esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Model and Assumptions
### Topology and Communication  
Peer-to-Peer Network: The blockchain network consists of \(N\) nodes interconnected in a peer-to-peer fashion. Nodes communicate directly with one another without relying on centralized servers, thereby forming a decentralized network topology [26]. This structure is fundamental for ensuring that control remains distributed across the network.  
Network Connectivity: We assume that the network is connected, meaning that a path exists between any two nodes, ensuring that information can propagate throughout the network. This can be represented as a connected graph (refer to Figure 7). For the purpose of this analysis, we will focus on undirected graphs, which assume bidirectional communication links between nodes. An example of a disconnected graph is shown in Figure 8.  
Synchronous Network: For the analysis, we adopt a synchronous network model, where all messages are delivered within a known maximum delay \(\Delta\). This assumption simplifies the analysis by removing the uncertainties introduced by variable message delays, thus representing a best-case scenario for both security and consensus mechanisms.  
Uniform Latency and Bandwidth: All nodes are assumed to experience uniform network latency and have equal bandwidth limitations. In other words, \(\delta\) and \(B\) are constant across all nodes in the network where latency (\(\delta\)) represents the average network latency between nodes and bandwidth (\(B\)) refers to the maximum bandwidth available to any node. This homogeneity ensures that the observed effects in our model are the result of the properties being analyzed--decentralization, security, or scalability--rather than disparities in network resources between nodes.  
Figure 6: Blockchain performance frontier.
# Mobile Robot Path Planning Considering Obstacle Gap Features
###### Abstract  
In order to fully harness obstacle information in path planning and improve the coordination between global and local path planning, a novel mobile robot path planning method is proposed. The novelty of the proposed path planning strategy lies in its integration of obstacle gap characteristics into both global and local planning processes. Specifically, this method addresses the issues of low search efficiency, excessive redundant points, and poor path quality in the traditional A* algorithm for global path planning by extracting gap grids in the global grid map and incorporating their influence into the heuristic function, thereby guiding the search more effectively. The generated global path is further optimized at gap points to remove redundant nodes. For local path planning, which employs the Dynamic Window Approach (DWA) and often exhibits weak compatibility with global planning and a lack of smoothness through obstacle gaps, this method calculates feasible steering angles based on the distance between the robot and obstacles as well as gap attributes. Additionally, the geometric relationship between global and local paths is established using the Bernstein equation, generating segmented guidance control points for DWA. Simulation experiments demonstrate that the proposed algorithm significantly enhances path efficiency and obstacle avoidance capability in tight space environments, reducing path length by approximately 4.79% and motion time by approximately 15.22% compared to conventional algorithms.  
robots; path planning; gap; guide angle; Bernstein +
Footnote †: journal: Applied sciences  
1
# The Application of Computed Tomography to Study the Soil Porosity of Mountain Red Earth
###### Abstract  
Mountain red soil, as a special type of soil in the South, has received widespread attention for its soil erosion problems. Its pore structure restricts water infiltration, thereby affecting the occurrence and development of soil erosion. In order to systematically obtain the distribution characteristics of the pore structure within the surface mountain red soil, this paper uses non-destructive CT detection technology to scan the soil column samples taken from the typical mountain red soil distribution area in Chenggong District, Kunming City, Yunnan Province. Image processing technology is applied to CT slices, and ImageJ (1.46r) software is used to obtain the distribution characteristics of pores within the soil column, including pore sizes and the number of pores at each depth, the proportion of pore area, roundness, and box-counting dimension. The results show that with the increase in depth, the proportion of pore area decreases linearly from the maximum value of 52.25% at the top to the minimum value of 2.02% at the bottom; the roundness of pores fluctuates between 0.8 and 0.9, overall increasing; the total number of pores generally first increases then decreases, and small pores are predominant, with the least number of large pores in the topsoil layer; the box-counting dimension shows a gradual linear decrease, with a maximum value of 1.7980 and a minimum value of 0.9878. The number of pores affects both roundness and the box-counting dimension, and the proportion of pore area also affects the box-counting dimension. There is a negative correlation between roundness and the box-counting dimension. The 3D visualization reconstruction of pores shows that most are interconnected, with the pore size significantly reducing with increasing depth. The quantitative analysis of parameters and 3D visualization reveal, to some extent, the impact of pore structure on the occurrence and development of soil erosion in mountain red soil. These research findings form the foundation for studying soil erosion in this region and provide a basis for systematically understanding its processes and mechanisms.  
