esa-sceva/satcom-chunk-collection huggingface dataset:
# A Division-of-Labour Approach to Traffic Light Scheduling
## 1 Introduction  
Much of the productivity of modern society has become dependent on transport. The efficient management of traffic plays a particularly important role in the transport industry and, to an extent, society. Poor traffic management can cause accidents, and productivity is reduced by the resulting congestion. A study in Ghana found that traffic congestion reduced worker productivity by 9% per workday [1]. It is also estimated that 27% of global warming has been caused by transportation [2].  
One of the most important aspects of traffic management, especially in urban areas, is traffic light scheduling. Many traffic light scheduling algorithms have been developed, as reviewed in [3; 4; 5], and newly developed algorithms tend to incorporate computational intelligence approaches to accommodate for the dynamic nature of traffic [6; 7; 8; 9]. Despite the availability of smart traffic light scheduling algorithms, it is still the case that static and deterministic timing schedules are utilised that do not take the dynamic nature of this optimisation problem into consideration. This is mainly due to the ease of implementation of these static approaches and the little demand for software and hardware resources.  
This paper presents a flexible and efficient model based on the division of labour within ant colonies [10] for the traffic light scheduling problem. The dynamic self-organising behaviour seen in the division of labour in insect colonies makes models of division of labour for task allocation an ideal approach to cope with the dynamic nature of the traffic light scheduling problem. In addition, the task allocation models of division of labour are very simple to understand and implement. The proposed traffic scheduling algorithm is applied to eight different traffic scenarios. The effectiveness of the algorithm is assessed by comparing the performance of this algorithm to the performances of five other traffic scheduling algorithms. The developed division-of-labour algorithm and another dynamic algorithm, the max pressure algorithm [11], generally performed best across these scenarios.  
The remainder of the report is structured as follows: Section 2 provides background on traffic signal control and division of labour in insect colonies. The division-of-labour approach to traffic light scheduling is proposed in Section 3. Section 4 describes the empirical procedure followed to analyse the performance of the division-of-labour traffic light scheduling model. The results are presented and discussed in Section 5.
## 3 Methodology
### GPU-Based PBD Cloth Framework (Reality Collision Version)
#### 3.2.3 Compute Shader Kernel Design  
To achieve real-time cloth simulation in XR environments with limited computational resources, the proposed system implements the core Position-Based Dynamics (PBD) algorithm using Unity's Compute Shader functionality. Compute Shaders enable highly parallel processing by utilizing the GPU, allowing thousands of particles to be simultaneously simulated at interactive framerates.  
The simulation workflow is decomposed into multiple independent compute kernels, each responsible for a specific stage of the PBD pipeline. This modular design maximizes parallelism, optimizes memory access patterns, and reduces synchronization overhead.  
The main Compute Shader kernels designed for the system are summarized below:  
* **ApplyExternalFores**: Applies external forces such as gravity to the velocities of particles.
* **DampVelocities**: Applies damping to particle velocities to simulate energy dissipation.
* **ApplyExplicitEuler**: Predicts new particle positions using explicit Euler integration.
* **ProjectConstraintDeltas**: Projects particles to satisfy distance and bending constraints, including atomic operations for parallel accumulation.
* **AverageConstraintDeltas**: Averages accumulated constraint deltas and updates particle positions accordingly.
* **SatisfySphereCollisions** and **SatisfyCubeCollisions**: Handles collision responses against primitive colliders such as spheres and cubes.
* **SatisfyMeshCollisions**: Detects and resolves collisions between cloth particles and RoomMesh triangles.
* **SatisfyHandMeshCollisions**: Handles dynamic collision interactions with the user's hand mesh obtained via Meta Quest OVR Mesh data.
* **UpdatePositions**: Updates final particle velocities and positions after constraint satisfaction and collision response.  
Figure 3: Flowchart of the GPU-based PBD cloth simulation system.  
Each kernel is carefully optimized by selecting appropriate thread group sizes and minimizing conditional branching inside the shader code. Through this highly parallelized architecture, the system achieves stable real-time performance even for moderately high-resolution cloth meshes on standalone XR devices.
