esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multipath Data-Scheduling Strategy Based on Path Correlation for Information-Centric Networking
## 5 Evaluation
### Basic Performance Evaluation
#### 5.1.1 The Effect of Delay on Performance  
In this subsection, we set the default bandwidth and packet loss rate of path E1-R3-R4-E2 to 100 Mbps and 0%, respectively. The default packet loss rate is set to 0%, which means that no noise packet loss occurs simply because the router has a buffer overflow. In addition, we set the default delay of path E1-R3-R4-E2 to 5 ms, 10 ms, 15 ms, 20 ms, 25 ms and 30 ms to analyze the transmission performance of the proposed method in scenarios with different link delay differences. We used R1 and R3 to construct two disjoint relay paths between the source and destination nodes and transfer 100 chunks.  
Figure 5 shows the average throughput of the four data-scheduling strategies under different link delay differences. When the delay of path E1-R3-R4-E2 is 5 ms, that is, when the delay of two paths is the same, the average throughput of the four scheduling strategies is similar. When the delay of path E1-R3-R4-E2 is 5 ms, that is, when the delay of two paths is the same, the average throughput of the three scheduling methods is similar. As the delay of path E1-R3-R4-E2 increases, the throughput of RRDS decreases gradually and is always lower than that of the other methods. This is because RRDS mechanically distributes data across sub-paths, ignoring path state differences and resulting in a lower throughput. In addition, compared with LRFDS and FDPS, the proposed PSDS can maintain the highest average throughput. This is because the proposed PSDS can estimate the real state of the path more accurately by integrating the two factors of RTT and packet loss rate.  
Figure 6 shows the proportion of out-of-order chunks of four data-scheduling strategies under different link delay differences. As the delay of path E1-R3-R4-E2 increases, the out-of-order chunks of the methods increase gradually. When the delay of two paths is equal, the out-of-order chunks of the four scheduling methods are close. However, with the increase in the delay difference between two paths, the out-of-order chunks of RRDS remain the highest, indicating that the method has difficulty solving the out-of-order problem. In addition, compared with the LRFDS and FDPS, the proposed PSDS can still maintain the proportion at a lower level when the path delay difference is large.  
Figure 4: Simulation topology 1.  
Figure 5: Average throughput under different link delays.
# Free Vibration Analysis of Arches with Interval-Uncertain Parameters
## 4 Numerical Simulation and Discussion
### Uncertain Natural Frequency Investigations Considering Different Uncertain Parameters
#### 4.3.2 Multiple Uncertain Parameters  
In order to investigate the characteristics of frequency under the influence of multiple uncertain parameters, all the aforementioned uncertainties are considered simultaneously. However, employing the MCS requires a substantial number of samples for each parameter in order to ensure accuracy, resulting in unacceptable computational costs. Utilizing the CPS model can significantly reduce these costs and provide notable efficiency advantages. The numerical simulation results depicting multi-dimensional uncertainty are presented in Figures 7 and 8. The changing rates of frequencies surpass those observed when considering individual parameters separately, indicating a linear superposition of the effects of all uncertainties. Furthermore, although resembling single cross-sectional height considerations in terms of image shape, fluctuations exhibit smaller amplitudes. Additionally, as the radius increases, the phenomenon that the position of the minimum value shifts to the left along the horizontal axis representing the central angle does not change.  
Figure 6: The changing rates of the first fourth-order natural frequencies of an arch with uncertain cross-sectional width, Young’s modulus, and density of mass, respectively: (**a**) first order; (**b**) second order; (**c**) third order; and (**d**) fourth order.  
Figure 7: The changing rates of the first fourth-order natural frequencies of an arch with multiple simultaneously uncertain parameters and a radius of 1 m: (**a**) first order; (**b**) second order; (**c**) third order; and (**d**) fourth order.