computed tomography; mountain red soil; soil erosion; pore quantification; box-counting dimension Article  
The Application of Computed Tomography to Study the Soil Porosity of Mountain Red Earth  
[PERSON], [PERSON], [PERSON] and [PERSON]  
Faculty of Geography, Yunnan Normal University, Kunming 650500, China; [EMAIL_ADDRESS] (H.Y.); [EMAIL_ADDRESS] (L.Z.); [EMAIL_ADDRESS] (Y.C.)  
* Correspondence: [EMAIL_ADDRESS]
## 5 Experimental Results
As expected, an increase on demand levels produces increments on both the distance driven and the lead times. Nevertheless, it must be highlighted that those increments feature different patterns depending on the cooperation settings. Actually, as it can be observed in Figure 11, the greater the increase on demand figures is, the greater the positive effects on distances are. This is particularly noticeable in the no cooperation settings, where the distance driven increases a 29% and a 65% when demand increases a 25% and a 50%, respectively. However, these increments are lower in the full cooperation environment, a 20% and a 51%, respectively. That is, the slope (i.e., the negative effects) is softer when cooperation is implemented, which leads to a better response to unexpected demand increases. Similar insights can be obtained when analysing the effects on leading times in Figure 12. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Base**} & \multicolumn{4}{c}{**25\% Demand Increase**} & \multicolumn{2}{c}{**50\% Demand Increase**} \\ \hline  & **No Cooperation** & **Limited** & **Full** & **No Coop** & **Limited** & **Full** & **No Coop** & **Limited** & **Full** \\ \hline S1 & 166.69 & 126.74 & 80.36 & 220.94 & 166.62 & 99.78 & 280.82 & 204.08 & 122.54 \\ S2 & 147.77 & 115.58 & 84.97 & 187.44 & 142.69 & 100.74 & 243.99 & 187.29 & 130.99 \\ S3 & 134.14 & 106.41 & 80.19 & 171.64 & 131.33 & 95.79 & 224.75 & 175.21 & 124.42 \\ S4 & 112.27 & 90.00 & 73.17 & 137.53 & 107.98 & 83.83 & 163.64 & 127.79 & 101.56 \\ \hline Total & 149.74 & 115.99 & 80.55 & 717.55 & 548.61 & 380.15 & 913.20 & 694.38 & 479.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average lead times (minutes) in the 100 replications for non-, limited, and full cooperation settings for each supermarket by demand increase scenario. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Base**} & \multicolumn{4}{c}{**25\% Demand Increase**} & \multicolumn{2}{c}{**50\% Demand Increase**} \\ \hline  & **No Coop** & **Limited** & **Full** & **No Coop** & **Limited** & **Full** & **No Coop** & **Limited** & **Full** \\ \hline S1 & 694.79 & 511.16 & 352.43 & 920.92 & 672.00 & 437.60 & 1,170.52 & 823.10 & 537.42 \\ S2 & 357.24 & 297.21 & 210.68 & 453.13 & 366.93 & 249.79 & 589.85 & 481.62 & 324.80 \\ S3 & 299.92 & 257.12 & 184.28 & 383.76 & 317.32 & 220.14 & 502.50 & 423.37 & 285.92 \\ S4 & 192.27 & 166.04 & 133.48 & 235.53 & 199.21 & 152.93 & 280.24 & 235.75 & 185.27 \\ \hline Total & 1544.21 & 1231.53 & 880.87 & 1993.34 & 1555.46 & 1060.45 & 2543.11 & 1963.84 & 1333.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average distance driven (km) in the 100 replications for non-, limited, and full cooperation settings for each supermarket by demand increase scenario. Figure 11: Mean plots by demand scenario and cooperative setting for total distances. Figure 12: Mean plots by demand scenario and cooperative setting for average lead times.
