esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### About the Structural Typology  
The first question that must be reflected on before considering the incorporation of dynamic parameters in an SHM system for bridges is whether the structural typology is appropriate for this or not.  
This is because, to determine with certain precision the natural frequencies and modal damping ratios, accelerograms of a certain duration are required.  
Regarding natural frequencies, according to the Nyquist-Shannon Sampling Theorem, a higher sampling rate allows a wider range of frequencies to be covered. However, in the case of bridges, their importance is relative since the values of their natural frequencies are usually low, so the areas of greatest interest for study are usually between 0 and 10 Hz. Therefore, attending strictly to this issue, a sampling frequency of 20 Hz (data every 0.05 s) could be enough. On the contrary, what is of greatest interest is that this frequency range is defined with the greatest possible precision, which is the result of having accelerograms of a certain length. For example, an accelerogram of 50 s duration would allow the study frequency range to be analysed with a discretization of 0.02 Hz; while one of 5 s duration would only achieve a discretization of 0.2 Hz.  
In the case of the modal damping ratio, something similar happens. The most common situation is that available accelerograms correspond to specific events. In these circumstances, to determine the modal damping ratio, the free vibration sections must be located. Therefore, the longer the duration of the free vibration sections within the accelerogram, the greater certainty in the calculation of the modal damping ratio.  
Therefore, if the modal damping ratio of the structure is lower, the recorded accelerograms will be more appropriate. In other words, a priori, compared to structures where concrete is the predominant construction material, metallic and composite structures would be more suitable for the implementation of SHM systems that incorporate dynamic parameters.  
As an example, Figure 3a,b includes, respectively, two accelerograms corresponding to the E-1 and E-2 structures. As can be seen, the first accelerogram has a length in free vibration regime much greater than the second, practically its entire length, about 55 s in the first case compared to barely 2 s in the second case, between t = 40 and t = 42 s, which allows the dynamic parameters to be determined with much greater precision in the E-1 structure. Furthermore, all this, regardless of the fact that in absolute value, the accelerations recorded in the first structure are lower, of the order of a tenth (maxima of 0.010 g in E-1 compared to 0.10 g in E-2).
# Evaluation of Horizontal Shear Performance of Composite Girders Using Separable Shear Connectors
## 3 Evaluation of Horizontal Shear Performance
### Structural Characteristics of Embedded Separable Shear Connectors
The deck dismantling process involved drilling the deck surface to a specified depth, removing only the stud bolts, and reversing the construction order. This procedure disengages the connection between the deck and the girder, facilitating simple dismantling. Thus, the stud bolt bears the actual shear strength, whereas the U-shaped component enables the dismantling process. When the stud bolt is tightened, a compressive force is introduced on the U-shaped component, causing the tapered surfaces of the nut anchor and the U-shaped component to wedge tightly. This configuration eliminates tolerance and prevents initial slippage after integration, even under horizontal shear forces. A detailed finite element analysis and further evaluation of horizontal shear tests for this embedded separable shear connector can be found in previous studies [14; 15].
## Abbreviations  
The following abbreviations are used in this manuscript:  
LRNTS  
Lunar Regional Navigation Transceiver System  
GNSS  
Global Navigation Satellite System  
ISRU  
In Situ Resource Utilization  
ISRO  
Indian Space Research Organization  
SLAM  
Simultaneous Localization and Mapping  
SINS  
Strapdown Inertial Navigation System  
QUEST  
QUaternion ESTimator  
SNE  
Satellite Navigation Equipment  
SCPA  
Self-Calibrating Pseudolite Array  
GPS  
Global Navigation System  
MMS  
Magnetospheric Multiscale mission  
GOES  
Geostationary Operational Environmental Satellite  
PSR  
Permamentably Shadowed Region  
LRO  
Lunar Reconnaissance Orbiter  
SPA  
South Pole Aitken  
DEM  
Digital Elevation Model  
LLR  
Lunar Laser Ranging  
AFS  
Atomic Frequency Standard  
SIC  
Successive Interference Calculation  
STP  
Space Time Processing
# A Weighted PageRank-Based Bug Report Summarization Method Using Bug Report Relationships
## 8 Conclusions  
In this paper, we proposed RBRS a method for automatically summarizing bug reports using three associations (duplicates, blocks, depends-on). To evaluate RBRS, we constructed a corpus of 36 bug reports including the correlation information of bug reports. We then compared RBRS with PRST [6]. Experimental results showed that our method RBRS yields 6.41% higher precision, 6.14% higher recall, and 6.28% higher F1 score than those of the previous method PRST.  
