esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Stability Parameters for Wide Body Aircraft Using Computational Techniques
## 4 Comparison of Results  
Results obtained from both pieces of software are presented in terms of dimensionless aerodynamic coefficients for both longitudinal and lateral stability at low-cruise-flight conditions. To validate the results expressed in terms of coefficients obtained from both the commercial packages, these were compared with the analytical results available in the literature [62]. The comparison of results shows that results obtained from computational techniques are quite close to each other and the analytical results. However, a small difference has been found in some of the coefficients. The comparison of results for both longitudinal and lateral coefficients obtained from both pieces of software with the results available in the literature is summarized in Tables A1 and A2 of Appendix A. The comparison of results shows that longitudinal coefficients obtained from DATCOM are quite close to the analytical results; however, there is some difference in longitudinal and lateral values. On the other hand, the XFLR results are very close to the analytical results in both the cases. An old version of US DATCOM is available, and it was used during the project. It has limitations for some input parameters and Mach number in some of the analysis.
# Uncertainty Theory-Based Resilience Analysis for LEO Satellite Communication Systems
## 2 Preliminaries  
In this section, we introduce some important concepts of the uncertainty and chance measures.
## 2 System Model
### The Hard-Delay Constraint  
In this real-time transmission system, the delay of data packets in the system consists of two components: the queueing delay of the packet queue and the transmission delay of the symbol queue. The maximum waiting time of a data packet in the system, defined as \(W\), is given by  
\[W=W_{q_{1}}+W_{q_{2}}, \tag{12}\]where \(W_{q_{1}}\) and \(W_{q_{2}}\) represent the maximum queueing delay in the packet queue and the maximum transmission delay in the symbol queue, respectively. To satisfy the hard delay constraint, it is necessary to limit the waiting time of data packets within the system. This indicates that the waiting time of the data packets in both the packet queue and the symbol queue should be limited. In the following, we separately consider the maximum waiting time of the data packets in these two queues.  
In light of Equation (7), the hard delay constraint in the packet queue is characterized by an integer \(K\). Notably, \(K\) represents the maximal number of time slots that a packet is allowed to be backlogged in the packet queue, with \(K\geq 1\). The nature of the deterministic packet arrival process leads us to the fact that if \(\left(q_{1}[t]-s_{1}[t]\right)^{+}+1>K\), it indicates that the earliest \(\left(q_{1}[t]-s_{1}[t]\right)^{+}+1-K\) packets have been in the queue for more than \(K\) time slots. Consequently, these initial \(\left(q_{1}[t]-s_{1}[t]\right)^{+}+1-K\) packets violate the hard delay constraint and should be dropped. In real-time communication systems, it is necessary to design policies to ensure that all data packets are transmitted within their designated time limits to achieve a packet loss rate of zero.  
The delay-violation probability is defined as the proportion of data packets that violate the hard delay constraint. We use \(\xi\) to denote the delay-violation probability, which is given by  
\[\xi=\lim_{N\rightarrow+\infty}\frac{1}{N}\sum_{t=1}^{N}\max\{\left(q_{1}[t] -s_{1}[t]\right)^{+}+1-K,0\}. \tag{13}\]  
As long as the packet queue does not exceed the maximum queue length, the symbol queue will necessarily have the maximum length, according to Equations (8)-(10). Therefore, as long as the overflow of the packet queue is constrained, this tandem queuing system will not experience overflow. Then, we do not need to incorporate the symbol dropping mechanism. Therefore, the condition \(\xi=0\) implies that all packets obey the hard delay constraint.  
For the symbol queue, it is also necessary to restrict the maximum waiting time of the symbol blocks. In the next section, we will provide a detailed explanation of how to limit the maximum waiting time in the symbol queue using embedded Markov chains.  
Our aim is to obtain a variable-length coding policy that minimizes the transmission power while ensuring the hard delay constraint. The explicit design of our variable-length coding policy is addressed in the next section.