# Communication Architecture in Mixed-Reality Simulations of Unmanned Systems
## 7 Experiments
### Testbed Configurations  
Two testbed configurations are considered in the presented results for the proposed experimental setting: one with three HWEs, which is hereafter referred to as the HW scenario, and one with the simulation platform (represented by the GCS) to communicate with one HWE, and two remaining HWEs modeled in the MR simulation. The latter configuration is hereafter referred to as the MR scenario. These two configurations are schematically depicted in Figure 8. The MR simulation used the architecture with SWEs modeled within the simulation platform according to Figure 2a.  
The main evaluation indicator of the communication characteristics is measured as the number of bytes per second successfully transferred between the three entities. All entities have been static and positioned at the same distances from each other. During the experiments, all of the entities broadcasted packets of 1 kB at a rate of 15 kBps using the UDP protocol, and successfully received data was measured every second for each entity.  
Three Microhard nVIP2400 modems with the TX power set to 14 dBm were used as the communication hardware. One of the modems (in particular the one utilized by HWE\({}_{3}\)) was equipped with an antenna that was superior to the others (8 dBi vs. 2 dBi). Thus, in a case of simultaneous communication of all entities, it was capable of transmitting approx. 12 kBps through the wireless medium, while the other two modems (used by HWE\({}_{1}\), HWE\({}_{2}\), and by the GCS in the second testing configuration) were only capable of successfully transmitting approx. 9 kBps in the HW scenario.
# Horticulture 4.0: Adoption of Industry 4.0 Technologies in
### Motivation, Novelty, and Contribution of the Study  
As discussed above, the traditional approaches lack appropriate monitoring of pest control, water management, soil management, light control, and temperature control, as these parameters are crucial for the effective production of horticulture with a sustainability aspect. The previous study found that the different issues in this group highlight the necessity for more environmentally sound processes, in addition to a rise in the utilization of automated systems and accurate processes. Industry 4.0 technology has proven its capability in various applications through the fusion of advanced technologies such as the Internet of Things, artificial intelligence, blockchain, robotics, digital twins, and big data. The amalgamation of Industry 4.0 and horticulture provides the opportunity to transform industrial agriculture into the next generation, namely Horticulture 4.0. Moreover, Industry 4.0 is capable of achieving sustainable and intelligent ecosystem with real-time management of farming, automation, intelligent decisions, real-time processing, and analysis [8]. It has proved that Industry 4.0 technologies have already delivered exceptional results in greenhouse farming for a sustainable food supply chain with the integration of hardware and software algorithms and giving rise to higher yields using fewer resources and less water. Although many studies have addressed Industry 4.0 technologies individually for fruitsand vegetables, they are limited studies that merely address multiple Industry 4.0 digital technologies as single concepts for discussing their significance in horticulture. Based on the aforementioned information, this study analyzed the value and applications of Industry 4.0 technologies in horticulture for real-time monitoring of fruit-and-vegetable conditions in various environmental settings, intelligent and predictive analytics for fruit-and-vegetable growth and disease estimation, and virtual horticulture plants. The contribution of the study is as follows:  
* The significance of using Industry 4.0 technologies for horticulture is discussed in the study for sustainability.
* We also discuss the significance and applications of the Internet of Things, cloud computing, artificial intelligence, blockchain, and big data for horticulture for enhancing traditional practices for disease detection, irrigation management, fertilizer management, maturity identification, marketing, and the supply chain, soil fertility, and weather patterns at pre-harvest, harvest, and post-harvest
* Finally, the article discusses the challenges and suggests recommendations for the wide adoption of any future enhancement in the vicinity of horticulture. In horticulture settings, robotics, drones with vision technology and AI for the detection of pests, weeds, plant diseases, and malnutrition, and edge-computing portable devices that can be developed with IoT and AI for predicting and estimating the disease of crops are vital recommendations suggested in the study.
