esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Multi-Sensor GNSS-IoT System for Precise Water Surface Elevation Measurement
## 2 Methodology
### Hardware Device  
WSE estimation relies on a local network of low-cost hardware devices being deployed, which each consist of a GNSS receiver and antenna, LTE CAT-M1 modem, battery, solar panel, ultrasonic and accelerometer sensors, outer radome and mounting parts, as shown in Figure 1[25; 26]. The device has dimensions of 208 mm (Length) \(\times\) 169 mm (Width) \(\times\) 272 mm (Height) and weighs 1325 g, including the supplied external comms antenna. It is a plug-and-play compact device that can operate in ambient environmental temperatures of \(-\)10 \({}^{\circ}\)C to +65 \({}^{\circ}\)C. The LTE CAT-M1 modem provides better coverage and signal penetration in remote or hard-to-reach areas compared to traditional LTE/4G/5G networks. It supports bi-directional communication for GNSS and sensor data and enables regular status updates, remote commands, configuration and Firmware-Over-the-Air (FOTA) updates. The internal rechargeable Lithium-Ion Phosphate (LiFePO4) battery is designed for 2-4 weeks of operation without recharging. Coupled with a 1.5 W integrated solar panel, the device is capable of permanent autonomous site operation without external power inputs.
# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
## 3 Motivation and Proposed Scheme  
The RACH congestion problem can be fatal for a network system and devices. As described in the RA procedure in Section 2.1, a collision occurs when multiple devices select the same preamble and send their Msg1s. Because this Msg1 collision cannot be detected by the eNB during the Msg1 reception, these devices suffer from an Msg3 collision that also wastes the PUSCH resources. Moreover, the congestion problem gets worse due to the retransmission by the RA-failed devices and can even cause a connection failure. In summary, the RACH congestion problem causes (1) degradation of the RACH throughput, (2) increased probability of connection failures, and (3) wasting of the uplink resources (PUSCH). Exploiting the capture effect positively affects the network system [15; 16]. Intuitively, the RACH congestion problem can also benefit from exploiting the capture effect. Even though collisions occur, if an Msg3 of a specific device is captured, the eNB can send an Msg4 to the device, and, therefore, the device would successfully connect. The received signal powers at the eNB are differentiated to exploit the capture effect. However, as seen in Equation (4), there is no difference between the target powers of the received signals when perfect power control for M2M devices is performed. Hence, we propose adopting a power ramping scheme for Msg3 transmission to exploit the capture effect. The proposed scheme leads the device to transmit at higher power as more retransmissions occur. There are some advantages using the power ramping scheme: first, it is easy to implement because it has been adopted for Msg1 transmission; and, second, the more retransmitted devices there are, the higher the probability of being captured, reducing the probability of connectionfailure. Because the proposed scheme uses a power-ramping scheme, the Msg3 transmission power can be reformulated as follows:  
\[P_{msg3}=\min(P_{Cmax},P_{target}+PL+(r-1)PRS). \tag{5}\]  
As a result, the received power can be differentiated based on the ramping number (\(r\)) of the device.
## 2 Specim Design and Test Method
### Layout of Monitoring Points  
The appropriate positions of measurement were defined to capture the overall time-history dynamic responses of the test model during seismic loadings, as well as any local effects such as yielding of LYP steel energy absorbers. Figure 13 shows the arrangement of monitoring points where the strain gauges and displacement sensors for the measurement of each component are placed. The strain gauges used for acquiring the maximum stress to monitor the damaged condition of frame members were respectively glued on the flanges at both ends of columns or beams. They were located at the area where the stress induced by bending moment under the horizontal ground motion was much higher than that at the middle or other parts. The measurement points of strains in the energy absorbers were arranged at the center of each LYP steel plate, where the stress caused by the bending deformation under the action of horizontal shear force transferred from the frame beam was the maximum. The waterproof treatment and protection measures were performed to prevent dampness and damage after the strain gauges had been glued on the polished steel surface with the dirt removed. The YHD type of displacement sensors were fixed on the outer flange of the frame column, at the beam-column connections of each story, by a magnetic suction seat to measure the inter-story drift of the specimen. Another TST type of displacement meter was installed on the bracket by a steel wire to obtain the absolute displacement at the monitoring point.  
Figure 12: Measuring devices of the specimen. (**a**) Connection of strain gauges; (**b**) YHD sensors; (**c**) TST sensors; (**d**) data acquisition instrument.
