esa-sceva/satcom-chunk-collection huggingface dataset:
# Centralized Task Allocation and Alignment Based on Constraint Table and Alignment Rules
## 2 Preliminaries  
### Task Allocation and Alignment Problems  
The task allocation problem is to find matching of tasks to agents that maximizes global reward as below [12; 16]:  
\[\max\sum_{i=1}^{N_{u}}\left(\sum_{j=1}^{N_{t}}c_{ij}(\mathbf{x}_{i},\ \mathbf{p}_{i})x_{ij} \right), \tag{1}\]  
subject to  
\[\sum_{i=1}^{N_{u}}x_{ij}\ \leq\ 1\ \ \forall j\in J, \tag{2}\]  
\[\sum_{j=1}^{N_{t}}x_{ij}\ \leq\ L_{t}\ \ \forall i\in I, \tag{3}\]  
\[\sum_{i=1}^{N_{u}}\sum_{j=1}^{N_{t}}x_{ij}=N_{min}, \tag{4}\]  
\[x_{ij}\in\{0,1\}, \tag{5}\]  
where \(N_{u}\) is number of agents, \(N_{t}\) is number of tasks, and \(L_{t}\) is maximum number of tasks each agent can be allocated. \(x_{ij}\) is the flag that is 1 if task \(j\) is allocated to agent \(i\) and is 0 otherwise, \(\mathbf{x}_{i}\) is the flag vector whose \(j\)th element is \(x_{ij}\), and \(\mathbf{p}_{i}\) is the ordered sequence of tasks for agent \(i\). \(N_{min}\) is the lowest number between \(N_{t}\) and \(N_{u}L_{t}\). \(c_{ij}\) is scoring function and \(c_{ij}(\mathbf{x}_{i},\ \mathbf{p}_{i})\) means reward of task \(j\) if agent \(i\) completes tasks along \(\mathbf{p}_{i}\) based on \(\mathbf{x}_{i}\). \(I\) and \(J\) are agent group and task group, respectively, so number of elements for \(I\) and \(J\) are \(N_{u}\) and \(N_{t}\), respectively. As in Equation (2), one task is allocated to only one agent.  
The task alignment problem is to calculate the arrival time, start-time, and end-time of each task satisfying temporal constraints and allocated sequence of tasks for each agent. If the temporal constraints have logical error, for example a task1 has to be started after finishing task2, and task2 has to be started after finishing task1, the task alignment result cannot be calculated. To prevent this, we assume that temporal constraints have no logical error so there must be at least one task alignment solution satisfying temporal constraints.
### Theoretical Background
#### 1.1.2 Measurement of the Vertical Parachute Speed Using a FLYTEC 4030 Variometer  
This variometer (vertical speed indicator) is dedicated to the operational measurement of the vertical speed of hang-gliders and parachute gliders for paragliding. The above-mentioned facts result in some limits for its utilization for skydiving parachutes. Predominately, it has limited resistance against free-fall conditions, and a powerful effect during the parachute opening and landing. All of these are performed with a higher vertical descending speed. Generally, the FLYTEC 4030 was used for the specification of the vertical speed parachute value that had been previously achieved using the classical method, and for the specifications of the whole flight envelope of the parachute. The method is applicable for parachutes and parachute gliders [27].  
The measurement of the vertical parachute speed using the FLYTEC 4030 device has significant disadvantages:  
* Uncertainty resulting from turbulence of agitated air that causes the device to be blown over.
* Inlet of a dynamic air compression into a pressure sensor cannot be completely avoided during the functional flight.  
Materials and Methods of the Measurement of the Parachute Vertical Speed Using the Photo-Optical Method  
The basic principle of this measurement method lies in assembling a compilation of sequential snapshots of the parachute movement trajectory with a noted time interval between shots [27] (Figures 2 and 3).  
The detailed procedure for defining the vertical parachute speed by the photo-optical method is as follows [27].
