esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Capturing QoS Measurements  
The cellular measurement data used in this work was captured by Teragence using sampling. The captured data consist of measurements of PLR, delay, jitter, the location of the mobile device and the mobile operator. We did not consider delay, location and the mobile operator in this work, as PLR and jitter are relatively stronger influencing factors of the QoE of VoD applications [29]. All the measurements used in this paper were treated as anonymous in accordance with the privacy policy of the UK.  
The measurements use packet probing via the android mobile application installed in the user device. The probing packets travels through the network and are captured at the servers, where the packets are processed to retrieve different measurements and are stored in a database. The administered access to the database was provided to us by Teragence to use their measurement data for analysis in this paper. We cannot disclose the details of the setup further due to commercial confidentiality.
# Experimental Study on Dynamic Response Characteristics of RPC and RC Micro Piles in SAJBs
## 2 Experimental Program
### Soil Container and Soil Parameters
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Water** & **Density** & **Void Ratio** & **Cohesive** & **Internal** & **Poisson** \\
**Content** & **\(\boldsymbol{\omega}\) (\%)** & **\(\boldsymbol{\rho}\) (g/cm\({}^{3}\))** & **\(\boldsymbol{e}\)** & **Strength** & **Friction Angle** & **\(\boldsymbol{C_{u}}\)** & **Ratio** \\  & & & **\(\boldsymbol{c}\) (kPa)** & **\(\boldsymbol{\varphi}\) (\({}^{\circ}\))** & & **\(\boldsymbol{v}\)** \\ \hline
2.2 & 1.98 & 0.80 & 4 & 32 & 3.15 & 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Physical and mechanical parameters of sand. Figure 3: Details of the soil box and layout of pile. (**a**) Steel box. (**b**) Model pile in box. Two different boundaries were employed in the inside of the sand box, and some angle steels were welded into the bottom of the box to form an anti-skid compartment. The relative movement between the bottom sand and the box can be prevented. In addition, the flexible boundary was established inside of the sand box by sticking 10-cm thickness polyethylene foam board to the inner of the steel box walls. Therefore, the boundary condition of the micro pile-soil interaction can be as closed as possible to the real situation [38; 39; 40]. In this experiment, the embedded depth of each micro-pile was 1.8 m, which left the above-ground part of 0.4 m, as reference literature [41]. After the micro pile was in position, it began to fill the box with soil up to 1.8 m of height, as shown in Figure 4a. The soil filled in the box was tamped by each layer of 20 cm height until the embedded depth is 1.8 m.  
Elastic pile is widely used in SAJB to satisfy the longitudinal deformation of the superstructure under the temperature load or earthquake load. According to a technical code for the building pile foundation (China) [42] and literature [43], it is found that, when the equivalent length (\(\tilde{l}\)) is larger than 2.5, the pile belongs to the elastic pile. The equivalent length is calculated by the following. \[\tilde{l}=\alpha l_{0} \tag{1}\]  
\[\alpha=\sqrt[5]{\frac{mb_{0}}{EI}} \tag{2}\]  
\[b_{0}=1.5b+0.5 \tag{3}\]  
where \(l\) is the equivalent length of the micro pile (m), \(\alpha\) is the relative flexibility coefficient, \(l_{0}\) is the embedded depth of pile (m), \(m\) is the coefficient of horizontal subgrade reaction (MN/m\({}^{4}\)), \(b_{0}\) is the calculating width of pile (m), \(E\) is the elastic modulus of the pile (kN/m\({}^{2}\)), \(I\) is the moment of inertia of pile (m\({}^{4}\)), and \(b\) is the diameter of pile (m). Based on Equations (1)-(3), the equivalent length (\(\tilde{l}\)) of the RPC and RC micro-piles are 3.49 m and 3.78 m, respectively, which meets the requirement of the elastic pile in the code.
