esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Atmospheric Flow Structure on Optical Turbulence Characteristics
## 4 Discussion
However, an inter-comparison of these vertical profiles showed that a meso-scale jet stream was formed above Lake Baikal at a height of 1600 m. The wind speed on the axis of this jet stream varied from 8.8 to 10 m/s for the selected measurement periods. Over land, the jet stream was less pronounced. Figure 11: Energy spectra of angle-of-arrival fluctuations caused by turbulence within a stably stratified atmospheric surface layer. Lines 1–5 correspond to different measurement hours. Line 6 corresponds to the averaged spectrum  
Figure 12: Vertical profiles of wind speed (**a**) over the observatory and (**b**) over Lake Baikal.
## 3 Experimental Results  
### The Oscillation of the CFRP Hub and the Steel Hub Grinding Wheel in the Grinding Process  
This study used a static state to evaluate the different vibrations that emerge when an external force touches the grinding wheel of the CFRP hub and the steel hub. In particular, it used soft and hard hammers to knock the diamond wheel to produce higher, middle, and lower vibration frequencies. The higher frequency was defined as 5500 Hz, the middle frequency was defined as 500 Hz, and the lower frequency was defined as 300 Hz. The goal was to investigate the vibration in the CFRP hub and the steel hub grinding wheel within a certain period in the static test. The experimental results revealed that the initial amplitude of the CFRP hub grinding wheel exceeds that of the steel hub, as seen in Figure 5(a1,b1). However, the amplitude of the steel hub grinding wheel is slightly over that of the CFRP hub when the vibration time exceeds 0.05 s. The experimental findings show that the CFRP hub grinding wheel is more significant than the steel hub in the initial amplitude value. However, the CFRP hub of the grinding wheel can stabilize the vibration when an external force strikes the grinding wheel at a low frequency.  
The middle frequency was defined when the tapping frequency reached 500 Hz. The initial amplitude of the CFRP hub grinding wheel is smaller than that of the steel hub and can stabilize the oscillation at 0.1 s, as seen in Figure 5(a2,b2). The chatter vibration of the CFRP hub grinding wheel is also more moderate than that of the steel hub grinding wheel when the time is lower than 0.05 s.  
Figure 4: Schematic diagram of the knocking test for the grinding wheel of the CFRP hub and steel hub.  
The CFRP hub and the steel hub grinding wheel amplitude values are quite similar when the tapping frequency reaches 5500 Hz. However, the CFRP hub grinding wheel can stabilize the oscillation time quickly, ranging between 0.025 and 0.05 s. The opposite happens when the steel hub grinding wheel exceeds 0.075 s, and the chatter vibration becomes more obvious than that of the CFRP hub grinding wheel. The experimental results are shown in Figure 5(a3,b3).  
The abovementioned experimental results suggest that high-frequency vibration can be easily produced when the peripheral speed of the grinding wheel increases, and the grinding wheel grinds a workpiece with extreme hardness. The CFRP substrate has better damping ability for grinding the performance products [11]. Meanwhile, the grinding wheel of the steel hub can produce a chatter vibration and a larger amplitude to influence the quality of the grinding process.
# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 5 Discussion
### Cross-Validation and Statistical Significance Analysis  
The use of a rigorous a five-fold cross-validation protocol and statistical significance testing greatly enhances the reliability of the reported findings; the fact that TSA-GRU showed high accuracy in each fold with minimal variance and narrow confidence intervals lends evidence to TSA-GRU's robust generalization capacity. The paired \(t\)-test assures that the improvements in performance over MSA-GRU were not the result of chance/randomness and could be considered truly statistically significant. These tests, supported by detailed visualizations (Figures 14-19), represent evidence that the TSA-GRU architecture can achieve meaningful and reproducible improvements in sequential modeling tasks for knowledge tracing activities.
