esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Multi-Robot Collaborative Planning
### Priority Obstacle Avoidance  
In multi-robot path planning, the prioritized obstacle avoidance strategy is a core approach intended for resolving potential path conflicts, task coordination, and obstacle avoidance issues in multi-robot systems. The strategy guarantees the efficiency and safety of the system by assigning distinct priorities to each robot and forming an execution sequence based on task priorities. During actual execution, the robots determine when to execute tasks, how to avoid other robots, and how to coordinate their movements efficiently based on their respective priorities, thereby avoiding or resolving conflicts and ensuring the smooth operation of the system.  
1. Collision course. As shown in Figure 5a. When two robots are traveling in opposite directions on a path and there is a possibility of collision at a certain location, the robot with higher priority will be granted precedence in passing through, and the robot with lower priority will need to temporarily stop and wait. This typically occurs in high-conflict areas such as narrow passages or intersections. To prevent a collision, the lower-priority robot may need to opt for temporarily stopping, backing up, or taking an alternate path.
2. Clash of nodes. As shown in Figure 5b. When multiple robots plan to travel to the same or adjacent nodes, a competitive state might arise, especially when these robots plan to reach the same goal location simultaneously in a limited space. To address this conflict, robots with higher priority will be granted priority to pass through, while robots with lower priority need to remain in place and wait until the higher-priority robot completes its task or passes through the node.
3. Path deadlock. As shown in Figure 5c. Deadlocks are a common issue in multi-robot systems, especially during path planning and execution among robots, which occur when the robots mutually block each other, making it impossible to proceed. This typically occurs in path planning that lacks sufficient flexibility. To avoid deadlocks, the prioritized obstacle avoidance strategy takes the possibility of deadlocks into account in the planning stage and reduces the mutual interference among robots by assigning reasonable paths to robots with different priorities.
4. Reprogramming mechanisms. The replanning process is initiated when the system detects that a low-priority robot is trapped in a prolonged waiting or deadlock state. At this point, the low-priority robots recalculate their paths in accordance with the system's dynamic environment and identify new feasible paths to bypass the conflict region. The high-priority robots continue to follow the original path to carry out their tasks without being influenced. This mechanism can effectively prevent a long-term  
Figure 5: Types of conflict.  
stagnation state of the system and ensure that the tasks of the high-priority robots can be accomplished successfully.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 3 IEEE 802.15.7 Overview
### MAC Layer
#### 3.3.1 Superframe Structure  
The MAC layer allows the use of a time based structure called superframe, whose characteristics are defined by the coordinator. The superframe is a timing pattern that is delimited by beacon frames. The coordinator transmits beacon frames at the beginning of each superframe to synchronize devices, identify the network and define the characteristics of the superframe. The superframe can have an active part and an inactive part. The active part is divided in 16 equally spaced intervals or time slots, during which all communications are performed. The inactive part allows devices to pass to an energy saving state. The active part is divided into a Contention Access Period (CAP), where devices compete with each other to access the channel using a random access procedure, and a Contention Free Period (CFP), intended for applications with latency and bandwidth requirements. In the CFP channel, access is centrally managed by the coordinator through the use of time slots called Guaranteed Time Slot (GTS). The structure of the superframe is defined by the values of several parameters, described below:  
* The attribute _macBeaconOrder_, _BO_, describes the interval used by the coordinator to transmit beacon frames. _BO_ and _aBaseSuperframeDuration_ attributes define the time interval between beacon frames, _BI_ (beacon interval, specified in clock cycles) according to the following expression:\[\begin{array}{l}B1=aBaseSuperframeDuration\times 2^{BO}\hskip 113.811024 ptfor:0\leq BO \leq 14\\ \hskip 113.811024 pt\text{If }BO=15\text{ the coordinator does not transmit beacon frames.}\end{array} \tag{6}\]  
where _aBaseSuperframeDuration_ is defined as the number of clock cycles that form the superframe when the attribute _macSuperframeOrder_ (see below) is equal to 0.
