esa-sceva/satcom-chunk-collection huggingface dataset:
# A Sketch-Based Fine-Grained Proportional Integral Queue Management Method
## 3 System Model
### Traffic Statistics  
The flow statistics part of the S-PIE is mainly implemented using the measurement structure Sketch [29]. Sketch is a lightweight data structure that maps packet information arriving into a two-dimensional array by means of key-value pairs. A more accurate evaluation of the message frequency is achieved with a small time-space overhead. Thus, our goal is to introduce the measurement structure Sketch into the queue-management algorithm, use the frequency counting function of the Sketch structure to identify large flows, implement a discriminatory packet drop policy based on rapid network congestion relief, ensure fairness among data flows, and maintain the availability of the network. Typical Sketch has count-min Sketch (CM Sketch) [30], conservative update Sketch (CU sketch) [31], count Sketch (C Sketch) [32], etc.
# Contrast Analysis of Polarization in Three-Beam Interference Lithography
## 3 Contrast in Three-Beam Interference Patterns
### TE-TE-TE Polarization
\begin{array}{cc}i\Big{(}\frac{1}{2}\cos\theta\sin\delta_{2 }-\frac{\sqrt{3}}{2}\cos\delta_{2}\Big{)}&j\Big{(}-\frac{\sqrt{3}}{2}\cos \theta\sin\delta_{2}-\frac{1}{2}\cos\delta_{2}\Big{)}&\mathbf{k}\sin\theta\sin \delta_{2}\\ \mathbf{e}_{3}=\left[\begin{array}{cc}i\Big{(}\frac{1}{2}\cos\theta\sin\delta_{ 3}+\frac{\sqrt{3}}{2}\cos\delta_{3}\Big{)}&j\Big{(}\frac{\sqrt{3}}{2}\cos \theta\sin\delta_{3}-\frac{1}{2}\cos\delta_{3}\Big{)}&\mathbf{k}\sin\theta\sin \delta_{3}\end{array}\right]\end{array}  \tag{23}\]  
It can be concluded from Equation (23) that when there is a deviation in the polarization angle, the contrast loss is related to the incident angle. Under the condition that the polarization angle deviations \(\delta_{1},\delta_{2}\) and \(\delta_{3}\) are not more than \(\pm 5^{\circ}\), we calculated the minimum value of contrast when the incident angle is increased from \(5^{\circ}\) to \(75^{\circ}\). As shown in Figure 9, when the incident angle is not more than \(40^{\circ}\), the contrast loss does not exceed 0.015. Even if the interference angle reaches \(60^{\circ}\), the contrast still has 0.975. At this time, we have a sufficient contrast loss margin to consider the standing wave effect caused by large angle incidence.
# The Influence of Hard Protection Structures on Shoreline Evolution in Riohacha, Colombia
## 4 Results and Discussion
### Zone 2: Urban Cliff Area with Seawull  
Since the construction of coastal structures, sediment accumulation in this area has been remarkable (see NSM in Table 4). In all profiles, the coefficients of determination indicate that the linear model used is reliable. However, once again, applying prediction models in this area is not appropriate, as the sediment retention zone of these structures is nearly filled (Figure 6A). The southern part of the seawall has experienced particularly significant growth, with an average gain of approximately 89 m over just 13 years (2009-2022), most of which has occurred since 2018 (yellow line in Figure 6A).  
