esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Performance of a New-Type PSC I-girder for Railway Bridge Application
## 2 Dynamic Performance Evaluation of New Type of PSC I-girder Bridge
### Performance Evaluation by Moving Load Analysis
#### 2.2.2 Analysis Method  
The dynamic moving load analysis is carried out using the in-house programs HFC and hfAnalyzer developed at Korea Institute of Civil Engineering and Building Technology (KICT). HFC is a kind of library designed by object-oriented programming that allows the user to implant his/her own material model and analysis method. The hfAnalyzer was developed using HFC and its applicability has been validated by applying various modeling and analysis methods on diversified railway bridges [9]. The model adopts 2-node beam elements for the girder and cross beams to reflect the features of the girder bridge and 4-node shell elements for the deck. The concrete ballast track composed of PCL (protection concrete layer) and TCL (track concrete layer) is also reflected in the model to achieve accurate analysis. Specifically, the PCL is modeled as a distributed mass load and TCL is modeled by solid elements. The rails are modeled by 2-node beam elements to consider the entrance and exit of the traveling train on the bridge. For the bridge bearings, beam-type restraints are applied with deck considering the gap with the girders. Figure 7 depicts the analytical FE model for the girder bridge with span length of 35 m. The damping ratio in the moving load analysis has the most sensitive effect on the amplitude of the response of the bridge at resonance. Therefore, this study applies a damping ratio of 1% as suggested in the Railway Design Code (KDS 24 10 10) [10; 11].  
The moving load analysis was carried out for the train since its access on the structure to its complete crossing for traveling speeds increased by step of 10 km/h within the maximum speed range and including the critical and sub-critical speeds listed in Table 4. The train load was applied on one line to consider the effect of eccentric loading, and the amplitudes of the vertical deflection, vertical acceleration of the deck as well the twist of the track were checked.  
Figure 6: Resonance suppression span length per type of train.
# Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform
## 5 Experimental Evaluation and Results
### Hardware and Software Configuration
#### 5.1.2 Flight Experiment  
The flight experiment was performed in the Joliette airport near the Montreal City. A light aircraft was equipped with a Novatel GNSS/INS RTK connected to two GNSS antennas. An Iridium-Next L-band antenna was used to record the Iridium downlinks during the flight. The utilized front-end and the LNA were the same as the ground experiment. The position output of the RTK was considered as the true reference trajectory. The receiver's sampling frequency and bandwidth were also followed by the specifications of the ground test. The time duration of the flight test was 10 min. The GNSS measurements were blocked after 5 min of the experiment. More details about the overhead satellites and the Doppler shift measurements are discussed later. Figure 5 shows the installed antennas for the LEO satellites as well as the GNSS receiver in the aircraft.  
Figure 4: The ground vehicle and the equipment used in the ground experiment.
# Wireless Accelerometer Architecture for Bridge SHM: From Sensor Design to System Deployment
## 4 Laboratory Tests Results
### Metrological Verification
#### 4.2.2 Metrologic Certification  
The accelerometer underwent metrological certification to validate its accuracy and ensure compliance with rigorous measurement standards. The test procedure involved applying a sinusoidal acceleration input of unknown amplitude at various frequencies. To evaluate the sensor's performance, simultaneous measurements were conducted using a high-precision laser interferometer provided by the Istituto Nazionale di Ricerca Metrologica (INRIM). This reference device, traceable to internationally recognized metrological standards, served as the benchmark for comparison.  
The acceleration data obtained from the tested sensor were compared against the INRIM laser measurements across a range of frequencies. Any deviations observed between the two sets of measurements were quantified and computed in terms of percentage. This approach not only validated the sensor's performance but also established a calibration framework that enhanced its reliability in practical applications. Table 3 below reports some of the results.  
The process further demonstrated the accelerometer's ability to produce results within an acceptable tolerance range, even at high frequencies where dynamic errors are more likely. By leveraging a certified metrological reference, the test confirmed that the device meets stringent requirements for precision, laying the foundation for its use in high-accuracy measurement environments.
## 3 Algorithm Design  
### Overall Process Design  
According to the previous paper, the core problem of reliability based on road network capacity is actually to obtain the reserve capacity coefficient. By the reserve capacity coefficient and travel demand, the reliability of the road network can be obtained by Equation (2). The overall process of the algorithm design can be as follows:  
Step 1: For a given OD matrix, the lower-level SUE model with constraints is solved to obtain the link flow.  
