esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
1. The increase in burial depth reduces the deformation of the pipeline and the pressure on the pipe top under the action of vibration load, but when the burial depth exceeds 1.8 m, the increase in burial depth contributes little to the reduction of the pressure on the pipe top.
2. With the increase of compaction times, the backfill soil keeps changing between over-compaction (loose) and compaction states, and the pipe top pressure also keeps changing. Moreover, at a shallow burial, the pipe top pressure is more obvious when the compaction degree changes.
3. The strain at the top of the pipe is mainly axial strain, while at the side of the pipe, circumferential strain is greater than axial strain. In addition, the strain values at the side of the pipe are all greater than those at the top of the pipe. The axial strain at the pipe top is greatly affected by the adjacent parts, so the trend of its change is different from the pipe pressure with the increase of compaction times.
4. Under earth pressure, with the increase of buried depth, the soil arching effect at the top of the pipeline becomes more obvious. The calculation results of soil pressure at the top of the pipeline obtained by Chinese specification are quite different from the finite element results, and with an increasing buried depth of the pipeline, the difference between them increases.  
Figure 21: The displacement and soil pressure at the top of the pipeline under the strong vibration mode.  
Figure 22: The hoop strain of the pipeline under the strong vibration mode.  
Under the self-weight of backfill, with the increase of pipeline burial depth, due to the increase of side soil compaction with the increase of burial depth, the increment rate of lateral deformation of the pipeline is less than that of vertical deformation. At the same time, the increase of pipeline burial depth also increases the compaction of bottom backfill, so the increment rate of pipe top displacement decreases with the increase of buried depth. (6) Under earth pressure, the elastic modulus of the filling soil has a significant influence on the soil arch effect, and they are positively correlated. However, the increase of elastic modulus of the backfill weakens the attenuation of vibration load in soil. Under the comprehensive effect, the elastic modulus of the backfill within a certain range can effectively reduce the stress deformation of the pipeline under vibration compaction. However, if the elastic modulus of the backfill exceeds 10 times the initial elastic modulus, the deformation of the pipeline will be greater than the initial value.  
Writing--original draft preparation, C.M.; writing--review and editing, X.W.; formal analysis, J.Z.; software, H.L.; methodology, Y.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data is unavailable due to privacy restrictions.  
The authors declare no conflict of interest.
# Estimating Destination of Bus Trips Considering Trip Type Characteristics
## 2 Literature Review
### Methodology for Estimating Destination Based on Pattern Using Historical Data  
Studies on various techniques, including artificial intelligence (AI) and probability models, have been conducted to estimate the alighting stops for trips with missing alighting stop information, when the trip-chain-based alighting estimation is impossible because of lack of information on the next trip. Regarding analysis-based studies for the past travel history, a study analyzed the distribution of the boarding time of the trip for each passenger, and estimated the alighting stops by assuming that the usual boarding stop in a similar time band as the estimation-target trip was the origin, while the usual boarding stop in a different time band was the destination [19]; another study analyzed the past travel history of a similar card ID to estimate the alighting stop based on the similarity to the estimation-target trip [20]. Moreover, a study estimated alighting stops by applying a three-dimensional latent Dirichlet allocation model [21], and another study analyzed major trip origins, destinations, and transfer locations by spatially clustering the locations of the stops and estimated alighting stops [20]. Other studies estimated alighting stops by developing machine learning models and deep learning models [22; 23; 24; 25].