## 3 Results and Discussion
### Total and Free Amino Acid Composition in Frozen and Freeze-Dried Materials  
In both frozen and freeze-dried samples, the most abundant amino acid was glutamic acid (6.8 g/100 g and 4.3 g/100 g dry weight), followed by glycine/arginine (6.1 g/100 g and 8.4 g/100 g dry weight) and aspartic acid (5.1 g/100 g and 3.5 g/100 g dry weight). Those three amino acids together represent about 53% and 57% of the TAAs profile (31.6 g/100 g and 30.4 g/100 g dry weight), respectively (Table 2). Glutamic acid, glutamine, leucine, methionine, serine, and tyrosine in frozen samples (theoretical calculation, dry weight) showed a significantly higher (\(p<0.05\)) concentration compared to freeze-dried samples. Previous studies have found glycine to be the major amino acid in both frozen and freeze-dried _P. tremulus_(2021; 2021). In addition, glutamic acid, aspartic acid, and arginine have also been shown to be prominent in sea cucumbers (2021; 2021).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Samples** & **Frozen [\%]** & **Freeze-Dried [\%]** \\ \hline Water & 89.7 \(\pm\) 0.8 & 3.4 \(\pm\) 1.0 \\ Protein & 4.4 \(\pm\) 0.7 & 43.1 \(\pm\) 2.4 \\ Ash & 4.0 \(\pm\) 0.4 & 36.3 \(\pm\) 2.8 \\ Lipid & 1.3 \(\pm\) 0.4 & 8.5 \(\pm\) 2.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The proximate composition in frozen and freeze-dried red sea cucumber (_Parastichopus tremulus_). Results are presented as average \(\pm\) standard deviation, \(n=10\).  
In both frozen and freeze-dried samples, glutamic acid (139 and 414 mg/100 g dry weight), glycine/arginine (46 and 258 mg/100 g dry weight) and alanine (13 and 43 mg/100 g dry weight) are the dominating FAAs. Those dominating FAAs represent about 82% and 85% of the total FAAs content, respectively (Table 2). All FAAs in freeze-dried samples showed a significantly higher (\(p<0.05\)) amino acid concentration compared to the theoretical calculation of frozen samples (dry weight), except aspartic acid. This is in accordance with a previous study [7]. The glycine concentration may be prominent in red sea cucumber, but it could not be separated from arginine during analysis. The levels of amino acid content have been shown to vary with seasonal variations, age, habitat area, and location [36]. In this study, the red sea cucumbers were harvested only once, in October 2020.
## 4 Tree-Based Protocols
### Manchester Coding  
Some tree-based protocols work with Manchester coding, which can be used to locate bits that have collided [2; 5; 53]. The use of Manchester coding to trace the collision to an individual bit is called bit-tracking in the literature [54]. In Manchester coding, the value of a bit is defined by the change in the voltage level: a negative or positive transition. A logical 0 is coded by a positive transition; a logical 1 is coded by a negative transition. In the case in which a minimum of two tags simultaneously  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Protocol Name** & **QT** & **STT** & **QwT** \\ \hline \multirow{6}{*}{**Protocol feature**} & \multirow{6}{*}{Tags respond when their ID match the query transmitted by the reader.} & \multicolumn{1}{p{113.8 pt}}{Defines the generation of the new queries according the several predefined rules. These rules take into account the number of consecutive collisions and slots.} & \multicolumn{1}{p{113.8 pt}}{The reader transmits the number of bits tags must respond in addition to the query. This number is calculated using an heuristic function at the reader side.} \\ \hline \multirow{6}{*}{**Disadvantages**} & \multirow{2}{*}{Tags respond when their ID match the query transmitted by the reader.} & \multicolumn{1}{p{113.8 pt}}{It transmits the full tag ID with each response, therefore a high number of bits are wasted with every collision.} & \multicolumn{1}{p{113.8 pt}}{The reader command needs a high number of bits to represent the size of the tags’ responses.} \\ \hline \multirow{2}{*}{**Efficiency**} & \multirow{2}{*}{34.6\%} & \multirow{2}{*}{34\%} & \multirow{2}{*}{35\%} \\ \cline{2-2} \cline{5-5}  & & & \\ \cline{1-1} \cline{2-2} \cline{5-5}  & & & \\ \cline{1-1} \cline{2-2} \cline{5-5}  & & & \\ \cline{1-1} \cline{2-2} \cline{5-5}  & & & \\ \cline{1-1} \cline{2-2} \cline{5-5}  & & & \\ \cline{1-1} \cline{2-2} \cline{5-5}  & & & \\ \cline{1-1} \cline{2-2} \cline{5-5}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: A comparison of standard tree-based protocols.  