## 3 Tracking Loops Error Characterization
### Vibration-Induced Phase Noise  
The vibration-induced phase noise is associated to the jitter in the receiver clock because of environmental vibrations. The model of the phase noise induced by environmental vibrations is similar to the Allan deviation phase noise. The model is written as follows:  
\[\sigma_{\phi}^{2}=\frac{1}{2\pi}\int_{0}^{\infty}G_{\phi}\left(\omega\right) \left|1-H\left(\omega\right)\right|^{2}\,d\omega \tag{7}\]  
In Equation (7), \(G_{\phi}\left(\omega\right)\) is the single-sideband oscillator phase noise spectrum density and expressed as Equation (8) for the vibration-induced phase noise [20,24]:  
\[G_{\phi}\left(\omega\right)=\left(2\pi L_{c}\right)^{2}\cdot k_{g}\left(\omega \right)^{2}\cdot\frac{G_{g}\left(\omega\right)}{\omega^{2}} \tag{8}\]  
where \(k_{g}\left(\omega\right)\) is the oscillator's g-sensitivity in parts-per-g (parts/g). \(G_{g}\left(\omega\right)\) is the single-sided power spectrum density of vibrations (g\({}^{2}\)/Hz).  
Substituting \(G_{\phi}\left(\omega\right)\) from Equation (8) into Equation (7), the vibration-induced phase noise is derived for second-order PLL:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Oscillator** & \(h_{0}\left[s\right]\) & \(h_{{}_{-1}}\left[-\right]\) & \(h_{{}_{-2}}\left[1/s\right]\) \\ \hline TCXO & 1.00 \(\times\) 10\({}^{-21}\) & 1.00 \(\times\) 10\({}^{-20}\) & 2.00 \(\times\) 10\({}^{-20}\) \\ OCXO & 2.51 \(\times\) 10\({}^{-26}\) & 2.51 \(\times\) 10\({}^{-23}\) & 2.51 \(\times\) 10\({}^{-22}\) \\ Rubidium & 1.00 \(\times\) 10\({}^{-23}\) & 1.00 \(\times\) 10\({}^{-22}\) & 1.30 \(\times\) 10\({}^{-26}\) \\ Cesium & 2.00 \(\times\) 10\({}^{-20}\) & 7.00 \(\times\) 10\({}^{-23}\) & 4.00 \(\times\) 10\({}^{-29}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Clock parameters of different oscillators.  
\[\sigma_{\phi}^{2} = 2\pi{L_{c}^{2}}{k_{s}^{2}}{G_{s}}\int_{\omega_{L}}^{\omega_{2}^{2}} \frac{\omega^{2}}{\omega_{L}^{4}+\omega^{4}}d\omega\] \[= \frac{\pi{L_{c}^{2}}{k_{s}^{2}}{G_{s}}}{\sqrt{2}\omega_{L}}\left\{ \arctan\left(\frac{\omega_{2}\sqrt{2}}{\omega_{L}}+1\right)+\arctan\left( \frac{\omega_{2}\sqrt{2}}{\omega_{L}}-1\right)\right.\] \[\left.-\arctan\left(\frac{\omega_{1}\sqrt{2}}{\omega_{L}}+1 \right)-\arctan\left(\frac{\omega_{1}\sqrt{2}}{\omega_{L}}-1\right)+\frac{1}{ 2}\ln\right.\] \[\left.\times\left[\frac{\left(\omega_{1}^{2}+\omega_{L}\omega_{1} \sqrt{2}+\omega_{L}^{2}\right)\left(\omega_{2}^{2}-\omega_{L}\omega_{2}\sqrt{2 }+\omega_{L}^{2}\right)}{\left(\omega_{1}^{2}-\omega_{L}\omega_{1}\sqrt{2}+ \omega_{L}^{2}\right)\left(\omega_{2}^{2}+\omega_{L}\omega_{2}\sqrt{2}+\omega _{L}^{2}\right)}\right]\right\}\hskip 28.452756 pt\left(rad\right)\]
# Effects of Drying Process on the Volatile and Non-Volatile Flavor Compounds of _Lentinual edodes_
## 3 Results and Discussion
### HS-SPME-GC-MS Analysis of Volatile Compounds of L. edodes  
In order to further investigate the effects of different drying methods on the flavor of _L. edodes_, the specific volatile components of different mushroom samples were measured by HS-SPME-GC-MS. Semi-quantitative analysis with an internal standard was used to compare differences among samples. The volatile compounds of fresh and dried mushroom samples were shown in Table 1. A total of 55 volatile compounds from _L. edodes_ were tentatively identified and quantified, including 11 alcohols, 9 aldehydes, 6 ketones, 11 sulfur  