## 3 Materials and Methods
### Architecture
#### 3.2.3 Self-Temporal Compression Mechanism  
The use of distillation layers has been widely applied in many time series forecasting models (such as LSTM and Transformer) and has been proven effective for long-sequence time series forecasting tasks. Therefore, this mechanism also plays a significant role in the task of long-sequence elevator fault detection. Temporal compression is achieved by downsampling the data through convolution and pooling operations, reducing the length of the time series to half its original size. Traditional Transformers use stacked self-attention blocks, which require computationally intensive calculations when dealing with large amounts of time series data from thousands of elevators. This can lead to significant computational overhead and longer processing times [11].  
\[Q =X_{\text{input}}\cdot W_{q} \tag{7}\] \[K =X_{\text{input}}\cdot W_{k}\] (8) \[V =X_{\text{input}}\cdot W_{v}\] (9) \[Distilled=MaxPool(Conv1d(Attention(Q,K,V))) \tag{10}\]In this context, \(Q\) represents the query vector. \(K\) represents the key vector. \(V\) represents the value vector. \(X_{\text{input}}\) denotes the input feature matrix. \(W_{q}\), \(W_{k}\), and \(W_{v}\) respectively denote the linear projection matrices used for query, key, and value. _Distilled_ represents the temporal compression operation. \(Conv1d\) refers to one-dimensional convolution operation, and \(MaxPool\) denotes the maximum pooling operation.  
\[\text{Output}_{\text{next}}=\text{AttentionBlock}(Distilled) \tag{11}\]  
The output of the temporal compression operation, referred to as _Distilled_, is used as the input for the next self-attention block in a recursive manner until it reaches the final self-attention block. \(\text{Output}_{\text{next}}\) represents the input for the next self-attention block, which is the output of the temporal compression operation (_Distilled_). \(AttentionBlock\) refers to a block that contains self-attention mechanisms. This process continues iteratively, allowing information to flow through multiple self-attention blocks, with the output of one block serving as the input for the next.
# A Routing Optimization Method for LEO Satellite Networks with Stochastic Link Failure
## 3 Optimization Method
### Improved Genetic Algorithm Based on A* (GA-AS)  
GA is a self-organizing and self-adaptive artificial intelligence technology that simulates the evolutionary process and mechanism of natural organisms to solve problems. The basic framework of GA-AS is similar to GA's but the initial solution generator has been significantly modified. Here, we give the framework of GA-AS, and the flow of it is shown in Algorithm 1.  
* Initialization: The initial population needs to be generated before the iterative calculation is performed. Assuming that the population size is \(N\), we adopt the improved A* strategy and run the algorithm \(N\) times to generate the initial population in this work.
* Coding: The coding is the process of establishing the mapping relationship between phenotype and genotype. We use natural number coding to transform the problem into an optimal combination problem.  
Selection: In this process, the principle of competition in nature is simulated by calculating the fitness value, which is the criterion for assessing individuals' performance, and then the outstanding individual is selected.
* Heredity: This part includes crossover and mutation. Inspired by biology, the offspring is produced by gene crossover and mutation of parents. Usually, the offspring shares almost features of the parents, and it is possible for the offspring to have properties that the parent does not have due to the mutation operator.  
In GA-AS, when the genotypes change, the population needs to be filtered to screen out the individuals who violate the constraints. In this work, for addressing the situation, we use the penalty strategy by reducing the objective function value \(f\) to obtain fitness value \(\mathcal{F}\), as shown in Equation (26), and therefore \(\mathcal{F}\) may be negative.  
\[\mathcal{F}=f-n\cdot M \tag{26}\]  
where \(n\) is the number of constraint violations, and \(M\) is a constant.  