# A Review of Autonomous Tugboat Operations for Efficient and Safe Ship Berthing  
[PERSON]  
1 Department of Naval Architecture and Ocean Systems Engineering, Korea Maritime and Ocean University, Busan 49112, Republic of Korea; [EMAIL_ADDRESS] (J.-H.C.); [EMAIL_ADDRESS] (J.-Y.J.)1  
[PERSON]  
1 Department of Naval Architecture and Ocean Systems Engineering, Korea Maritime and Ocean University, Busan 49112, Republic of Korea; [EMAIL_ADDRESS] (J.-H.C.); [EMAIL_ADDRESS] (J.-Y.J.)1  
[PERSON]  
2 May 19, 2023231 May 2023231 May 2023231 May 2023231 May 2023231 May 2023231 May 2023231 May 2023231 May 2023  
vessel technology, operating costs and human error are reduced, and communication with other vessels can be facilitated using various wireless technologies such as satellites, mobile networks, and dedicated narrowband systems. This can compensate for economic, safety, and communication issues [5; 6; 7]. In addition, unlike existing ships, autonomous tugboats can increase efficiency by minimizing power and increasing maneuverability by applying improved thrust allocation algorithms, such as the RPI (redistributed pseudoinverse) algorithm [2]. Therefore, there is a need for an automated system for ship berthing that can reduce human involvement and improve efficiency, while addressing the challenges of communication and control during berthing [3].  
The need for autonomous tugboat technology in ship berthing is becoming increasingly important due to various challenges involved in the process. To enable this technology, several capabilities, such as cognitive technology to recognize and integrate the surrounding situation, task allocation within the group, maintenance of formation, decision-making technology to plan routes, modeling technology to consider multibody dynamics of the mother-ship, and precise control technology for a group of tugboats for berthing, are essential. Incorporating these technologies in autonomous tugboats will reduce human costs and time, as well as improve safety, efficiency, and accuracy in ship berthing.  
This paper presents an examination of the research trends in recognition, judgment, modeling, and control technologies for ship berthing using autonomous tugboats from the early 2000s to recent years. Section 2 of this paper provides a brief overview of the technology trends and previous studies on the development of unmanned/autonomous towing technology. Section 3 discusses the need for recognition, judgment, modeling, and control technologies, and provides detailed information on each of these technologies. Finally, Section 4 outlines the current limitations of autonomous towing technology, presents the conclusions drawn from this study, suggests future directions for development, and proposes research areas that require further investigation.
# Heterogeneous Electro-Fenton-like Designs for the Disposal of 2-Phenylphenol from Water
## 2 Materials and Methods
### Sample Preparation and Analytical Methods
#### 2.5.1 2-Phenylphenol Concentration  
2-phenylphenol degradation was determined by HPLC and spectrophotometrically. For HPLC determination, 2-phenylphenol concentration decrease was measured with an Agilent 1100 HPLC equipped with Diode Array Detector and using a Zorbax Eclipse XD8-C8 reverse-phase column (150 \(\times\) 4.6 mm i.d. 5 \(\upmu\)m). Mobile phase composed of water:acetonitrile (40:60) was pumped in isocratic mode at 1 mL/min for 20 min. Separation was carried out at room temperature and the column effluent was monitored spectrophotometrically at 252 nm wavelength. Prior to chromatographic analysis, all samples were filtered through 0.45 \(\upmu\)m PVDF filters. Spectrophotometric measurements were performed with a GENESYS 150 UV-Vis Spectrophotometer from 240 to 350 nm, and the pollutant removal was associated with the area under curve decrease and expressed in terms of percentage in relation to the initial sample.
# Effect of the Surface Roughness on the Shear Strength of Granular Materials in Ring Shear Tests
###### Abstract  
Surface roughness plays an important role in estimating the shear strength of granular materials. A series of ring shear tests with different surface roughnesses (i.e., smooth and rough surfaces) were performed. A large-sized ring shear device, which is applicable for fine- and coarse-grained sediments, was developed to examine the shear strength of large particle sizes (i.e., commercial gravels with a mean grain size of 6 mm). In terms of surface roughness, the drainage- and shear-velocity-dependent shear strengths of the granular materials were examined. In this study, different shear velocities of 0.1, 0.5, and 1 mm/s were applied under drained and undrained conditions. The test results clearly show that shear stress is affected by drainage, shear velocity, and surface roughness. In particular, a typical strain-hardening behavior is exhibited regardless of the drainage and shear velocity condition. The measured shear strength obtained from both drained and undrained conditions increased with increasing shear velocity. All tests showed a large fragmentation using rough surfaces compared to the smooth surfaces of the device. The grain crushing was significant during shearing, even when normal stress was not applied. For a given shear velocity, surface roughness is an important feature in determining the shear strength of granular materials.  