## 4 Conclusions  
The laboratory uniaxial compression test was conducted accompanied by DIC monitoring and AE positioning monitoring. As the inclination angle of preset twin flaws increases, the distribution and development characteristics of surface cracks and internal local failures illustrate different characteristics, and the main conclusions are as follows:  
1. The inclination angle of preset twin flaws gradually changes the intensity of the final failure of the sample, surface strain field, and distribution characteristics of secondary cracks. In addition, the internal 3D flaws show little influence on the distributions of the maximum principal strain fields and visible cracks on the sample surface, which are mainly controlled by the 2D preset flaws. The above results are consistent with previous similar test results.
2. The gradient inclination angle of preset twin flaws shows a slight influence on the distribution of AE events before reaching the UCS status of samples, while the destructiveness of the middle rock between the parallel flaws declines gradually as the flaw inclination angle increases. Furthermore, as the flaw inclination angle increases, the central position of the internal free surfaces (built by the internal 3D open-type preset flaw) is more prone to typical tensile failure, and the boundary position is more prone to shear failure.
3. Based on the acoustic data monitored by the InSite-Lab system, the long-term accumulation of small-scale damage is observed during the majority of the test duration; there will be clear premonitory features in the b-values and S values before the formation of local failures; the final failures quickly form at various scales and energy levels, then the macroscale destruction dominates in subsequent periods.
4. When calculating the b-values, the two influencing factors (data window width and moving step length) have little impact on the evaluation results due to the instantaneous occurrence of massive AE signals; in addition, it is suggested to set the bin width of magnitude to 0.5 or 1.0 for obtaining more stable statistical results.
5. The laboratory test only focused on the failure characteristics of the fractured brittle rock under the uniaxial compression condition, which can only simulate an underground pillar; however, the surrounding rock of underground engineering is mostly in a biaxial or even true triaxial stress state. By applying the true triaxial compressive loading system at Fuzhou University, we will continue to prepare relevant samples (the true 3D preset flaws) in future work and expand the experiment to true triaxial conditions.  
Conceptualization and methodology, T.L.; formal analysis and writing, S.C.; review, B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Fujian, grant number 2022J01567.  
Not applicable.  
The data used to support the results of this study are included in the paper.  
The authors declare no conflict of interest.
# BERT- and BiLSTM-Based Sentiment Analysis of Online Chinese Buzzwords
## 3 Proposed Model
### Network Buzzwords Sentiment Analysis Model  
This paper adopts a combined prediction model, namely Bidirectional Encoder Representation of Bidirectional Long Short-Term Memory Transition (BERT-BiLSTM), to con  
Figure 5: BiLSTM network model.  
struct a sentiment analysis model for OCBs. It is worth noting that the BERT-BiLSTM proposed in this paper uses BERT as the upstream module and BiLSTM as the downstream module to fine-tune the BERT pre-trained model and then transfer it into BiLSTM for training. The BERT-BiLSTM OCBs structure is shown in Figure 6.  
As mentioned above, OCBs have some problems, such as language fragmentation, incomplete semantic structure, and irregular features, while BERT has a strong ability to learn the features of nearby words. In Figure 6, \(E_{1},E_{2},\cdots,E_{N}\) is the word input vector of the BERT layer, \(E_{[CLS]}\) is the buzzword start bit indicator, \(T_{0}\) is the output vector of the OCBs start symbol after BERT training, and \(T_{1},T_{2},\cdots,T_{N}\) is the word output vector of the BERT layer. Subsequently, a BiLSTM model is used to extract contextual features in the input sequence data, where \(\overset{\rightarrow}{h}  
vector as \(\overleftarrow{h}\) and finally combines the two for output vector \(v_{i}\), as shown in Figure 7. The calculation formula is as follows:  
\[v_{i}=\overrightarrow{h_{i}}+\overleftarrow{h_{i}} \tag{10}\]  
where \(\overrightarrow{h_{i}}\in R^{d_{h}}\)\(\overleftarrow{h_{i}}\in R^{d_{h}}\).  
