esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Selected Results of Hub Height Winds
The only filtering criteria used are related to the quality of the signals of the lidar; for the WindCubes the CNR and availability parameters and for the ZephIR we use the points of fit-figure, the degree of activity of the cloud correction algorithm and the precipitation signal, see [8]. If we start to look at particular conditions, the amount of data highly reduces. We need to maximize the amount of data for the analysis of the wind shear. As we have many pulsed lidars where the amount of data decreases with height, then we need to make a compromise between how much data we want to have and how high we want to observe. Since we want to see the shear at 100 m then we selected the two heights closest to it. Fitting a curve would bias the alpha estimation to the form of the fitted curve (the ideal will be to have two measurements very close to each other since alpha is in fact a very local parameter). Figure 12: Normalized distribution of \(\alpha\)-values from the power law at around 100 m for a number of NORSEWInD wind lidar nodes for all wind speed ranges. It is noticed that:  
* For all the nodes, there is a broad range of \(\alpha\)-values, mostly in the positive side of the distribution, which contrasts with the common value of 0.2 used for load calculations offshore. See [32] for further discussion of the \(\alpha\)-value. * A higher amount of positive \(\alpha\)-values is found since wind speeds are generally higher above than below 100 m, as expected, but at all nodes it is also observed a good amount of negative \(\alpha\)-values. The latter are normally found either under conditions where the atmosphere is very unstable and the wind speed does not change much with height (and so due to the nature of the atmosphere dynamics higher wind speeds are observed below 100 m) or conditions where the atmosphere is very stable and so low-level jets or shallow boundary layers influence the wind profile so that it bends backwards. It can straightforward be seen that predictions of the distribution of \(\alpha\) using Equation (3) might only fit a range of positive values (no negative values can be estimated from it, although the conditions are very unstable and the sea roughness is high). * Most distributions peak on a positive value between 0 and 0.05. The clearest exception is Horns Rev 2, which observes most of the time the wake of the wind farm that increases the wind shear at this particular height [8]. * Most distributions lie on each other; the greater exceptions are those at Jacky and Beatrice (the two with the fewest high-quality data for the analysis by far), and Schooner that shows a bump at about \(\alpha\) = 0.2 which might not be real since we found a systematic problem with data, although the data here shown should be \"correct\" according to the NORSEWInD standards (see Table 1).
## 2 Materials and Methods
### Experimental Procedure  
At the beginning of the study, 51 reflective spherical markers were adhered to the participants' anatomical landmarks based on the Plug-in-Gait marker setting (VICON, UK) for full-body models [26]. After 10 min of warm-up, the body hairs over the RA, erector spinae (ES), rectus femoris (RF), biceps femoris (BF), tibialis anterior (TA), and gastrocnemius (GN) muscles of each athlete were removed, and the surface electrodes were adhered to the skin in the area of these muscles to record maximal voluntary isometric contraction (MVIC) values for each muscle group [27; 28; 29]. The electrodes were positioned as suggested by [PERSON] [30]. Subsequently, three sessions of pre-test data for successful CMJs and SPJs were collected. The jumping conditions (CM) and SPJ) were randomized by drawing lots. To achieve RA fatigue, the participants first laid flat on the ground, lifted their thighs 90 degrees to the torso, and placed their feet on a chair. Then, they raised their shoulders by approximately 10 cm, 50 times, at a rate of 45 repetitions per minute (rpm). This procedure was repeated thrice, with 30 s rest intervals, to induce abdominal fatigue. The abdominal fatigue was assessed through EMG signals. Then, post-test data for three trials of successful CMJs and SPJs were collected (kinematic, kinetic, and EMG data).  
The required actions for the CMJ and SPJ are described as follows.  
A successful CMJ test required both feet to be on the 1 st force plate when taking off and on the 4 th force plate on touchdown.  