The contributions of this paper are as follows. First, we proposed a weighted PageRank-based bug report summarization method that uses three kinds of association information for bug reports. Second, we built a corpus that can be used for bug report summary studies using the relationships of bug reports. Third, by analyzing the change in summary quality as the number and weight in relationships vary, we found a variable setting that maximizes summary quality. Fourth, we implemented the proposed method as a tool and performed a simulation to compare it with the existing method.  
The bug report summary corpus constructed in this paper has a limitation that it is small and is written by persons who did not participate in building the bug reports. Previous bug report summarization research also had such limitation. So, for future research, we plan to use a larger bug report corpus to address this issue. We also found the shortcomings of the commonly used natural language processing library NLTK for preprocessing bug reports. We realized that the styles and formats of the sentences in bug reports are different from those of the normal sentences used in newspapers, novels, scientific papers, etc. Therefore, we also plan to conduct research on preprocessing techniques to enhance the quality of bug report summaries. As a final remark, although we improved the accuracy of summarizing bug reports with additional relationships of bug reports, we are not satisfied with the 50.92% F-score. For the improvement of summary accuracy, we are going to explore other machine-learning techniques, including a sequence to sequence model.  
Conceptualization, B.K. and S.L.; methodology, B.K.; software, B.K.; validation, B.K., S.K. and S.L.; data curation, B.K.; writing-original draft preparation, B.K.; writing-review and editing, S.K. and S.L.; supervision, S.K.  
This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT (NRF-2017M3C4A7066210) and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2019R1F1A1053262). This research was also supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2018R1D1A1A02085551).  
The authors declare no conflict of interest.
# A TCP Acceleration Algorithm for Aerospace-Ground Service Networks
## 4 Experimental Results
### Results Analysis
#### 4.2.1 Average Throughput
Therefore, if the BBR algorithm continues to estimate the congestion window using the detected minimum roundtrip time, the estimated CWND value of the congestion window will be less than the link's actual ideal capacity. Further, reduced CWND limits the sender's sending rate, causing the bandwidth value measured by the BBR algorithm in detecting the link's maximum bandwidth to be less than the link's best achievable bandwidth. For instance, a lower maximum bandwidth results in a lower CWND value. As a result, the BBR algorithm can operate only at a reduced rate, thus causing significant network resource waste. BoostTCP dynamically learns the network path characteristics of each TCP connection during transmissions, such as the end-to-end delay and its variation characteristics, the arrival interval and variation characteristics of the receiver's feedback data packet (ACK), the degree of data packet reversal and its variation characteristics, delay and jitter caused by deep data inspection by security equipment, and random packet loss caused by various factors. While tracking these characteristics in real time, BoostTCP analyzes them holistically and derives precursor signals that reflect congestion and packet loss along the TCP connection network path. Further, it determines the congestion degree based on the results of the dynamic, intelligent learning processes; determines the transmission rate and the congestion recovery mechanism that are compatible with the available bandwidth on the current path, and then performs the packet loss judgment and recovery accurately and in a timely manner. The BoostTCP algorithm can detect congestion in real time, automatically slow down, avoid mechanism congestion caused by excessively aggressive transmission, and accurately identify packet loss caused by random error codes. Thus, high-speed transmission is maintained and transmission behavior is smoother, which indirectly increases the effective data transmission rate.