In addition, we define the hidden layer of the model as follows:  
\[h_{i}^{d}=g_{2}\Big{(}W_{h}^{d}a_{i}+Uh_{i-1}^{d}+b_{h}^{d}\Big{)} \tag{11}\]  
where \(g_{2}\) is the Tanh function, \(W_{h}^{d}a_{i}\in R^{d_{h}\times d_{a}}\) is the weight matrix of \(a_{i}\), \(d\in\{0,1\}\) represents two different directions in the hidden layer, \(U\) is the weight matrix of the hidden layer output sequence \(h^{d}\) at moment \(i\). \(b_{i-1}^{d}\) corresponds hidden layer output sequence of the previous moment \(i-1\); \(b_{h}\) represents offset vector of d direction. Then, the output sequences \(h_{d}\) of all hidden layers are combined to obtain the feature vector \(H\) at the end of the sentence. Then, the feature vector \(H\) is transferred to the fully connected layer with the Relu function and the Softmax function to classify the emotional tendencies. The probability calculation formula of sentiment tendency classification is as follows:  
\[p(y|H,W_{s},b_{s})=\text{softmax}(W_{s}H+b_{s}) \tag{12}\]  
where \(W_{s}\in R^{|s|\times|I|}\) (\(|I|\) is the number of classes) and \(b_{s}\in R^{|I|}\) are the learnable parameters of the output layer.
## 3 Simulation Studies and Results
### Stationary Test of Time Series  
The smoothness of the sequence affects the performance of the LSTM model. As a result, we first perform a stationarity test on the time series [55] before modeling. The augmented Dickey-Fuller (ADF) test is an effective method [56]. For the 8 data sets shown in Table 1, the ADF test method was used to test the stationarity of the time series. The test results are shown in Table 6. It is glaringly apparent that the ADF test results of all data sets fall into the rejection domain, indicating that all sequences are stationary.
## 3 Experimental Validation of the Hydroelasticity Theory
### Comparison of Numerical and Experimental Spectra  
The frequency response spectra are usually used to describe the ship short-term responses in irregular waves. Comparisons of the frequency spectral results of ship heave, pitch and VBM amidships in the two afore-mentioned conditions are shown in Figures 7-9. The spectra are obtained by the auto-correlation function method with Hamming smooth processing based on the corresponding time series. As can be seen, the frequency distribution and peak value of the response spectra show good agreement between the numerical and experimental results in both the two conditions. The peak frequency of heave, pitch and VBM spectra increases from Condition 1 to Condition 2 due to the increasing wave encounter frequency. However, the fill area under the curve, which denotes variance decreases from Condition 1 to Condition 2 due to the decreasing wave state. The spectral density concentrates within the narrow range of 0.3-0.8 rad/s for heave and pitch motions. However, to be different from the motion spectra, the VBM spectra comprise both low frequency and high frequency components, which is consistent with the phenomena observed from the time histories in Figures 5 and 6. The frequency of whipping loads coincides with the two-node wetted natural frequency of the hull. Moreover, the slamming loads are more pronounced at the high-speed condition.  
Figure 6: Time series of motion and loads in Condition 2. **(left)** Numerical results; **(right)** experimental results.  
Figure 7: Comparison of heave spectra.
## 2 Maritime Cognitive Radio Communication Model
### Design of Service Flow for SUs and CBS  
In the maritime cognitive radio communication system, the demand of communication is different depending on the service. Therefore, in the CBS, it is necessary to classify the communication requests according to service type. The CBS needs to make different responses to different types of requests. When the PU uses the channel, it does not need to notify the CBS. This requires that the CBS needs to have the ability to perceive whether a PU is using the channel. If a PU and an SU use the channel at the same time, the CBS must inform the SU to suspend the current communication.  
As shown in Figure 2, when an SU makes a communication request, it will send a request packet that contains the SU's information to the CBS. The CBS receives the request, makes a decision, and then informs the SU of which channel the SU is allowed to use. After receiving the information from the CBS, the SU sends back status information, indicating that the SU is beginning to use the channel. When the SU has finished the communication, status information will also be sent to the CBS to indicate that the channel is not being used anymore. The CBS periodically senses the status information of SUs; the time period during which this occurs is called the sensing period.