# FlexBFT: A Flexible and Effective Optimistic Asynchronous BFT Protocol
## 4 FlexBFT Design
### Fast Mode Protocol
snw-ABC first outputs \(\mathcal{H}(TXs)\) and the aggregated threshold signature as BAP to the buffer and proceeds to download the complete transactions after the function \(retrieve\) is invoked. Block downloading commences in parallel immediately after the output of \(\mathcal{H}(TXs)\). The next stage of the fast mode protocol involves using ABA to complement snw-ABC, as shown in Algorithm 2, which lacks agreement properties, achieving pace synchronization. Due to snw-ABC's notarizability, it can be assumed that the consensus progress of correct nodes is mostly in the state of \((s,s-1)\) or \((s+1,s)\), where \(s\) represents the slot number. Thus, achieving agreement among correct nodes only requires agreeing on one of two consecutive natural numbers. We employ ABA to achieve consensus on the maximum slot number. For each node, ABA takes the input max(slot number)%2. Subsequently, based on the output of ABA, the current consensus progress \(syncPace_{e}\) for correct nodes is determined. Since pace synchronization may result in a consensus progress rollback, the protocol introduces a pending buffer. The fast mode protocol remains pending as TXs ready to be committed to the log. During the pace synchronization phase, based on the return value of ABA, two cases exist:  
- If \(syncCee=p_{e}\), it indicates that the TXs in pending status are the latest and should be committed directly. - If \(syncCee\leq p_{e}+1\), it means that for that node, all blocks have not been entirely retrieved. In this case, nodes must retrieve the chunk root of the \(p_{e}\) block, which may have already reached consensus through AVID, or even copy it from the log of correct nodes. Subsequently, they commit it to their blocks. Malicious actions by the leader, snw-ABC timeouts, or censorship threats trigger a transition to pace synchronization. Pace synchronization can handle most of the issues in a partially synchronous Byzantine environment and aims to keep the protocol in the fast mode protocol as much as possible. However, it can still encounter fully asynchronous network problems, in which case, the fast mode protocol loses liveness. Pace synchronization identifies that snw-ABC has made no progress through ABA and then enters back-up mode until returning to fast mode.
## 5 Discussion
### Comparison of LEACH, A-LEACH, and LEACH DCS  
The stability period is indicated by calculating the number of rounds until the network's first node dies out. It is an important parameter to predict how stable the protocol is. Lifetime and stability period analysis for 100 nodes deployed over 100 \(\times\) 100 m\({}^{2}\) with an initial energy of 5 J shows LEACH-MAC outperforms LEACH, A-LEACH, and LEACH DCS, as indicated in Figure 10.  
Figure 10: Lifetime metrics comparison for BS location (50, 175).
###### Abstract  
The movable super-diffraction optical needle (MSON) is a tightly focused beam like a \"needle\", which can realize vector scanning on the focusing plane. Not only does it have a long focal depth, but its resolution also exceeds the diffraction limit. The modulation and control technology required for generating MSON by oblique incidence is explored in this manuscript for the purpose of processing high-aspect-ratio, sub-wavelength structures. As the optical needle generated by traditional methods is static and sensitive to variation of the angle information of the incident beam, here we introduce a confocal scanning system by using a two-dimensional galvanometer system, a scan lens, and a tube lens to control the oblique incidence angle. The effects of the oblique incidence angle on the resolution, depth of focus, uniformity, and side lobes of the MSON were analyzed. Further, the voltage-controlled liquid crystal located between the scan lens and the 2D galvanometer system can be used to compensate for the additional phase difference caused by oblique incidence. The aspect ratio is defined as the ratio of depth of focus to resolution. By modulating and controlling the light field, the MSON with high aspect ratio (7.36), sub-diffractive beam size (\(0.42\lambda\)), and long depth of focus (\(3.09\lambda\)) has been obtained with homogeneous intensity, and suppressed side lobes. High speed, high axial positioning tolerance, and high-resolution laser processing can also be achieved, which removes the restrictions presented by traditional laser processing technology, for which high resolution and long depth of focus cannot be achieved simultaneously.  
optical needle; resolution; depth of focus; high aspect ratio; movable +
Footnote †: journal: Applied Sciences
# Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks
## 3 Surface Inspection Process
### Contour Detection  
In this part, we fitted the outer contour of the end surfaces of the roller by using the Hough transform [25]. The pipeline can be seen from Figure 6 below.  
