esa-sceva/satcom-chunk-collection huggingface dataset:
# Establishing a Geological Knowledge Base for Braided River Deltas Using Google Earth
## 3 Results and Discussion
### Sedimentary Parameter Distribution Characteristics
#### 3.1.5 Adjacent Bifurcation Spacing  
In deltas such as Wax Lake, Kotlik, Lena River, Po River, Poyang Lake, and Orinoco, 80% of bifurcation angles concentrate below 90\({}^{\circ}\), with Poyang Lake (65%) and Lena River (53%) showing the highest proportions in the 30-60\({}^{\circ}\) interval (Figure 7). This reflects that braided channels primarily bifurcate at moderate angles, corresponding to a depositional environment with balanced hydrodynamic energy and frequent channel migration, where sandbodies are mostly distributed in network or dendritic patterns. Moderate bifurcation angles (30-60\({}^{\circ}\)) commonly develop in gentle-slope settings, such as the Poyang Lake delta, where stable hydrodynamic conditions promote moderate channel bifurcation, forming dendritic sandbar complexes [32].  
The Lena River delta, driven by a massive sediment supply, exhibits moderate yet sustained hydrodynamic energy, leading to dominant moderate-angle bifurcations and strong sandbody stacking.  
The bifurcation angle characteristics of the braided river delta in the Yao second and third members of the Yingtai area, Songliao Basin, are closely related to its \"proximal source and short flow path\" depositional background. Influenced by the east-dipping steep slope topography, concentrated hydrodynamic energy causes channels to primarily bifurcate at 60-80\({}^{\circ}\), forming tabular and trough cross-beddings in underwater distribu  
Figure 7: Eight groups of braided river deltas are adjacent to bifurcation spacing distribution and synthesis data.  
channel microfacies. Such high-angle bifurcations result in lenticular sandbody stacking, which, despite good connectivity, exhibits strong heterogeneity due to local topographic abruptions, making it a favorable reservoir unit for hydrocarbon enrichment.
# Which, When, and How: Hierarchical Clustering with Human-Machine Cooperation
## 5 Algorithm Overview  
### Algorithm Design  
The whole algorithm is presented in Algorithm 3, which is the detailed implementation of Algorithm 2. In Algorithm 3, line 5 corresponds to the \"when\" problem: the key insight is that questions are asked to limit the error propagations. Line 6 corresponds to the \"which\" problem: the key insight is that local structures of pairs of data points can be used to determine the most questionable pair of data points. Then, line 7 shows the transitive relations that can be used to derive more answers. If one of these answers disagrees with the current clustering result (i.e., the machine has made some errors), then adjustments are applied (lines 8 to 13), which correspond to the \"how\" problem. The key insight is that the back-tracing scheme can balance the time complexity and the adjustment accuracy. Line 14 shows that one question operation is used up. In the next subsection, further analysis shows that the time complexity of Algorithm 3 stays asymptotically the same with Algorithm 1, which is \(O(N^{2}\log N)\). We do not further discuss the stop criterion (line 3 in Algorithm 3). A simple stop criterion is used: the iteration stops, when the number of existing clusters reduces to a threshold. A better stop criterion is to be addressed in our future work.  
```
Data: Distances between pairs of data points; The number of available questions (i.e., \(Q\)). Result: The clustering result.
1 Treat each data point as a singleton cluster;
2 Save the current state as the trace back state;
3 whilethe stop criterion is not metdo
4 Merge the closest two clusters;
5 ifencounter a certainty gain and \(Q>0\)then
6 Select the most questionable pair of data points in the dirtiest cluster, and then, use the question operation to ask for the human help;
7 Derive the transitive relations of questions;
8 ifone answer disagrees with the machinethen
9 Record the current clustering hierarchy;
10 Trace back to the saved state and apply local adjustments;
11 Iteratively merge the closest two clusters, until returning to the recorded hierarchy;
12 end if
13 Save the current state as the trace back state;
14\(Q=Q-1\);
15
16 end if
17
18 end if
19
20 end for return the remaining clusters.