## 7 Proposed Architecture for Cellular System Based on SDN, NFV, and C-RAN
* PHY (Physical Layer): The layer in the BBU that handles the physical connection to the network. * MAC/RLC: These layers manage multiple access protocols and data transfer reliability. * RRC/PDCP: These layers manage radio resources and the convergence of data from different sources. * **NFV in C-RAN:** When NFV and C-RAN are combined, they offer more energy-efficient, flexible, and scalable cellular networks. In C-RAN, functions with stringent latency requirements, such as Digital Signal Processing (DSP), are deployed on the dedicated hardware and co-located with RRHs. On the other hand, BBU functionalities such as packet scheduling and user management could be virtualized since these functions are not as latency sensitive and could be decoupled from hardware and deployed as software instance [95]. * **User Equipment and Applications:** This represents the devices and applications that use the cellular network, such as smartphones, wearables, and vehicles. This integrated architecture demonstrates a practical implementation of SDN, NFV, and C-RAN technologies for next-generation cellular networks. Using SDN programmable control, NFV flexible virtualization, and centralized processing for RAN using C-RAN, the proposed cellular architecture addresses the critical energy efficiency challenges identified throughout this article while maintaining the performance requirements of 5G and beyond. Table 5 presents the key components of the proposed cellular architecture and their role in addressing some challenges in the development of a sustainable cellular network. The integration of centralized SDN control, the virtualized network functions of NFV, and the resource optimization of C-RAN plays a key role in improving network efficiency and reducing energy consumption. This holistic approach demonstrates how software-driven architectures can significantly improve scalability, flexibility, and sustainability in next-generation cellular networks.
## 4 Discussion  
### Ideal and Non-Ideal Packing for Sand-Fines Mixtures  
The results presented in Figure 3 show a significant deviation from ideal packing for sand-clay and sand-silt mixtures despite of the small size ratio, \(R\). The current model could explain this deviation through the incorporation of fines cohesive behavior by using \(d_{cri}\) of 0.15 mm and \(p\) = 0.15 in Equations (5) and (6). Nonetheless, the experimental and modeling results for mixtures M10 and M11 largely match with the ideal packing condition (Figure 4). Unlike the other mixtures, M10 and M11 share one important characteristic: \(d_{l}\) is very large (\(>\)0.70 mm). Consequently, the parameter \(x\) in Equation (6) approaches 1 (\(>\)0.95), as shown in Figure 5 and thus the first term in Equation (5) approaches zero and the effect of \(p\) on \(r\) diminishes. That is, the cohesive packing behavior of the fine particles does not significantly disturb the packing of large particles once the latter becomes very large. In such case, \(r\) becomes predominantly function of \(R\) alone, as suggested by Equation (5), which can lead to near-ideal packing case if \(R\) is very small.  
The ideal and non-ideal packing behavior of sand-fines mixtures is demonstrated in Figure 6. Both curves are constructed using exactly the same parameters including size ratio (\(R\) = 0.003) but the absolute large and fine particle sizes are different. For the near-ideal packing case, the large particle size is 0.750 mm while it is 0.250 mm for the non-ideal packing condition. The largest difference between the two cases can be observed around the minimum porosity value near \(f_{f}^{\star}\) which corresponds to the maximum packing density condition. Error in predicting the actual porosity at such condition may result in significant underprediction of unconsolidated sediments initial volume and permeability in sedimentary basins [21; 22; 23; 24; 25]. At such maximum packing density, the maximum P-wave velocity of sand-fines sediment mixtures is also expected [20; 27]. The prediction of such elastic property relies significantly on accurate prediction of the mixtures packing density [20; 27]. Therefore, the following sections discusses how the current model can improve the prediction of maximum packing density.  
Figure 5: Parameter x as function of large particle size \(d_{l}\) as described by Equation (6) for \(d_{cri}\) = 0.15, while \(d_{l}\) = 0.75 mm is highlighted by dashed vertical red line, for which x = 0.95.
## 2 Problem Statement
### Methodology  
To enable a companion computer onboard a UAV for simultaneous object detection and 3d path planning in real-time, it is essential to integrate the fastest object detection algorithm and 3D path planner that requires less computational burden. YOLO, as explained in Section 4.1, is selected as the fastest object detection algorithm. In addition to object detection, this algorithm also localizes the object(s). The proposed 3D path planner relies on the relative locations of the detected objects to calculate a collision-free path for the UAV. Although the proposed 3D path planner resembles \(A^{*}\) path planning algorithm in implementing heuristic function for cost minimization, it avoids an exhaustive search for consecutive collision-free nodes and storage method of \(A^{*}\). Unlike \(A^{*}\), the proposed 3D path planner maps the current location of the UAV to a few nodes between consecutive obstacles. These few nodes are determined based on the size of the UAV and the gap between consecutive obstacles, as explained in Sections 5.1.1 and 5.1.2. A Euclidean function is used as a heuristic function in this 3D path planner.  