* The attribute _macSuperframeOrder_, \(SO\), defines the length of the active part of the superframe, including the beacon frame. The relationship between the value of \(SO\) and the duration of the active part of the superframe, \(SD\) (superframe duration, measured in clock cycles) is given by: \[\begin{array}{l}SD=aBaseSuperframeDuration\times 2^{SO}\hskip 42.679134 ptfor:0\leq SO \leq BO\leq 14\\ \hskip 113.811024 pt\text{If }SO=15\text{ the frame does not have active part.}\end{array}\] (7)  
The beacon frame is transmitted at the beginning of the first slot of the superframe without the use of any random access method. The CAP begins immediately after the beacon frame, and the CFP--if it exists--begins following the CAP and extends to the end of the active part of the superframe.
## 4 Problem Formulation  
The task completion delay of all IoT devices can be expressed as:  
\[U =\sum_{i\in\mathcal{N}}\bigg{(}y_{i,0}D_{i,0}+y_{i,1}D_{i,1}+\sum_ {j\in\mathcal{M}}y_{i,j}D_{i,j}\bigg{)}\] \[=\sum_{i\in\mathcal{N}}\bigg{[}y_{i,0}\frac{c_{i}a_{i}}{f_{i,0}}+ y_{i,1}\bigg{(}\frac{a_{i}}{r_{i,1}}+\frac{2d_{i,1}}{c}+\frac{c_{i}a_{i}}{f_{i,1}} \bigg{)}+\sum_{j\in\mathcal{M}}y_{i,j}\bigg{(}\frac{a_{i}}{r_{i,1}}+\frac{2d_{i,1}}{c}+\frac{a_{i}}{r_{i,j}}+\frac{2l_{1,j}}{c}+\frac{c_{i}a_{i}}{f_{i,j}} \bigg{)}\bigg{]}. \tag{6}\]  
The optimization objective of this paper is minimizing the task completion delay of all IoT devices. The optimization variables are \(y_{i,0}\), \(y_{i,1}\), \(y_{i,j}\), \(f_{i,1}\), and \(f_{i,j}\). The problem \(\mathcal{P}\) is as follows.  
\[\mathcal{P}:\min_{\begin{subarray}{c}y_{i,0},y_{i,1},y_{i,j},\\ f_{i,1},f_{i,j}\end{subarray}}U \tag{7a}\] \[\text{s.t.}\ \sum_{i\in\mathcal{N}}y_{i,1}f_{i,1}\leq F_{1},\] (7b) \[\sum_{i\in\mathcal{N}}y_{i,j}f_{i,j}\leq F_{j},\forall j\in \mathcal{M},\] (7c) \[0\leq f_{i,1}\leq F_{1},\forall i\in\mathcal{N},\] (7d) \[0\leq f_{i,j}\leq F_{j},\forall i\in\mathcal{N},\forall j\in \mathcal{M},\] (7e) \[y_{i,0}+y_{i,1}+\sum_{j\in\mathcal{M}}y_{i,j}=1,\forall i\in \mathcal{N},\] (7f) \[y_{i,0},y_{i,1},y_{i,j}\in\{0,1\},\forall i\in\mathcal{N},\forall j \in\mathcal{M}. \tag{7g}\]  
Equation (7a) is the objective function. Equation (7b) represents that computing resources allocated to task \(i\) cannot surpass the resources owned by the local satellite's MEC server, i.e., \(F_{1}\). Equation (7c) shows that computing resources allocated to task \(i\) cannot exceed the resources of the MEC server of neighboring satellite \(j\), i.e., \(F_{j}\). Equations (7d) and (7e) mean that the MEC server's computing resource allocation for task \(i\) is non-negative. Equation (7f) denotes that only one strategy is selected to complete task \(i\). Equation (7g) means that \(y_{i,0}\), \(y_{i,1}\), and \(y_{i,j}\) are 0 or 1.  
\(\mathcal{P}\) consists of the IoT devices' task offloading decision and the computing resource allocation of the local satellite's MEC server and neighboring satellites' MEC servers. \(\mathcal{P}\) includes discrete variables and continuous variables, so it is a mixed-integer nonlinear programming problem (MINLP), which is NP-hard.