We performed the Hough circle detection 10 times for each end surface, and then took the average of the radius and the average of the center coordinates as the actual radius and center coordinates of the outer contour of the end surface. Then we used the Canny algorithm to extract the outer contour and calculated the standard deviation of the distance between the actual center coordinates and all points on the contour. The formulas were as follows:  
\[\begin{split} x_{s}=\sum\limits_{i=1}^{10}x_{si}\\ y_{s}=\sum\limits_{i=1}^{10}y_{si}\\ r_{s}=\sum\limits_{i=1}^{10}r_{si}\\ std=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}\left(d_{j}-r_{s} \right)^{2}}\end{split} \tag{1}\]  
where \((x_{si},y_{si})\) and \(r_{si}\) are the center coordinates and the radius of the \(i\)-th circle detected by Hough circle detection. \((x_{s},y_{s})\) and \(r_{s}\) are the actual center coordinates and the actual radius of the outer contour of  
Figure 5: Surface inspection process. CNN: Convolutional Neural Networks; ROI: Region of Interest.  
Figure 6: Contour detection process.  
the end surface, \(d_{j}\) is the distance between the \(j\)-th point on the contour and the coordinate (\(x_{s}\),\(y_{s}\)), and \(std\) is the standard deviation of \(d\).  
If the \(std\) was less than the set threshold (set to 0.4 by experiment), it meant that the outer contour of the current end surface was a circle, and the sample would be sent into the shared convolutional neural network to extract a feature map for defect classification and localization. On the contrary, if there was a defect at the contour of the end surface, and the outer contour was not a circle, then the bearing roller would be judged as unqualified.
## 4 Optical Methods
### Preliminary Operations
#### 4.1.2 Filtering  
A smoothing procedure was applied to each frame in order to reduce noise. For this purpose, a two-dimensional (2D) Gaussian filter (i.e., the MATLAB _ingaussfilt_ built-in function) with a standard deviation of \(\sigma\) = 1.5 was used. This value, which belongs to a typical range that ensures effective Gaussian filtering [47], was found to be a good compromise between noise reduction and boundary conservation.
###### Abstract  
Because wireless sensor networks (WSNs) have been widely used in recent years, how to reduce their energy consumption and interference has become a major issue. Topology control is a common and effective approach to improve network performance, such as reducing the energy consumption and network interference, improving the network connectivity, etc. Many topology control algorithms reduce network interference by dynamically adjusting the node transmission range. However, reducing the network interference by adjusting the transmission range is probabilistic. Therefore, in this paper, we analyze the probability of interference-optimality for the WSNs and prove that the probability of interference-optimality increases with the increasing of the original transmission range. Under a specific transmission range, the probability reaches the maximum value when the transmission range is 0.85\(r\) in homogeneous networks and 0.84\(r\) in heterogeneous networks. In addition, we also prove that when the network is energy-efficient, the network is also interference-optimal with probability 1 both in the homogeneous and heterogeneous networks.  
Keywords:probability; interference-optimal; energy-efficient; topology control; wireless sensor networks +
Footnote †: journal: Applied Sciences
# Theoretical Design of a Novel Vibration Energy Absorbing Mechanism for Cables
## 3 Theoretical Modeling of the VEAM  
The VEAM is composed of six identical damper assemblies, where each pair of opposing damper assemblies is connected through a hydraulic circuit to form a sub-system. Thus, each sub-system is composed of two magnetic non-linear stiffness modules and two hydraulic viscous damping modules. The isolation and damping performance of the VEAM can be evaluated by calculating its transmissibility [51]. The harmonic balance method (HBM) is an efficient method for modeling periodic problems and it is widely used to derive transmissibility [41]. Therefore, in this paper, HBM is used to establish theoretical models for the non-linear stiffness and the viscous damping modules. In this section, a mathematical model for the mechanism of a subsystem of VEAM in a single degree of freedom is established.