# Investigation of Vehicle Stability with Consideration of
## 6 Discussion
Spectral analysis of the vertical displacement of the vehicle body and other suspension mechanisms measured on the suspension test bench confirmed correspondence of the properties of the quarter-car model with corresponding characteristics of the real vehicle. Therefore, it can be stated that the real quarter-car model corresponded to the theoretical model, as well as to real car movement. The developed model is appropriate for further investigation and modeling of the suspension properties of cars. Minor inaccuracies were also observed, which may have affected the reliability of the results. The limited capacities and slowness of the pneumatic chamber simulating the excitation of the road surface, which allowed testing only at a low-frequency (<20 Hz) of excitation should be mentioned. Another issue was the attachment of the vehicle body to the test bench frame, as a sufficiently rigid attachment may inhibit the movement of the model sprung mass. However, the conditions simulated by the test bench offered a fairly accurate reflection of the real conditions; therefore, it is reasonable to further investigate the characteristics of the real quarter-car module, as well as possibilities for its improvement. A vehicle stability model was developed for the assessment of vehicle-road interaction. The model comprehensively evaluated the dynamic characteristics of the suspension, suspension kinematics, and road conditions, and enabled the development of methodology for the determination of the safe speed of the vehicle on roads of known quality using numerical models. As a result of the combination of the vertical displacement of the quarter car and the kinematic models, a suspension model was developed and described changes in the spatial position of the wheel during suspension operation. When driving on roads of different quality, the data obtained allowed estimation of the directional stability of the vehicle, including the lateral displacement of the wheel, camber, and the turn about the vertical axis. To simplify the analysis, all these changes were reduced to one parameter, i.e., the calculated tire slip angle. Simulations showed that the kinematic properties of the front suspension needed to be refined in the directional simulation application, as the boundary travel of the suspension resulted in large total slip angles (in particular, the front wheels). This causes the risk that the vehicle may lose its transverse stability at higher speeds. The effect of changes in the geometric position of the wheel on the directional stability parameters of the vehicle was determined using kinematic models. The total slip angle was used for this. The software application AUTOSTAB was developed under this methodology taking into account the effect of the above factors on the dependence of the vehicle's limit turning radius on speed. Following the example of the VW Golf, changes in the geometric position of the wheel during suspension operation were found to have a significantly greater effect on directional stability than body tilt due to centrifugal forces.
## 1 Introduction
Experiments and simulations indicate its ability to dramatically cut system reaction time. In terms of handling parameter fluctuations and load disruptions, the SMRL method beats conventional strategies. It provides stable and accurate control, improving system efficiency and stability. The experimental results confirm its efficacy. Overall, the SMRL approach contributes significantly to control systems, helping applications such as robots, electric vehicles, and industrial automation. This paper proposes to model and simulate the control system of the variable mechanism of the axial piston pump using the simulation technique of robust control. Simulink is used to model the control system parts separately in order to establish a more realistic and reliable model, which can reduce the cost and increase the design efficiency in comparison to an actual physical experiment. Starting with PID control, the effect of this control is evaluated in connection with the degree of stability of the PID for the research of variable mechanism control of axial piston pumps. In this paper, we examine the control system of the variable mechanism of an axial piston pump, design the robust controller by applying the theory, and investigate its ability to assure motion accuracy in the presence of an external load disturbance; solenoid valves improve the integration of the hydraulic system and control, as well as the electrical impulses from sensors and control signals.
## 2 Materials and Methods
### Variables  
Player tracking data. The external training load was extracted using the APEX Pro Series Software (v. 2.0.2.4) with the following variables: total distance (TD) covered (m), average speed (AvS), maximal running speed (MRS) (m/s), relative high-speed running (rHSR) distance (m), high metabolic load distance (HMLD) (m), sprinting distance (SPR) (m), dynamic stress load (DSL) (a.u.), number of accelerations (ACC), and number of decelerations (DEC). The GPS software tracked locomotor activities exceeding 19.8 km/h, categorizing them into rHSR (19.8-25.1 km/h) and SPR (>25.1 km/h). Sprint performance was assessed by the number of sprints and average sprint distance (m). HMLD used the distance covered when a player's metabolic power surpasses 25.5 W/kg. The ACC and DEC focused specifically on movements within the highest intensity ranges, with ACC exceeding 3 m/s\({}^{2}\) and DEC falling below \(-\)3 m/s\({}^{2}\). DSL was determined using a 100 Hz triaxial accelerometer integrated into the GPS units, which combined accelerations along the three orthogonal axes (X, Y, and Z) to generate a total vector magnitude expressed as G-force [6; 32].  