Step 2: Construct the sensitivity matrix to obtain the linear programming functions of the link flow and travel time with respect to the given OD matrix, simplify the upper-level model problem, and obtain the \(\mu_{r}{}^{(n)}\).  
Step 3: After expanding the OD matrix \(\mu_{r}{}^{(n)}\) times, repeat steps 1 and 2 to get new \(\mu_{r}{}^{(n+1)}\).  
Step 4: Convergence judgment. When \(\left|\mu_{r}{}^{(n+1)}*\mu_{r}{}^{(n)}-\mu_{r}{}^{(n)}\right|\leq\varepsilon\) (where \(\varepsilon\) is a very small real number, it can be 0.01) or \(\mu_{r}{}^{(n+1)}<1\), the reserve capacity coefficient \(\mu_{r}=\max(\mu_{r}{}^{(n)}*\mu_{r}{}^{(n+1)}\), \(\mu_{r}{}^{(n)})\) is obtained. Otherwise, taking \(\mu_{r}{}^{(n)}=\mu_{r}{}^{(n)}*\mu_{r}{}^{(n+1)}\), repeat the above steps until convergence.  
When \(\left|\mu_{r}{}^{(n+1)}*\mu_{r}{}^{(n)}-\mu_{r}{}^{(n)}\right|\leq\varepsilon\) (where \(\varepsilon\) is a very small real number, it can be 0.01), it means that there is no big difference between \(\mu_{r}{}^{(n+1)}*\mu_{r}{}^{(n)}\) times expansion and \(\mu_{r}{}^{(n)}\) times expansion of the given OD matrix. It is approximately considered that the reserve capacity coefficient \(\mu_{r}=\mu_{r}{}^{(n)}*\mu_{r}{}^{(n+1)}\) has been obtained. Or when \(\mu_{r}{}^{(n+1)}<1\), it means that the given OD matrix cannot be expended any more, then the reserve capacity coefficient is \(\mu_{r}{}^{(n)}\).  
Step 5: By comparing the relationship between the reserve capacity and the growth threshold of traffic demand, according to Equation (2), the reliability of the road network can be obtained.  
In addition, the flowchart of the algorithm is as shown in Figure 1. Where, at the first time, \(\mu_{r}{}^{(1)}=1\), and \(n=1,2,\cdots,m\), \(m\) is a positive real number.
## 1 Introduction
The study examines how bending radius and wavelength influence transmission loss, providing valuable insights for optimizing fiber optic performance in applications requiring tight bends. Their findings indicate that as the bending radius decreases, transmission loss increases exponentially, emphasizing the importance of adhering to minimum bend radius specifications to minimize signal attenuation. Most of the work on calculating the bending loss was concentrated on the loss in the cladding area to determine the remaining transmitted power. In this paper, we derived a formula to calculate the losses in the outside area of the cladding region. This escaped power represents a transmission to the surrounding region required in some applications, such as wireless sensor networks, confined area communications, and underwater optical data collection. The rest of the paper is organized as follows: In Section 1, we derived a closed formula that describes the fiber bend behavior and calculates the amount of escaped light power to the surrounding region as a function of bend radius. Our experimental work and how it aligns with the formula derived is shown in Section 2. In Section 3, we present an OLF Transmission Case Study, and finally, the conclusion and future work are given in Section 4.
# Design Analysis of a Passive Buffer System for Space-Assembled Segmented Mirrors
## 3 Buffer System Validation
It is obvious that the deformation value rises considerably as the driving force increases from 100 N to 500 N, revealing that the buffer system has a good absorption of impact energy. As shown in the curve, the deformation at \(Z=0.020\) m is far beyond the others, generally appearing in the span from \(1\times 10^{5}\) m to \(3\times 10^{5}\) m, which shows the effect of deformation on the bottom of the chamfered hole is evident. However, it is necessary to point out that the deformation value still does not exceed the specified requirements. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Young modulus E (Pa) & \(1.09\times 10^{11}\) \\ Poisson’s ratio v & 0.34 \\ The radius of the pin head (m) & 0.020 \\ The depth of the conical hole (m) & 0.020 \\ Half-cone angle (\({}^{\circ}\)) & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The docking parameter specifications. Figure 6: The relationship between the driving force and the maximum deformation under different depths of propulsion. In this, to make the graph easier to read, trend lines have been added. The relationship between the maximum compressive stress and the driving force is delivered by Figure 7. The rising trends of the compressive stress, under different depths of propulsion, always keep at a slow rate, showing this system is very effective in damping the impact force. Although the compressive stress remains in a secure range, it is worth mentioning that the line at \(Z\) = 0.020 m is still much higher than the others, going up from 1.5 \(\times\) 10\({}^{9}\) Pa to 2.5 \(\times\) 10\({}^{9}\) Pa. Therefore, efforts should be made to avoid the contact position too close to the bottom of the chamfered hole, in case of damage to the PMAs.