surface roughness; granular material; gravel; ring shear test; grain crushing +
Footnote †: journal: Applied Physics Letters
# NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology
## 3 Data Services
### Dataset and Information Search
FAQs contain answers for frequently asked questions, while how-to's provide step-by-step data recipes. Some how-to's can be downloaded in the Jupyter Notebook format. Key dataset-related publications are available. More dataset-related referral research papers are being developed. For datasets archived and distributed at other NASA DAACs, users can search them through the NASA Earthdata homepage [47]. Search results can also be filtered and the retrieved information will guide users on where and how to download data and the associated documents. Finally, all users are required to register with Earthdata [47], which is a simple, easy and one-time process. Once registered, users can access data and services at all NASA DAACs. Details about how to download data with different software packages (e.g., wget, curl) are available [48].
# An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks
## 4 Simulation  
In this section, we conduct experimental studies in a realistic network mobility model where all the theoretical assumptions of the proposed algorithm do not hold, which provides a fair comparison among it and the compared protocols. The simulation not only illustrates the improved performance under DTCM algorithm, but also provide insight in the suitable selection of network parameters such as the remaining level of network energy \(L^{e}_{\min}\) and the remaining level of network cache \(L^{c}_{\min}\), etc. In this simulation, we first evaluate the energy and cache assumption of DTCM algorithm via a benchmark protocol, and then give a fair comparison between five routing protocols under the same experimental environment. The result demonstrates that the proposed protocol outperforms other ones in terms of delivery ratio and end-to-end delay within a reasonable fault-tolerant interval.
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)
### The Persistence Layer and Persistence Security Layer  
On top of the Data Layer, we can find the Persistence Layer. This layer handles the logic needed to access and manage the data, making the operations in the database abstract. The Persistence Layer outlines the capabilities and functions made available to the user in order to perform CRUD (Create, Read, Update, Delete) operations [51] on the database assisted by Object-Relational Mapping (ORM).  
The Persistence Layer is enhanced with security measures by the Persistence Security Layer, which ensures that the data are accessed securely and only by authorized entities. It prevents common attacks and enforces strict access controls, maintaining data integrity and security.  
The Persistence Security Layer is responsible for strong authentication and authorization mechanisms; it defines roles and permissions to limit access according to user roles. This ensures that data access is restricted to authorized users and applications. Methods like SQL Injection Prevention, Secure Access Controls, and Role-Based Access Control [52] are used to achieve this.  
Figure 3 illustrates the relationships, technologies, and roles associated with both the Persistence Layer and the Persistence Security Layer:
# Energy Cost Minimization with Hybrid Energy Storage System Using Optimization Algorithm
## 3 Results
### Energy Balancing
Figure 13: Results for the energy balancing method—data for 4 October: (**a**) grid power balance, (**b**) energy usage and production (input data), (**c**) batteries power, (**d**) batteries state of charge. In winter (Figure 14), the energy storage is not active as there is little overproduction to be used. Figure 15 summarizes the financial aspect of the HESS operation. Figure 15a depicts cost of energy purchased in each tariff zone, which clearly demonstrates that the high afternoon peak is not avoided, especially in winter months. In summer months, the PV production combined with the storage can substantially reduce the exchange with the grid. The overall costs and profits are presented in Figure 15b showing that the overall financial outcome is positive in June. Figure 15c gives the values of the surplus RES energy used directly or captured by the HESS. It is calculated as the costs that would have been if there was no energy production. In Figure 15d, the costs saved by the energy storage are presented, this includes the costs that are caused by the degradation of the storage--it is calculated from the difference of the total outcome with and without HESS. The overall cost balance has substantially decreased compared to the situation without the batteries at all, the value is PLN 266,028. This clearly shows that even the simple algorithm of battery management can lower the yearly costs of the operation of the facility.