# A Light-Weighted Machine Learning Approach to Channel Estimation for New-Radio Systems
## 6 Conclusions  
In this paper, we provided the light-weighted ML approach to channel estimation for NR systems. The main intuition was that there exists an equivalence between the CIR in the time domain and its corresponding CFR in the frequency domain. Based on the CFR rather than the CIR, it was shown that the more light-weighted ML model can be established in comparison with the existing ML-based channel estimator. Specifically, regardless of the number of CIR's channel taps, it was observed that the ML model configuration with one input layer, one hidden layer, and one output layer was sufficient to learn the channel estimation, which was confirmed from the computational complexity comparison in Table 2. Furthermore, the proposed light-weighted ML-based channel estimator was shown to be robust when the quantized weights were applied to reduce memory overhead, which opened the availability for practical use. From the perspective of the MSE performance, the proposed ML-based estimator has better performance than the existing ML-based channel estimator. One interesting thing is that the proposed ML-based estimator had better performance even than MMSE when the number of channel taps is equal to 1, which showed the effectiveness of the proposed one especially for poor scattering channel environments. Instead of naive use of the proposed ML-based estimator training for each SNR, it was observed that with a trained proposed ML-based estimator, the specific SNR is sufficient to cover all the SNR regimes without significant performance degradation. As a further work, the proposed ML-based estimator can be naturally extended to CFR interpolation based on the inference of the CIR, which can be further elaborated.  
**Author Contributions:** Conceptualization, H.W.L. and S.W.C.; methodology, H.W.L. and S.W.C.; software, H.W.L.; validation, H.W.L. and S.W.C.; formal analysis, H.W.L. and S.W.C.; investigation, H.W.L.; resources, H.W.L.; data curation, H.W.L.; writing--original draft preparation, H.W.L.; writing--review and editing, S.W.C.; visualization, H.W.L.; supervision, S.W.C.; project administration, H.W.L. and S.W.C.; funding acquisition, S.W.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IIITP) grant funded by the Korea government (MSIT). (No. 2021-0-00165, Development of 5G+ Intelligent Base Station Software Modem).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Model Calibration and Verification
#### 3.3.2 Structure Assessment Test  
The simulation results of the model with the corresponding initial values for various variables show a behavior corresponding to the expected behavior of those variables. In this section, we have examined the structural behavior of one of the most significant variables in this model.  
The relationship between dumping sites and water pollution is a critical aspect to consider when assessing the environmental impact of waste disposal. Dumping sites, where waste materials are discarded, can have a significant influence on water pollution in various ways. Therefore, the impact of dumping sites on water pollution is significant. It highlights the importance of implementing proper waste management practices, such as appropriate site selection, containment measures, and adequate treatment of leachate, to minimize the release of pollutants and protect water quality. Proper regulation, monitoring, and enforcement of waste disposal practices are essential to mitigating the adverse effects of dumping sites on water pollution and preserving the health of aquatic ecosystems.  
Figure 9 correctly shows the relationship between the impact of dumping sites on water pollution in this study. As shown in this figure, during 2010-2017, the rate of dumping materials and wastes on the sea was constant, so water pollution decreased during these years. After increasing the amount of dumping materials, water pollution increased gradually.  
Figure 8: Calibration of the simulation with historical population data.
## 2 Related Research
### Position Fingerprinting Method
#### 2.1.2 k-NN-Based Fingerprinting Method  
The k-NN (k-Nearest Neighbors) is a classification algorithm commonly used in machine learning algorithms. k-NN-based fingerprinting [14] is often applied to the multi-classification problem corresponding to location estimation. Fingerprinting with k-NN is achieved by using fingerprint points around a location point by calculating the Euclidean distance between the location point and the fingerprint points. Figure 2 illustrates the concept of the k-NN-based fingerprinting method.  
In the fingerprinting method using k-NN, the estimation accuracy may be significantly affected by the distribution of fingerprint points and the noise of observed RSSI.  
Figure 1: Procedure of fingerprint positioning method.
# A Sketch-Based Fine-Grained Proportional Integral Queue Management Method
## 3 System Model  
This section details the algorithmic idea of the Sketch-based fine-grained proportional integral queue-management algorithm S-PIE that is proposed in this paper. As shown in Figure 1, S-PIE adds a Sketch-based traffic statistics module to PIE, uses the Sketch measurement structure to assist in mapping the frequency of packets passing through the queue, and evaluates the data flows that account for a larger proportion of the queue, that is, large flows, based on the frequency of current packet occurrences. Then, the drop policy of the original queue-management method PIE is adjusted to achieve a fine-grained queue-management policy. The architecture of S-PIE is divided into a traffic statistics part and a queue-management part, which are described in detail below.