For the SPJ, the participants began from the starting line and could take a two-step approach. They had one foot each on the 1 st and 4 th force plates. Then, they took off vertically with their hands swinging forward to attempt a spike. On touchdown, they had one foot each on the 2 nd and 3 rd force plates (Figure 1b). In this study, one leg was the braking leg and the other was the jumping leg, which is characteristic of the SPJ. During the two-step approach, the braking leg made the first step in the SPJ (the dominant leg), and the jumping leg made the second step (non-dominant leg).
# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 4 Resource Management and Network Planning
### Energy Efficiency and Power Control
MFG models the interactions among the behavior of an individual player and the aggregate behavior of the collective players. The numerical results indicated that the proposed schemes can improve the efficiency of wireless charging, being practically applicable due to their offline operation, solving the MFG and thus promoting their practical implementation without demanding significant processing power from the UAVs. Moreover, when compared to a uniform power control policy without ML, the signal-to-interference plus noise ratio (SINR) threshold imposed by the network is not violated, thus providing resiliency against outages.
## 3 Results
### Simulation and Regional Adaptability Test  
After calibrating and validating the parameters of the typical vegetation types, this study simulated the monthly GPP from 2000 to 2021 at each grid with the meteorological, soil, initialization files, etc.  
The adaptability of a model refers to its ability to cope with changes in the environment. Although Biome-BGC had high accuracy at the seven parameter optimization sites, the performance of the model at the regional scale was uncertain. The mismatch in spatial resolution among the input data of the model would lead to unavoidable errors, and the accumulation of these errors might weaken the simulation effectiveness at the regional scale. It was unknown how much the performance of the model would be reduced. Therefore, a further regional adaptability test was needed to judge the overall simulation performance of the optimized parameters of different typical vegetation types.  
The optimized parameters exhibited great adaptability for the typical vegetation types in this study, as shown in Figure 4. For the temperate grass grids, the R\({}^{2}\) and RMSE values of the simulated results after parameter optimization were mostly centered (25-75%) in the range of 0.76-0.91 and 27-60 g C m\({}^{-2}\), respectively (Figure 4a). Similarly, for the alpine meadow grids, the R\({}^{2}\) and RMSE values were concentrated within 0.90-0.94 and 18-33 g C m\({}^{-2}\), respectively (Figure 4b). For the shrub grids, R\({}^{2}\): 0.84-0.94, RMSE: 34-66 g C m\({}^{-2}\) (Figure 4c); for the evergreen needle leaf forests, R\({}^{2}\): 0.83-0.93, RMSE: 48-68 g C m\({}^{-2}\) (Figure 4d); for the deciduous broadleaved forests, R\({}^{2}\): 0.76-0.95, RMSE: 40-55 g C m\({}^{-2}\) (Figure 4e); for one crop a year, R2: 0.83-0.93, RMSE: 44-77 g C m\({}^{-2}\) (Figure 4f); for two crops a year, R2: 0.72-0.83, RMSE: 54-72 g C m\({}^{-2}\) (Figure 4g). Overall, our results demonstrated the great simulation accuracy of the Biome-BGC model at the regional scale.  
Figure 3: The scatter plots of monthly simulated and observed GPP values for typical vegetation types in the YRB.  
On the whole, simulating the GPP of the typical vegetation types with the optimized eco-physiological parameters, the average R\({}^{2}\) value between the simulated GPP values and observed GPP values in the Yellow River Basin from 2012 to 2014 was 0.85 and the average RMSE was 50.92 g C m\({}^{-2}\). Therefore, this model could well adapt to the Yellow River Basin (Table 2).
# High-Throughput Cross-Technology Communication via Chip-Level Side Channel
## 4 Evaluation
### Transparency  
This experiment aims at showing that our design would not affect ZigBee communication too much. After modifying some of the ZigBee chips, the Hamming distance would increase and the ZigBee packets accepted ratio would decrease. As displayed in Figure 14, our design has much better performance in the ZigBee-PAR than Symbee. As the distance increases, the performance gap becomes more pronounced. At the same time, the differences among ZigBee packets accepted ratio of three settings are not too significant when the distance between ZigBee CTC sender and WiFi CTC receiver is less than 10 m. As the distance increases, the more ZigBee bits could not be demodulated correctly due to error caused by ZigBee chips modification. More specifically, the cumulative distribution function (CDF) of Hamming distance when the distance is 3 m is plotted in Figure 15. As we can see, the Hamming distance increases as more chips are modified. However, if sender and receiver are not located further than 10 m, our design is transparent to the ZigBee communication.  