## 4 Simulation Evaluation
### Simulation Setup
#### 4.1.2 Simulation Scenario  
We evaluated all three methods in two scenarios, fixed sink and mobile sink. In both scenarios, one data collection is defined as a process during which the sink node collects sensor data from all sensor nodes once. Each sensor node generates one data frame for each data collection. When the number of sensor nodes is 86 or 196, the sensor nodes are arranged in a grid shape at intervals of 150 m in times of 86 nodes, and 100 m in times of 196 nodes. As for other cases, sensor nodes are randomly distributed in the communication range corresponding to the maximal power of the sink node.  
In the fixed sink scenario, the sink node stays at the center of the simulation area and collects data once. Three hundred simulations with random seeds are performed and the average results are computed.  
In the mobile sink scenario, there are four data collection points: (400, 0), (800, 400), (400, 800), and (0, 400). The sink node stays at one to four points with rotation per data collection. The movement pattern is as follows: (400, 0) for one point, (400, 0), (400, 800) for two points, (400, 0), (400, 800), (0, 400) for three points, and (400, 0), (800, 400), (400, 800), (0, 400) for four points. The simulated time is 24 h and the sink node collects data once per hour.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **SC-Sched** & **WuR-TDMA** & **ContikiMAC** \\ \hline Activation timing & A pair of nodes per hop & All nodes at once firstly & Duty cycling \\ Routing and transmission timing information & WuC per hop & All together by communication module & Routing is known \\ Channel access & TDMA & TDMA & CSMA/CA\({}^{*}\) \\ \hline \hline \end{tabular}  
* CSMA/CA: Carrier-sense multiple access with collision avoidance  
\end{table}
Table 1: Comparison of different methods.
## 4 Results and Discussion
### Analysis under Different Relative Gaps and Reynolds Number
In addition, when \(R/R_{0}=0.5\) and \(R/R_{0}=0.8\), the circumferential velocity of the core rotating fluid increases along the direction from stator to rotor, while at the position of \(R/R_{0}=0.2\), it decreases to a certain extent at \(R/R_{0}=0.2\). Figure 7: Results at different relative gaps. Figure 8: Velocity distribution of Geometry 1 at different \(Re\). (**a**) \(Re\) = 4.06 \(\times\) 10\({}^{5}\). (**b**) \(Re\) = 3.77 \(\times\) 10\({}^{6}\). (**c**) Circumferential velocity distribution at \(R/R_{0}\) = 0.2. Figure 9: Circumferential velocity distribution of Geometry 1 at different \(R/R_{0}\). Figure 10 is the velocity distribution and surface streamline in the gap of the cross-section. It can be seen that it is uncertain whether the boundary layer merging occurs for a larger \(S/R_{0}\) which may occur under the condition of a small \(Re\). In addition, there is also radial flow. The core rotating fluid outside the boundary layers exerts different degrees of external excitation and radial pressure on the boundary layer of the rotor wall. The radial pressure makes the radial flow subject to the inverse pressure gradient, resulting in higher torque. The vortex near the end wall is formed as \(Re\) increases which makes the flow in this area lose stability. In addition, compared with Figure 8, with the increase of \(S/R_{0}\), the influence of wall velocity on the core rotating fluid and centrifugal force in the gap are weakened. Figure 11 is the velocity vector in the rotor rim gap (end wall). The backflow phenomenon in the axial gap of the whole rim is not obvious (\(Re\) = 4.06 \(\times\) 10\({}^{5}\)). The flow is similar to the annular Couette flow of the two rotating cylinders and basically shows the characteristics of laminar flow. When \(Re\) reaches 3.77 \(\times\) 10\({}^{6}\) (as shown in Figure 10b), multiple sets of vortex pairs appear in the rim and the annular Couette flow in the axial gap loses stability and forms a Taylor vortex [24]. It can be inferred that there is a critical \(Re\) between 4.06 \(\times\) 10\({}^{5}\) and 3.77 \(\times\) 10\({}^{6}\). When \(Re\) exceeds the critical value, the fluid pressure gradient will increase sharply and the instability will be enhanced. In summary, the skin-friction coefficient model of Region III assumes that the boundary layers are merging which is mainly related to the flow on the rotor surface. It does not consider the influence of an unstable flow such as radial flow and Taylor vortex in the rim gap on the torque. In fact, an unstable flow occurs in the inlet and outlet parts of a gap behind the radial impeller. The size of the rim is not large and the influence on the windage loss of the whole back is not obvious.