# A Review of the Control Plane Scalability Approaches in Software Defined Networking
## 5 Conclusions  
SDN is a predominant paradigm for many networking environments such as data centers, enterprise, cloud networks. The major advantages of SDN are enabling efficient network administration, allowing its programmability and agility and achieving abstraction of lower-level functionality. This is due to decoupling the data plane from the control plane. Although SDN has many benefits regarding network and flow management, it still has many limitations on both performance and scalability. Control plane scalability in SDN is one of the most significant challenges. This paper sheds the light on SDN control plane scalability problem. It begins by providing a classification taxonomy of the existing approaches devoted to achieve higher performance in the control plane. An overview of these approaches is also provided assuming that the scalability challenges of the control plane can be tackled by using methods such as--reducing amount of requests to improve scalability of a single controller or distributing the traffic load between multiple controllers; other methods that look at the problem from different perspective employ various optimization mechanisms including--parallelism-based optimization, routing scheme optimization and more recently, machine learning based optimization. An assessment of the reviewed approaches performance in terms of flow setup latency and throughput is provided. Additionally, open research issues related to control plane scalability were also identified.  
Security was common concern among all the reviewed SDN approaches; hence, in future work, we plan to investigate the resilience of various control plane systems against a variety of security attacks [41; 42]. In particular, investigating how to leverage SND features to secure the Internet of Things [43; 44] presents an opportunity that is of great interest to both research communities. Finally, communication and synchronization between controllers in large-scale systems remains a central area of research in the SDN community.  
This research received no external funding.  
The author is grateful to the Middle East University, Amman, Jordan for the financial support granted to cover the publication fee of this research article.  
The author declares no conflict of interest.
## 4 Experiments and Analysis
### Experimental Design  
In order to verify the performance of the Chinese medical entity recognition model proposed in this paper based on the improved Tri-Training algorithm in related tasks, in this paper, four groups of experiments are selected to compare the performance of the model by comparing the supervised learning model, the original Tri-Training algorithm model, and the improved Tri-Training algorithm in different aspects.  
(1) Model 1: Supervised Hybrid Deep Learning Model BERT-IDCNN-BiLSTM-CRF Model (BIBC).  
(2) Model 2: The deep neural network model used by the three base classifiers of the original Tri-Training algorithm is the BERT-IDCNN-BiLSTM-CRF model (BIBC).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Medical Entities** & **Test Set** & **Training Set** \\ \hline Disease and Diagnosis & 2116 & 682 \\ Imaging Examination & 222 & 91 \\ Laboratory Test & 318 & 193 \\ Operation & 765 & 140 \\ Drug & 456 & 263 \\ Anatomy & 1486 & 447 \\ Total & 5363 & 1816 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics on the number of different types of entities (unit %).  
Figure 3: Example diagram of data annotated by BIO. (The example sentences: Cardiac ultrasound indicates right ventricular and biventricular enlargement. “B-IMA” indicates that the element is in a segment of the entity type Imaging Examination, and that the element is at the beginning of the segment. “I-IMA” means that the element is in a segment of entity type Imaging Examination and the element is in the middle of the segment. “O” indicates that the element does not belong to any type.)  
(3) Model 3: The deep neural network models used in the three base classifiers of the improved Tri-Training algorithm are all BERT-IDCNN-BiLSTM-CRF models (BIBC).  
(4) Model 4: The improved Tri-Training algorithm proposed in this paper, in which the deep neural network models used in the three base classifiers are the BERT-IDCNN-CRF model (BIC), BERT-BiLSTM-CRF model (BBC), and BERT-IDCNN-BiLSTM-CRF model (BIBC).
###### Abstract  
The proton exchange membrane fuel cell as a green power source is a suitable replacement of the engine mounted generators in the emergency power unit of a more-electric aircraft. Most existing energy management methods for operation of fuel cells in the more-electric aircraft refer to the hydrogen consumption minimization. But due to the increasing number of electrical components and hence electrical demand in the aircraft, demand-side management should be considered in these methods. In order to determine the effect of demand-side management on the fuel cell operation and size, an efficient load priority model is presented and integrated into an optimization framework. The proposed optimization framework is formulated as mixed-integer quadratic programming using Karush-Kuhn-Tucker optimality condition and is solved by CPLEX optimization tool. The Boeing 787 electrical distribution system is considered as a single-bus case study to evaluate the performance of the proposed optimization framework. Numerical results show that the size of fuel cell as an emergency power unit resource depends on the type and importance of the system's loads in different emergency conditions. Also, with an efficient priority model, both hydrogen consumption and load shedding can be decreased.  
demand-side management; fuel cell; Karush-Kuhn-Tucker optimality condition; more-electric aircraft; mixed-integer quadratic programming Article
# Review of Ka-Band Power Amplifier
## 3 Topologies
### Power Combining  
For some occasions requiring high power, such as space communication and base stations, the power amplifier of single power cell is often unable to meet the demand, so it is necessary to adopt power synthesis technology [65]. The general power combining topology is shown in Figure 13.  
