esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Angel's Viola, Brovm Layer (Sampled Point 2)  
The non-invasive sampling protocol based on the pHEMA/PVP hydrogel was applied on the most superficial dark brown layer of the angel's musical instrument (Figure 1 Sampled point 2). After a ZipTip purification step, the MALDI-MS spectrum of the peptide mixture was acquired (Figure 5). The resulting PMF was interpreted as follows; most peak signals were typical of collagen peptides, among which, for example, peaks at _m/z_ 1105.4, 1267.5, 1427.5, 1459.5, and 2130.8 were due to peptides of collagen \(\alpha\)-1(I) or collagen \(\alpha\)-2(I) chains [20; 21; 38; 39; 40]. A detailed interpretation of MS signals is reported in Table 1. For the sake of clarity, we refer to \"animal collagen\" because it is not possible to establish the exact taxonomy of mammal's collagen-type proteins due to the sequences digestion similarity of collagen between bovine, goat, and rabbit [20; 41]. Moreover, it should be noted that chymotrypsin/trypsin digestion of mammal collagen sequences produces a PMF which is somewhat overlapping that one generated from the only trypsin digestion. This is consistent with our previous findings [20] and it may be due to a rather low number of chymotryptic cleavage sites i.e., phenylalanine, tyrosine, or tryptophan [42] occurring in collagen. Signals at _m/z_ 1330.5 and 1540.5--appearing relatively intense in the spectrum and labelled with E--are ascribed to enzymes' auto-proteolytic digestion confirming the low amount of proteinaceous material on the investigated layers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experimental** & **Theoretical** & **Sequence** & **Identified Protein** \\ _m/z_ & _m/z_ & **Sequence** & **Identified Protein** \\ \hline
1105.53 & 1105.57 & (R)GVQGPGPGAGPR(G) + 1 ox & Collagen alpha-1(I) \\
1128.50 & 1128.55 & (R)GLGPGPGTDGKR(G) + 2 ox & Collagen alpha-1(II) \\
1241.59 & 1241.60 & (R)GSPGGPGAAGFPGGR(G) & Collagen alpha-1(III) \\
1267.62 & 1267.68 & (R)GIPGPVGAAGATG(G) + 1 ox & Collagen alpha-2(I) \\
1289.53 & 1289.59 & (R)GSPGGPGAAGFPGGR(G) + 3 ox & Collagen alpha-1(III) \\
1358.75 & 1358.67 & (R)ADDANVVRDRDL(E) & Collagen alpha-1(I) \\  & 1427.70 & (R)GSAGPGATGFGAGR(V) & Collagen alpha-1(I) \\
1427.73 & 1427.73 & (R)GIPGFELPGGAG(G) + 2 ox & Collagen alpha-2(I) \\  & 1427.79 & (K)ALLIGOSNDVEL(A) & Collagen alpha-1(II) \\
1435.69 & 1435.68 & (R)GEPGPAGLGPGPGPGER(G) + 3 ox & Collagen alpha-1(I) \\
1459.70 & 1459.69 & (R)GSAGPPGATGFAAGR(V) + 2 ox & Collagen alpha-1(I) \\
1473.68 & 1473.67 & (R)GDGGPPGAAGR(T) + 2 ox & Collagen alpha-2(I) \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of the identified peptides in MALDI MS spectrum of dual enzyme digest of angel’s viola brown layer with sequences and relevant protein.  
Figure 5: MALDI-MS spectrum of a tryptic/chymotryptic digestion of Angel’s viola into a brown layer. The letter E indicates enzyme auto-proteolytic peptides. The complete list of peptides is reported in Table 1.