## 5 Implementation
### Technology Stack Used  
This subsection describes the technology utilized in the implementation of our proposed system.  
1. Ethereum and Solidity: It is an open-source blockchain-based platform employed to create and share business, financial services, and entertainment applications. These contracts are written for various points in the supply chain, especially for interactions between two adjacent entities, such as breeders and FCI, farmers and warehouses, etc. These smart contracts help implement features provided by blockchain, such as storing immutable records of purchases by farmers from the government or documenting the quality of seeds approved by FCI.
2. Web3 JS Library: Web3 is a collection of libraries of the World Wide Web that incorporates and facilitates blockchain applications. We have used web3.js, which is an Ethereum JavaScript API that helps in connecting the smart contracts deployed on the chain at the back end to the front end, which is the portal for breeders and FCI and the website available to farmers for purchasing seeds. Therefore, while the purchase of seeds is initiated at the front end by the farmer, the recording of the purchase and the transfer of the order value from the farmer's wallet to the warehouse address are executed by the contract and are synced with the help of web3.js.  
Figure 11: Flowchart for ordering seeds.  
Ganache and Truffle: Ganache and Truffle are essential for testing the smart contracts and assist in creating a virtual environment to understand the behavior of the deployed contracts in different scenarios. Thus, with the help of these tools, we were able to develop test cases and expand the scope of the contract so that the contract does not fail or produce unexpected results in case of exigency.
* React JS: The React JavaScript library is the open-source library used to develop the front-end user interface and add functionalities to different portals, such as navigation between different pages and registration and the login of different entities. Thus, the web pages displayed to the users and the interaction of users with the portals are made possible with the help of this library.
* Inter Planetary File System (IPFS): IPFS is a \(p2p\) method of efficiently storing data and sharing data in a distributed file system. It is designed to hold data in a manner such that no single entity in a network holds the entire table of data. We have utilized IPFS to store data from seed applications and will employ the details in the future with the help of the generated hash.
* Metamask: This wallet is used to assist in the transfer of Ethers among different parties and between parties and smart contracts in instances where the smart contracts store Ethers until an event has been successfully concluded.
* MongoDB: Since every single detail, like user login details, for entities such as breeders, farmers, and logistics companies cannot be stored on-chain, we have used MongoDB services to create a back-end environment and store these details, and it is integrated with the user interface.
## 3 Results  
### Evaluation of EBs  
On Days 2 and 5, the expression of Nanog was observed in almost the entire area of each EB (Figure 2a,b), but the expression of Nanog was clearly attenuated in EBs cultured under normal oxygen conditions on Days 8 or 10 (Figure 2d,g). The size of EBs on Day 2, equivalent to the point in time when the hanging drop method was finished, was 318.65 \(\pm\) 3.44 \(\upmu\)m (N = 20, range 295 to 368 \(\upmu\)m) (Figure 3) and by Day 5 it had increased to 519.80 \(\pm\) 18.44 \(\upmu\)m (N = 20, range 362 to 704 \(\upmu\)m). The size of EBs on Day 8 in the hypoxia group was 635.05 \(\pm\) 19.25 \(\upmu\)m (N = 37, range 503 to 837 \(\upmu\)m), in the normoxia group it was 740.83 \(\pm\) 22.89 \(\upmu\)m (N = 115, range 413 to 1234 \(\upmu\)m) and in the hyperoxia group it was 744.37 \(\pm\) 12.74 \(\upmu\)m (N = 49, range 444 to 1205 \(\upmu\)m). EBs in the normoxia and hyperoxia groups were significantly larger on Day 8 compared with EBs in the hypoxia group, but there was no significant difference between the normoxia and the hyperoxia groups. The size of EBs on Day 10 in the hypoxia group was 637.43 \(\pm\) 26.91 \(\upmu\)m (N = 56, range 529 to 888 \(\upmu\)m), in the normoxia group it was 943.16 \(\pm\) 41.52 \(\upmu\)m (N = 92, range 532 to 1692 \(\upmu\)m) and in the hyperoxia group it was 945.67 \(\pm\) 10.27 \(\upmu\)m (N = 51, range 611 to 1956 \(\upmu\)m). EBs in the normoxia and the hyperoxia groups were significantly larger compared to EBs in the hypoxia group on Day 10, and, as was the case on Day 8, there was no significant difference between EBs in the hyperoxia and normoxia groups. EBs in the hyperoxia group had a significantly larger size than EBs in the hypoxia group, and the standard errors tended to be smaller. The size of EBs became larger from Days 2 to 5. The size of EBs in the normoxia and hyperoxia groups became larger from Day 5 to Day 10. The size of EBs in the hypoxia  
\begin{table}
\begin{tabular}{c c c} \hline
**Primer** & **Gene Name** & **Assay ID** \\ \hline Collagen Type I & Collagen type I alpha 1 & Mm00801666\_g1 \\ Runx2 & Runt-related transcription factor 2 & Mm00501580\_m1 \\ GAPDH & Glyceraldehyde-3-phosphate dehydrogenase & Mm99999915\_g1 \\ \hline \end{tabular}
\end{table}
Table 1: qRT-PCR Primers Used in this Study.  