The input signal is divided into N signals through the power distribution network, and each path can be equally distributed or non-evenly divided. The power distribution network can be a binary cascade network (binary power synthesis network) or non-binary power splitter (non-binary power synthesis network). The input signal is amplified by an amplifier and then combined with the output through a power synthesis network.  
In the binary power synthesis network, Wilkinson synthesizer [66], 90\({}^{\circ}\) orthogonal branch line [67] and rat-race hybrid ring [68] can be used as power synthesizers. In the non-binary power synthesis network, microstrip lines [69], coaxial lines [70] and waveguide couplers [71] can be adopted in the power synthesizer. Power combination technology can also be divided into corporate combiner, balanced amplifier and differential amplifier.
## 3 Results and Discussion
### Determination of Total Flavonoid (TFC) and Total Anthocyantin Content (TA)  
The TPC in P1E, P2E, and P3E was determined at 430 nm using the reagent aluminum trichloride (AlCl\({}_{3}\)) technique, and the findings were calculated in mg of quercetin equivalent per one gram of extract (mg QE/g). Figure 2 shows that the TPC in P1E, P2E, and P3E is as follows 4.47 \(\pm\) 0.30, 4.44 \(\pm\) 0.286, 4.72 \(\pm\) 0.346 mg QE/g of dry extract, respectively. The variation in total flavonoid between the three samples is not significant (_p_ > 0.05). Comparing our obtained TPC values with previous literature, in Morocco, [PERSON] et al.  
Figure 1: Polyphenol content in the P1E, P2E, and PE3; GAE: gallic acid equivalents; a and b—values with a significant difference (_p_ < 0.05).  
found that the TFC in methanolic flowers extract is 8.67 mg QE/g [39]. The TFC in the extract of fresh petals of _P. rhoas_ is 9.07 mg QE/g [41], while in the _P. rhoas_ basal leaves' extract is 12 QE mg/g [42]. The total flavonoid content in these works are relatively higher than our obtained values. Moreover, the values obtained of TFC in the three studied extracts of the whole plant are relatively similar to those obtained in the extracts of four organs (flower, leaf, stem, and root) of _P. rhoas_: 4.50, 4.39, 4.49, and 4.38 mg QE/g, respectively [33]. In this regard, a research from 2004 found that the methanolic extracts included the kaempfer flavonoids, luteolin, hypolaetin, and quercetin as well as the glycosides, flavonoid, hyperoside, astragalin, and isoquercitrin [18]. Further research revealed that the hydro-alcoholic extracts included vitexin, rutin, coumarin, malvidin, and luteolinidin [14].  
The TA in the studied extracts PE1, PE2, and PE3 was highly unified 1.71 \(\pm\) 0.008, 1.66 \(\pm\) 0.070, 1.77 \(\pm\) 0.026 mg CGE/g extract, respectively (Figure 3). The difference in TA values in the three samples was statistically not significant (_p_ > 0.05). Comparing our TA values with previous studies, a Serbian study showed that the total content of monomeric anthocyanins in all studied extracts ranged from 4.72 to 5.3 mg CGE/g of fresh petals. The values of total monomeric anthocyanin content do not differ significantly in ethanolic, aqueous, and methanolic extracts [41].  
Variability in plant metabolites may be caused by environmental factors (geographical location, cultivar genotype, soil type, etc.) and experimental variables (period of harvest of the plant studied, the part of the plant used, the polarity of the solvent used, and the extraction method used) [51; 52].