Figure 17: Flow chart of the tag ID estimation phase of the proposed FQwT protocol: (**a**) for reader; (**b**) for tags.  
transmit bits with different values ('0' and '1'), the positive and negative transitions of the received bits violate the coding rules, and a collision can be tracked.  
As shown in Figure 18, Tag 1 is 1100 and Tag 2 is 1010. Both tags synchronously transmit data. The reader can understand the first bit, but the second and the third bit cause a collision. The reader detects a violation of the Manchester codification on those bits, and this is interpreted as a collision located at bits 2 and 3.
# Aerodynamics and Motion Performance of the H-Type Floating Vertical Axis Wind Turbine
## 4 Results and Analysis
### Results and Analysis
#### 4.3.5 Comparison with \(\Phi\)-Type Floating VAWT  
[PERSON] et al. studied a 5 MW \(\Phi\)-Darrieus type wind turbine supported by the same truss Spar type floating foundation and with the same mooring system, and its parameters are presented in Reference [11]. Compared with the \(\Phi\)-type wind turbine (blade height is 129.56 cm, mass of two blades is 308 tons), the blade height and blades mass of this paper decreased by 39% and 75%, respectively. The two floating VAWTs have the same total mass.  
Motions of the two floating VAWTs were compared considering the wind loads and regular wave loads, and the calculation parameters are shown in Table 8. The mean value and STDs of the surge, heave, and pitch motions of the two floating VAWTs are presented in Figures 14-16. The mean generator powers of the two floating VAWTs are shown in Figure 17.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Items**} & \multicolumn{3}{c}{**Surge (m)**} & \multicolumn{3}{c}{**Heave(m)**} & \multicolumn{3}{c}{**Pitch(deg)**} \\ \cline{2-10}  & **STD** & **Mean** & **Max** & **STD** & **Mean** & **Max** & **STD** & **Mean** & **Max** \\ \hline LC4.1 & 0.36 & 0.32 & 1.46 & 0.27 & 0.17 & 0.52 & 0.19 & 0.42 & 1.14 \\ LC4.2 & 0.58 & 2.33 & 4.28 & 0.34 & 0.17 & 0.72 & 0.39 & 2.61 & 3.97 \\ LC4.3 & 1.04 & 2.95 & 6.45 & 0.49 & 0.17 & 1.15 & 0.72 & 3.28 & 5.67 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Response statistics of the floating VAWT (cases LC4).  
Figure 13: Pitch responses of the floating VAWT: (**a**) time histories; and (**b**) response spectra.  
Figure 16: Pitch comparison of two floating VAWTs (cases LC5).  
Figure 14: Surge comparison of two floating VAWTs (cases LC5).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Wind Turbine Type** & **Wave Height (m)** & **Wave Period (s)** & \(V_{\infty}\) **(m/s)** \\ \hline LC5.1 & H-type & & 2.10 & 9.74 & 5 \\  & \(\Phi\)-type & & 3.62 & 10.29 & 15 (rated wind speed) \\ LC5.2 & H-type & & 14 (rated wind speed) \\ LC5.3 & H-type & & 6.02 & 11.38 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison cases (LC5).  
Figure 15: Heave comparison of two floating VAWTs (cases LC5).  
Figures 14-16 show that compared with the \(\Phi\)-type floating VAWT, the mean values of the H-type floating VAWT's motions are much smaller. Taking the rated wind speed case (LC5.2) as an example, the mean values of surge, heave and pitch of H-type floating VAWT decrease 38%, 74.2% and 38.6%, respectively. Therefore, H-type VAWT withstand smaller wind loads compared with the \(\Phi\)-type VAWT. The STDs of motions of the two floating VAWTs are almost same, as they are supported by the same floating foundation, and the STD of the floating VAWT motions mainly depends on the wave loads. Figure 17 shows that the motion of floating VAWTs has insignificant effect on the mean generator power for both type wind turbines. It is a satisfactory result for the prospective development of a floating VAWT.