## 3 Design of Isl Network for IoT
### Medium Access Control
Furthermore, the time slots limit the allowable propagation delay and, thus, the distance between satellites. For S-NET missions, synchronization is carried out by ground command. A dedicated master S/C is used for time synchronization and control of the network. This asymmetrical organization of function/responsibility, however, leads to higher resource consumption in the master. Additionally, two channel access methods based on asynchronous TDD rules for packet switching will be demonstrated: Token passing and random access. In both cases, P2P protocols are used while only one P2P connection is allowed at a time. Bi-directional P2P data transfer is time-duplexed and organized in short sessions of variable length. Each session consists of a number of TDD frames. In this context, suitable channel coding is important (see Section 3.3). The token passing scheme is collision-free and simple to implement, and is thus a suitable choice for formations with a small number of nodes. For a line topology, the token is passed back and forth from one end to the other and, hence, a routing algorithm is not required. For a fully connected topology, a token ring organization is suitable. For a higher number of nodes and multi-hopping, the more advanced random access method is preferable, due to higher data throughput while allowing package collision. Even simultaneous links on the same channel are possible, if proper spacial separation can be achieved. As each session consists of several TDD frames, only collided frames are lost in the case of a collision (see Figure 5). This scheme can be extended by a collision avoidance rule and/or a power control technique.
# Analysis and Prediction of Carsharing Demand Based on Data Mining Methods
## 4 Results and Comparison
### GDBT Prediction Result  
The best iteration times are selected according to the parameter adjustment method. The variation trend of mean absolute error and root mean square error with iteration times is shown in Figure 9. With the change of iteration times, the average absolute error fluctuates from 2.1 to 2.5. When the number of iterations is 250, the average absolute error reaches the lowest. The root mean square error fluctuates from 2.1 to 2.7. When the number of iterations is 100, 250, and 300, the error is relatively small. Considering the average absolute error and root mean square error, the best number of iterations is 250.  
Next, the optimal max depth needs to be determined. The error statistical result is shown in Figure 10. The mean absolute error and root mean square error fluctuate with the increase in the maximum depth of the tree. When the depth is 9, both are small; that is, the optimal max depth is 9.  
Similarly, parameters such as step size and loss function are searched in grid format. The optimal parameters of GBDT are obtained, as shown in Table 6.  
The GBDT is run according to the model's optimal parameters in Table 6. The iterative results of the training set error and test set error are shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{v}\) & **Max Depth** & **N** & **L** \\ \hline
0.05 & 9 & 250 & Square loss function \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimal parameter list.  
Figure 11: Error training results.  
Figure 9: Model iteration error.  
In accordance with Formula (6), the relative importance of some characteristics is derived. The GBDT, which plays a key role in predicting users' demand, includes the time information, the number of vehicles picked up and returned in the previous period, the number of picked-up vehicles at the same time of the previous day, and so on.
# Blockchain Architecture for Lightweight Storage
## 3 Reputation Model
### Equity Reward  
The block is successfully uploaded to the chain, and the nodes participating in this block receive a certain amount of equity as a reward. The formula for the equity reward is as follows:  
\[Reward_{i}=c\cdot B+\frac{\beta\cdot Pi}{Ri}\cdot E \tag{6}\]  
The rewards obtained by nodes are divided into two parts: the base reward \(B\) and the extra reward \(E\). The coefficient for the extra reward \(E\) is linked to the proportion of the node's positivity \(P_{i}\) and its reputation value at that moment (Equation (6) has the same value of \(\beta\) as in Equation (1)), encouraging honest behavior to yield more rewards. The number of blocks different types of nodes participate in during a consensus cycle varies, and the coefficient \(c\) adjusts the base reward for each node. The formula is as follows:  
\[c\left\{\begin{array}{l}b^{-1},agent\ node\\ 1,verification\ node\end{array}\right. \tag{7}\]  
As shown in Equation (7), \(b\) represents the planned number of blocks produced in a consensus cycle. The same agent node participates in block packing only once per consensus cycle, so the coefficient \(c\) is set to 1. The verification node participates in the verification of a total of \(b\) blocks in a consensus round, adjusting \(c\) to \(b^{-1}\).  