Table 5 summarizes the DSAS data for cliff Zone 2 (Figure 6), where most transects (T12-T20, except T16 and T17) show significant erosion, particularly T18. Indeed, in recent years, there have been several instances of houses collapsing or facing imminent collapse north of the seawall. The reliability of the linear fit is high for these transects, but low LR2 values in T11, T16, and T17 indicate poor model fit. T11, located downstream of groin No. 6, is influenced by its construction [66], while T16 and T17 have a developing beach and vegetation growth, complicating cliff crest identification. Due to these factors, prediction models were not applied to these transects.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Transect** & **NSM** & **LRR** & **LCI** & **LR2** \\  & **(m)** & **(m/yr)** & **(m/yr)** & **(m/yr)** \\ \hline T7 & 67.7 & 6.2 & 2.9 & 0.90 \\ T8 & 65.1 & 5.9 & 2.7 & 0.90 \\ T9 & 87.6 & 8.6 & 2.3 & 0.98 \\ T10 & 90.9 & 10.0 & 4.7 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Displacement rates of the coastline for the different transects created during the period 2009–2022 for the beach area in Zone 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**DSAS Years 2003–2022**} & \multicolumn{4}{c}{**HTA**} & \multicolumn{4}{c}{**EHD**} & \multicolumn{2}{c}{**KFB**} \\ \cline{2-10}
**Transect** & **NSM** & **LRR** & **LCI** & **LR2** & **Lower** & **Upper** & **Lower** & **Upper** & **Lower** & **Upper** \\  & **(  
The HTA and EHD models simulate shoreline changes from the last available coastline (2022) to 2045, while the KFB model in DSAS estimates 20 years from 2024, rounding up to provide predictions for 2045. All three models project significant shoreline recession, particularly south of the seawall in T18, T19, and T20 (Figure 7), with lower erosion estimates in the northern part. In the KFB model, accretion estimates for the lower bound at T13, T14, and T15 stem from data uncertainty rather than a physical process. The HTA model shows the smallest prediction variability, while the KFB model has the largest range. In most transects, the three models overlap or closely align (Figure 7), making this common range the most reliable forecast for territorial planning.
# High-Speed Autonomous Robotic Assembly Using In-Hand Manipulation and Re-Grasping
## 4 Perception
### Depth-Based Block and Target Shape Detection  
In addition to the RGB-based block detection, we also implemented a perception module based on the wrist-mounted RGBD camera. Recently, several studies have focused on detecting and estimating 6D pose of known objects from RGBD data [17]. Although such general RGBD-based object detection algorithms can robustly detect and estimate poses of complex-shaped objects even under heavy occlusion, we decided not to use them as the Soma cube assembly task requires a very high positional accuracy to reliably assemble the blocks using a single robotic manipulator, 1 mm or less for Soma cube blocks with 25 mm unit size we used. Instead, we devised a custom cube perception pipeline based on the 2D heightmap generated from the depth image, exploiting the specific shapes of Soma cube blocks.  
Using the camera transform calculated from manipulator forward kinematics, the depth image is projected into 3D space to form the point cloud of the blocks. Then, a 2D heightmap is generated based on the distance of each point in point cloud from the supporting surface, and a 2D detection pipeline similar to the RGB-based detection is used afterward. As we can arbitrarily move the RGBD camera using the robotic manipulator, we use multiple different camera positions, which includes the whole block observation position, individual block observation positions and target shape observation position, to overcome the limitation of lower resolution of the RGBD camera compared to the high-resolution ceiling camera. We found that the RGBD camera used at a close range provides accuracy excelling that of high-resolution ceiling camera, while having two issues: (a) close camera position makes some area of the blocks occluded with no registered point on it, and as a result single block tends to show up as separate blocks instead of a single connected one in heightmap image; and (b) depth readings have noise at the edge of the blocks, which shows up as spike-shaped artifacts in the point cloud. We used multi-stage filtering to handle those issues, which includes k-nearest neighbors filtering in the depth space and erosion/dilation of the heightmap image. After filtering, each connected block is segmented and a rotated bounding box is fitted for each block. Then, each segmented heightmap is rotated to be aligned with the X-Y axis of the image space and discretized into a small 2D height array. This discretized target array can be directly used for target shape or further matched with individual building blocks with all possible orientations to detect each block type, orientation and pose. We found this depth based block detection  
Figure 6: RGB and depth-based block detection: (**a**) RGB block detection pipeline; and (**b**) depth block detection pipeline.  
algorithm, while not using the GPU, takes less than 20 ms for processing on the core i7 laptop with GTX 1080 GPU, is lighting independent and has superior accuracy to the RGB-based detection algorithm. The depth-based block detection pipeline is shown in Figure 6b.
# Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models
## 2 Experimental Datasets and Methods
### Experimental Methodology  
#### 2.2.1 Logging Interpretation Method  
Well logging interpretation involves analyzing the physical properties of subsurface formations to infer rock composition, porosity, permeability, and lithology. It provides fundamental data and theoretical support for geological analysis and in situ stress prediction [32; 33; 34; 35]. Logging is conducted during or after drilling by lowering logging tools (sensors) into the well to measure and record various parameters of subsurface formations, such as resistivity, acoustic wave slowness, density, and gamma rays. Figure 1 uses data like acoustic wave slowness and density logging to calculate rock mechanical parameters such as elastic modulus and Poisson's ratio, which directly affect the magnitude and distribution of in situ stress. Resistivity logging helps estimate formation porosity and water saturation, which are closely related to the behavior of rock under in situ stress.  
In the process of predicting in situ stress, well-logging interpretation supplies fundamental data to pinpoint the key factors affecting in situ stress. By examining various logging datasets, a comprehensive analysis of the physical properties of subsurface formations can be conducted, and characteristic parameters linked to in situ stress can be identified.
## 2 System Description
### The Necessity of Considering Multiple Control Coupling  
In certain special cases, the bandwidth of the PLL cannot be designed to be too small. In such cases, a model only considering PLL dynamics may not accurately capture the system's dynamics. To illustrate this, consider a double line-to-ground fault, PS and NS current reference values \(I_{\text{dref}}\), \(I_{\text{tupref}}\), and \(I_{\text{tuf}}\), \(I_{\text{tupref}}\) are selected as 0.4, 0.5, and 0, 0.5, respectively, referred to as Case 1. Figure 6 compares the simulation results of the fourth-order model, which only considers PLL dynamics, with those of the detailed electromagnetic transient switching model. System parameters are listed in Appendix A, Table A1. As shown in Figure 6, the fourth-order model (dashed line), which only considers PLL dynamics, indicates transient instability during asymmetric faults. However, the detailed electromagnetic transient switching model (solid line) shows that transient synchronization instability will occur.  
This result in Figure 6 demonstrates that the fourth-order model, which considers only PLL dynamics, fails to accurately reflect the dynamic characteristics of the system when the PLL bandwidth is relatively large. The erroneous conclusion from the fourth-order model arises because, with a larger PLL bandwidth, the transient synchronization process of the VSC is no longer solely dominated by the PLL dynamics. It is also influenced by the LC filter and AC network transient, PR-based current control dynamics, and the DSOGI//PNSC dynamics. Ignoring the coupling effects between multiple control loops will inevitably lead to incorrect analysis results.  
Therefore, it is crucial to consider the detailed nonlinear model of the system to study the transient stability considering the coupling of multiple control loops. This is the main motivation for this paper.  
Figure 5: The equivalent circuit of the fourth-order model.  
Figure 6: Simulation results of the fourth-order model and the detailed switching model for Case 1.
# On-Board Parameter Optimization for Space-Based Infrared Air Vehicle Detection Based on ADS-B Data
## 2 Methodology
### Electro-Optical Performance Model of a Space-Based Infrared System
#### 2.2.2 Noise Model  
The system performance was limited by noise, which was characterized by temporal noise and spatial noise. Temporal noise represented the sensitivity of the payload itself, which can be expressed as the root mean square (RMS) of the electron collected by the integrating capacitance, also known as \(NEC\). The equation of \(NEC\) is given by  
\[NEC=\sqrt{n_{\rm ph}^{2}+n_{\rm read}^{2}}, \tag{5}\]  
where \(n_{\rm read}\) is the RMS readout noise and \(n_{\rm ph}\) represents the RMS noise of the sum of all photon noise, which can be divided according to different radiation sources into  
\[n_{\rm ph}=\sqrt{N_{\rm s}+N_{\rm bg}+N_{\rm instr}+N_{\rm dark}}, \tag{6}\]  
where each term has units of electrons, \(N_{\rm s}\) is the photon signal, \(N_{\rm bg}\) is due to atmospheric path radiance and earth background radiance, \(N_{\rm instr}\) is the thermal emission from the optical elements and \(N_{\rm dark}\) is due to dark current generation.  
When it is a point target detection scenario, the system sensitivity \(NET\) can be expressed as:  
\[NET=\frac{NEC\cdot\frac{hc}{\lambda_{0}}l^{2}}{A_{0}\tau_{0}\tau_{a}QE\cdot T_ {int}}, \tag{7}\]  
where \(A_{0}\) is the effective area of the optical system.  