Prior to a real flight test, the performance of the proposed 3D path planner must be checked in a simulated environment. For this performance test, software tools are essential components. One of the software tools specifically designed for such task is Gazebo 3D dynamic environment simulator. This software was primarily designed to evaluate algorithms for robots [18] and provides realistic rendering of the environment in which the robot navigates. Moreover, it is enriched by various types of simulated sensors. We designed a simulated cluttered 3D environment in Gazebo and used it to test performance of the proposed 3D path planner during its successive development.
## 2 Theoretical Background
 \tag{7}\]  
If \(\sum\limits_{n=1}^{i-1}L_{n}<x\leq\sum\limits_{n=1}^{i}L_{n}\), \(x\) lies in the region that \(i\)th LGSS covers. \(\sum\limits_{n=1}^{0}L_{n}\) is defined as zero. \(\int\limits_{L_{n}}\) represents the integral within the region covered by \(n\)th sensor. \(\bar{\epsilon}_{i}(t)\) represents the dynamic strain at time \(t\) from the \(i\)th sensor. For clamped-clamped beam (Figure 1(c)), the beam cannot rotate at support A and the rotating angle at support A is zero. In other words, the value of \(\theta_{A}\cdot x\) is zero and the value of \(\Delta\) is equal to  
Figure 2: Deflection estimation for single-span beams. the value of \(d(x)\). Therefore, the deflection of point \(x\) of a clamped-clamped beam at time \(t\) can be expressed as:  
\[ \begin{array}{rl}d(x,t)&=-\int_{0}^{x}\frac{\varepsilon(z,t)}{h}(x-z)\,\mathrm{ d}z\\ &=-\left[\sum\limits_{n=1}^{i-1}\frac{\overline{\varepsilon}_{n}(t)}{h}\int \limits_{L_{n}}(x-z)\mathrm{d}z+\frac{\overline{\varepsilon}_{i}(t)}{h}\int \limits_{\sum\limits_{n=1}^{i-1}L_{n}}^{x}(x-z)\mathrm{d}z\right]\end{array}  \tag{8}\]  
According to the aforementioned analysis, the method can be extended to continuous beams and other beams with different boundary conditions.
# Subtitling 3D VR Content with Limited 6 DoF: Presentation Modes and Guiding Methods
## 4 Results: User Tests
### Sample of Participants  
In total, 24 users participated in the study (of which, half were female). They were aged between 18 and 65 years (average 35.1, standard deviation 14.7); 13 were young adults (18-35 years), nine were middle-aged adults (36-55 years); and two were older adults (>55 years).  
Regarding their study level, 20.8% of participants had a secondary school level, 25% were undergraduate university students, 29.1% held a university degree, 20.8% held a PhD degree, and 4.1% preferred not to indicate their study level. All participants were hearing users, but the audio was muted, so the subtitles became a key element to understand the story. A portion of 58.3% of the participants had previous experience with VR content consumption using VR headsets (half less than once per year, 28.5% between 1-5 times per year, 14.2% on a monthly basis, and 7.1% on a weekly basis).  
It must be noted that no particular filter was applied for the participants' recruitment, beyond having a good level of English, because the subtitles were presented in that language. This was because the considered subtitling modes and use of indicators can provide benefits to the general audience, and can potentially be applied in many different scenarios [5].