# Traffic Monitoring System Based on Deep Learning and Seismometer Data
## 1 Introduction
However, these signals can be hard to identify owing to the complexities of the seismic waveform and the influence of the underlying geology on the propagation of the seismic wave. We have overcome these problems by using artificial intelligence (AI) techniques. Moreover, seismic data are relatively smaller in size than videos recorded by a camera. One hour of a single-channel seismic record is 5 MB, while one hour of video can be 1 GB. For long-term monitoring, smaller data size has a large advantage in data management. In practice, seismic signals generated by vehicles are hard to distinguish, as most civilian vehicles generate similar vibrations at frequencies below 20 Hz. However, because these signals travel through the ground, they are less sensitive to wind noise, which is an advantage for vehicle detection [25]. Because AI has been instrumental in the dramatic improvement of voice recognition technology in the last decade, such as voice analysis [26], we chose to test the application of similar techniques to recognize vehicles from seismic waves. Furthermore, AI has been widely applied for the classification of seismic events [27; 28; 29; 30]. The application of AI to seismic information for monitoring traffic promises to offer the advantages of low power requirements, easy implementation, and low cost in addition to its advantages in occupant privacy. A study published in 2010 used a neural network to classify vehicles on the basis of seismic data [31]. The study used acoustic data recorded with a microphone to supplement the seismic data, and the best classification accuracy achieved was 92%. Another study published in 2019 relied exclusively on seismic signatures [25]. That study proposed extracting spectral features of vehicle seismic signals, using a log-scaled frequency cepstral coefficient (LFCC) matrix, a step that requires preprocessing the seismic data in the frequency domain. This method achieved classification accuracy as high as 91.39%. However, both studiesconcerned heavy military vehicles and cannot be generalized to civilian vehicles. Moreover, both approaches could not use raw seismic data without preprocessing or supplementation by other data. This paper describes our proposed traffic monitoring system for civilian applications. Our purpose was to build and optimize a neural network that takes a window of waveform data as input, labels it as either seismic noise or a vehicle signal, and identifies the type of vehicle. The proposed approach relies on seismic data alone without preprocessing. In this study, we tested three different neural network architectures that are widely used for the analysis of time series data, including voice recognition. Our approach was applied to civilian traffic and achieved 99% classification accuracy in the training process and 96% accuracy in the validation process.
# Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
## 3 Methods
### Simulation of Runoff Based on SWAT Model  
Hydrological models are widely used to simulate the runoff process, and many scholars have employed some common models such as the Variable Infiltration Capacity (VIC) model [25; 26; 50], Soil and Water Assessment Tool (SWAT) model [51; 52; 53], and Identification of Hydrographs and Components from Rainfall, Evaporation, and Stream (IHACRES) model [50; 51] to simulate the runoff in various river basins. However, the condition of each basin is different, especially the difference in climate, which makes the selection of hydrological models critical to the accuracy of runoff simulation [52]. In terms of this issue, [PERSON] et al. [54] evaluated the performance of 9 hydrological models for 12 large-scale river basins around the world. In their conclusions, the SWAT model performs well in simulating runoff in the Yangtze River Basin. Some other studies in the literature have also shown that the SWAT model shows good results in the simulation of runoff in China compared with other models, e.g., the VIC model and Xinanjiang model [55; 56; 57]. As a physically based model, the SWAT model can simulate the short-term and long-term hydrological process, chemical process, erosion process, soil hydrological cycle, and crop production process of the basin rapidly [23; 58]. Compared with other models, the SWAT model can simulate the response of runoff to LUCC in most basins in China more obviously and accurately [55; 59]. The SWAT model considers the longitudinal movement of water flow in each small unit and the lateral exchange of water flow between each unit and each other, so it is suitable for the simulation of lateral runoff at small scales. Therefore, the SWAT model was used to estimate the outbound runoff at small-scale basins in this study.  
The SWAT model of the GRB was established by using the DEM, soil type, land cover, precipitation, and meteorological data. CN2.mgt and 11 other parameters related to runoff were selected by referring to other studies [59]. SWAT-CUP 2012 was used to obtain the ranges of each parameter and sensitivity order through multiple iterations. The sequential uncertainty fitting (SUFI-2) algorithm was used to update parameter values. Daily precipitation and monthly runoff data in 2002-2011 and 2012-2018 were used for the calibration and validation, respectively. The applicability of the model was evaluated by the Nash-Sutcliffe Efficiency (NSE), coefficient of determination (R\({}^{2}\)), and Percent Bias (PBIAS) indexes.