Since the earth clutter characteristics are related to spectral band, \(GSD\), atmospheric transmission and background type, they vary dramatically with the observation scenario. Assuming that the total radiance of the earth clutter is a normal distribution, \(CET\) can be expressed as the standard deviation of the earth background fluctuation [32; 33], which is given by  
\[CET=\sigma_{clutter}\cdot GSD^{2}\cdot Rad_{bg}, \tag{8}\]  
where \(GSD\) is ground sample distance, \(Rad_{bg}\) is background radiance and \(\sigma_{clutter}\) is the background fluctuation as a percentage. \(NET\) and \(CET\) unify the performance unit to radiant intensity (W/Sr), which is consistent with the object space. Under the condition that the detection range remains unchanged, the normalization of the detection efficiency analysis can be realized. Since the spatial and temporal components are independentlydecoupled, it simplifies the complexity of system performance evaluation. \(JSNCR\), which combines temporal noise and spatial noise terms in radiant intensity, is expressed by  
\[JSNCR=\frac{K\cdot EE\cdot J}{\sqrt{NET^{2}+CET^{2}}}, \tag{9}\]  
where \(K\) is the target cross-pixel factor caused by the relative position change of target and satellite during observation period.
# Research on Driving Scenario Knowledge Graphs
## 3 Methodology: DSKG Construction
### Knowledge Extraction and Fusion
#### 3.3.2 Knowledge Fusion  
Knowledge fusion is a process that integrates, disambiguates, processes, verifies, and updates heterogeneous data, information, methods, experiences, and human thoughts into a high-quality knowledge base through high-level knowledge organization under the same framework. Considering that this project involves the fusion between the top-level ontology and a small-scale knowledge graph, a text-based approach can be used for entity matching.  
A text-based approach refers to matching entities based on their textual description information. Descriptive information of concepts from two graphs can be extracted, and the similarity between two concepts can be measured by calculating the similarity. For example, in different graphs, the concepts \"vehicle\" and \"car\", despite having different names, have the same meaning and description, requiring the establishment of a matching relationship between these two concepts. Figure 7 depicts the graphical representation of classes defined in the Traffic Genome dataset, mapped to their equivalent DSKG classes. The equivalence between elements is defined by the \"owl:sameAs\" relation, indicating that one element can be replaced by another without changing the meaning, and vice versa.  
Through extraction and fusion, we constructed the initial knowledge graph of driving scenarios. The final merged DSKG contains a total of 178 classes, 34 object properties, and 43 data properties. For convenient association, it has been converted and stored in the Neo4j graph database. Neo4j supports large-scale data storage, effectively addressing issues such as low data density, large volumes, and rapid updates in the traffic domain. Additionally, the Cypher graph query language supports relevant queries and graph algorithms, facilitating data querying and value mining. Therefore, Neo4j graph database  
Figure 7: Entity matching diagram.  
was chosen for knowledge storage in this study. Figure 8 presents the final driving scenario domain knowledge graph (partial).
# The Influence of Pillar Displacements on Geodetic Measurements
## 3 Results and Discussion
Figure 7: Standard error ellipses for the influence of displacements at orientation point \(B\) on the position of control point \(C\). Figure 8: Standard error ellipses for the influence of displacements at control point \(C\) on the position of control point \(C\). Figure 9: Standard error ellipses for the influence of displacements of the pillar at survey point \(A\) on the position of the control point \(C\). If we look at the influence of the temperature displacements at survey point \(A\) on the position of control point \(C\), Equations (52)-(54), in combination with the representations of the error ellipses in Figure 9 and the values in Table 2, we see that:  
* the error ellipses increase with distance from the control point \(C\),
* the major semi-axis of the error ellipses reaches a size of 2 mm, which is significant if we take into account the fact that for the survey point \(A\) we assumed the variances to be \(\sigma_{y_{A}}^{2}=\sigma_{x_{A}}^{2}=1\) mm\({}^{2}\) and \(\sigma_{y_{A}}=0\) mm\({}^{2}\),
* that the standard deviation of the distance between measured and exact position of the control point \(\sigma_{C}=\sqrt{\sigma_{y_{C}}^{2}+\sigma_{x_{C}}^{2}}\) exceeds the value of 2.2 mm, which is significant in precise measurements,
* with increasing distance between survey point \(A\) and orientation point \(B\), the influence of the horizontal angle error on the precision of control point \(C\) decreases and the distance error becomes more and more influential. The displacement of survey point \(A\) thus influences all measurements, i.e., the measured horizontal direction to points \(B\) and \(C\), and the distance between survey point \(A\) and control point \(C\). The error of determining the position of the control point \(C\) has a considerable influence on the error of the measured direction from survey point \(A\) to control point \(C\) and consequently on the horizontal angle with its apex at survey point \(A\) and the intersections in the direction of orientation point \(B\) and control point \(C\) as well as the measured distance between survey point \(A\) and control point \(C\).