# Sparse Weighting for Pyramid Pooling-Based SAR Image Target Recognition
## 3 Contrast Experiment Results and Analysis
### Experiment 1: Investigation of the Proposed Method  
In order to verify the superiority of the presented algorithm, the presented sparse weighting sparse pyramid pooling (SWSPP) algorithm was compared with the state-of-the-art methods: namely sparse coding SPM (ScSPM) [13] and locality-constrained linear coding SPM (LLC) [16]. The classification results are listed in Table 2. The performance of the codebook was compared under different dimensions \(dim\) from 100 to 1024. The greater the dimensionality, the more the feature extracted was discriminative. Therefore, the results improved as the dimension increased. In all three scenarios, the method presented in this study was obviously superior to the ScSPM and LLC methods under all the dimensions of the codebook. Since the proposed SWSPP took the sub-regions dependability into account,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target** & **Name of Training Set** & **Training Set** & **Name of Testing Set** & **Testing Set** \\ \hline BTR70 & Sn-c71 & 233 & Sn-c71 & 196 \\ \hline BMP2 & Sn-9663 & 233 & \
\begin{tabular}{c} Sn-9663 \\ Sn-9666 \\ Sn-c21 \\ \end{tabular} & 195 \\ \hline T72 & Sn-132 & 232 &
\begin{tabular}{c} Sn-132 \\ Sn-812 \\ Sn-s7 \\ \end{tabular} & 191 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The dataset of training and testing targets.  
Figure 3: Visible light images for three targets in the MSTAR database. (a) BMP2. (b) BTR70. (c) T72.  
it enhanced the discrimination of the recognition target. From the experiment results, we verified that the dependability generated according to the sparse representation was effective for classification. The curves of the classification average results under the three algorithms are shown in Figure 4.
## 3 Results
### Validation of the Derived Wind
#### 3.2.3 Validation of Wind Profiles  
The Doppler beam-swinging (DBS) scanning mode, emitting five beams towards the east, south, west, north, and vertical in sequence at a 75\({}^{\circ}\) elevation angle, acquires radial wind speeds in five directions: V\({}_{\text{E}}\), V\({}_{\text{S}}\), V\({}_{\text{W}}\), V\({}_{\text{N}}\), and \(\omega\). When the Lidars are operating in the DBS mode, they can provide files containing reconstructed wind, including the meridional (u) and the zonal (v) wind components, derived using a method similar to [PERSON] et al. [21]. These data are used to further evaluate the performance of the optimized method, with a purpose for its potential to extend the height with acceptable accuracy, as it is known that the assumption of a horizontally homogeneous atmosphere used in the inversion method is increasingly difficult to satisfy with the increasing detection height, and the accuracy of inversion results might decrease, particularly for low-elevation-angle VAD scans. A wind profile spanning 8-800 m can be obtained by the inversions from VAD scans with 10\({}^{\circ}\) (8-300 m) and 30\({}^{\circ}\) elevation angles (25-800 m). Here, the wind profiles derived from the optimized method are evaluated using the wind profiles obtained from DBS scanning as a benchmark.  
Figure 12 shows the MAE (Figure 12a), RMSE (Figure 12b) and R\({}^{2}\) (Figure 12c) between the wind speed derived from the optimized/the traditional methods and those from DBS reconstruction at S3 from VADs with both elevations. It can be observed that the accuracy of the wind speed derived from the optimized method is superior to that from the traditional method in terms of all the three statistics. Notably, at certain heights, such as near the hub height of 78 m, the error from the traditional method shows abnormally high values (pink line and light blue lines in Figure 12), while this error is significantly reduced after using the optimized method (red and blue lines in Figure 12), indicating an improvement in the performance of the optimized method. In addition, the optimized method has the potential to extend the height with acceptable wind accuracy, especially for low-elevation-angle scanning. For example, in the last gate of the 10\({}^{\circ}\) elevation VAD scan, the statistics of the wind speed derived using the optimized method were significantly improved. Generally, the results obtained using a 30\({}^{\circ}\) elevation VAD scan are better than those obtained using a 10\({}^{\circ}\) elevation VAD scan. The above analysis indicates that the wind speed derived from the optimized method is more consistent with the wind speed reconstructed by DBS. Therefore, the optimized method can be used to derive wind profiles from below the minimum range of a vertically operating scanning Doppler Lidar to above the height affected by wind turbines.  