## 3 Results
### The Coupled Effect of Key Parameters on Methane Plume Retrieval
In addition, it can be seen from the figure that changes in spectral resolution and SNR would also have a marked impact on the estimated results. The correlation of the two parameters is not linear, but rather follows a concave distribution, suggesting that increasing the spectral resolution can quantify the plume more precisely than enhancing SNR, in agreement with the verification of the retrieval precision by [PERSON] [25]. This is due to the fact that increasing the spectral resolution can effectively enhance the number of spectral samples in the retrieval spectrum, which in turn makes the methane absorption features more pronounced and improves the spectral contrast relative to the continuous spectrum. The limitations of spectral resolution and SNR can be mitigated by applying advanced algorithms. Methods such as matched filter could maximize the SNR, while TOA reflectance can be utilized to achieve effective retrievals, even at a coarse spectral resolution. Figure 9b illustrates the relationship between spatial resolution and SNR at the specific spectral resolution of 2 nm. Regardless of changes in spatial resolution, the low SNR notably impacted the estimated results. When the SNR was lower than 200, the estimated errors were generally more than 30%. In addition, there was little improvement in estimation when the SNR increased from 400 to 800, indicating similar performance at these high SNRs. Figure 9c presents the results when slicing the matrix at the fixed SNR of 800. It shows that underestimation was consistently exhibited at the coarser spectral resolutions, irrespective of the spatial resolution. This finding emphasizes the importance of achieving high spectral resolutions. Figure 9: (**a**) Estimated emissions of retrieval with different key parameters for 1000 kg/h simulated emissions; (**b**) the relationship between spatial resolution and SNR at 2 nm spectral resolution; (**c**) the relationship between spatial resolution and spectral resolution at SNR of 800.
# Optimization of Multi-Mission CubeSat Constellations with a Multi-Objective Genetic Algorithm
## 3 Problem and Optimization Formulation
### Objective Functions Formulation
It is given in terms of the \(N_{G}\) number of accessed points by the constellation from the \(N_{TG}\) total number of grid points. \[Max\ f_{4}=\frac{N_{G}}{N_{TG}}\times 100\% \tag{9}\]
## 4 MG Communication Technologies
### Wireless Technologies
#### 4.2.4 5g  
Cellular networks such as GSM, GPRS, 3G, 3.5G, 4G, and 5G have high data rate communication and bandwidth. This characteristic facilitates the transfer of a huge amount of data. Therefore, these technologies can be used in applications such as interchange among smart meters, MG control center, and supervisory level of the main grid, i.e., DMS in WAN environments. High-cost licensed spectrum and the uncertainty of stable connectivity in severe weather conditions are drawbacks of this technology, although cellular technologies could defeat interference and security issues of free bands by applying licensed bands. Cellular communication, which was initially introduced in the 1980s based on analogue signal communication, has been developed to IP-based communication such as 4G and 5G for increasing bandwidth and sustaining real-time communication. 4G, which is called Long-Term Evolution-Advanced (LTE-A), increased the data rate of its predecessor generation, i.e., 3G from the range of 2-14 Mbps to the range of 100 Mbps-1 Gbps as well as latency and improvement of infrastructure energy consumption. Meanwhile, noticeable advancement in this technology is happening by the introduction of 5G to facilitate IoT application penetration in MGMS. The high performance of 5G has three characteristics, including Millimeter-Wave (mm-Wave), Multiple-Input Multiple-Output (MIMO), and ultra-dense cellular network. This technology brings benefits such as lower latency, higher bandwidth, a larger number of participants and nodes in MGMS, higher security, and so on. Enhanced Mobile Broadband (eMBB), Ultra-reliable and Low-latency Communications (uRLLC), and Massive Machine Type Communications (mMTC) are services offered by 5G as The International Telecommunication Union (ITU) determined. While an eMBB service is a response to individual aspects of the world digitalization communication requirements, uRLLC and mMTC are related to higher scales of this trend, including industry and society aspects, respectively. Hence eMBB provides higher bandwidths for applications such as 3D and High-Definition (HD) Video and Virtual Reality (VR). This ultra-bandwidth can reach to 10 Gbps. On the other hand, uRLLC offers low latency limited to 1 ms for time and reliability sensitive applications such as driverless cars and industrial automation. Finally, mMTC represents the number of connected devices up to 1 million/km\({}^{2}\), facilitates the implementation of smart homes and smart cities [50].