# Vehicular Edge-Computing Framework for Making Use of Parking and Charging Electric Vehicles
## 4 Problem Description and Optimal Solution  
### Problem Description  
There are four types of participants in VEC including the requesting vehicle, the edge server, the ECP, and the parking vehicles, and they all expect to maximize their interests. The interest of a participant has the impact on the others' interests, and there is a competition-cooperation relationship among them, which can be abstracted as the following three problems.  
**Problem 1**.: _Maximizing the Utility of Requesting Vehicles_  
\[\begin{split}&\max_{q_{it},p_{it},p_{it}}U_{R_{i}}(q_{it}^{*},p_{ it}^{*},p_{ie}^{*})\\ & s.t.\ Q_{i}-q_{it}^{*}\geq 0\end{split} \tag{14}\]  
In (14), the \(q_{it}^{*}\) denotes the optimal amount of tasks offloaded to the edge server, and the \(p_{i\upsilon}^{*}\) and \(p_{ie}^{*}\) are the optimal prices of the edge server and ECP, respectively. The constraint indicates that the optimal task volume offload to the edge server cannot be greater than the total volume of the offloaded tasks.  
**Problem 2**.: _Maximizing the Utility of the Edge Servers_  
\[\begin{split}&\max_{q_{it},p_{it}}U_{VEC}(q_{it}^{*},p_{it}^{*})\\ & s.t.\ p_{i\upsilon}>\frac{\varphi\alpha}{c_{\upsilon}}\end{split} \tag{15}\]  
In (15), the constraint indicates that the reward received by the edge server should be greater than the cost of task processing.  
**Problem 3**.: _Maximizing the Utility of ECP_  
\[\begin{split}&\max_{q_{ie},p_{it}}U_{ECP}(q_{ie}^{*},p_{ie}^{*})\\ & s.\ t.\ C_{1}:q_{ie}^{*}=Q_{i}-q_{i\upsilon}^{*}\\ &\ C_{2}:p_{ie}^{*}>\frac{\sigma}{d}+p_{new}\end{split} \tag{16}\]  
In (16), the first constraint indicates that the sum of the tasks that the requesting vehicles offloaded to the ECP and the edge servers are equal to the total task volume, and the second constraint indicates that the reward received by the ECP is greater than the sum of its energy consumption and the payment to the parking vehicles.
## 3 Results and Discussion  
In this section, measurements using the prototype system and the rapid system are discussed. Furthermore, the rotation speed in the rapid measurement system is discussed.  
The four steps of the program were described in Section 2.6: (1) real and virtual conversion, (2) filtering, (3) eccentric correction and calibration, and (4) output. The program uses the first step of real and virtual conversion to perform the calculation. The filtering requirements are discussed later in the measurement results. In the prototype system, the measurement results are used directly, without filtering. In the rapid measurement system, noise must be filtered out from the measurement results in order to achieve a more accurate eccentric correction. Experiments were conducted to verify the stability, measurement repeatability, and accuracy of the system.  
In this study, an optical projector and CMM were used to verify the measurement results. The CMM measurement data are provided by the QC department in the factory, and the CMM measurement results are used for comparison and adjustment in the final rapid measurement system. Therefore, this system can be transferred directly to the production line.