## 3 Results and Discussion
### Spatiotemporal Variations of Mangrove Forests in the Study Area
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Transition of Land Use**} & \multicolumn{3}{c}{**Zones**} & \multirow{2}{*}{**Total Area**} \\ \cline{2-2} \cline{4-5}  & **Transition** & **Buffer** & & **Core** \\ \hline
**Aquaculture** & **6411.5** & **141.91** & **0** & **6553.41** \\ \hline Aquaculture to Crop Land & 19.37 & 0.02 & 0 & 19.4 \\ \hline Aquaculture to Forest & 81.08 & 66.21 & 0 & 147.29 \\ \hline Aquaculture to Water Bodies & 0.04 & 941.58 & 0 & 941.62 \\ \hline Aquaculture to Rest of Land & 13.24 & 1.46 & 0 & 14.7 \\ \hline
**Built-up** & **824.52** & **170.75** & **0** & **995.28** \\ \hline Built-up to Aquaculture & 25.57 & 7.8 & 0 & 33.37 \\ \hline Built-up to Crop Land & 13.7 & 0.88 & 0 & 14.58 \\ \hline Built-up to Forest & 0.93 & 14.05 & 0 & 14.99 \\ \hline Built-up to Rest of Land & 19.84 & 7.93 & 0 & 27.77 \\ \hline
**Crop Land** & **3869.62** & **93.27** & **0** & **3962.88** \\ \hline Crop Land to Aquaculture & 7.76 & 0.02 & 0 & 7.78 \\ \hline Crop Land to Built-up & 4.43 & 0.38 & 0 & 4.81 \\ \hline
**Forest** & **1.19** & **27,487.57** & **6143.17** & **33,631.93** \\ \hline Forest to Aquaculture & 0 & 1330.3 & 0 & 1330.3 \\ \hline Forest to Built-up & 0 & 69.71 & 0 & 69,71 \\ \hline Forest to Water Bodies & 1.41 & 366.89 & 7.75 & 376.05 \\ \hline Forest to Rest of Land & 0 & 257.48 & 0 & 257.48 \\ \hline
**Rice of Paddy Land** & **180.38** & **0** & **0** & **180.38** \\ \hline
**Water Bodies** & **6225.83** & **12,393.61** & **1934.28** & **20,553.72** \\ \hline
**Rest of Land** & **2247.67** & **1086.86** & **9.81** & **3344.34** \\ \hline Rest of Land to Forest & 1.54 & 11.94 & 0 & 13.48 \\ \hline Rest of Land to Water Bodies & 62.59 & 62.78 & 0 & 125.36 \\ \hline
**Total Area** & **20,012.21** & **44,513.1** & **8095.01** & **72,622.62** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The pattern of mangrove forest area followed the land use changes between 2015 and 2019 (ha). pose a threat to the diversity of mangrove species, or even prevent new immigrant species in the near future. This will be affected by the sustainability of ecosystem services around the buffer and transition zones in the study area.
# An Effective and Efficient Adaptive Probability Data Dissemination Protocol in VANET
## 3 Effective and Efficient Adaptive Probability Data Dissemination Protocol (EEAPD)
### Requirements and Premises  
In this paper, only vehicle-to-vehicle communication as depicted in Figure 1 was contemplated by assuming each node is provided with onboard unit device having IEEE 802.11p standard. Likewise, every node can deduce its current position by considering GPS. This utilization is accountable for generating an emergency message that needs to spread within the network area. In this paper message and data packet will be used interchangeably.
# Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine
## 1 Introduction
Since entropy-based feature extraction is solid to discriminate speech and noise, it fails to discard cough and excessive breath from speech signals, which are treated as non-speech. Based on fuzzy set theory, to measure the complexity of the time series data, fuzzy entropy (FuzzyEn) [22] was introduced. FuzzyEn is a modified algorithm of sample entropy (SampEn) [23; 24; 25; 26; 27]. Since then, FuzzyEn has been successful in feature extraction. Similar to SampEn-based algorithms, FuzzyEn retains certain characteristics like excluding self-matches. Additionally, by inheriting the similarity measurement using fuzzy sets, the limitations cited by SampEn--which uses the Heaviside function as the tolerance to select or discard the similarities between the two vectors--was overcome by FuzzyEn, as FuzzyEn transits smoothly through varying parameters with the use of the exponential function. In this study, FuzzyEn was used as a feature to provide the input into the SVM for VAD, and its performance was investigated under various noisy conditions (airport, babble, car, and train) at different SNR levels (\(-10\) dB, \(-5\) dB, \(0\) dB, \(5\) dB, and \(10\) dB). Also, the significance of the FuzzyEn feature was tested using \(k\)-NN classifier and the results were compared against the different algorithms and the algorithm proposed. This article follows with Section 2, which describes the proposed methodology, and Section 3, the result analysis, and finally concluding with Section 4.
# MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning
## 4 Experiments
### Instance Segmentation Experiment
In order to compare with the \"MU R-CNN + HMM\" scheme proposed in Figure 7, we add HMM module to the algorithm in Ref. [38] for comparison. In Ref. [38], we proposed a QR code detection algorithm based on BING and AdaBoost-SVM, which can quickly detect QR tags, but does not have the function of instance segmentation. Observations state \(v_{0}\) and \(v_{1}\) in formula (5) can be directly obtained by using the algorithm in Ref. [38], but \(v_{2}\) cannot be directly obtained. Since \(v_{2}\) represents the ratio of the white pixels number divided by black pixels number in binary mask image, we used the traditional Gaussian and Median filtering method to de-noise the detected target region, and then binarization by OTSU algorithm to obtain the \(v_{2}\) state. The HMM model was also trained on the data set \(D_{6t}\), and the obtained HMM model was named as \(HMM_{1}\). Then, \(HMM_{1}\) was tested in data set \(D_{6p1}\), \(D_{6p2}\) and \(D_{6p3}\) respectively. The experimental results are shown in Table 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Network** & **APm** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **APb** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** \\ \hline \multirow{4}{*}{\(D_{1p}\)} & MASK & 8.48 & 27.85 & 0 & 66.66 & 90.34 & 77.63 \\  & R-CNN & 10.29 & 29.34 & 4.91 & 66.75 & 89.79 & 77 \\  & MU & & & & & \\  & R-CNN & 16.93 & 40.12 & 13.89 & 68.73 & 92.5 & 78.43 \\  & MASK & & & & & \\  & R-CNN & 8.46 & 27.98 & 0 & 63.35 & 87.43 & 73.83 \\  & MS & 10.45 & 29.41 & 5.4 & 62.39 & 88.25 & 74.27 \\  & R-CNN & & & & & \\  & MU & 17.2 & 40.02 & 13.64 & 64.53 & 88.93 & 75.64 \\  & R-CNN & & & & & \\  & MASK & 8.8 & 28.27 & 0 & 68.17 & 91.83 & 78.44 \\ \(D_{3}\) & MS & 10.81 & 29.83 & 5.66 & 69.28 & 91.97 & 78.94 \\  & R-CNN & & & & & \\  & MU & & & & & \\  & R-CNN & 17.49 & 40.22 & 13.97 & 70.82 & 93.59 & 80.53 \\  & MASK & & & & & \\  & R-CNN & 8.49 & 28.05 & 0 & 62.54 & 87.71 & 73.69 \\  & MS & & & & & \\  & R-CNN & & & & & \\  & MU & & & & & \\  & R-CNN & 17.24 & 39.94 & 13.61 & 64.89 & 89.52 & 75.97 \\  & MASK & & & & & \\  & R-CNN & & & & & \\  & MS & & & & & \\  & R-CNN & & & & & \\  & MU & & & & & \\  & R-CNN & & & & & \\  & R-CNN & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparing MU R-CNN and related networks in \(D_{1p}\), \(D_{2}\), \(D_{3}\), \(D_{4p}\) and \(D_{5p}\) dataset. The backbone of MU R-CNN, MS R-CNN and MASK R-CNN are all ResNet-101 FPN.