As nodes actively participate in voting, their reputation values will gradually increase. Even if their weight is low, they still have a chance to be selected as a consensus node to participate in the block. Conversely, if a node engages in malicious behavior, its reputation value will decline rapidly, making it difficult to be selected as a consensus node in the next cycle. The reputation model ensures that block generation and verification behaviors are carried out by stable and reliable nodes, which improves the security of the system.  
The formula for the reputation value of a node can be derived as follows:  
\[Ri=\alpha\cdot\frac{wi}{\sum_{i=1}^{n}wi}+\beta\cdot m\cdot(P_{i}^{j-1}+\frac{e_{i }}{a_{i}}) \tag{8}\]
## 3 Results and Analysis  
### Experimental Environment and Parameter Settings  
The experiment was conducted on a system with CentOS 7.9.2009, utilizing a 12 vCPU Intel(R) Xeon(R) Platinum 8255C CPU @ 2.50 GHz and an NVIDIA GeForce RTX 3090 graphics card. Anaconda served as the Integrated Development Environment (IDE), the Python version is 3.8.10, the YOLO series of target detection models were constructed and trained using version 1.11.0 of the PyTorch deep learning framework. The single-stage SSD target detection model and the FasterCNN two-stage target detection model were constructed and trained using version 2.25.3 of the mmdetection deep learning framework, with CUDA version 11.3 facilitating the acceleration of the training process. Parameters included an image resolution of 640 \(\times\) 640, the Adam optimizer, an initial learning rate of \(1\times 10^{-3}\), the momentum value of 0.937 and weight decay coefficient of \(5\times 10^{-4}\), which were incorporated along with a batch size of 64 for network training and 200 training rounds for both the baseline and improved models based on these settings. Mosaic data augmentation was used in the YOLO target detection algorithms.
# State-of-the-Art Review of Structural Vibration Control: Overview and Research Gaps
## 3 General Limitations of Structural Control Systems
### Modeling Errors  
The accuracy of structural control depends on the ability to model the actual structure effectively. However, limitations in modeling, often arising from a reduced number of degrees of freedom, can introduce errors. Consequently, control algorithms based on idealized models may struggle to effectively manage the actual structure's dynamic behavior. The modeling errors are classified into four types: parameter errors, model order errors, neglected disturbances, and neglected nonlinearities. No controller can treat all four at once [8]. Disregarding various errors and concentrating solely on one category may result in inaccurate conclusions. Most hardware and design suggestions and results are based on satisfactory computer simulations. It is challenging to draw clear conclusions about hardware and design recommendations because the research papers heavily rely on numerous referenced hardware tests. The available results predominantly stem from computer simulations, which are effective in conservative systems where errors in design parameters cannot destabilize the system. However, active feedback control can introduce instability or suboptimal performance if significant modeling errors and control energy are substantial. Relying solely on computer simulations may not offer sufficient evidence for a robust design. Simulations may or may not encompass enough modeling errors to assess the stability of the actual system. The lack of addressing the interdependency between modeling and control design is one of the severe issues in control systems. The study by [PERSON] et al. [8] stated that robust control theories, such as adaptive control and H\({}_{\infty}\) can treat the errors and stabilize the system if the unbounded error is known. However, such systems usually trade performance for robustness.