Wearable features. The internal training load was captured using an HR sensor by Garmin HR-band devices with a 1 Hz short-range telemetry system (International Inc., Olathe, KS, USA). Metrics included maximum heart rate (HRmax), average heart rate (AvHR), and the percentage of HRmax (%HRmax) [33]. Akubat TRIMP was used to quantify the training impulse, calculated as training duration \(\times\) 0.2053e\({}^{3.5179}\)\(\times\) HRratio, where HRratio was derived from players' iTRIMP values and HRmax was determined via the Yo-Yo Intermittent Recovery Test Level 1 (YYIR1) [34; 35].  
Psychological perceived features. The Borg Rating of Perceived Exertion 6-20 scale was utilized to evaluate subjective exertion, fatigue, and recovery [36]. The session RPE (sRPE) was computed by multiplying each player's RPE score by the session duration (sRPE = RPE \(\times\) session time) [18; 19]. Recovery perception was assessed using the Total Quality Recovery (TQR) scale, scored from 6 to 20. TQR and RPE were collected 30 minbefore and after training sessions, respectively, to gauge players' recoveries and efforts. Both scales have been validated in prior studies involving youth football athletes [5].
## 2 Materials and Methods
### Determination of VOCs Using GC-MS  
The analysis of the VOCs was performed by HS-SPME combined with GC-MS (7890A GC, Agilent, USA, 5975C MS; Agilent Technologies, Santa Clara, CA, USA). The paocai brine (5 mL) were accurately weighed into 15 mL headspace glass vials containing 1 g of salt, the vials were immediately placed in a heating block to equilibrate for 30 min at 50 \({}^{\circ}\)C and the VOCs were extracted using a 65 \(\upmu\)m 50/30 um DVB/CAR/PDMS optical fiber for 10 min at 50 \({}^{\circ}\)C. VOCs were desorbed from the SPME fiber at 250 \({}^{\circ}\)C for 5 min in the injector in splitless mode. The carrier gas was helium, and it was supplied at a flow rate of 1.2 mL/min (constant linear velocity). The column temperature was programmed as follows: maintained at 36 \({}^{\circ}\)C for 3 min, increased at 5 \({}^{\circ}\)C/min to 65 \({}^{\circ}\)C, increased at 3 \({}^{\circ}\)C/min to 155 \({}^{\circ}\)C, increased at 10 \({}^{\circ}\)C/min to 200 \({}^{\circ}\)C, and maintained at 200 \({}^{\circ}\)C for 3 min. The MS condition was set as: 280 \({}^{\circ}\)C for the transfer line, 230 \({}^{\circ}\)C for the ionization, 70 eV for the ionization energy [10]. The quantification analysis was done by using cyclohexanone (27.424 g/L, \(w/v\)) as an internal standard. The NIST11.L standard mass spectral databasewas used to identify VOCs based on retention time and mass spectral similarity match. Each sample was analyzed in triplicates at each sampling time.
# Electrochemical Control of Biofilm Formation and Approaches to Biofilm Removal
## 3 Biofilms in Food Industry
### Detection of Biofilm in Food Processing Plants  
The detection of the presence of a biofilm is more important in closed circuits, i.e., tubes, cooking tanks, filling machines, or other hidden places where biofilm may grow without being observed by the naked eye. Prior to cleaning, the manufacturing process has to be stopped, and all the closed circuits have to be emptied. The early detection of biofuling may save on the cost of frequent cleaning. In the literature, 23 various biofilm detection techniques have been identified, including those based on physical, chemical, microscopical, and biological principles [129]. However, most of them are either not applicable to in-line monitoring of biofilm formation or do not provide the results in real time. Conventional methods for detecting bacterial pathogens in biofilm are still based on culturing the microorganisms on agar plates after swabbing biological material from the food contact surface [130]. In order to overcome the problem with bacteria in viable but nonculturable states, direct epifluorescence microscopy, enzyme-linked immunosorbentassays, or PCR have been proposed as more sensitive, albeit more expensive and time-consuming, methods for the detection of pathogens in biofilm [131].