group on Day 8 became larger compared to Day 5 but was smaller than EBs in the other two groups, and was about the same size on Days 8 and 10, meaning that they had not increased in size.
# A Programmable SRv6 Processor for SFC
## 4 Application and Validation
### Remarks  
The design of the SRv6 processor shows that complex packet operations can be handled in a programmable pipeline. It has not been optimized for performance. For instance, the data width of the pipeline could be increased with additional design effort, improving both throughput and latency. Latency can be improved by optimizing how packets are pulled out of the packet cache, updated, and sent out. Moving from an FPGA to an ASIC would improve frequency and data width. Deeper pipelining or several parallel parsers can also increase throughput.
## 3 Discussion of Results  
In order to validate the curve fitting algorithm based on the 2 DOF model, BTT sampled data in different resonance conditions are generated by the numerical simulator introduced in the previous section. For all the test cases investigated hereafter, the proportional damping constant \(\alpha\) is always set to zero, while only the parameter \(\beta\) is changed. For providing a better understanding, the corresponding damping ratios \(\zeta\) have been reported instead of \(\beta\)[31].  
The results obtained with the proposed 2 DOF algorithm are here compared with those obtained by the classical \"Sine fitting method\" used in many commercial softwares. The two methods are here used to reconstruct the frequency response for a certain mode starting from the sensors data. The reconstructed response will then be compared with the response curve obtained by the mathematical model.
## 3 Experimental Results
### Event-Related Potential (ERP) Analysis
An interaction effect between fatigue state and trial type was observed for mean P200 amplitude in the frontal (\(F(5,442)=5.766\), \(p<0.01\), \(\eta_{p}^{2}=0.061\)), central (\(F(2,445)=7.539\), \(p<0.01\), \(\eta_{p}^{2}=0.063\)), and parietal regions (\(F(2,429)=10.723\), \(p<0.01\), \(\eta_{p}^{2}=0.048\)). Further analysis of simple effects revealed that the trial type primarily amplified the P200 amplitude in the frontal region. In the fatigued state, significant differences in P200 amplitude were also noted among trial types in the central and parietal regions. These findings suggest that the frontal region plays a relatively prominent role in cognitive tasks. Additionally, an interaction effect between trial type and interruption type on P200 amplitude was significant in the frontal region (\(F(7,440)=6.530\), \(p<0.01\), \(\eta_{p}^{2}=0.094\)), but not in the central (\(F(7,440)=1.266\), \(p=0.265\)) or parietal regions (\(F(7,440)=1.117\), \(p=0.351\)). This further underscores the importance of the frontal region in processing these tasks. For P200 peak  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Task Type**} & \multirow{2}{*}{**Trial Type**} & \multirow{2}{*}{**Trial Type**} & \multicolumn{2}{c}{**P200 Mean Amplitude (\(\mu\)V)**} & \multicolumn{2}{c}{**P200 Peak Latency (ms)**} \\ \cline{4-9} \multicolumn{2}{c}{} & & & **Frontal** & **Frontal** & **Frontal** & **Frontal** & **Frontal** & **Frontal** \\ \hline \multirow{4}{*}{ \begin{tabular}{c} Non- \\ Fatigue State \\ \end{tabular}  } & \multirow{4}{*}{Simple} & Pre- & & **Frontal** & **Frontal** & **Frontal** & **Frontal** & **Frontal** & **Frontal** \\  & Interruption & & **Region** & **Region** & **Region** & **Region** & **Region** & **Region** \\ \hline \multirow{4}{*}{\ \begin{tabular}{c} Non- \\ Fatigue State \\ \end{tabular}  } & \multirow{4}{*}{Complex} & Pre- & & 1.966 \(\pm\) 1.69 & 1.706 \(\pm\) 1.47 & 1.422 \(\pm\) 1.12 & 170 \(\pm\) 12 & 172 \(\pm\) 16 & 171 \(\pm\) 12 \\  & & Interruption & 