Figure 14: ZigBee packets accepted ratio.  
Figure 13: CTC bits accepted ratio.
## 2 Related Work & Background
### System Architectures for Industry 4.0
#### 2.1.9. Summary  
System architectures for Industry 4.0 are critical for integrating intelligent manufacturing processes, with reference frameworks like RAMI 4.0 and IIRA guiding the path toward digitalization and automation. The synergy of IoT, Edge Computing, Cloud Technologies, and AI not only streamlines real-time data acquisition but also enables more efficient and flexible production workflows. Communication protocols such as MQTT and OPC UA ensure interoperability across diverse industrial settings, while big data analytics and digital twins further optimize production by enabling advanced process simulations and predictive maintenance. Recent advances in modular architectures, cyber-physical production systems (CPPS), and ontology-based knowledge systems have also underlined the potential for adaptive, domain-specific knowledge modeling. Choosing the appropriate maturity model (e.g., RAMI 4.0 Maturity Index, Impuls Model) helps organizations systematically progress along their digital transformation journey. At the same time, cross-company data sharing and real-time capabilities play a pivotal role in supporting increasingly interconnected manufacturing environments.  
Building on these established frameworks and technologies, the following sections detail our novel approach to seamlessly integrating heterogeneous data sources with knowledge modeling, ensuring real-time capabilities for practical Industry 4.0 deployments. This comprehensive approach aims to fill current gaps in integrating legacy systems, edge-based analytics, and continuous learning processes within a single scalable architecture.
## 3 Proposed Framework
### Localization with Combined Dictionary
#### 3.4.6 DBN Structure During Training  
The training phase of DBN as shown in Figure 7 incorporates data from both the odometry sensor and the LiDAR training dataset to build a robust model of the environment. The odometry sensor provides precise ego vehicle positions, forming a reliable foundation for motion estimation, while the LiDAR dataset captures interactions with surrounding objects. From this data, six static tracks (corresponding to stationary objects such as buildings, poles, and trees) were classified and stored for future reference.  
The DBN in the training phase operates as follows:  
1. Sensor Integration: The lower-level nodes in the DBN represent the ego vehicle's state, including its hidden state \(\bar{S}_{0}\), predicted position \(\bar{X}_{0}\), and observed position \(\bar{Z}_{0}\). These nodes are connected via transition probabilities \(P(\bar{X}_{0}|\bar{S}_{0})\), modeling the odometry-based motion dynamics.
2. LiDAR-Based Track Classification: At the track level, the DBN models individual static tracks, such as \(SLT_{1}\), where \(SLT_{1}\) represents the hidden state of the track and \(XLT_{1}\) represents its observed position. Transition probabilities \(P(XLT_{1}|SLT_{1})\) capture the temporal evolution of these static objects in the LiDAR dataset.
3. Dictionary Fusion: A higher-level node integrates the outputs of all track-level nodes into a unified combined dictionary. This dictionary serves as a probabilistic repository of interactions between the ego vehicle and the classified tracks. It encodes cross-track consistency and enables robust ego position estimations across different scenarios.  
This approach ensures that the training phase produces a comprehensive model of the environment, capturing both the static structure and the temporal dynamics of track interactions. The combined dictionary becomes a central component for downstream testing.  
Figure 7: DBN structure during the training phase. Odometry sensor data and LiDAR observations are used to classify and learn tracks.
