that forms the cooperative infrastructure of community [39].  
In the context of SNSAs, online social relationships can help promote social trust and social capital [40; 41]. [PERSON] and [PERSON] [42] suggest that the power of relationships of SNSAs users could be different, especially when online relationships are considered complementary to existing offline social relationships. [PERSON] et al. [43] reported that social capital was influenced positively by social trust on SNSAs. Moreover, [PERSON] et al. [44] found that social trust was associated positively with the perception of activities made possible via SNSAs, particularly in relation to communication. This indicates that social trust plays an important role in the online community, where power and relationships can be acquired through interpersonal interaction [43]. Social trust, therefore, appears to be an essential requirement in online communities where lack of direct contact increases the possibility of deception occurring [43].  
With regard to forming trust among users, [PERSON], [PERSON], [PERSON], and [PERSON]'s [45] large USA study involving 2255 adults (including 975 SNSAs users), found that SNSAsusers were 46% more trusting than the 27% who did not use the Internet. Likewise, in another USA study by [PERSON] et al. (2016), 117 college student users of Facebook expressed a high level of trust and were more willing to share interpersonal information. However, [PERSON] and [PERSON] (2017) suggest that forming trust based on the limited information available on SNSAs leads to some difficulties in the process of trusting interpersonal interactions within the online environment. In a Jordanian study by [PERSON] and [PERSON] (2018), involving 1135 university students, SNSAs were found to have a positive influence on relationships between students through removing the psychological and social barriers among them. However, relationships could be negatively impacted through assertion of regional, tribal and ethnic intolerances. In conclusion, studies illustrate both types of potential impacts on developing social trust among students through the use of SNSAs, indicating a need for further research, particularly in non-Western regions.
## 3 Results  
Table 2 and Figure 6 illustrate the impact of different knee braces on the maximum range of motion of the knee joint. The research findings indicate a significant interaction effect between the group and time (F = 5.063, \(p\) = 0.005, ES = 0.275). The analysis of the simple effects of the group shows that at time point T2, the experimental group (EG) is significantly higher than the control group (CG) (\(p\) = 0.000, ES = 0.292), and at time point T3, EG is also significantly higher than CG (\(p\) = 0.001, ES = 0.253). The analysis of the simple effects of time reveals that for both EG and CG, compared with T0, there are significant differences in the joint range of motion at time points T1 and T2 (\(p\) < 0.05). However, the difference between T0 and T3 is not significant (\(p\) > 0.05).  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & **EG** & **CG** & \(p\) & **ES** & **Group \(\times\) Time** \\ \hline T0 & 45.6 \(\pm\) 6.9 & 43.2 \(\pm\) 6.2 & 1.000 & 0.063 & \\ T1 & 13.2 \(\pm\) 8.8 & 16.1 \(\pm\) 9.0 & 0.297 & 0.026 & \\ T2 & 34.4 \(\pm\) 6.6 & 25.4 \(\pm\) 7.4 & 0.000 & 0.292 & \(p\) = 0.005 \\ T3 & 47.1 \(\The results of Table 3 demonstrate that at time point T1, there are no significant differences in joint coordination across all joints between the two groups. At T3, compared with CG, EG exhibits higher hip-ankle in-phase coordination (\(p\) = 0.013), increased proximal hip-knee coordination (\(p\) < 0.001), and reduced knee-ankle anti-phase coordination (\(p\) = 0.028) (see Table 4 and Figure 7).  