## 4 Discussion
In this study, apart from methyl hexadecanoate, methyl octanoate, and ethyl dodecanoate, other esters detected in the Riesling wines all showed increases during 6 to 12 months of bottle storage. However, only the increase in the concentrations of ethyl butanoate, ethyl 3-methylbutanoate, and ethyl acetate was closely correlated to the decrease in pH, whose OAV and concentrations were small in wines. The changes of other esters that had significantly higher concentrations, such as ethyl lactate and ethyl hexanoate, were not significantly correlated with the change in pH values. Therefore, the change of ethyl butanoate, ethyl 3-methylbutanoate, and ethyl acetate exerted less influence on the flavor quality of the wine, which may explain why there was no statistically significant difference in aroma sensory characteristics between different treatment groups and different bottle-storage durations. It has been widely known that many wine odorants exist as glycosylated bound precursors that will convert into free, volatile forms via acid catalysis during storage, and lower pH could promote the hydrolysis of glycoside, as well as the isoprenoid rearrangements observed in the resulting aglycone intermediates [34]. TDN is considered a characteristic aroma component of Riesling wines, particularly aged Riesling [35]. This compound has an aroma reminiscent of \"ketrosene\" and a sensory threshold of 2 \(\mu\)g/L [36]. In this study, TDN was present at a concentration over 100 \(\mu\)g/L and dominated the aroma of the wines. Previous studies have demonstrated that TDN can be generated by acid-catalyzed hydrolysis of Riesling acetal (an intermediate of C13-norisoprenoid glycoconjugates in grapes) during wine storage, and wines with lower pH usually have higher concentrations of TDN [35; 36]. This phenomenon was also observed in our study, where the concentration of TDN was significantly negatively correlated with the pH value, and the concentration was increased with the cold stabilization treatment duration and storage duration. In our study, furfural presented a similar variation trend with TDN, which is closely associated with the pH decline. Through acid-catalyzed degradation, furfural can be converted from pentoses in wine [37]. This result may indicate the existence of pentoses. For linalool, same as the results of other research, linalool decreased after bottle storage for 12 months as the pH decreased, which might be caused by the acid-catalyzed transformations into furan linalool oxides [38; 39].
## 2 Materials and Methods
### Experimental Campaign  
The objective of this research was to analyze the technical viability of dispensing with the compressor anti-surge valve in a turbocharged SI engine equipped with a low-pressure EGR loop. To this end, a series of tip-outs at 1500 rpm from full to zero load (the most critical surge conditions) was tested. Two strategies were studied in these tip-outs. First, the throttle closure was optimized to avoid instabilities in the compressor while ensuring a fast load decrease. Second, different closings on the intake flap were applied, and the throttle closure was re-optimized for each of them in order to evaluate the reduction of the compressor inlet pressure as a potential strategy to accelerate the maneuver. It must be remarked that the use of the intake flap does not involve an additional cost, given it is not an ad hoc solution to enhance the engine transient response without the ASV. As mentioned above, the installation of the intake flap is required to increase EGR rate and, consequently, fuel efficiency. Indeed, notable improvements in fuel consumption were achieved with high EGR operation in the same engine, as shown in a previous study [33]. In pursuit of simplicity, however, no EGR was used in the tests to reduce the degrees of freedom.  
To optimize throttle closure, its actuation was divided into five transitions in which the time to reach five different openings was varied to find the surge limit. The throttle actuation of the 20 tip-outs tested for its optimization is shown in Figure 2. For all cases, the throttle position was automatically moved from 60 (fully open) to 40\({}^{\circ}\) in just one engine cycle (0.08 s), given that the air mass flow (AMF) was marginally modified in this opening range. For closer positions, the four transitions between openings equal to 40, 30, 20, 10, and 3\({}^{\circ}\) (minimum value to compensate friction losses) were optimized by testing five transition durations (1, 3, 5, 7, and 9 cycles), as seen in Figure 2. In descending order of duration, the optimum case was the one before the first in which compressor surge was detected.  
Later, the effect of the intake flap on the surge limit was studied. Inducing a reduction in the compressor inlet pressure (\(p_{1}\)) through the intake flap led to an increase in the corrected mass flow, thus moving to the right the operating point on the compressor map. In this research, three \(p_{1}\) levels were evaluated if considering its minimum value during the tip-out maneuver: 0.33, 0.65, and 0.87 bar. Likewise, the throttle actuation was re-optimized for each \(p_{1}\) level by following a similar procedure to the one described above.
# Latency Analysis of Blockchain-Based SSI Applications +
## 4 Evaluation Results  
In this section, we present the latency measures we obtained by deploying our scripts on multi-regional cloud servers and also on a separate local server.