# The Tractor-Cart System Controller with Fuzzy Logic Rules
## 2 Tractor and Cart System Modeling  
Achieving a high level of precision in controlling off-road vehicles in unstructured agricultural environments is not as easy as on-road vehicles. Because agricultural machines operate in highly uncertain and uneven terrain conditions, self-aligning systems of these vehicles need further study with respect to their varying environments. There are many research studies dedicated to the auto steering and self-aligning whose controllers are used on tractors. Although the towed cart is the main source of damage to crop plants in a field, there are few studies on precise navigation of towed carts. To design an automatic guidance system for a towed cart, it is essential to understand steering kinematics and motion dynamics of the tractor and cart in various field conditions. In several studies [17; 18; 19] of tractor and cart tracking controller design, because of the low operating speeds (i.e., less than 10 m/s) of agricultural machines, the kinematic model was considered due to its simplicity. In this approach, it is assumed that the sideway slippage of tires is negligible, the inertia of the system is minimal and air-drag acting on the vehicle does not influence the system dynamics [20]. The demonstrated results [21; 22] for the kinematic model in control design showed highly accurate results within the range of interest. However, in higher operating speeds, the tire sideslip was not negligible, and forces acting on dynamics of cart need to be taken into account and analyzed. This study analyzes the behavior of tractor and its towed cart (single axle) motion of the system was modeled with a kinematic model approach. Accuracy, robustness, and overall performance of the tractor-cart automatic guidance controller based on the kinematic bicycle model were simulated and analyzed.
## 6 Evaluation
### Evaluation Based on the Questionnaire Survey
#### 6.1.2 Evaluation Concerning the Compatibility with the Method to Obtain Information  
Regarding the use of applications using AR, 20% of the users used them regularly, while 80% did not regularly use them. However, 89% of the users obtained sightseeing information, and 85% obtained disaster information using the Internet. From these, while many users were not familiar with applications using AR, there was a need for the system, as it effectively provided information concerning sightseeing spots, tourism-related facilities, and disaster support facilities through the Internet.
# Towards Understanding Neural Machine Translation with Attention Heads' Importance
###### Abstract  
Although neural machine translation has made great progress, and the Transformer has advanced the state-of-the-art in various language pairs, the decision-making process of the attention mechanism, a crucial component of the Transformer, remains unclear. In this paper, we propose to understand the model's decisions by the attention heads' importance. We explore the knowledge acquired by the attention heads, elucidating the decision-making process through the lens of linguistic understanding. Specifically, we quantify the importance of each attention head by assessing its contribution to neural machine translation performance, employing a Masking Attention Heads approach. We evaluate the method and investigate the distribution of attention heads' importance, as well as its correlation with part-of-speech contribution. To understand the diverse decisions made by attention heads, we concentrate on analyzing multi-granularity linguistic knowledge. Our findings indicate that specialized heads play a crucial role in learning linguistics. By retaining important attention heads and removing the unimportant ones, we can optimize the attention mechanism. This optimization leads to a reduction in the number of model parameters and an increase in the model's speed. Moreover, by leveraging the connection between attention heads and multi-granular linguistic knowledge, we can enhance the model's interpretability. Consequently, our research provides valuable insights for the design of improved NMT models.  
Keywords:neural machine translation; interpretability; linguistics +
Footnote †: journal: Applied sciences
# Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT
## 3 Theoretical Backgrounds
### Effects of Non-Uniform Node Topology on the Localization Accuracy of the DV-Hop Algorithm  
The original DV-Hop algorithm estimates the distance between unknown nodes and anchor nodes based on the average hop distance. In networks with uniformly distributed nodes, the average hop distance more accurately reflects the actual physical distance between nodes. In contrast, non-uniform topologies are often affected by obstacles in the monitoring area, which may prevent sensor deployment in specific regions. Obstacles may force nodes that could originally communicate within one or two hops to rely on multiple hops for connection. Changes in shortest routing paths disrupt the correlation between physical distance and hop count. Under such complex network conditions, the original DV-Hop algorithm fails to achieve the localization accuracy required for practical applications.  
Although numerous enhancements to the original DV-Hop algorithm have been proposed, their performance often degrades significantly in the presence of irregular network topologies. Figure 1 illustrates the communication paths in a network with a non-uniform topology. It is evident that the hop count from node \(S\) to node \(A_{1}\) is 3, whereas due to obstacles, the hop count from node \(S\) to node \(A_{2}\) increases to 6. However, the actual distance \(d_{1}\) between node \(S\) and node \(A_{1}\) is evidently greater than \(d_{2}\), the distance between node \(S\) and node \(A_{2}\). In uniformly distributed networks, it is generally assumed that a higher hop count indicates a greater physical distance. This assumption, however, does not hold in non-uniform topologies, where the correlation between hop count and physical distance is often disrupted. As a result, based on the principles of the original DV-Hop algorithm, the estimated distances between certain unknown nodes and anchor nodes may become unreliable.  
Figure 1: Communication paths between nodes in a non-uniformly distributed topology network.