Figure 1: PCA of E-nose data for fresh and dried _L. edodes_; F: fresh sample; FD: freeze drying sample; HAD: hot-air drying sample; ND: natural drying sample. compounds and 17 hydrocarbons. A total of 41, 36 and 24 compounds were detected in FD, HAD and ND samples, with contents of 171.29 \(\upmu\)g/g, 206.88 \(\upmu\)g/g and 256.67 \(\upmu\)g/g, respectively. It is notable that only 22 compounds were detected in fresh mushroom (1451 \(\upmu\)g/g). It was consistent with the reports that more volatile compounds were formed in dried _L. edodes_[1]. After drying treatment, the total volatile components increased, but the total volatile compounds content decreased.
# Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses
## 6 Conclusions  
In this paper, we mainly looked into energy-efficient IoT strategies for precision agriculture in greenhouse. We have summarized the prospects and challenges for precision agriculture in greenhouse and discussed the implication of sensor box design scheme over sensor readings. It is crucial to deploy the sensors in the air circulated box for precise data and longer lifetime of the sensors. We highlight the divergent control systems used in the greenhouse and propose the sequential steps for WSN deployment and monitoring of the environment. This paper demonstrates an end-to-end complete WSN architecture and deployment of LoRaWAN-based network for the monitoring of tomato crop in the greenhouse. Progressive WSN deployment, in different phases, helps in understanding the challenges of the greenhouses and applying the mitigation scheme at every next phase. The system monitors different values like light, temperature, CO\({}_{2}\) and humidity and shows the variation throughout the crop season. Therefore, it is important to keep monitoring the environmental data for precision agriculture. For future work, we would work towards the increased energy efficiency of the LoRaWAN network by energy harvesting.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] classified and did the conceptualization of the back-end database; [PERSON] contributed in hardware setup along with designing of the proposed sensor box; [PERSON] wrote the paper; [PERSON] and [PERSON] supervised the project. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Research Road Map** & **Optimization and Impact** \\ \hline Cognitive design technique & Electronic design and radio optimization (dile time, re-transmission etc.) \\ Energy consumption model & Connection strategy and power optimization techniques \\ Radio optimization & Passive wake up radios, time sync, data rate and coding schemes \\ Frequency of data transmission & Edge computing and combining multidisciplinary approach (ML, AI etc.) \\ Adaptive ML technique [44] & Adapt and tune future behavior and predictions. \\ Cost of power management scheme & Trade-off between packet transmission and computation \\ \hline \hline \end{tabular}
\end{table}
Table 7: Future work.  
Figure 13: Power measurement of the LoRaWAN communication.  
**Funding:** This research is funded by EU - Interreg project on greenhouse monitoring (Grow!) [52].  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 3 Materials and Methods
### Experimental Setup and Test Scenarios  
#### 3.6.1 Test Environment  
To evaluate the practical feasibility of the proposed system, all tests were conducted in an indoor environment designed to simulate typical residential conditions. The test area included common household furniture to create natural obstacles and simulate re-al-world spatial constraints.  