```  
**Algorithm 3**The Proposed Algorithm.
# WLAN Aware Cognitive Medium Access Control Protocol for IoT Applications
## 3 Overview and Operation of IEEE 802.11 and IEEE 802.15.4
### IEEE 802.15.4
#### 3.2.1 Beacon Mode Versus Non-Beacon Mode Comparison  
Beacon-enabled mode has several advantages over non-beacon-enabled mode of sensor nodes. In beacon-enabled mode, regular beacons are sent by network coordinator (sink node) to synchronize all the sensor nodes and allocate Guaranteed Time Slot (GTS) for data transmission. Moreover, it supports a flexible Duty Cycle (DC), where the active time of the nodes can be adjusted as per the network scenario. In comparison, non-beacon enabled mode is suitable for an ad hoc communication scenario where there is no coordinator node.
## Materials and Methods
### Model Validation  
To explore the effectiveness and potential contribution of the backscatter coefficient of multi-temporal Sentinel-1 images to STN mapping, different combinations of environmental variables were constructed (Table 2). We then compared the predictive performance of different combinations based on RF, BRT, and SVM methods. To compare and evaluate the prediction accuracy of these models, a 10-fold cross-validation technique was used to calculate the following three commonly used validation indices: The root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R\({}^{2}\)). These validation indices were defined as shown in Equations (1)-(3):  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|\,P_{i}-O_{i}\right| \tag{1}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(P_{i}-O_{i}\right)^{2}} \tag{2}\]  
\[R^{2}=\frac{\sum_{i=1}^{n}\left(P_{i}-\overline{O_{i}}\right)^{2}}{\sum_{i=1}^ {n}\left(O_{i}-\overline{O_{i}}\right)^{2}} \tag{3}\]  
where \(n\) represents the number of sample points; and \(P_{i}\) and \(O_{i}\) represent the estimated and observed STN content at site \(i\), respectively.
## 5 Conclusions  
This paper proposed a novel temperature-based mean-shift tracking algorithm (TIR-MS) in IR-based pedestrian tracking. A search of the IR-based pedestrian tracking techniques showed that the most research focused on the brightness feature-based tracking algorithm. On the other hand, the brightness can be distorted easily by the image contrast, causing degradation of the pedestrian tracking performance. This paper presents the radiometric temperature-based method and how to convert the histogram. In addition, the histogram similarity map is based on the temperature histogram model and Epanechnikov kernel function. The temperature data-based histogram was more stable and compact than the brightness histogram, i.e., the temperature data has a high weight value and enables a more efficient kernel density estimation. In the experiments, temperature improved the mean-shift tracking performance for the pedestrian by the discriminative IR feature with thermoregulation and the physical normalized temperature enhanced the pedestrian tracking performance [13]. The temperature histogram was evaluated by comparing the image contrast with the brightness and 14-bit digital count histogram. In future work, because the temperature was not applied to the various tracking algorithms, particularly deep learning, will be used in models, such as RNN-based and CNN-based deep learning tracking algorithms, to improve the tracking performance. In addition, the radiometric temperature-based deep neural network model will be developed with the construction of a large database to track pedestrians and drones, and the results will be compared with the brightness-based deep neural network, such as re3 [34], GOTURN [33], and ADNet [35].  
Conceptualization, S.Y. and S.K.; methodology, S.Y.; software, S.Y.; validation, S.Y. and S.K.; formal analysis, S.Y.; investigation, S.Y.; resources, S.K.; data curation, S.Y.; writing--original draft preparation, S.Y.; writing--review and editing, S.Y. and S.K.; visualization, S.Y.; supervision, S.K.; project administration, S.K.; funding acquisition, S.K. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A3B07049069). This work was supported by the Civil Military Technology Cooperation Research Fund (19-SN-MU-03, Development of Military Technology for Intelligent Ship Navigation Information) of Agency for Defense Development. This research was funded and conducted under the Competency Development Program for Industry Specialists of the Korean Ministry of Trade, Industry and Energy (MOTIE), operated by Korea Institute for Advancement of Technology (KIAT). (No. P0008473, The development of high skilled and innovative manpower to lead the Innovation based on Robot)  
The authors declare no conflict of interest.  
Figure 15: Tracking performance in different distances: (**a**) 5 m, (**b**) 15 m, (**c**) 35 m, (**d**) 50 m.