# A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications
###### Abstract  
With the real restriction of packet size and transmission power, a joint UL-AoA (Uplink Angle of Arrival) and UL-TDOA (Uplink Time Difference of Arrival) positioning method is discussed for the Industrial Internet of Things (IIoT), which demands high accuracy of position information. One motivation of the UL positioning method of IIoT is an asymmetric network and uplink traffic is dominant in most identified scenarios. Further, IIoT sensors are power limited for low cost, compared to the transmission power of base stations and other normal user equipment. This paper considers an environment of a 5G-enabled IIoT network with massive Multiple input multiple output (MIMO) antennas, beamforming and millimeter operating bands. After reviewing downlink positioning reference signal (PRS) and uplink sounding reference signal for positioning (SRS-Pos), a general system model and a theoretical localization problem is derived under the joint positioning method. The provided simulation results help to investigate the impact of system configurations on positioning measurements. Further, through the test in this paper, it is proved that joint positioning outperforms that of the time only or angle only method.  
positioning: PRS; SRS-Pos; IIoT; TDOA; AoA; joint positioning +
Footnote †: journal: _Symmetry_
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 2 Advantages and Challenges of mmWave Industry 4.0 and beyond Communication
### Challenges of mmWave Communication
#### 2.2.2 Path Loss  
mmWave propagation at 60 GHz faces severe path loss because of oxygen absorption, as well as according to Friis' equation. The free-space propagation loss is directly proportional to the square of the frequency. The path loss at 60 GHz is about 21 to 28 dB (at 10 m distance) more as compared to conventional sub-6 GHz communication bands [45; 52]. Furthermore, 60 GHz communication exhibits quasi-optical propagation, in which the received signal is dominated by the LOS path and first-order reflections from strong reflecting materials [74].  
In order to combat path loss issues, 60 GHz communication leverages the beamforming antenna gain. As a result, array gain helps to improve the signal-to-noise ratio (SNR) to mitigate path loss at 60 GHz. Thanks to the smaller wavelength at 60 GHz, array form factors are much lower as compared to those of sub-6 GHz designs. Moreover, for the same physical antenna size, mmWave transmissions allow for a better antenna gain to mitigate path loss [69]. An improvement of 28.1 dB in path loss was achieved using a multibeam antenna system by combing the strongest received signals coherently at mmWave bands [75]. Besides the antenna array gain, the implementation of multiple-input-multiple-output (MIMO) in IEEE 802.11 ay also helps to enhance the multiplexing gain. However, due to directional communication using beamforming, interference can occur if the receive beam aligns with unwanted transmitters, causing potential disruption.
## 5 Discussion and Operational Applications
Thus, it has significant potential to advance various scientific and technological domains.
# A Novel Virtual Power Plant Uncertainty Modeling Framework Using Unscented Transform
## 4 Problem Definition  
The proposed case study considers a VPP aggregator that aims to make profit and avoid losses in the electricity market by properly forecasting short-term generation and demand. Two days of historical data are considered, and the UT method is applied so that the third day is predicted by the proposed methodology and compared with the Persistence Method [24].  
Figure 3 illustrates the VPP considered. It comprises three solar photovoltaic (PV) generators, three load sets (i.e., EV charging stations, a residential condominium, and a shopping mall, respectively) connected to a 13.8 kV bus, and three load sets (i.e., four households without EV, one household with EV, and one street light) connected to a 0.22 kV bus.  
Figure 3: Electrical Region with VPP.  
The main challenge for this VPP is to obtain, for the next day, a forecast interval mapped with the smallest possible generation error, and individual demand, minute-by-minute. By accurately predicting generation and load, the VPP aggregator can reduce its risks when purchasing and selling power in the electricity market.  
Figure 4 shows the energy production of the three photovoltaic generators, connected to the 13.8 kV bar during three consecutive days. Data from the first two days are used to train the model. Two days are used in the model to buy, with the last day containing the error of the proposed model.  
Figure 5 shows the 3 days of demand data connected to the 13.8 kV bus.  
Finally, in Figure 6, the demands connected to the low voltage are shown.  
Figure 7 shows the total aggregated generation and load data for the VPP considered in this study.  
Figure 4: Photovoltaic power generation.  
Figure 5: Electricity demand (13.8 kV).