Video data were captured using two INSTA360 GO3 cameras, positioned to provide complementary angles and reduce blind spots. The lighting was kept at standard indoor levels, with no special adjustments to enhance visibility, to assess the system's performance in typical usage conditions.  
Five volunteers (aged 25-58, with varied height and body build) participated in the experiments. Each participant was instructed to perform predefined sequences of movements simulating both fall events and activities of daily living (ADLs).
## 3 Materials and Methods
### Operations on Spatial Data  
Querying on spatial data is considered an operation that is usually coupled with indexing methods. Several indexing methods have been considered in literature as they are crucial for the performance of spatial data query processing algorithms, since they are used in order to reduce the query run time. The most representative are the ones based on \(R\)-tree and Quad-tree.  
Further, to support efficient query processing on moving objects, grid-based space partitioning methods can be adopted [27]. Moving objects data are indexed in the grid cells they belong to for facilitating the queries and avoiding the check of all the objects. The aim of a spatial partitioning technique is to improve the query time as well as to keep all the partitions balanced in terms of memory and computations, which is known as the term \"load balancing\" [28].  
The equal grids partitioning uniformly divides the whole region, providing thus a good data locality but not load balancing. Also, Quad-tree is another data structure based on the principle of \"divide and rule\" that recursively divides two-dimensional space into four quadrants and needs a merging operation to construct the specified number of partitions. On the other hand, the \(R\)-tree provides an efficient data partitioning strategy to efficiently index spatial data. It is considered a balanced search tree that improves both search speed and storage utilization. Another space partitioning strategy is based on \(KDB\)-tree, a balanced binary tree, which has been used in load balancing of spatial databases for fast querying. It is worth mentioning that the locality of data and load balancing are important to speedup queries performance [29].
# Research on the Dynamic Control Method of CFETR Multi-Purpose Overload Robot
## 2 CMOR Modelling  
### Kinematic Modelling  
CMOR is a multi-degree-of-freedom robotic system for the maintenance of internal components of CFETR vacuum chambers [35]. It consists of CASK parts transfer vehicle, CMOR robot arm body, multifunctional toolbox, and CMOR end-effectors as shown in Figure 1. To describe the motion of the CMOR, the first step requires accurate modelling of the kinematics of the CMOR. In this paper, the improved DH parameter method is used to model the forward kinematics of CMOR, and the range of DH parameters and joint variables of each link are taken as shown in Table 1. The kinematic relationship between two adjacent links of CMOR is  
\[\begin{array}{c}\underset{i}{-1}T=\begin{bmatrix}c\theta_{i}&-s\theta_{i}c \alpha_{i}&s\theta_{i}s\alpha_{i}&a_{iC}\theta_{i}\\ s\theta_{i}&c\theta_{i}c\alpha_{i}&-c\theta_{i}s\alpha_{i}&a_{iC}\theta_{i}\\ 0&c\alpha_{i}&c\alpha_{i}&d_{i}\\ 0&0&0&1\end{bmatrix}\end{array} \tag{1}\]  
where \(c\) denotes \(cos\), \(s\) denotes \(sin\), \(a_{i}\) is the linkage length, \(\alpha_{i}\) is the joint torsion angle deviation, \(d_{i}\) is the linkage deflection, and \(\theta_{i}\) is the joint rotation angle. To eliminate the singularity problem arising from the mutual parallelism between the two axes of CMOR joint 1 joint 2, the rotation \(R_{i}\) is introduced to modify the DH model as follows:  
\[R_{i}=\begin{bmatrix}c\beta_{i}&0&s\beta_{i}&0\\ 0&1&0&0\\ -s\beta_{i}&0&c\beta_{i}&0\\ 0&0&0&1\end{bmatrix} \tag{2}\]  