# Cyclic Behaviors of Geopolymeric Recycled Brick Aggregate Concrete-Filled Steel Tubular Column
###### Abstract  
Incorporating geopolymeric recycled brick aggregate concrete into steel tubes provides a promising solution to reduce environmental impact of construction and demolition waste. In this paper, geopolymeric recycled brick aggregate concrete-filled steel tubular column (GRBACFST) was developed to improve the environmental sustainability of composite column. Considering the replacement ratio of recycled brick aggregate (RBA), the thickness of the steel tube, type of cementitious materials and the axial compression ratio as the variation parameters, experimental research was performed to explore the cyclic behavior of GRBACFST columns, including the failure mode, bearing capacity, hysteresis curve, ductility and degradation characteristics. Results demonstrated that the failure of GRBACFST columns occurred in the region at column bottom, with the bulge of steel tube and crush of geopolymeric recycled brick aggregate concrete. The proposed GRBACFST columns exhibited favorable hysteretic behaviors with desired bearing capacity, excellent ductility, and energy dissipation behavior, which were enhanced by the increased thickness of the steel tube. The bearing capacity and ductility were reduced with the increase of axial compression ratio, while enhanced with thicker steel tube. Moreover, the degradation of stiffness and strength was more obvious under larger axial compression ratio. The increase of replacement ratio of RBA caused a significant reduction of bearing capacity, while it had few effect on the hysteretic index. It was concluded that the hysteretic behavior of proposed GRBACFST column was not sensitive to the types of cementitious material and geopolymers could serve as an eco-friendly binder for concrete.  
geopolymeric recycled brick aggregate; cementitious materials; concrete-filled steel tube; cyclic behavior +
Footnote †: journal: Applied Sciences
## 1 Introduction  
The foot, as the distal part of the limb, supports the body weight, facilitates movement, and plays a critical role in locomotion. It consists of various anatomical structures, including bones, muscles, tendons, joints, ligaments, nerves, and blood vessels, and features three arch structures: the medial longitudinal arch (MLA), the lateral longitudinal arch, and the anterior transverse arch. These arches are crucial for shock absorption during activities such as walking and running [1]. In particular, the MLA is significant for forming the instep and primarily absorbing and dispersing shocks applied to the foot [2]. This arch facilitates proper lower-extremity function during the gait cycle and plays an essential role in shock absorption and propulsion during walking. Congenital deformities or acquired trauma affecting any element of the MLA can lead to varying degrees of clinical consequences [3].  
The feet, used daily and subject to various stressors, are prone to numerous diseases due to morphological reasons, overuse, and other factors. Musculoskeletal injuries (MSKIs) to the foot are common, especially in soldiers, who frequently overuse their feet, accounting for 80% of all injuries during military training [4]. Plantar fasciitis and Achilles tendonitis are particularly prevalent, with stress fractures also commonly originating in the feet [5]. Such damage often results from the stiffness of soft tissues, including muscles and fascia, due to the prolonged use of heavy shoes and overuse [6; 7]. Overuse or weakening of the internal and external foot muscles can lower the height of the MLA, increasing tension in the foot's muscles and connective tissues [8].  
For healthy foot function, it is important to maintain the height of the medial longitudinal arch. It is known that the foot's intrinsic muscles interact with the extrinsic muscles, fascia, and ligaments to maintain arch height and contribute to stability [9]. Due to their characteristics, soldiers spend a long time wearing hard combat boots. However, due to the military performing combat and missions, not many studies have verified the efficacy of toe exercises in soldiers and compared them to human asymmetry, and studies conducted especially on Asian soldiers have not been documented to the best of our knowledge. Therefore, this study assessed the impact of toe-strengthening exercises on improving the lower-extremity function and functional movement in cadets undergoing training.  
The hypotheses of this study are as follows.  
1. Cadets living in combat boots will have a difference in the height of the medial longitudinal arch and an imbalance in the lower extremities.
2. Toe exercises will alleviate the height and muscle tension of the medial longitudinal arch, causing positive changes in improving the lower extremity imbalance.
## 4 Problem Description
### Simulator  
The dynamic behavior of the drum boiler is simulated by solving an ordinary-differential-equation model. The simulation model represents the drum boiler in terms of a water inlet, a heat supply, a water-level PI-controller, and a saturated steam outlet. A schematic representation of the drum boiler is shown in Figure 4.  
Water from the condenser enters the drum through the water inlet and the saturated steam is extracted. The behavior of the boiler furnace in a coal-fired power plant or exhaust gases of a gas turbine is modeled using a heat supply system to heat and evaporate the water in the rising tubes. For simplicity, the model assumes thermodynamic equilibrium  
Figure 4: Schematic diagram of the drum boiler.  
