esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 3D BEM-DMST Code Experimental Validation
### DeepWind Demonstrator
Since the rotor tilt involves a reduction of the normal wind velocity component, the angle of attack slightly decreases, but the effect is more noticeable at low wind speeds where a limited change of angle of attack leads to a remarkable change in lift, while drag is almost constant. At high wind speeds, where the turbine is operating in deep stall for the largest part of the rotation, small changes of angle of attack provide a drag contribution which nullify the increase of lift. Besides, 3D BEM tilted rotor results overestimate the power at high wind speed respect to experiments because the flow might be severely disturbed in the presence of combined tilted configuration and turbulence associated to higher wind speeds. The physical phenomenon can be imputed to high disturbed flow and vortex detachment, in fact differences in configurations are expected at low speeds where the flow is attached: in tilted layout every streamline (supposed to be all parallel to flow direction) will approach a thicker airfoil section due to the inclination of the rotor; this implies a completely different aerodynamic behaviour which should lead to modified polars. The effect of the combination of rotor tilt and turbulence could be accurately modelled only with a high fidelity tool such as CFD. Better experimental accordance is found at the higher rotational speed of 300 rpm (Figure 8c). The agreement at higher Re numbers (see Reynolds regimes in Table 4 at optimal TSRs, sampled at equatorial plane), is justified by considering the challenge in accurately modelling the aerodynamic phenomena where laminar-to-turbulent transition plays a key role. The polar construction uncertainty reduces as Reynolds number increases because transition is less likely to occur. The power curve does not feature a saddle within the wind speed tested; however the gap between 3D BEM and empirical sampling increases with wind speed magnitude, although it remains within 5% of the experimental datum. The motivations are similar to the ones reported for the case at 200 rpm, that are all relaxed by the 50% increase in the Reynolds number. In light of the three comparisons reported above, the in-house 3D BEM code is deemed to be validated to performance predictions at medium and high TSRs with low tilt angles. In the successive sections, the application of medium and large scale VAWTs will be examined. However, as a final step of the validation, the tip loss model is considered in detail and discussed in the following subsection.
## 4 Drug Resistance Mechanisms in TB  
If the 6-months combination therapy for the treatment of drug-susceptible TB is adequately taken, patients achieve cure rates of > 95%, and the development of resistance by simultaneous mutations to various drugs is very rare [13]. The resistance developed by Mtb to any antimicrobial agent is not due to a single mechanism, but to the interplay of biological, clinical and microbiological reasons, including:  
1. Nonadherence of patients to their 6-months therapy and/or errors of physicians in the therapy management (human errors), that increases the risk of developing genetically drug resistant bacilli [14; 15];
2. Complexity and poor vascularization of granulomatous lesions, which obstruct drug distribution to some sites, further leading to suboptimal drug concentration and the development of phenotypic and genetic resistance [16; 17];
3. Naturally occurring high levels of antibiotic resistance in tubercle bacilli (intrinsic resistance) [18; 19; 20];
4. Formation of non-replicating (NR) drug-tolerant bacilli inside the granulomas (phenotypic resistance) [21; 22];
5. Development of genetically resistant bacilli by chromosomal mutations (acquired resistance) [23; 24; 25].
## 6 Field Application  
In order to verify the application of the 3-RDE PDC bit, two 3-RDE PDC bits were applied in the gravel formation and migmatitic granite formation in the instinct of Liaohe Oilfield, China. The effects of the 3-RDE PDC bit, the Hybrid bit and the conventional PDC bit are compared when the application condition, such as the wellbore dimension, drilling parameters, depths and the lithology, are similar.  
Figure 20a shows the use of the 3-RDE PDC bit and the Hybrid bit whose diameter are both 241.3 mm in Mesozoic gravel formation in the first trial. It can be seen that compared with the Hybrid bit, the use of 3-RDE PDC bit can increase the ROP by 10.9%-21.0%, along with the drilling footage increased by 1.91-2.41 time [24]. Moreover, the tripped-out bit has not been damaged and new enough to be re-tripped in, while the Hybrid bit has been worn and damaged at the shoulder (Figure 21a,b). Additionally, the application of 3-RDE PDC bit saves a Hybrid bit and times on trips.  
Figure 20b illustrates the uses of 3-RDE PDC bit and conventional PDC bit with the diameter 152.4 mm for drilling the migmatitic granite formation in the second trial. Field data reveals that in a comparison of conventional PDC bit, the 3-RDE PDC bit has a 60.3% increase in ROP and a 1.2-fold increase in drilling footage, with a higher wear resistance and impact resistance (Figure 21c,d).  