# Energy-Efficient IoT-Based Light Control System in Smart Indoor Agriculture
## 3 Plants Light Requirements
### Photosynthetically Active Radiation  
PAR is the portion of the light spectrum used by plants in the photosynthesis process. PAR is also the visible light spectrum which contains wavelengths between approximately 400 nm and 700 nm. PAR is measured in PFD, which means the number of photons emitted on a unit area per unit of time. Plants convert PAR light energy into chemical energy in the form of glucose and other sugars through the photosynthesis process. In general, a minimum PAR intensity of around 100-200 \(\upmu\)mol/m\({}^{2}\)s is required for vegetative growth, and 400-600 \(\upmu\)mol/m\({}^{2}\)s or more is required for flowering and fruiting [62]. The optimal PAR intensities for a particular plant species and growth stage can be determined through experimentation and monitoring of plant growth and development under different intensity values.
# A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN
## 4 Experiments
### Static Experiment
#### 4.1.2 Experimental Results of Multipath/NLoS Estimation
It is observed that the positioning errors are smaller in magnitude than the estimated \(\hat{F}^{s}(t)\) for the dataset without multipath/NLoS compensation. This occurs because the locations are computed based on both the pseudorange measurements with and without multipath/NLoS effects. The pseudorange measurements without multipath can provide accurate positioning information, thereby mitigating the multipath/NLoS positioning accuracy degradation. The Cumulative Distribution Functions (CDFs) of these positioning errors are provided in Figure 10. The CDF illustrates the distribution of positioning errors, serving a crucial role in assessing the enhanced positioning accuracy after multipath/NLoS compensation. The 3D Root Mean Squared Error (RMSE) can be improved from 13.92 m to 3.01 m (reduced by 78%) using the new dataset that compensates for multipath/NLoS biases. Figure 8: Positioning scatters before and after pseudorange multipath/NLoS compensation in the horizontal plane. Table 2 presents the horizontal and vertical errors (at the 95 th percentile of the CDFs) derived from the GNSS datasets both before and after the multipath/NLoS compensation. The positioning solutions after the multipath/NLoS compensation in Figure 8 still contain slight biases. However, these positioning results meet the standard accuracy performance criteria for GPS, as outlined in [25]. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Horizontal [m]** & **Vertical [m]** \\ \hline Before Compensation & 10.94 & 31.04 \\ After Compensation & 2.54 & 4.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Horizontal and vertical positioning errors at 95 th percentile of the CDFs. Figure 9: Positioning error time series before and after pseudorange multipath/NLoS compensation in East, North, and Up directions.
# Paneth Cells and Lgr5+ Intestinal Stem Cells in Radiation Enteritis
## 2 The Structure and Functions of the Small Intestine
### The Lgr5 Intestinal Stem Cells
The Lgr5\({}^{+}\) ISCs are intermingled with the Paneth cells and are heavily reliant on the Paneth cells which secrete antimicrobial peptides such as human \(\alpha\)-defensin 5 to sterilize the stem cell zone, provisioning of lactate for their aerobic respiration and regulation of their proliferation and differentiation through elaboration or secretion of niche factors and ligands. The Lgr5\({}^{+}\) ISCs normally undergo symmetrical or asymmetrical divisions daily [43]. The symmetrical division of Lgr5\({}^{+}\) ISCs ensures clonal expansion whereas asymmetrical division leads to the production of progenies which differentiate to become enterocytes, goblet cells, enteroendocrine cells, Tuft cells, M-cells, cup cells, and Paneth cells [15; 47; 64].
# 6G Goal-Oriented Communications: How to Coexist with Legacy Systems?
## 3 Coexistence of a Goal-Oriented and a Data-Oriented Communication System  
In this section, we apply the GO approach to a scenario in which two different systems coexist: _(i)_ a GO communication system, consisting of a User Equipment (UE) offloading inference tasks to an MEH collocated to an AP, sharing radio spectrum with _(ii)_ a classical DO communication system, consisting of another UE connected to another AP, where the device continuously uploads content, such as a video stream, to be cached in the network. As an example, the focused scenario can refer to an industrial setting for low-power networking, where a device uploading product images for anomaly detection (GO transmitter/ receiver pair) coexists with a security camera providing continuous video feed of the factory floor (legacy DO eMBB service). We believe that such a scenario will be of extreme interest in the future, as in beyond 5G and 6G systems, and the same system architecture will need to support both such services. As spectrum resources under 6 GHz will continue to be pivotal in providing wide radio coverage [3], (non-orthogonal) spectrum sharing is envisioned to be a standing feature, especially for low-power networking, e.g., in an industrial environment.  