# Curved Path Following Control for a Small Fixed-Wing UAV with Parameters Adaptation
## 4 Simulation  
The proposed curved-path following control method with adaptive parameters is presented as Figure 8.  
The bank-to-turn maneuver with no sideslip is applied to the small fixed-wing UAV. It follows that [25]  
\[\dot{\chi}=\frac{\mathrm{g}}{V}\tan(\phi) \tag{28}\]  
where \(\mathrm{g}\) is the magnitude of gravity at sea level. It is assumed that the inner loop PD controller can quickly track the commanded bank angle. At the same time, the dynamics of the bank angle are much faster than those of heading and altitude. In this case, the roll angle can be approximately considered as the commanded bank angle \(\phi_{cmd}\). Therefore, Equation (28) becomes  
\[\dot{\chi}=\frac{\mathrm{g}}{V}\tan(\phi_{cmd}) \tag{29}\]  
Considering the relationship \(\dot{\chi}=u_{cmd}\) shown in Equation (1), the bank angle command \(\phi_{cmd}\) can be derived as  
\[\phi_{cmd}=\tan^{-1}\biggl{(}\frac{Vu_{cmd}}{\mathrm{g}}\biggr{)} \tag{30}\]  
The simulation was studied in the Matlab/Simulink environment. The Aerosonde UAV model with a wingspan of 2.9 m was used as the test vehicle [29]. Figure 9 shows the simulation in the Matlab/Simulink environment. The parameters used in the simulation are shown in Table 2. The initial \(V\) was set as 25 m/s. The sample time was set as 25 ms. A constant wind at 8 m/s from 270\({}^{\circ}\) W was also added to the model of the UAV, which will verify the effectiveness of the proposed method under the wind disturbance. The initial position and course of the UAV were set as \((0,0)\) and 0\({}^{\circ}\) N, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Initial \(V\) & 25 m/s & Wind speed & 8 m/s \\ Sample time & 25 ms & Wind direction & 270\({}^{\circ}\) West \\ \(x_{0}\) & 20 m & \(k_{1,max}\) & \(6.0\times 10^{-4}\) \\ \(V_{p1}\) & 40 m/s & \(k_{1,min}\) & \(3.0\times 10^{-4}\) \\ \(V_{p2}\) & 15 m/s & \(k_{20}\) & \(8.0\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters for simulation.  
Figure 8: The structure of the proposed controller.
# A Novel Consistent Quality Driven for JEM Based Distributed Video Coding
## 3 Proposed JEM Based DVC (JEM-DVC)
### JEM Based Key Frame Coding
In Figure 3, 33 angular modes are numbered from 2 to 34, and each represents a distinct direction. Among them, special modes 10 and 26 are referred to as Horizontal and Vertical, respectively. The non-angular modes 0 and 1 are Planar and DC, respectively, which are defined as the same as in the H.265/HEVC Intra coding [15]. Additionally, in the case of CU coding with several candidates of sizes, such as \(CU_{size}=64\times 64\), \(32\times 32\), \(16\times 16\), and \(8\times 8\), it is reported from our experimental results that a larger \(CU_{size}\) is suitable for flat and homogeneous regions. Additionally, for these regions, DC, Planar, and simple directional modes (e.g., Vertical and Horizontal modes) are the most likely to be chosen as the best mode candidate. In contrast, a smaller \(CU_{size}\) is suitable for high activities and significantly detailed regions. Therefore, the exhaustive mode searching for all candidates of CU sizes is unnecessary, since it is very time consuming and complex. As a good example to verify the above assessment, Figure 4 shows the rate of optimal mode selections that were obtained from our experiments (in the case of large CU size, \(CU_{size}=64\times 64\) and \(32\times 32\)). As shown in Figure 4, among a total number of 35 directional predictions, most of the decisions for the best prediction in this case are Planar, DC, Horizontal, and Vertical, which account for up to 91% of total mode candidates. Therefore, we can determine these prediction modes for large CU sizes (\(CU_{size}=64\times 64\) and \(32\times 32\)), while the remaining modes are decided for smaller CU sizes (\(CU_{size}=16\times 16\) and \(8\times 8\)). These can be referred to as the fast Intra mode decision scheme that is proposed for the JEM based key frame coding.