2.242 \(\pm\) 1.89 & 1.896 \(\pm\) 1.67 & 1.618 \(\pm\) 1.56 & 171 \(\pm\) 15 & 176 \(\pm\) 14 & 172 \(\pm\) 14 \\  & & Recovery & 2.363 \(\pm\) 1.99 & 1.957 \(\pm\) 2.26 & 1.639 \(\pm\) 1.30 & 176 \(\pm\) 14 & 176 \(\pm\) 14 & 176 \(\pm\) 15 \\ \cline{2-9}  & \multirow{4}{*}{Complex} & Pre- & & 2.871 \(\pm\) 2.50 & 1.914 \(\pm\) 1.77 & 1.529 \(\pm\) 1.33 & 175 \(\pm\) 14 & 173 \(\pm\) 14 & 170 \(\pm\) 12 \\  & & Interruption & 3.052 \(\pm\) 2.49 & 2.178 \(\pm\) 2.14 & 1.657 \(\pm\) 1.13 & 175 \(\pm\) 16 & 178 \(\pm\) 13 & 176 \(\pm\) 14 \\  & & Recovery & 3.037 \(\pm\) 2.41 & 2.488 \(\pm\) 1.92 & 1.715 \(\pm\) 1.13 & 176 \(\pm\) 16 & 181 \(\pm\) 16 & 176 \(\pm\) 14 \\ \cline{2-9}  & \multirow{4}{*}{Pause} & Pre- & & 2.173 \(\pm\) 1.63 & 1.791 \(\pm\) 1.20 & 1.175 \(\pm\) 1.12 & 171 \(\pm\) 16 & 173 \(\pm\) 15 & 174 \(\pm\) 15 \\  & & Recovery & 2.392 \(\pm\) 1.66 & 1.755 \(\pm\) 1.54 & 1.249 \(\pm\) 0.93 & 172 \(\pm\) 14 & 172 \(\pm\) 14 & 174 \(\pm\) 15 \\ \hline \multirow{4}{*}{ \begin{tabular}{c} Fatigue State \\ \end{tabular}
## 2 Related Work
[18] developed a complete UAV autonomous landing system using a hybrid camera array (fish-eye and stereo cameras) and a state estimation algorithm based on motion compensation and tested them with multiple platforms (Parrot Bebop and DJI M100). A common assumption in many of these systems is that the speed of the mobile target is low enough for the UAV to be able to land on it without compromising the integrity of both robotic platforms. However, experiments carried out by the German Aerospace Center (DLR) have demonstrated that it is possible to land a fixed wing drone on a net attached to the top of a car moving at 70 km h\({}^{-1}\)[19]. Note, however, that in this experiment the ground vehicle followed a linear trajectory, which is not always possible in SaR missions, where the debris forces the UGV to make turns almost continuously. Indeed, there are substantial differences when trying to land a UAV on a terrestrial moving platform describing either a linear or a circular trajectory. Most of the research so far focused solely on the former, without thoroughly considering that the movement of the target can also be circular or even a mixture of both, thus producing random trajectories. This is, therefore, an interesting line of work, since in SaR tasks we want to provide the terrestrial robot with complete freedom of movement. In such a scenario, the UAV has to adapt to the trajectory described by the ground robot for a successful landing. The work presented in this paper takes a step forward in this direction by demonstrating a system capable of autonomously landing a UAV for both a linear and a circular trajectory of the moving landing platform. Finally, all of the works above described strategies towards precise landing in moving platforms, but none presented a full system capable of operating for extended periods of time. In our work, a robust state machine together with a recovery and re-localization module allows for life-long operation, as we show in Section 4.
# Automatic Bird Species Recognition from Images with Feature Enhancement and Contrastive Learning
## 2 Materials and Methods  
### Dataset  
To benchmark our method's effectiveness, we utilized the CUB-200-2011 (CaltechUCSD Birds-200-2011) dataset. This widely used resource for fine-grained image classification, particularly in bird species recognition, features 11,788 images across 200 North American bird species (at least 30 images per species). Split into training (5994 images) and testing (5794 images) sets, CUB-200-2011 offers valuable annotations beyond image labels, including bounding boxes, keypoint regions, and bird attributes. This comprehensive dataset provides all the necessary attributes for fine-grained bird classification research.