```
Input: communication tasks Output: routing optimization results
1 initialization: generate initial population by the improved A* strategy;
2 whileiteration number less than the set value or average population fitness value greater than the threshold valuedo
3 calculate the objective function value \(f\);
4 count the number of constraint violations \(n\) for per individual;
5 calculate fitness value \(\mathcal{F}\) according to Equation (26);
6 heredity (crossover and mutation);
7
8 recalculate \(\mathcal{F}\);
9 generate new population;
10 selection;
11
12 end while
```  
**Algorithm 1** Improved Genetic Algorithm Based on A* (GA-AS)
## 5 Passenger Vehicle Demonstrator for Low Speed Autonomy (PVLSA)
### Conclusions  
The PVLSA demonstrator was utilised to develop a set of perception, planning and control algorithms to realise an SAE Level 3+ automated parking solution. As part of this,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Success Rate** & **Execution Time (s)** & **Number of Nodes (\#)** & **Path Length (m)** & **Reverse Path** & **Collision Checks (\(\times\)1000)** \\ \hline RRT & \(37/50\) & \(0.12\pm 0.3\) & \(200.38\pm 465.0\) & \(71.62\pm 13.5\) & \(17.23\pm 18.8\) & \(6.19\pm 11.8\) \\ RRT Connect & \(48/50\) & \(0.85\pm 1.7\) & \(953.08\pm 1794.3\) & \(63.56\pm 8.9\) & \(6.89\pm 4.9\) & \(56.04\pm 77.9\) \\ RRT* & \(49/50\) & \(3.32\pm 0.1\) & \(200.00\pm 0.0\) & \(59.48\pm 6.3\) & \(5.46\pm 4.3\) & \(338.76\pm 12.2\) \\ RRV & \(50/50\) & \(0.11\pm 0.1\) & \(40.60\pm 22.6\) & \(58.94\pm 9.7\) & \(24.64\pm 25.0\) & \(7.05\pm 3.4\) \\ RRTx & \(50/50\) & \(0.27\pm 0.2\) & \(39.24\pm 26.3\) & \(64.55\pm 10.4\) & \(13.83\pm 12.4\) & \(17.14\pm 13.7\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Real-life validation KPI results of the PVLSA demonstrator for parking at spot-2.  
Figure 14: Parking scenario description of the PVLSA demonstrator validation test.  
Figure 15: An example test video of the PVLSA demonstrator is available on Youtube, access date: 4 June 2021. The main visualisation on the left shows the localisation and the tracked path during the manoeuvre. On the top right, the dilated occupancy map together with the planned path is shown. The bottom right is the birds-eye view of the vehicle during the experiment.  
we described in this paper a specific implementation of RRT-based algorithms for path planning purposes in low-speed autonomy use-cases. Scenario-based real-life validation of the algorithms was performed in a use case involving a static environment.  
The main contribution of the PVLSA demonstrator is a benchmark of the respective algorithms in real-life validation tests. Test results indicate high success rates for the RRV and RRTx methods at very low computational overheads, which are promising for utilisation in further analysis. The utilised use-case scenarios were selected in the interest of achieving a simple basis of comparison for the selected algorithms. On the other hand, these scenarios were overly simplistic to assess their performance in a typical usage cycle of the provided solution.  
It remains to extend the evaluation in more challenging scenarios and further develop the existing solution, potentially, as a part of commercial exploitation. There are many potential implementation areas of the developed solution including:  
* Self parking solutions,
* Automated personal mobility services (i.e., Robo-Taxi),
* Autonomous delivery of goods (parcel, cargo, food, etc.),
* Road cleaning robots,  
which shall form future areas for potential exploitation.
## 4 Relationship between Corrected PLS and UCS  
According to the proposed relationship equations by ISRM and the 'Code for Rock Test of Railway Engineering (China)', the UCS calculated results are generally larger than the UCS test results. Thus, these equations are not applicable for predicting the UCS of carbonaceous slate in the Luang Prabang Suture Zone. For this reason, and based on the PLS test and UCS test results of carbonaceous slate, four types of fitting correlations between PLS and UCS were established that are widely used in related research [32], and fitting equations were obtained as follows:  
Zero-intercept linear equation:  
\[UCS=18.45I_{s(50)} \tag{7}\]  
Non-zero-intercept linear equation:  
\[UCS=17.34I_{s(50)}+0.9 \tag{8}\]  
Power equation:  
\[UCS=18.05I_{s(50)}^{0.849} \tag{9}\]  