# Ultra-Wideband Compact Fractal Antenna for WiMAX, WLAN, C and X Band Applications
###### Abstract  
In this paper, a compact dual-wideband fractal antenna is created for Bluetooth, WiMAX, WLAN, C, and X band applications. The proposed antenna consists of a circularly shaped resonator that contains square slots and a ground plane where a gap line is incorporated to increase the gain and bandwidth with a small volume of 40 \(\times\) 34 \(\times\) 1.6 mm\({}^{3}\). The patch was supported by the FR4 dielectric, which had a permittivity of 4.4 and tan \(\delta\) = 0.02. A 50 \(\Omega\) microstrip line fed this antenna. The antenna was designed by the HFSS program, and after that, the simulated results were validated using the measured results. The measurement results confirm that the suggested antenna achieves dual-band frequencies ranging from 2.30 to 4.10 GHz, and from 6.10 GHz to 10.0 GHz, resonating at 2.8, 3.51, 6.53, and 9.37 GHz, respectively, for various applications including commercial, scholarly, and medical applications. Moreover, the antenna's ability to operate within the frequency range of 3.1-10.6 GHz is in accordance with the FCC guidelines for the use of UWB antennas in breast cancer detection. Over the operational bands, the gain varied between 2 and 9 dB, and an efficiency of 92% was attained. A good agreement between the simulation and the measured results was found.  
ring antenna; ultra-wideband; wireless communications; transmission-line; Ansys HFSS simulation; slot antenna; microstrip patch antenna 2023  
27 March 2023  
16 April 2023  
1
## 4 Extension of Traditional Impedance Tracking Topology
### Switching Criteria between CCM and DCM Modes in the Buck Circuit  
The buck circuit topology is shown separately in Figure 7, where Vb represents the input voltage after the rectifier circuit, Sb represents the control switch transistor of the buck circuit, Db represents the unidirectional diode, Lb represents the buck circuit inductor, Cb represents the buck circuit capacitor, and Rb represents the system's equivalent resistance.  
The buck circuit can operate in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM). Based on the fundamental principles of the buck circuit, the current in the inductor does not drop to zero throughout the entire switching period in CCM. By contrast, in DCM, the current in the inductor drops to zero in each switching period. Mode switching is determined by the following equation:  
According to the above equation, switching between CCM and DCM modes depends on duty cycle D controlled by Sb. Based on this equation, we modeled and analyzed the CCM and DCM modes separately.
## 3 The Train-to-Ground Wireless Communication Needs
### The Needs for Services to Passengers  
Wireless communications have been deeply integrated into people's life. The theoretical throughputs of LTE can reach 300 Mbps using 100 MHz bandwidth. Furthermore, The transmission rate of the future 5G network can reach 10 Gbps. All these recent evolutions concern also the vacuum tube transportation domain. For the vactrain, passengers' communication demands is related to the type of services. As for business users, for example, the main Internet application needs are web browsing and emailing, which do not need real-time process and then have a limited throughput. The entertainment users requires large bandwidth and often real-time web applications, such as games, video, chatting, etc. The different applications can be classified depending on the QoS criteria. They are presented in Table 3.  
Currently, LTE has been largely deployed all around the world. 5G network, the next generation of mobile internet connectivity, is also about to be put into commercial application in the near future. Taking the future 5G network as an example, the single-user throughput of the 5G network is approximately 0.1 Gbps to 1 Gbps using a bandwidth of 1 GHz. The throughput of the whole train can be presented as  
\[T_{train}=T_{user}N_{user}P_{tp}P_{ua}P_{c}, \tag{1}\]  
where \(T_{train}\) represents the throughput of the whole train, \(T_{user}\) is the single-user throughput of the 5G network, \(N_{user}\) is the train's passengers capacity, \(P_{tp}\) is the 5G terminal penetration rate, \(P_{ua}\) is the user attachment ratio, and \(P_{c}\) is the concurrency rate. The passenger capacity of vactrain pod is generally ~15 passengers (refer to XP-1 [2]). Suppose the 5G terminal penetration rate is 80%, the user attachment ratio is 70%, the concurrency rate is 10%, then the throughput of the vactrain could be calculated as 84-840 Mbps with an average single user throughput of 7-70 Mbps.