\begin{table}
\begin{tabular}{l c c c c} \hline  & **In-Phase** & **Antiphase** & **Proximal Phase** & **Distal Phase** \\ \hline Hip-ankle & & & & \\ CG & 24.63 \(\pm\) 10.12 & 15.85 \(\pm\) 5.35 & 39.96 \(\pm\) 6.98 & 19.56 \(\pm\) 10.76 \\ EG & 31.51 \(\pm\) 6.13 & 15.25 \(\pm\) 5.72 & 38.63 \(\pm\) 6.69 & 14.61 \(\pm\) 10.72 \\ \(p\) & 0.013 & 0.292 & 0.202 & 0.483 \\ Hip-knee & & & & \\ CG & 38.89 \(\pm\) 12.35 & 7.69 \(\pm\) 4.64 & 4.44 \(\pm\) 2.85 & 48.98 \(\pm\) 16.48 \\ EG & 39.77 \(\pm\) 10.51 & 6.79 \(\pm\) 4.49 & 7.26 \(\pm\) 1.47 & 46.18 \(\pm\) 15.70 \\ \(p\) & 0.164 & 0.958 & \textless{}0.001 & 0.968 \\ Knee–ankle & & & & \\ CG & 3.26 \(\pm\) 2.91 & 14.25 \(\pm\) 5.75 & 79.28 \(\pm\) 8.17 & 3.21 \(\pm\) 2.33 \\ EG & 3.93 \(\pm\) 2.96 & 9.16 \(\pm\) 5.76 & 83.27 \(\pm\) 9.15 & 3.64 \(\pm\) 2.53 \\ \(p\) & 0.280 & 0.015 & 0.096 & 0.236 \\ \hline \end{tabular}
\end{table}
Table 4: Influence of different knee braces on lower limb joint coordination at time point T3.  
Figure 6: Maximum range of motion during the knee support phase.  
The results of Table 5 show that compared with CG, EG experiences a smaller ACL force during walking at T4 (t = \(-\)2.24, \(p\) = 0.030, ES = 0.69).
## 5 Conclusions  
We have analyzed a MU MISO downlink systems with combining precoding and NOMA approaches, and proposed the OP as well as EC calculation method for all users. Furthermore, the approximation of OP and EC is derived in both perfect and imperfect SIC schemes. The proposed method was compared with the conventional OMA method, and the numerical result indicated that the combining precoding and NOMA approach outperforms the conventional OMA method in case of perfect SIC. Whereas in case of imperfect SIC, the total EC of proposed method decreases and becomes lower than that of conventional OMA method when the SNR is high. Moreover, the theoretical analysis was verified by simulation in both cases of perfect and imperfect SIC.  
The ZFBP method was equipped to remove the inter-cluster interference, however, the performance of system was analyzed under the assumption of perfect implementation of ZFBP, and thus the inter-cluster interference was removed clearly. The analysis of system performance under the assumption of imperfect ZFBP will be proposed in our future works. Moreover, another precoding method will be taken into consideration to further improve the performance of downlink MU systems.  
Conceptualization, P.T.H.; methodology, V.V.S.; software, T.M.H.; validation, V.V.S. and T.M.H.; formal analysis, N.T.P. and N.L.C.; investigation, T.M.H.; resources, N.T.P.; data curation, N.T.P.; writing--original draft preparation, V.V.S.; writing--review and editing, N.T.P. and N.L.C.; visualization, V.V.S.; supervision, T.M.H.; project administration, P.T.H. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.04-2017.311.  
Figure 7: Comparing total EC of 3 users of proposed method with OMA method in the case of BPSK modulation.
## 2 Materials and Methods
### Methodology
#### 2.4.2 Mann-Kendall Test Method  
Addinsoft's XLSTAT Software was used to perform the statistical Mann-Kendall test. In climatologic and hydrologic time series, the Mann-Kendall test is a widely used statistical approach for trend analysis [51]. In this test, the null hypothesis H0 indicates that there is no trend in the time series and that the data are independent and randomly ordered. Conversely, the alternative hypothesis H1 assumes that there is a trend in the time series. In reality, the null hypothesis H0 is rejected in favor of H1 when \(p\)-value exceeds the alpha value (0.050) [51]. [PERSON]'s parameters were used to analyze the trend of climatologic and hydrologic data. The positive or negative value of Statistic \"S\" was used to infer an upward or downward trend, while the statistic Kendall's _tau_ was used to measure the relationship and correlation between two variables.  
4.3 Supervised Classification of Satellite Imageries and Digital Elevation Model (DEM) Reconditioning  
ArcGIS 10.5 was used for the supervised classification of satellite imageries. Specifically, after the image processing, atmospheric corrections, and radiometric calibration steps, the satellite images were used for supervised classification to categorize the study area into different LULC classes [50]. Similarly, Digital Elevation Model (DEM) reconditioning was performed using Arc-Hydro tools for watershed delineation. As a digital visualization of ground surface terrain, DEM contains several hydrological data from which can be extracted relevant surface information such as flow direction or slope, drainage line, and catchment polygons [52].