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 2 Related Work
### ML-Based High-Precision Indoor Positioning  
[PERSON] et al. [28] proposed an indoor fingerprinting system based on calibrated channel state information (CSI). The raw-phase information is extracted from the multiple antennas and subcarriers of the network interface card, and then the phase information is further compressed by a linear transformation. In the offline phase, a deep network with three hidden layers is designed to train the data, and a greedy learning algorithm is introduced to reduce the computational complexity. In the online stage, the probability method based on radial basis function is used to estimate the online position, and good performance is achieved. [PERSON] et al. [20] proposed a two-stage neural network to extract features from the acquired data according to the information obtained by different base stations, with a precision of 0.28 m and a mean square error of 0.30. However, in the design stage of the network, the interaction information between each base station was not considered, but the individual information of each base station was modeled separately and then superimposed, which failed to capture the nonlinear relationship between the data of each base station. It is difficult to improve the accuracy further. In the lower part of the model, the speed of convergence suffers due to the two channels, and a superposition operation is performed during the convergence. Additionally, because the network depth is too large (63 layers), it takes nearly seven hours of training time and the time cost is too high. [PERSON] et al. [29] utilized the diversity of channel impulse responses collected by multiple access points, and combined artificial neural networks with traditional positioning technologies such as received signal strength (RSS) and AOA for co-location, thereby improving the accuracy of traditional positioning techniques. [PERSON] et al. [22] proposed a free positioning technology device based on machine learning. It removes the sub-carrier noise affected by multipath, selects the most reliable localization features from the channel response, and uses support vector machines (SVM) for identification; this results in a better positioning accuracy, even in the presence of passive devices.
## 2 Mathematical Modeling of HESS
### Bidirectional DC/DC Converter  
A hybrid energy storage system requires the bidirectional connection of lithium batteries, supercapacitors and the \(DC\) bus for energy transfer. Therefore, this paper selects a bidirectional \(DC\)/\(DC\) converter, with its topology shown in Figure 4.  
The operating principle is illustrated in Figure 5.  
Figure 4: Bidirectional \(DC\)/\(DC\) converter topology.  
Figure 3: Equivalent model of supercapacitor.  
ESD represents the energy storage device, Inv represents the inverter and Rec represents the rectifier.  
Combining Figures 4 and 5, it can be seen that when the energy storage device releases energy, the _DC/DC_ converter operates in boost mode, controlling the release of power through real-time energy feedback to the storage device. Similarly, when the energy storage device absorbs energy, the _DC/DC_ converter operates in buck mode.
# Practical Application of Sustainable Road Structure: Mechanical and Environmental Approach
## 3 Materials, Testing Methods and Trial Sections
### Road Structure Trial Sections  
The test section was located in the east-central part of Poland, 50 km south-west of Warsaw on a local city road. It consisted of two parts: the experimental section developed in the APSE project, located on the west part, and reference one, located on the east part of the road. This road has a medium traffic level (KR3), which means that the expected traffic during the service life period will be between 500,000 and 2,500,000 ESALs (equivalent standard axles loads)--KR is a Polish equivalent of ESALs traffic load-seven KR classes are observed in Poland.  
The reference road section has the following structure, from surface to base layers: 5 cm of surface course (AC11--asphalt concrete 11), 6 cm of binder course (AC22), 7 cm of base course (AC22) and 20 cm of subbase with mechanically stabilized aggregates 0/31.5 mm (Figure 1a). The APSE experimental road section has the same asphalt layers thickness as reference but exhibits some differences in materials composition (Figure 1b).  
Both sections were only different in terms of the asphalt binder and base layers (30% RAP and BIO-additive used in the experimental section while only 20% RAP in the reference section) and subbase (CDW in the experimental section substituting virgin aggregates in the reference section). During the construction process, no technological differences between the experimental and the reference section were observed. Only visual differences could be seen on the sub-base layers (Figure 2a,b), however comparable compaction and load capacity of 160 MPa at the top of the layer were obtained. Upper layers e.g., base, binder, and wearing courses were visually the same (Figure 2c,d).  