## 4 Target State Prediction with Uncertainty Estimation
### Stochastic Model Predictive Controller
Therefore, the position constraints are redefined as follows:  
\[ \begin{array}{l}\textit{Yield Mode}\,:\,\Pr(p_{[k]}\leq P_{ego,[k]}-c_{[k]})> \beta\\ \textit{Cross Mode}\,:\,\Pr(P_{ego,[k]}+c_{[k]}\leq p_{[k]})>\beta\end{array}  \tag{20}\]  
where \(\beta\) is the lower boundary for the probability of the inequality equations. The standard deviation of the probability distribution is defined as an estimation result of the recursive covariance matrix. It means that the inequality constraints are adaptively adjusted according to the perception performance and the accuracy of the behavior predictor. The position constraint is defined differently for the corresponding driving mode. For the yield mode, the ego vehicle avoids the collision using braking, which means an upper limit is assigned. For the cross mode, the ego vehicle has a priority to pass the conflict point first then the target vehicle. In this case, a lower limit has been applied to the position constraint. The constraints for \(v_{[k]}\), \(a_{[k]}\), and \(\hat{a}_{[k]}\) are defined as a deterministic constraint as follows:  
\[ \begin{array}{l}0\leq v_{[k]}\leq\min\big{(}[\begin{array}{cc}v_{max}&v_{ curve}\end{array}]\big{)}\\ a_{min}\leq a_{[k]}\leq a_{max}\\ \frac{|a_{[k+1]}-a_{[k]}|}{dt}\leq\hat{a}_{max}\end{array}  \tag{21}\]  
where \(v_{max}\) and \(v_{curve}\) are the velocity limit and velocity derived from the maximum lateral acceleration and road curvature. \(a_{min}\), \(a_{max}\), and \(\hat{a}_{max}\) are the minimum acceleration, maximum acceleration, and maximum jerk for longitudinal motion, respectively. The parameters used for the SMPC problem are summarized in Table 1.  
The initial conditions are defined as the current states of the ego vehicle. \[p_{[1]}=0,\,\,\,v_{[1]}=v_{ego},\,\,\,a_{[1]}=a_{ego} \tag{22}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Values** & **Symbol** & **Values** \\ \hline \(dt\) & 0.2 s & \(v_{max}\) & 50 km/h \\ \(\tau_{x}\) & 0.5 s & \(a_{max}\) & 1 m/s\({}^{2}\) \\ \(N_{p}\) & 25 & \(a_{min}\) & \(-\)5 m/s\({}^{2}\) \\ \(TTC_{conf,min}\) & 2.0 s & \(a_{max}\) & 2 m/s\({}^{3}\) \\ \(C_{conf,min}\) & 5.0 m & \Based on these constraints, the SMPC problem has been designed to calculate the desired longitudinal acceleration. The cost function for the SMPC problem is formulated as follows:  
\[J=\sum_{k=1}^{N_{p}}E\left(\left\|x_{[k]}-x_{ref,[k]}\right\|_{Q}^{2}+\left\|u_{ [k]}\right\|_{R}^{2}\right) \tag{23}\]  
where \(x_{ref,[k]}\) is the ego vehicle prediction results, which are depicted as a green dotted line in Figure 10b. \(Q\) and \(R\) are the weighting matrices of the cost function. \(N_{p}\) is the size of the prediction horizon of the SMPC problem. The cost function is designed to minimize the deviation from the ego vehicle prediction results and control effort. Therefore, the SMPC problem calculates the control inputs that minimize the change of behavior of the cross mode and the acceleration usage. The solver FORCES is utilized to solve the SMPC problem in the MATLAB/Simulink environment [33].