Figure 19: Response of tangential force in heterogeneous formations: (**a**) The sandstone formation with a high content of gravels; (**b**) The soft-hard staggered formation.  
Figure 20: Application of (**a**) 3-RDE PDC bit and Hybrid bit in gravel formation; (**b**) 3-RDE PDC bit and Hybrid bit in migmatitic granite formation.  
Field trials show that in the deep formations, both in comparison with the conventional PDC bit and the Hybrid bit, the 3-RDE PDC bit achieves a better performance in drilling footage and ROP, showing a stronger drilling capacity, higher wear resistance and impact resistance. The uses of 3-RDE PDC bit can enhance the drilling efficiency and save costs.
## 4 Analysis of the Dynamic Response Characteristics of Tunnel Lining
### Acceleration Response Analysis of Tunnel Lining with Vibration Load  
Figure 14 shows the acceleration time curve of the axial measurement points in the tunnel structure. Table 4 displays the peak values of acceleration of the axial measurement points of the tunnel. From Figure 14, it can be seen that there are significant differences in the peak value of the acceleration at different measurement points. The farther away the measuring point is from the vibration source, the smaller the acceleration response is. The peak acceleration of test point A1 is 3.11 m/s\({}^{2}\), a 55.6% increase over that when the base does not have a void. This indicates that the base voids significantly amplify the vibration response of the structure in the void region. Points A2-A4 are outside the base void range, and the peak acceleration growth rate is significantly smaller than point A1. The farther away from the vibration source the location is, the lower the acceleration peak growth rate is.  
Figure 14: Acceleration time course curve of the tunnel axial measuring points: (**a**) no base void; (**b**) with base void.  
Figure 13: Simulated train load.  
Figure 15 shows the peak acceleration distribution of each measuring point of the tunnel invert. The blue line is the peak acceleration of each measuring point when the base does not have a void, and the red line is the peak acceleration of each measuring point when the base has a void. Because point A1 is closest to the vibration source, the peak value of the acceleration is the largest. The distance between the other points and the vibration source increases, and the acceleration gradually decreases. When there is a void in the tunnel base, the peak acceleration of each measuring point increases. A1, A11, and A12 are within the range of the base void, and the acceleration growth is the most obvious, increasing by 55.6%, 53.7%, and 54.5%, respectively. The farther away the location is from the base void area, the smaller the peak acceleration increase in the measurement points is. The furthest test point A18 shows an increase of only approximately 9.4% in peak acceleration over that when the base does not have a void.  
The decay rates of the peak acceleration of the tunnel invert measuring points are slightly slower than that of the axial measuring points in the tunnel. The main reason for this may be that the circumferential construction joints in the tunnel reduces the conduction efficiency of the vibration wave in the structure.
# A Comprehensive Analysis of Real-Time Car Safety Belt Detection Using the YOLOv7 Algorithm
## 4 Performance and Experimental Results
### Analysis of the Trained YOLO Model Based on Real-Time Experiments
#### 4.2.1 Experiment 1: Concept Testing  
The underlying premise of our study necessitates the installation of a camera on the windshield with appropriate adjustments to ensure the visibility of the safety belt pillar loop. The detection system is designed to categorize the safety belt status as \"unbuckled\" if the pillar loop remains invisible, and conversely as \"buckled\" if the person is securely fastened and the pillar loop is visible. Additionally, in cases where an individual secures the safety belt behind their back but the pillar loop is visible, the system must correctly identify the status as \"unbuckled\". Consequently, a buzzer is activated whenever the safety belt status is detected as unbuckled. The choice of using the pillar loop is motivated by unforeseen scenarios where occupants could be wearing clothing with stripes that are similar to a safety belt or when a person is carrying a bag that has webbing that looks like a safety belt. In both of these cases, if a pillar loop is not used as the determining factor, the model would detect the status of a safety belt as buckled even though people might not necessarily be buckled.  
The pillar loop approach was validated in four cases, as shown in Figure 14:  
1. When a vehicle occupant buckled behind their back and the pillar loop of the safety belt was visible.
2. When two vehicle occupants wore their seat belts and their pillar loops were visible.
3. When two vehicle occupants were unbuckled and their pillar loops were visible.  
Figure 13: The setup of all the experiments that were conducted inside the vehicle.  
4. A detection status when one vehicle occupant was buckled but had an invisible pillar, and a detection status when the other occupant was buckled with a visible pillar loop.