The GO approach may find direct application to system scenarios involving coexistence of services of different types. For such services, the most relevant and important communication KPIs (e.g., data rate, latency, reliability) may even be the same, however, the tasks performed may substantially differ in scope, hence design trade-offs need to be addressed to concurrently guarantee all application-related goal values by keeping goal costs as low as possible for all tasks. To further elaborate on the problem of _conflicting goals_, in this paper, we choose to investigate the coexistence of the two systems, inspired by the Cognitive Radio (CR) networking paradigm [47; 48], whose scope is to opportunistically use a portion of the spectrum that is licensed to another usage [49]. Recently, the CR paradigm has been investigated in several contexts, including that of relay networks [49; 50].  
The fundamental difference lies in that, for the CR problem, where a primary and a secondary system operate together in space and time, focus is, in general, on allocating the available secondary radio resources to maximize secondary system performance, subject to the non-violation of _radio_ performance constraints imposed by the primary system operator. Nonetheless, for the focused GO/DO system coexistence, the ultimate design target is _service-effective communication_, even with \"softened\" radio communication requirements for the GO system that can be learned during goal accomplishment.
## 1 Introduction
### Influence of the Perforation Parameters
#### 2.2.2 Perforation Phasing  
The productivity ratio curves under four commonly used phase angles (\(0^{\circ},90^{\circ},120^{\circ}\), \(180^{\circ}\)) in engineering are shown in Figure 5. It can be observed that in isotropic formations, the productivity ratio is highest at \(90^{\circ}\) and lowest at \(0^{\circ}\). The phase angles are ranked in order of decreasing productivity ratio as follows: \(90^{\circ},120^{\circ},180^{\circ},0^{\circ}\). This trend can be attributed to the more uniform distribution of perforation holes in the circumferential direction of the wellbore at lower phase angles, such as \(90^{\circ}\), when the shot density remains constant.  
Furthermore, there is an interaction between phase angles and formation anisotropy. Figure 6 shows the influence of phase angles and formation anisotropy on the productivity ratio when the perforation hole penetrates the drilling contaminated zone. In cases where the anisotropy is not severe (\(0.3<K_{\rm v}/\rm K_{h}\leq 1.0\)), the phase angle of \(90^{\circ}\) exhibits the highest performance, while the phase angle of \(0^{\circ}\) demonstrates the lowest. The phase angles can be ranked in decreasing order of productivity ratio as follows: \(90^{\circ},120^{\circ},180^{\circ},0^{\circ}\). Conversely, when the anisotropy is severe (\(K_{\rm v}/\rm K_{h}\leq 0.2\)), the phase angles can be ranked in decreasing order of productivity ratio as follows: \(180^{\circ},120^{\circ},90^{\circ},0^{\circ}\). This indicates that higher phase angles result in higher productivity ratios. This phenomenon can be attributed to the  
Figure 4: Influence of the perforation depth and shot density on the productivity ratio.  
reduction in longitudinal spacing between adjacent perforation holes in the same direction at higher phase angles, thereby minimizing the impact of anisotropy to the greatest extent possible, when the shot density remains constant.
## 4 Experimental Results
### Qualitative Results  
Figure 5 illustrates the grounding process of the proposed RL framework. It shows that the agent moves and adjusts the candidate window step by step according to its alignment with the query. Figure 6 presents visual comparisons of the proposed method and CPL. Our method achieved more accurate prediction results than CPL, even with more complex language descriptions (second example). Finally, Figure 7 shows some failure cases  
Figure 4: Influence of hyper-parameters. (**a**) Influence of epoch number \(E\) in Equation (6) and (**b**) influence of different maximum number of steps in RL.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**R@1**} \\ \cline{2-4}  & **IoU = 0.3** & **IoU = 0.5** & **IoU = 0.7** & **Steps** \\ \hline BAR & 64.01 & 50.13 & 23.01 & 20 \\ TSL-PRL & 66.10 & 51.38 & 23.38 & 20 \\ Ours & **66.14** & **51.41** & **23.42** & **12** \\ \hline \hline \end{tabular} Bold mark represent the optimal performance across various methods evaluated under same metric.  