Logarithmic equation:  
\[UCS=6.63\ln\left(I_{s(50)}\right)+16.55 \tag{10}\]  
According to the fitting equations, curves were drawn in Figure 7. Four fitting equations could reflect the relationship between the PLS and UCS of carbonaceous slate, and all determination coefficients are higher than 0.7. The logarithmic equation has the lowest fitting determination coefficient--the value is 0.76. The determination coefficients of the non-zero-intercept linear equation and the power equation are 0.83 and 0.84, respectively. The zero-intercept linear equation has the highest determination coefficient--the value is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Location** & \multirow{2}{*}{**Sample Types**} & **UCS Range** & **Average** & **Cylindrical Sample** & **Standard** & **Variation** \\
**Number** & & **(MPa)** & **(MPa)** & **UCS (MPa)** & **Deviation** & **Coefficient (\%)** \\ \hline
1 & Cubic & 1.01–1.54 & 1.30 & 1.15 & 0.20 & 15.1 \\
2 & Cubic & 1.31–2.17 & 1.84 & 1.63 & 0.26 & 14.4 \\
3 & Cubic & 3.33–4.91 & 4.07 & 3.62 & 0.51 & 12.5 \\
4 & Cubic & 5.08–6.29 & 5.82 & 5.18 & 0.43 & 7.5 \\
5-V & Cubic & 19.05–24.08 & 21.67 & 19.26 & 1.41 & 6.5 \\
6-V & Cubic & 16.99–20.13 & 18.28 & 16.25 & 0.98 & 5.4 \\
7-V & Cubic & 23.66–29.31 & 26.00 & 23.12 & 2.09 & 8.0 \\
8-V & Cylindrical & 19.13–20.19 & 20.18 & 20.18 & 0.79 & 3.9 \\
9-V & Cubic & 11.87–15.58 & 13.65 & 12.13 & 1.17 & 8.6 \\
10-V & Cylindrical & 14.39–16.62 & 15.34 & 15.34 & 0.80 & 5.2 \\
5-P & Cubic & 11.98–14.19 & 12.73 & 11.32 & 0.78 & 6.2 \\
6-P & Cubic & 12.61–15.37 & 13.61 & 12.09 & 0.86 & 6.3 \\
7-P & Cubic & 18.72–20.92 & 20.92 & 17.63 & 0.68 & 3.4 \\
8-P & Cylindrical & 11.02–13.76 & 12.18 & 12.18 & 0.93 & 7.7 \\
9-P & Cubic & 5.96–7.91 & 6.90 & 6.13 & 0.68 & 9.8 \\
10-P & Cylindrical & 7.15–10.53 & 9.19 & 9.19 & 1.13 & 12.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of uniaxial compressive strength testing.  
0.96. Accordingly, the relationship between PLS and UCS is close to a linear dependence, and it can be expressed as UCS = 18.45I\({}_{s(50)}\), which has higher accuracy in predicting the UCS of the carbonaceous slate in the Luang Prabang Suture Zone.
## 6 Discussion and Future Work  
The new metal frame coupled antenna system exhibits several advantages compared to previously reported sub-6 GHz MIMO designs. First, the measured isolation performance exceeds \(-12\) dB, significantly better than earlier systems with isolation levels from near \(-4\) to \(-10\) dB. The enhancement is attributable to the refined coupling geometry and the centralized grounding between the PCB and the metal frame. In terms of the ECC, the new antenna achieves values below 0.12 across the entire n78 band, with a minimum of 0.001-0.002 in the 3.5-3.7 GHz range. These figures are better than those of conventional uniplanar or loop-type antennas; the ECCs often range from 0.15 to 0.2. Compared to recent studies such as [1,2,7], the new antenna affords a better trade-off between performance and integration. Although [10] reports isolation above 15 dB, our ECC performance (minimum 0.001) is superior and the structure is inherently more adaptable to diverse smartphone architectures. Furthermore, unlike Reference [10], which presents results measured on a bare test board, our study evaluates the antenna performance when integrated into an actual smartphone platform. Therefore, the results in [10] are likely to exhibit degraded performance under realistic operating conditions, whereas our measurements already reflect the constraints and effects associated with practical implementation.  
Overall, the shared coupling architecture, combined with systematic isolation enhancement and ECC optimization, offers a practical and scalable antenna for sub-6 GHz 5G MIMO integration. The structure is especially well suited for high volume smartphone applications wherein the PCB space and frame access are limited. However, it should be noted that user interactions, especially hand proximity, may affect the current distribution on the metal frame and lead to performance variations. Accordingly, future work will include comprehensive evaluations using standardized hand phantoms and simulation models to quantify the potential impacts on impedance characteristics, efficiency, and MIMO correlation. Ensuring robust operation under realistic usage conditions is essential for practical deployment.