## 2 Literature Review
### Numerical Methods for the Prediction of Ground Settlementss  
Numerical methods, contrary to the empirical ones that consider only a mere handful of effective parameters on ground settlement, take into account the influence of a large number of factors, such as soil properties, the geometry of foundation pits, construction methods, and environmental factors, such as temporary stacking load and vehicle load [21; 22]. In the last four decades, numerical methods have been extensively applied in the analysis of ground settlement induced by the construction of tunnels, and the results of these studies indicate the reasonable performance of numerical methods [13; 23; 24; 25]. The settlement prediction accuracy of numerical methods greatly depends on the choice of the values of these factors, the constitutive model, and the accuracy with which the construction process is represented. However, the true values of some factors and parameters may be difficult to obtain, and inaccurate values could lead to unreliable predictions [26]. For example, the parameters of soil properties are generally obtained from laboratory tests; however, the test results are often not representative of the in situ soil behavior due to sampling disturbance, change in in situ environment, and the effects of construction, leading to marked discrepancies between the prediction results of ground settlement and the on-site settlement-monitoring data [27].
## 3 Methods
### End-To-End NN-Learning  
Neural networks-based learning methods becomes more and more popular over the few last years in Oceanography and satellite data processing. They can embed complex modeling of the geophysical dynamics with large number of parameters and learn from large training datasets how to reconstruct a given target according to a cost function to minimize. After training, the model can be used on other similar input datasets. In particular, it can efficiently be applied to real-time events.  
Recently, an end-to-end learning representation has been introduced in [15] to deal with image sequences involving potentially large missing data rates. In this framework, an energy-based representation \(U_{\theta}\) to minimize is introduced:  
\[U_{\psi}(d\mathbf{x})=\|d\mathbf{x}-\psi\left(d\mathbf{x}\right)\|^{2} \tag{3}\]  
where the operator \(\psi=\psi_{\theta}\) denotes a NN-based representation of the underlying processes and \(\|.\|_{\Omega}^{2}\) refers to the L2 norm evaluated on subdomain \(\Omega\). Within a Bayesian framework, the interpolator \(U_{\psi}\) of the irregular space-time dataset \(\{d\mathbf{y}_{k}(\Omega_{k})\}\), referred ad the hidden state in a classic data assimilation framework, can be obtained by solving the minimization statement:  
\[\widehat{d\mathbf{x}_{k}}=I_{U_{\psi}}\left(d\mathbf{y}_{k}(\Omega_{k})\right) =\arg\min_{d\mathbf{x}}U_{\psi}\left(d\mathbf{x}\right) \tag{4}\]  
such that \(I_{U_{\psi}}\left(d\mathbf{y}_{k}(\Omega_{k})\right)=d\mathbf{x}_{k}\) if no observational error are considered.  
Last, for a specific definition of interpolator \(I\), the learning problem for optimizing parameters \(\theta\) of the NN representation \(\psi\) can be stated as the minimization of the reconstruction error for the whole observed data time series:  
\[\widehat{\theta}=\arg\min_{\theta}\sum_{k}\left\|d\mathbf{y}_{k}(\Omega_{k})- I_{U_{\psi}}\left(d\mathbf{y}_{k}(\Omega_{k})\right)\right\|_{\Omega_{k}}^{2} \tag{5}\]
# Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration
## 5 Conclusions and Future Work  
This paper presents a brief overview of Saudi electricity consumption as well as the need for empowering renewable energy resources in the power grid. Saudi has an inductive grid due to the high use of air conditioning. Thus, this work presents the most common PQ issues in the grid due to the penetration of wind energy, namely voltage variation and harmonics. Several mitigation techniques such as DVR, STATCOM passive and active filters and inverters are discussed. However, a longer study is required to comprehensively account for wind energy integration into the Saudi electricity infrastructure. As a demonstration example for intermittent wind power supply, a typical summer load profile of a substation in Riyadh city is analyzed using the quasi-static time series simulator with a standard IEEE test systems. The IEEE 30-node is simulated to assess the role of reactive power of WPP in voltage mitigation. The results show that the larger WPP with reactive power capability could mitigate the voltage at the PCC of the WPP and hence improve the overall voltage profile of the study system. On the other hand, the analysis of radial distribution systems with WWP shows that the voltage regulators can keep the voltage profile within the allowable preset limits of the nominal voltages. However, regulators cannot eliminate the fluctuations in the voltage profile. Besides, excessive operation and movements of the regulator tap setting could affect the device's lifetime. Reactive power compensation provides an efficient and fast control action due to inverter circuits. The amount of reactive power depends on the type and size of the interface circuit. The future research seeks for robust control and management of various power resources in a competitive power pool with demand-side mechanism and at the same time mitigate any resulting power quality issues accompanied by RES integration. Figure 15: The reactive power compensation and voltage regulator action of Case 3: (**a**) The pu phase voltages, the available reactive from the WECS \(Q\), and reactive power compensation \(Q_{c}\) (**b**) Tap changes operation. **Author Contributions:** Conceptualization, S.A.A. ; methodology, S.A.A. ; software, M.A.-A. ; validation, M.A.-A. ; formal analysis, S.A.A. ; investigation, S.A.A. ; writing--original draft preparation, S.A.A. ; writing--review and editing, S.A.A. Both authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Acknowledgments:** Researchers would like to thank the Deanship of Scientific Research, Qassim University for funding publication of this project. **Conflicts of Interest:** The authors declare no conflict of interest.