## 1 Introduction  
The European Union (EU) has set ambitious recycling targets as part of its Circular Economy Action Plan, which requires member states to improve their recycling systems and reduce the use of single-use plastics. The primary key conditions that must be fulfilled include ensuring the proper sorting of materials, maintaining clean and contamination-free bottles, and providing convenient access to recycling facilities [1]. Additionally, users often face issues such as long waiting times at recycling stations, which can deter participation in recycling initiatives.  
In many countries, plastic recycling practices vary, but there are some common trends aimed at improving recycling rates and reducing plastic waste, as follows:  
* Extended Producer Responsibility (EPR). Countries like Germany, France, and Belgium have implemented EPR schemes, wherein manufacturers are responsible for the entire lifecycle of their products, including recycling [2];
* Deposit Return Schemes (DRSs). Countries such as Sweden, Finland, Spain, Italy, and Romania have a DRS strategy for beverage containers, wherein consumers receive a small refund for returning plastic bottles [3];
* Standardized Recycling Codes (SRCs). Some countries, including the United Kingdom and the Netherlands, use standardized recycling codes to inform consumers about recyclable materials [4]. This helps reduce confusion and increases participation in recycling programs;
* Comprehensive Collection Systems (CCSs). Austria and Switzerland have waste collection systems that include curbside pickup and recycling bins for plastics [5].  
Most European countries employ a system in which consumers pay an additional fee for plastic bottles at the point of purchase. To recover this fee, they must return undamaged bottles with unaltered labels that include the appropriate recycling code to designated collection machines.  
The frequent malfunctions of recycling machines further exacerbate the situation, leading to increased frustration among users. These obstacles highlight the need for a more robust and user-friendly recycling infrastructure to facilitate plastic bottle recycling and improve participation rates.
# Reviews Based on the Reconfigurable Intelligent Surface Technical Issues
## 3 RIS Field Trials
### RIS Performance Evaluation Parameters [28]
#### 3.1.2 Spectral Efficiency  
Spectral efficiency is the total throughput of all users divided by the channel bandwidth of a given band divided by the number of TRxPs and is measured in bit/s/Hz/TRxP. The channel bandwidth for this purpose is defined as the effective bandwidth multiplied by the frequency reuse factor, where the effective bandwidth is the operating bandwidth appropriately normalized to account for the uplink/downlink ratio.
# A Recursive Least-Squares with a Time-Varying Regularization Parameter
## 1 Introduction
The recursive solution helps in reducing the complexity associated with computations and memory storage requirements.
###### Abstract  
This study investigated the seismic performance of reinforced concrete columns retrofitted with Replaceable Steel Links (RSLs), focusing on the effects of varying sliding slot lengths and torsional loads. The RSL system, known for its simple construction and effective seismic performance, was analyzed to assess the feasibility of substituting damaged steel links post-earthquake, using the system as a structural fuse. The experimental results highlighted the role of sliding slot lengths in delaying the initiation of shear cracks, especially under eccentric lateral loads. The RSL system exhibited notable torsional resistance, showing only a 10% decrease in maximum load capacity, even with a two-fold increase in the eccentric distance. Furthermore, with an increase in sliding slot length, the difference in cumulative energy dissipation attributable to augmented eccentric distances reached approximately 50%, indicating a notable impact of sliding slot length on the system's ability to resist torsion. Consequently, it is recommended that the length of the sliding slot be based on the specific seismic design objectives when employing the RSL system as a structural fuse. The post-experiment inspection revealed no deformation in the steel plates, and the buckled steel links could be effortlessly replaced by loosening the high-tensile bolts in the slots. These findings demonstrate the RSL system's efficiency as a structural fuse.  
seismic retrofitting; brace; structural fuse; replaceable steel link +
Footnote †: journal: Applied  
Article An Experimental Study on the Seismic Performance of a Replaceable Steel Link System Acting as a Structural Fuse.