The CMOR kinematic model is established using the modified DH parameters, and the homogeneous coordinate transformation matrix between adjacent links is  
\[\begin{array}{c}\underset{i}{-1}T=\text{Rot}(\text{X},\alpha_{i-1})\text{ Trans}(\text{X},a_{i-1})\text{Rot}(\text{Z},\theta_{i-1})\text{Trans}(\text{Z},d_{i}) \text{Rot}(\text{Y},\beta_{i})=\\ \begin{bmatrix}c\theta_{i}c\beta_{i}-s\theta_{i}s\alpha_{i}s\beta_{i}&-s\theta _{i}c\alpha_{i}&c\theta_{iC}\beta_{i}-s\theta_{i}s\alpha_{i}s\beta_{i}&a_{iC} \theta_{i}\\ s\theta_{i}c\beta_{i}+c\theta_{i}s\alpha_{i}s\beta_{i}&c\theta_{i}c\alpha_{i}&c \theta_{iC}\beta_{i}-s\theta_{i}s\alpha_{i}s\beta_{i}&a_{iC}\theta_{i}\\ -c\alpha_{i}s\beta_{i}&s\alpha_{i}&c\alpha_{iC}\beta_{i}&d_{i}\\ 0&0&0&1\end{bmatrix}\end{array} \tag{3}\]  
where \(\theta_{i},a_{i},\alpha_{i},d_{i},\beta_{i}\) are the DH parameters of the CMOR joint coordinate system.  
Then the position relationship of the CMOR end-effector concerning the base coordinate system can be expressed as  
\[\begin{array}{c}\underset{8}{0}T=\underset{1}{0}TR_{2}\underset{2}{1}T \underset{3}{2}T\cdots\underset{7}{0}T\underset{8}{7}T\end{array} \tag{4}\]  
where \(\underset{8}{0}T\) denotes the position of the CMOR end-effector with respect to the base coordinate system and \(R_{2}\) denotes the rotation matrix around the \(Y\)-axis introduced by avoiding singularities when joints \(J_{1}\) and \(J_{2}\) are parallel.
## 1 Introduction
[28] simulated airborne LiDAR data to explore the optimal LiDAR data acquisition conditions to estimate forest height. The results showed that the estimated predominant height was affected by the LiDAR sampling interval, and they were linearly dependent. [PERSON] et al. [29] studied the influences of the scanning angle, flying height, and scanning mode on the inventory of forest parameters. The study found that the flying altitude had little influence on the estimation of plot volume and mean height. The LiDAR data with a scanning angle of 15\({}^{\circ}\) could obtain slightly more precise mean height and plot volume measurements than the data with a scanning angle of 20\({}^{\circ}\). [PERSON] et al. [30] simulated airborne LiDAR data using a ray-tracing method to explore the effects of the scanning angle on mean height and canopy closure estimations. The results indicated that the influence of the scanning angle on height percentiles was greater in the forests with low intervals than with high intervals, and the effect of the scanning angle on canopy closure was greater than the effect on laser height percentiles. [PERSON] et al. [31] explored the effects of pulse density and flight strip overlap on occluded and unobserved canopy volumes. The results indicated that the forest canopy volume was obviously occluded, even with a high pulse density, and larger flight strip overlap can significantly increase the amount of observed canopy volume due to the added observation angles and increased pulse density. However, to our knowledge, there has not been a study exploring the effects of LiDAR data acquisition conditions on forest foliage profile retrieval. To obtain a more accurate forest foliage profile at the lowest cost, it is vital to investigate the effects of data acquisition conditions on foliage profile estimations. The overall goal of our study is to explore the effects of the scanning angle, flying altitude, and pulse density on foliage profile retrieval using simulated airborne full-waveform LiDAR data. The specific objectives of our study are to: (1) simulate the 3-D forest scene and airborne small-footprint full-waveform LiDAR data; (2) retrieve the forest foliage profile from simulated LiDAR data based on the GORT model; and (3) assess the effects of the scanning angle, flying altitude, and pulse density on foliage profile retrieval.