# User Acceptance of Information Feed Advertising: A Hybrid Method Based on SEM and QCA
## Research Design
### Questionnaire Design and Data Collection  
The questionnaire in this study was measured by the Likert 5 scale. The respondents chose 1 (very disagree) to 5 (very agree) to rate the questions. The observation indicators are set based on classic literature and users' willingness to accept information feed advertisements. The questionnaire consists of three parts: sample screening questionnaire, demographic questionnaire, independent variable, and dependent variable questionnaire:  
1. Sample questionnaire. This part of the questionnaire mainly detects the useful samples, ensuring that the users are experiencing information feed advertising.
2. Demographic questionnaire. This part mainly measures the gender, age, and educational background of the respondents.
3. Independent and dependent variable questionnaire. This part of the questionnaire is the core of the whole questionnaire design, as shown in Table 1.  
In this study, 262 questionnaires were collected through an online survey. After eliminating invalid questionnaires such as data missing, 229 valid questionnaires were obtained, with an effective rate of 87.4%. The sample size is more than 200, and the ratio of a sample size to measurement items is 10.9:1, which meets the analysis requirements of the SEM model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Question** & **Source** \\ \hline \multirow{8}{*}{Consistency} & YZ1: In terms of form, I think the information feed & \\  & advertisement is very similar to other information on & \\  & the page, so it is not abrupt & \\ \cline{1-1} \cline{2-3}  & YZ2: From the design point of view, information feed & Fan et al. [35] \\ \cline{1-1} \cline{2-3}  & advertising is very similar to other information on & \\ \cline{1-1} \cline{2-3}  & the page & \\ \cline{1-1} \cline{2-3}  & YZ3: From the perspective of content language style, & \\ \cline{1-1} \cline{2-3}  & information feed advertising is very close to other information on the page & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variables and sources.  
The descriptive statistical results of the sample are as follows: the proportion of male and female is 47.6% and 52.4%; the age group is mainly between 18 and 30 years old, accounting for 88.2% of the total sample; the number of undergraduates and above accounts for 89.9%, and the sample of master's degree or above accounts for 49.7%; in the occupation, the proportion of students studying is 55.4%, and that of enterprise employees is 32.7%.
# Network Structure and Synergy Characteristics in the Guangdong-Hong Kong-Macao Greater Bay Area
## 1 Introduction
Additionally, it offers theoretical support and practical insights for development management and policy formulation for individual cities, as well as for the planning and construction of the Greater Bay Area. The study is organized as follows: Section 2 reviews relevant literature, while Section 3 presents the research data and methodology. Section 4 analyzes the results, quantifying the Greater Bay Area's structure, city node characteristics, and overall city cluster dynamics based on logistics and capital flow networks. This section reveals the structural traits and synergistic development trends within these networks. Section 5 concludes the study, summarizing key findings and offering recommendations for optimizing transportation infrastructure, constructing efficient logistics networks, and enhancing capital flow within the Greater Bay Area city cluster.