Figure 3: System architecture.  
between water and steam inside the drum. The mass balance in the drum boiler can be written as:  
\[(\frac{\partial\rho_{l}}{\partial p}V_{l}(l_{l})+\frac{\partial\rho_{v}}{ \partial p}V_{v})\frac{dp}{dt}+(\rho_{l}-\rho_{v})\frac{dV_{l}(l_{l})}{dt}=q_{l }-q_{v} \tag{1}\]  
where \(V_{l}\) is the water volume in the drum, \(V_{v}\) the steam volume in the drum, \(\rho_{l}\) and \(\rho_{v}\) the density of water and steam, \(l_{l}\) the water level inside the drum, \(p\) the pressure inside the drum, \(q_{l}\) feedwater flow and \(q_{v}\) the steam flow rate extracted from the drum.  
The energy balance in the drum boiler can be expressed as:  
\[(V_{v}(h_{v}\frac{\partial\rho_{v}}{\partial p}+\rho_{v}\frac{ \partial h_{v}}{\partial p})+V_{l}(h_{l}\frac{\partial\rho_{l}}{\partial p}+ \rho_{l}\frac{\partial h_{v}}{\partial p})-V+mC_{p}\frac{\partial Tv}{ \partial p})(\frac{dp}{dt})+(\rho_{l}h_{l}+\rho_{v}h_{v})\frac{dV_{l}}{dt}\\ =Q+(q_{l}h_{l}-q_{v}h_{v}) \tag{2}\]  
where \(Q\) is the heat flow, \(V\) the water-steam volume in the drum, \(h_{l}\) and \(h_{v}\) are the water and steam enthalpies, while \(C_{p}\) denotes the specific heat capacity of steam.  
According to [PERSON] et al. [18], thermal stresses occur in the thick-walled drum if there are spatial temperature differences. Thus, thermal stress is determined proportionately to the time derivative of the metal temperature, considering that the metal temperature is equivalent to the water saturation temperature inside the drum, as given in equation three.  
\[\sigma_{D}=k\frac{dT_{D}}{dt} \tag{3}\]  
where \(\sigma_{D}\) is the thermal stress in the thick-walled drum, \(k\) the thermal conductivity of the wall and \(T_{D}=T_{sat}(p)\) the inner temperature in the drum.  
For simplicity, bulk system flows, volumes, and masses are considered. Therefore, the model ignores spatial variations in the process variables such as individual geometric features and fin and pipes arrangements in the risers and downcomers. Moreover, this model does not consider heat losses between the water inside the drum and the walls of the drum and pipes. For that reason, the water and metal temperatures are assumed to be in thermal equilibrium within the drum. Despite these simplifications, the resulting lumped parameter model could capture the overall behavior of the drum boiler.
# Utilization of Machine Learning Algorithms
## 4 Discussion
### Strengths and Limitations of the Study  
This study followed a comprehensive review process, gaining deep insights into the extent of ML usage, its associated benefits, and its limitations. At first, the protocol was prospectively registered with PROSPERO to avoid duplication. In addition, the PRISMA guidelines were followed to generate consistent results and enhance the study's replicability. A comprehensive literature search was conducted in a global context across different journal databases. The Covidence software aided in the exclusion of duplicates and ineligible articles, and was quite fast and reliable. Two reviewers screened the articles, and a third party was involved in resolving conflicts regarding the articles to include in the analysis. We conducted a \(t\)-test to determine the accuracy of ML over TMs.  
However, a few challenges were encountered during the review process. This review excluded studies that used ML algorithms to predict HIV testing among individuals less than 15 years old, which limits the generalizability of our findings to only those that are 15 years or older. Additionally, the researchers could not access certain subscription-based articles, which led to the exclusion of some relevant articles. Furthermore, the TMs and ML models differ, as shown by the accuracies in eight studies that applied TMs, which were compared to those of 36 ML models. A normality test was conducting before running an independent \(t\)-test to ensure that the data were normally distributed. The \(t\)-test results show a statistically significant difference (\(p=0.0002\)) between ML and TMs. Future studies should investigate the application of ML HIV testing interventions in age categories below 15 years.