\end{table}
Table 8: Different movement strategies on the Charades-STA dataset.  
resulting from the limitations of the network. It is speculated that these failures may be attributed to the complexity of the data or premature termination during network training.  
Figure 5: Movement process of our method.  
Figure 6: Visual comparisons with CPL.
# An Interoperable Component-Based Architecture for Data-Driven IoT System
## 5 Experiment Results and Discussions
### System Interoperability  
The proposed IoT architecture focuses on providing a solution to support the system interoperation, reusability and instant prototyping of IoT system through a component-based approach. The loosely-coupled integration of devices enables the interaction with minimal standardization in terms of operating system and connectivity protocol. In addition, the data-driven model selector provides real-world evaluated model recommendation and high programmability for greater performance optimization for IoT applications.  
The DIAT architecture, however, requires the IoT daemon of the architecture to be installed in all the devices integrated into the architecture. Furthermore, despite having the flexibility to install only the required layer of the IoT daemon depending on the devices' functionalities in the system, these IoT devices are still restricted to operate with the devices or systems with the same IoT daemon installed. Moreover, DIAT does not assume that IoT systems' owner and manufacturers are driven by different interests and binds user with a specific manufacturer, which does not provide all the devices or services one needs [41]. Similarly, when compared with the CEB Atlas, our proposed architecture provides a higher degree of system flexibility and device heterogeneity support as the CEB Atlas requires the IoT architecture to utilize devices operating on the Atlas platform, whereas our proposed architecture can support devices with a variety of operating OS. As for MsM and IoT manager, they provided a similar level of requirement for communication protocol as our proposed architecture. However, both the MsM and IoT manager lacks data-driven modules.  
The architecture comparison is shown in Figure 14, which visualizes the IoT architectures based on the basic components that are required for the devices to interact with each other, which includes a standardized OS (IoT daemon) and a standardized connectivity protocol. In the figure, we can observe that our proposed IoT architecture is the same as MsM and IoT manager, which require only one component to be standardized between devices, whereas the DIAT and CEB Atlas architecture require at least two components to be standardized between devices. As a result, our proposed architecture provides the highest degree of system flexibility and support for device heterogeneity and enables the easiest device and system interoperability and reusability. Different from the MsM and IoT Manager, our proposal is equipped with a data-driven module, which is further discussed in the next subsection.  
Figure 13: The data visualization of PC MQTT Broker, MKR1000 and Arduino Uno through _Grafima_.
# Stability Analysis of Lane-Keeping Assistance System for Trucks under Crosswind Conditions
## 2 Materials and Methods  
### Simulation Model  
For vehicles with a LKAS, it is relatively easy to drive along the middle of the lane on a planned straight path. However, when the vehicle enters a curved road, in order to continue to remain in the middle of the lane, the vehicle will repeatedly adjust the steering angle, which can easily reduce the lateral stability of the vehicle, especially for larger trucks. When the vehicle encounters crosswind on a curved road, the stability will decrease even more severely. This study proposes a LKAS control strategy based on the path-tracking preview algorithm and the LQR controller, to improve the lateral stability of the vehicle. To achieve the control objectives, it is necessary to establish a reasonable vehicle dynamics model to describe the vehicle's motion state. Additionally, a path-tracking preview model is developed to determine the deviation between the vehicle and road centerline. Then, a feedforward controller is established using the linear quadratic regulator (LQR) algorithm and a linear two-degrees-of-freedom vehicle model, to eliminate steady-state errors generated during optimal control by the LQR algorithm, thereby further reducing the lateral deviation. Considering the influence of the vehicle's sideslip angle on lateral vehicle control, an observer for the sideslip angle is established based on the vehicle's lateral tracking deviation model and the Kalman filter algorithm. Using the yaw rate and lateral acceleration as the observation values, the deviation of the vehicle's sideslip angle is estimated. Finally, the LQR algorithm is used to optimize the estimated deviation and obtain the driver's steering wheel angle, therefore improving the control accuracy of the lateral deviation.