Figure 12: _Cont._
# Dynamic Traffic Scheduling and Congestion Control across Data Centers Based on SDN
## 3 The Design and Implementation of DSCSD
### System Model  
Our scheme was designed and innovated on the basis of data center networking. In the following presentation, we briefly describe the components of the system model in Figure 2. (i) Data center servers. PC1, PC2, and PC3 are three individual data centers, and the fs from PC1 to PC3 takes priority over the flows from PC2 to PC3. (ii) OpenFlow switches. With the five switches, two distinct paths are formed, and the path (S1, S4, S5) is the shortest path leaving (S1, S2, S3, S4) the second shortest. (iii) SDN controller. We selected the open source OpenFlow controller Floodlight as the centralized controller and it can be used to control the behavior of OpenFlow switches by adding, updating, and deleting flow table entries in the switches.
## 1 Introduction  
Due to the development of high throughput DNA sequencing technology, genome sequencing costs have been significantly reduced, which has led to a number of revolutionary advances in the genetics industry [1]. Next-generation sequencing (NGS) allows significant amounts of DNA to be sequenced in parallel, and minimizes the need for comparatively inefficient fragment-cloning methods that are usually used in Sanger sequencing technologies [2].  
However, the management of such large volumes of data is still an issue, and has thus been of interest to researchers [3, 4]. Management refers to the transmission and storage of the sequence data. For example, it is still very common in the field of biomedical research to wait hours or sometimes days for DNA data transmission requests to be completed. Also, security and reliability issues remain obstacles in public sequence databases [5], which is even more discouraging to researchers.  
This is also expected to be a problem for next-generation medical informatics, such as personal health record (PHR) systems [6], where healthcare consumers own their entire health data, and in m-Health systems, where health data is freely transmitted between servers and smart devices [7]. DNA sequence data is not an exception for future consideration. In fact, it has already been emphasized by prior research [8; 9; 10] that protective approaches are required to defend against genome data attacks.  
Data compression is considered a conventional solution that is often used to attempt to reduce dependency on storage, and at the same time, to reduce the resources needed for DNA transmission. Efficient sequencing data compression methods are in high demand, not only by researchers [11; 12; 13], but also by industry, considering the fact that Aspera (Everyville, California CA, USA), a high-performance file transfer (including DNA) software company, was acquired by IBM (Armonk, New York NY, USA) in December 2013. This is because data compression not only reduces transmission time, but also reduces the need for disk space.  
Therefore, this research proposes Blockchain Applied FASTQ and FASTA Lossless Compression (BAQALC), a lossless compression algorithm that allows for efficient transmission and storage of the immense amount of DNA sequence data that are being generated by NGS within a reliable blockchain network [14]. The results showed comparatively high-performance, not only compared to widely used compression algorithms, but also compared to algorithms described in prior research. The proposed solution is envisioned to contribute to providing an efficient transmission and storage platform for next-generation medical informatics based on smart devices [15] such as PHR, telemedicine [16], e-Health [17], or m-Health.
# On Multi-Hop Decode-and-Forward Cooperative Relaying for Industrial Wireless Sensor Networks
## 5 Numerical Results  
In this section, we will present and discuss the analytical results obtained in the previous sections. The validity of the theoretical results is confirmed with Monte Carlo simulations.
# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 4 Performance Evaluation
### Result Analysis  
This study focused on the evaluation and comparison of handover decision strategies, including ACHQ, CAP, and the proposed IHD-V2X-based algorithm for varying the UE velocity and density of SCs. Note that the network needs of UEs vary based on the priorities of applications, including basic services (audio, video streaming, and general traffic), data offloading, augmented reality, and relay is implemented in CAP and IHD-V2X mechanisms, which are not considered in ACHQ. The effects of the velocity and network density on the HO efficacy are important for different application types with distinct network performance requirements.