Figure 1: Structure of trial sections: (**a**) reference section and (**b**) APSE experimental section.  
During the project duration, the effectiveness of the asphalt was evaluated through all the phases, from early laboratory assessment to plant production, handling, application and post-trial evaluation. A direct comparison was made versus the reference asphalt mixes.  
This validation will provide an opportunity to observe construction from start to finish and to record material property data, take samples, record observations through the construction, and monitor early-life performance. All analysis presented in this study lead to some recommendations for future applications, and to discuss whether new sustainable pavement concept is suitable for industry and asset managers.
## 5 Discussion
### Use of UAVs in Impact Assessment
#### 5.3.1 Advantages of the Use of UAVs
In addition, compared to conventional methods such as manned aerial surveys or satellite imaging, UAVs offered us a highly cost-effective alternative for the post-flood assessments. UAVs eliminate the need for expensive equipment and specialized personnel, significantly reducing the operational expenses. Additionally, they can be deployed quickly and require minimal logistical support, further contributing to cost savings [31; 42; 43; 44; 45; 46]. Another top advantage is the increased safety of assessment teams. Post-flood environments often pose significant risks to ground-based assessment teams due to hazards such as unstable structures, debris, and potentially contaminated floodwaters. Our UAV provided a safer alternative by enabling remote data collection and eliminating the need to enter dangerous areas. We were able to pilot the UAVs from secure locations, while the devices, equipped with advanced navigation and obstacle avoidance technologies, autonomously navigated challenging environments [47; 48; 46]. This approach not only protected us from risk but also ensured the efficient collection of data in hazardous conditions, contributing to more effective assessments and recovery efforts.
## 3 Spectrum Management Issues
### M-Milmo
#### 3.5.2 Spectrum Sensing  
The authors in [229] have designed a unique compressed sensing framework that differentiates LOS from NLOS signal paths and leads to better performance in comparison with existing approaches. Even though it increases the localization accuracy and minimizes the execution time, a high computational complexity algorithm is found. In [230], the researchers have discussed the importance of throughput and spectral efficiency for higher user services and system performance. The technique called Matched Filter Precoding (MFP) improves the system performance and cell-edge user's throughput in a cooperative M-MIMO network because of an increase in antenna numbers at a BS. Additionally, MFP helps reduce intercell interference among users by using asymptotical orthogonality. Overall, the cooperative M-MIMO mitigates the intercell interference and pilot contamination using many antennas at a BS. However, for a pilot signaling, more comprehensive and refined channel information is required to explore the extensive research work.
## 3 Results and Discussions  
### Adjustment of Laser Focus Position and Results of Kerf Width Measurement  
This study used a fixed average power of 25.5 W, pulse width of 200 ns, pulse repetition rate of 170 kHz, one pass per path, and scan rate of 5 mm/s to determine the focal length of the laser processing system. Air blowing with clean dry air was also applied.  
Based on the judgment of the magnitude of audio frequency, the focal length of the laser beam was located in the range of 165.2 to 167.2 mm from the top surface of the workpiece to the laser beam source along the Z-axis (for which the height along the Z-axis is defined as 0 mm). The details of the calibration process of the focal length are as follows: a change in distance of 0.5 mm was adopted to observe the magnitudes of kerf width and kerf depth. The focal length reading was narrowed in the range of 165.7 to 166.7 mm. Furthermore, in this focal range, the change in distance of 0.05 mm was also observed. We found that the focal length of the laser beam along the Z-axis was in the range of 166.1 to 166.3 mm, in which the minimum kerf width was measured between 28 to 30 \(\upmu\)m, which was slightly less than the spot size of 36 \(\upmu\)m (calculated from Equation (1)). The smaller spot size was attributed to the laser beam energy density with Gaussian distribution, and the energy density in the fringe of the focal spot was less than the threshold for the PCD material ablation. As we adjusted the Z-axis reading to be slightly higher or lower than the measured range, the kerf width became wider and kerf depth became shallower. We considered an average of 166.2 mm from the range measured in the final testing as the focal length, which was used to set the height of the Z-axis in the subsequent testing.