It is worth noting that, due to the improvement in channel utilization, the retransmission delay after collisions is significantly shortened. The delay introduced by adding ACNs is much smaller than the retransmission delay; therefore, the overall delay will not increase. The main contributions of this paper are as follows:  
1. We propose the Separation of Transmission and Control ALOHA (STCA) protocol, which moves access control from the satellite to airborne access control nodes (ACNs), confining collisions to the ACN layer and improving satellite channel utilization. 2. A load estimation and control algorithm is designed to dynamically adjust the access probability of each piece of SUE based on IRSA, preventing performance degradation under high load. 3. By separating access control from data transmission, access packets contain only essential information. The use of minislots reduces frame length compared with IRSA, which requires many slots. 4. Simulation results show that STCA achieves significantly higher channel utilization and throughput stability than IRSA and traditional Slotted ALOHA, providing an efficient solution for multi-user access in satellite communications. The rest of this paper is organized as follows: Section 2 describes the system model, including the ISAG network architecture and three spectra; Section 3 formulates the problem and optimization objectives for systems with/without SIC and variable frame length; Section 4 details the STCA protocol and its three variants; Section 5 presents numerical results comparing STCA with existing protocols; Section 6 concludes the paper.
# Considering Uncertainties of Key Performance Indicators in Wind Turbine Operation
## 4 Method and Data
### Data Completeness  
To analyze the effect of missing air density measurements as well as their approximation on the calculation of KPIs, one year of 10 min data of the 200 m met mast was used. This datasetwas selected due to its comprehensive, reliable, and well-calibrated measurements. After filtering the data availability of the selected year was 99%. In addition, three years (99.9% data availability) of comprehensive data of a North Sea offshore WF was used, as the met mast does not include power measurements as well as to validate the results.  
In the first step, the air density and wind power density sensitivities were evaluated to the availability of temperature, pressure, and humidity measurements using Equation (6) [31], as well as to the site altitude above sea level. Furthermore, the air density was approximated by adding additional inputs step-by-step to assess their relevance starting with the standard atmosphere. All results were compared to a reference case comprised of all required inputs.  
\[\rho=\frac{1}{T}\left(\frac{B}{R_{0}}-\phi P_{w}\left(\frac{1}{R_{0}}-\frac{1 }{R_{w}}\right)\right) \tag{6}\]  
where  
\[\rho =\text{derived air density},\] \[T =\text{measured absolute air temperature},\] \[B =\text{measured air pressure},\] \[R_{0} =\text{gas constant of dry air 287.05 J/kgK},\] \[\phi =\text{relative humidity (range 0 to 1 )},\] \[R_{w} =\text{gas constant of water vapor 461.5 J/kgK},\] \[P_{w} =\text{vapor pressure for }T\]
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## 3 Methodology
\[\rho=R+I+\epsilon \tag{7}\]  
Above, \(\rho\) denotes the pseudorange between the satellite and receiver, \(R\) represents the true range, \(I\) is the ionospheric delay, and \(\epsilon\) represents other delays, such as tropospheric, clock, multipath, and hardware delays. The main objective is to observe the impact of the ionosphere on an LEO-PNT scenario, so we created a simple ionospheric model that provides a close approximation to the actual ionospheric values using the techniques outlined in [54]. To validate our model, we conducted GPS measurements in various scenarios and compared outputs of the model based on precise measurements from both ground-based GNSS receivers and Android smartphones. In the following sections, we will describe the sources and techniques used to develop the new IAMM model.
## 3 Results
### Expression of Chondrogenic Differentiation Marker Genes  
To evaluate chondrogenic behavior in 3D silica fabrics in more detail, the expression of chondrogenic differentiation marker genes, _COL1A1_, _COL2A1_, _COL10A1_, _ACAN_, and _SOX9_, was investigated by qRT-PCR (Figure 5). For almost all the genes, differences in expression were preferentially observed between normal and differentiation conditions. Under differentiation condition, the expression of _COL2A1_ in atelocollagen and 3D silica fabric culture was superior to that in spheroid culture, but the expression of _ACAN_ was the most elevated in spheroid culture. The expression of _SOX9_ was the same in all scaffolds. The expression levels of _COL1A1_ and _COL10A1_ in spheroid culture were superior to those in the other scaffolds, and they were the lowest in 3D silica fabric culture. Chondrogenic differentiation of MSCs in each scaffold was also evaluated by assessing GAG and COL accumulation. These results suggested that 3D silica fabric culture was useful in the development of tissue-engineered constructs, particularly for cartilage formation, and that it is equivalent or superior to atelocollagen gel culture as a commodity scaffold.  