## 4 2D Bayesian Compressive Sampling (2D BCS)  
Bayesian compressive sensing/sampling (BCS) is an application of Bayesian methods in compressive sensing/sampling (CS) to efficiently reconstruct signals or images at low sampling rates. Recently, 2D BCS for reconstructing high-resolution cross-sections of data points from sparse CPT soundings has gained considerable attention and is increasingly used in geotechnical field investigations [15; 16; 17]. Mathematically, the reconstruction of geotechnical data cross-sections with high resolution from sparse measurement data can be represented by an \(N_{x_{1}}\times N_{x_{2}}\) data matrix \(F\). An \(N_{x_{1}}\times\text{M}\) matrix \(Y\) and an \(N_{x_{1}}\times N_{x_{2}}\) matrix \(F\) represent the data points estimated directly from M CPT soundings on \(F\) and the geotechnical data cross-sections reconstructed from \(Y\), respectively [18]. Theoretically, \(F\) is represented by a weighted sum of \(N_{t}=N_{x_{1}}\times N_{x_{2}}\) 2D basis functions [33; 34].  
\[F=\sum_{i=1}^{N_{x_{1}}}\sum_{j=1}^{N_{x_{2}}}\mathbf{B}_{i,j}^{\text{2D}}\omega_ {i,j}^{\text{2D}}=\sum_{i=1}^{N_{x_{1}}\times N_{x_{2}}}\mathbf{B}_{i}^{\text{2D}} \omega_{i}^{\text{2D}} \tag{12}\]where \(\mathbf{B}_{t}^{\rm 2D}\) and \(\omega_{t}^{\rm 2D}\) are the \(t\)-th (\(t=1,\ 2,\ \cdots,\ N_{t}\)) 2D basis function and the corresponding weight coefficients, respectively. Let \(N_{x_{1}}\times N_{x_{1}}\) matrix \(\mathbf{B}_{1}^{\rm 1D}\) and \(N_{x_{2}}\times N_{x_{2}}\) matrix \(\mathbf{B}_{2}^{\rm 1D}\) represent two one-dimensional orthogonal matrices, obtained by the discrete cosine transform (DCT). \(\mathbf{B}_{t}^{\rm 2D}\) can be conveniently constructed by \(\mathbf{B}_{1}^{\rm 1D}\) and \(\mathbf{B}_{2}^{\rm 1D}\)[35]. It is a worthwhile note that for the spatially autocorrelated data points, the values are close to zero except for a few with non-trivial weight coefficients \(\omega_{t}^{\rm 2D}\), thus providing the theoretical feasibility of reconstructing geotechnical data cross-sections using sparse measurement data [36]. Mathematically, the CPT measurement data \(\mathbf{Y}\) can be constructed using the submatrix \(A_{2}=\mathbf{\Psi}_{x_{2}}^{\rm T}\mathbf{B}_{2}^{\rm 1D}\) of the 2D basis function \(\mathbf{B}_{t}^{\rm 2D}\)[20].  
\[\mathbf{Y}=F\mathbf{\Psi}_{x_{2}}=\mathbf{B}_{1}^{\rm 1D}\mathbf{\Omega}\left(\mathbf{B}_{2}^{\rm 1D} \right)^{\rm T}\mathbf{\Psi}_{x_{2}}=\mathbf{B}_{1}^{\rm 1D}\mathbf{\Omega}(\mathbf{A}_{2})^{ \rm T} \tag{13}\]  
where \(\mathbf{\Psi}_{x_{2}}\) is the measurement matrix specific to this study, reflecting the locations of the M CPT soundings along the horizontal direction. The superscript \"T\" indicates the transpose operation of the matrix.
## 4 Experimental Investigations
### Objective Experiments
#### 4.1.2 Results  
The results of the objective evaluation experiments are presented in Table 3, which validates the feasibility of the presented HRTF individualization method. The presented method achieved small synthesis error when compared with both the database matching one and general regression neural network (GRNN) base method. It is also notable that the proposed method achieved balanced synthesis errors for left and right ears, while the other two methods achieved relatively larger synthesis errors for left ear and smaller errors for right ear. Consequently, the proposed method was proved to be algorithmically effective and embodies the superiority in the balanced synthesis error of left and right ear.