# A Deep Learning Approach for Maximum Activity Links in D2D Communications
## 5 Deep Learning-Based Link Scheduling
### Deep Belief Network  
A deep learning framework is applied in the lower-levels of unsupervised learning networks [43]. We adopted the deep belief network (DBN) method to solve our link scheduling problem [44]. A DBN is a neural network composed of multiple layers of restricted Boltzmann stack machines (RBMs). An RBM consists of two kinds of layers. One is the visible layer for inputting samples. The other one, named the hidden layer, is used for extracting features. In an RBM, there is no connection in the same layer, and each visible layer is connected to a hidden layer via symmetric weights. Since the weights are all symmetric, an RBM can not only infer the state of neurons using hidden layers, it can also use them to reconstruct the input values. Since an RBM is not sufficient to extract complex information from the input, a DBN was adopted in the paper. A DBN consists of a visible layer as a bottom layer and other hidden layers, and the training process is mainly divided into two steps, as shown in Figure 3. Firstly, the layer-wise training strategy is adopted for an RBM. The input vector is used to train the hidden layer, and the output of the hidden layer is regarded as the input data vector for the higher layer. Secondly, the output layer of the DBN sets up the BP network, which receives the output vector from an RBM as its input vector and trains the classifier under supervised learning. Each layer can only guarantee the optimal output feature vector by adjusting weights, but it cannot make sure that the final output of a DBN is the optimal value. The DBN can use the difference between the output value and labeled value from the backpropagation network for the fine-tuning of the whole network. The training process of a DBN network can be used as the initialization of the weight parameter of a deep BP network, which makes the DBN overcome the shortcomings of the random initialization weight parameters that makes the BP network easily fall into the low optimum values and spend a long time training.  
Figure 2: Learning structure.  
indicates that the D2D pair is active. The evaluation of the output vector is used to tune the learning model weights by using the BP method.
## 4 Discussion and Conclusions
### Implications for Practice  
From the perspectives of technical function and instructional design, we propose the following implication for designing and implementing AR-supported professional training. Turning first to the technical functions of AR, the human-computer interaction mechanism can be further improved to allow more natural input for AR so that the perceived authenticity and ease of use can be further enhanced. Furthermore, we recommend increased wearable equipment to be used as a computing device of AR for improved portability and flexibility, allowing trainees to freely move in workplaces in order to operate and practice without restraint. Lastly, the co-presence of multiple media representations is a preferable AR display for professional training. Particularly, digital text overlay proves to be highly effective when used alone or in combination with other media types, despite its relatively low development cost.  
In terms of instructional design, instructors should not be limited to one single pedagogy when facilitating AR-supported professional training. Despite the popularity of trial-and-error and experiential learning, flexible usage of multiple strategies seems to yield the best instructional effect. Moreover, we recommend using AR mainly for content delivery rather than hands-on practice in professional training. While many researchers have explored the instructional function of AR for practice, its effect has been unsubstantial. One possible reason is that the current AR functionality cannot afford authentic practice simulation, and the development of highly interactive AR for practice activity would further increase training costs.
## 3 Histogram-Based Reversible Data Hiding with Quadtree Decomposition
### The Three-Round Extraction Process  
The goal for the extraction process is to recover both the original image **X** and the user-defined information \(S\) at the decoder, with the two-byte side information \(a^{\prime}\) and \(c^{\prime}\). These are the reverse operations to the embedding process, and they can be outlined as follows.  
**Round 1.**: Perform histogram-based reversible data extraction on **X\"** with the side information \(K\). Reconstruct the block map **B** and obtain the luminance value of the max point for each block, \(a_{i}\).
**Round 2.**: Reconstruct the image **X'**, which denote the original image **X** with the user-defined information \(S\).
**Round 3.**: Generate the user-defined information \(S\) and the original image **X** with the aid of **B** and the conventional histogram-based reversible data extraction.  
By following the descriptions above, we can effectively hide the user-defined information \(S\) into the cover image **X** with high capacity and little overhead. At the decoder, after the reconstruction of the block map associated with quadtree decomposition, both the user-defined information and the cover  
Figure 5: Depiction of 8 \(\times\) 16 image with three-level quadtree decomposition.  
image can be obtained. For verifying the reversibility of our algorithm, both the inputs to the encoder and the outputs to the decoder are compared by calculating the mean square error (MSE) of images and the bit-error rate (BER) between embedded and extracted information. When both the MSE and the BER values are 0.0, the image and the user-defined information at the encoder in Figure 1(a) and at the decoder in Figure 1(b) are identical, and it implies that the reversibility of the algorithm is verified.