Figure 4: Accumulation of collagen (COL) in MSCs on 3D silica fibers and spheroids cultured in normal medium (Diff (\(-\))) and chondrogenic differentiation medium (Diff (\(+\))) for 4 weeks.  
Figure 5: The expression of _COL1A1_, _COL2A1_, _COL10A1_, _ACAN_, and _SOX9_ in mesenchymal stem cells (MSCs) cultured on 3D silica fibers in normal medium (Diff (\(-\))) and chondrogenic differentiation medium (Diff (\(+\))). Signal intensity was normalized using that of a control housekeeping gene (human _GAPDH_ gene).
# Design and Validation of Rule-Based Expert System by Using Kinect V2 for Real-Time Athlete Support
###### Abstract  
In sports and rehabilitation processes where isotonic movements such as bodybuilding are performed, it is vital for individuals to be able to correct the wrong movements instantly by monitoring the trainings simultaneously, and to be able to train healthily and away from the risks of injury. For this purpose, we designed a new real-time athlete support system using Kinect V2 and Expert System. Lateral raise (LR) and dumbbell shoulder press (DSP) movements were selected as examples to be modeled in the system. Kinect V2 was used to obtain angle and distance changes in the shoulder, elbow, wrist, hip, knee, and ankle during movements in these movement models designed. For the rule base of Expert System developed according to these models, a \(2^{8}\)-state rule table was designed, and 12 main rules were determined that could be used for both actions. In the sample trainings, it was observed that the decisions made by the system had 89% accuracy in DSP training and 82% accuracy in LR training. In addition, the developed system has been tested by 10 participants (\(25.8\pm 5.47\) years; \(74.69\pm 14.81\) kg; \(173.5\pm 9.52\) cm) in DSP and LR training for four weeks. At the end of this period and according to the results of paired \(t\)-test analysis (\(p<0.05\)) starting from the first week, it was observed that the participants trained more accurately and that they enhanced their motions by \(58.08\pm 11.32\%\) in LR training and \(54.84\pm 12.72\%\) in DSP training.  
Keywords:expert system; movement modelization; training accuracy; performance enhancement; injury prevention; sport; human-machine interaction +
Footnote †: journal: Applied
# A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree
## 4 Results  
### Thresholds Setting  
In this part, the thresholds setting will be discussed firstly. We know that the \(Children\) or \(depth\) influences the performance of tree construction and query operation. We adopted a fixed \(depth\) for octree constructing and a linearly variable \(Children_{min}\) with the number of nearest neighbors needed to be searched for R*-tree constructing: \(depth=2\) and \(Children_{min}=n\cdot k\). As shown in Figure 5, time consumed by constructing tree and \(kNN\) searching varies with \(Children_{min}\). Note that there is no scale on the y-axis, because the time consumed by constructing tree is far more than \(kNN\) searching. For the sake of visualizing, both of them were normalized to the same range of [0,1]. The time consumed by constructing tree decreases with the increasing of \(Children_{min}\), which is reasonable. \(kNN\) searching takes the least time when \(Children_{min}=8\cdot k\). Synthetically consider both construction and search time, we came to a conclusion that \(Children_{min}=8\cdot k\) is an optimal choice. However, it is just empirical. In addition, \(Children_{max}=2\cdot Children_{min}\). Both times are the average of 100 loops.
## Appendix A Locations Selection, System Design, Dimensioning, and Components  
### Location Selection  
Table 11 shows some key figures characterizing Hamburg in Germany and Murcia in Spain and their climates. Respectively, 1.8 and 1.5 million inhabitants live in urban areas [231; 232; 233]. Hamburg has a temperate oceanic (Cfb), and Murcia a hot semi-arid (BSh) climate, according to the Koppen-Geiger climate classification [234; 235; 236; 237; 238]. Local weather station data from 2012 to 2016 [239; 240; 241] are used to calculate the five-year average (\(\upmu\)) and annual coefficient of variation (CV, also known as the relative standard variation) of the average annual wind speed, solar global horizontal irradiation, precipitation, air temperature, daily maximum and minimum air temperatures, heating degree days (HDD) [127; 241], and cooling degree days (CDD) [50; 51].