## 4 Discussion
The results reported here, where no relationship was observed between the percentage of affinity to xylene and the biomass of biofilms, suggest that biofilm formation in _Listeria_ spp. is regulated by factors other than hydrophobicity, depending on the strain. This conclusion is corroborated by the fact that some strains (LM29 and LI) presented high values for hydrophobicity but sparse biofilm formation on both the hydrophilic (glass) and the hydrophobic (polystyrene and graphene) surfaces. In contrast, LM26 presented low hydrophobicity together with a high capacity to form biofilm on all four surfaces, and markedly so on polystyrene. Along these lines, [PERSON] et al. [44] stated that biofilm formation by different strains of _Listeria_ spp. on glass was weakly correlated with the hydrophobicity of bacterial surfaces, coinciding with the results obtained in the present study. Likewise, previousresearch [30] has found weak hydrophobicity but extensive surface coverage in biofilms formed by strains of _Escherichia coli_ on polystyrene. However, comparisons between reports should be made with caution because the hydrophobic surface properties of bacterial cells can be modified by several factors such as the characteristics of the culture medium, growth conditions and the study methods used [48; 52]. Importantly, the biofilms with the smallest biovolume were produced on graphene surfaces. These results suggest that graphene monolayer coatings may contain surface properties that reduce the formation of biofilm, as has been suggested by other authors [21; 53].
# An Operations Chain Model for Automatic Assessment of Operation Procedure for Equipment Operators
## 3 Scoring by the Needleman-Wunsch Algorithm  
One or more standard operation sequences can be derived from the operations chain model for a specific operation task. The operator is considered to have completed the task along any standard operation sequence. Therefore, the actual operation sequence will be compared to each of the standard operation sequences. The best comparison result can be used as the operator's score.  
The Needleman-Wunsch algorithm [11], proposed by [PERSON] and [PERSON] in 1970, is a global sequence alignment algorithm. This algorithm requires substantial computation and comparison, resulting in high space and time complexity. The space and time complexity are both O (\(M\)\(\times\)\(N\)), where M and N are the two sequence lengths, respectively. Consequently, it becomes very time-consuming when the data sequence is long [12; 13]. However, since the operation sequence corresponding to device operations is relatively short compared to the gene sequence, the computational cost required by this algorithm need not be paid special attention.  
The basic principle of this algorithm is as follows: firstly, using the idea of dynamic programming to align and score the elements in two different operation sequences, constituting a scoring matrix; secondly, using backtracking technology to backtrack the score matrix to obtain an optimal path, which is the optimal comparison result; finally, calculating the similarity score for the two sequences to present the trainee's operation procedure score based on the comparison result.  
To illustrate how the algorithm is applied to the alignment of operation sequences, assume that the sequence of operations operated by a trainee is \(T\), and one of the standard sequences of operations is \(S\). The length of the sequence \(S\) is \(M\) and the length of the sequence \(T\) is \(N\). The details of the two sequences are shown in Table 2.
# Detecting Social Media Bots with Variational AutoEncoder and k-Nearest Neighbor
###### Abstract  
Malicious social media bots are disseminators of malicious information on social networks and seriously affect information security and the network environment. Efficient and reliable classification of social media bots is crucial for detecting information manipulation in social networks. Aiming to correct the defects of high-cost labeling and unbalanced positive and negative samples in the existing methods of social media bot detection, and to reduce the training of abnormal samples in the model, we propose an anomaly detection framework based on a combination of a Variational AutoEncoder and an anomaly detection algorithm. The purpose is to use Variational AutoEncoder to automatically encode and decode sample features. The normal sample features are more similar to the initial features after decoding; however, there is a difference between the abnormal samples and the initial features. The decoding representation and the original features are combined, and then the anomaly detection method is used for detection. The results show that the area under the curve of the proposed model for identifying social media bots reaches 98% through the experiments on public datasets, which can effectively distinguish bots from common users and further verify the performance of the proposed model.  
social networks; social media bot detection; anomaly detection; Variational AutoEncoder +
Footnote †: journal:detection has become a research focus in recent years and is a challenging and meaningful task. Social media bot detection aims to distinguish between bots and people in social networks. The number of bots is far less than the number of people in the real world, which leads to an imbalance in the training data, and the positive and negative sample ratio leads to a lack of credibility in the final results.  
Therefore, this paper proposes a social media bot detection method based on Variational AutoEncoder (VAE) [6] and the anomaly detection algorithm. The main contributions of this work are in the following aspects.  
1. Firstly, VAE is used to encode and decode the sample features. The features of normal samples are more similar to the initial features after decoding, while the features of abnormal samples are different from the initial features.
2. The original features and decoded features are fused, and then the anomaly detection method is used.
3. Our method considers that the number of abnormal users is lower than that of normal users in the social network environment, and it is difficult to separate the abnormal users in the process of data collection. Our method addresses the shortcomings of high labeling costs and unbalanced positive and negative samples in the existing methods for the detection of social media bots. By reducing the number of abnormal samples that participate in the model training, we can realize the efficient detection of social media bots in social networks.