esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Comparison of BRI with Other Global and Regional Infrastructure Initiatives  
China's first-mover advantage, being the major global infrastructure initiative of its kind in the 21 st century, has set the bar for other alternative initiatives. PGII/BDN, GG, FOIP/PQI, and 3 SI, while not just economic ventures, are widely seen as geopolitical responses to China's growing influence through the BRI. Since the inception of the BRI in 2013, China has also recognised the importance of sustainability development goals pertaining to green growth, health and digital infrastructure and critical resource extraction from less habitable regions. The BRI has evolved into an umbrella initiative comprising the Silk Road Economic Belt, the 21 st Century Maritime Silk Road, and several thematic extensions such as the Green Silk Road, Health Silk Road, Digital Silk Road, Polar Silk Road, and Space Silk Road. However, while China's domestic capabilities are impressive, the international impacts of these initiatives remain mixed. As a result, these alternative initiatives are expected to not only rival the BRI in terms of its existing substantial impact on investment and connectivity but also improve upon its weaknesses, particularly in areas such as transparency, governance, and sustainability. This expectation places more pressure on these initiatives to avoid mistakes, as they must demonstrate their ability to offer a superior alternative to the BRI.  
Besides competition, by focusing on different aspects of infrastructural development, and tapping on different markets and financial instruments, these alternative initiatives are said to be complementary to the BRI in some ways as well. The crucial question then is as follows: how successful would these initiatives be in putting pressure on China's evolving BRI framework and in covering the bases of fair, inclusive and sustainable trade interconnectedness?
## 2 Materials and Methods
### Methods
#### 2.2.5 The Ensemble Forecasting Method  
The most intuitive and popular way of forecast aggregation is to linearly combine the constituent forecasts [80]. There are various methods proposed in the literature for selecting the combining weights [81]. The most popular and widely used ensemble methods are the error-based and the simple average [82]. The easiest among them is the simple average in which all forecasts are weighted equally, often remarkably improving overall forecasting accuracy [82; 83].  
Considering that \(\mathrm{Y}=\left[y_{1},\ y_{2},\ y_{3},\ \ldots,y_{\mathrm{N}}\right]^{ \mathrm{T}}\) is the actual out-of-sample testing dataset of a time series and \(\hat{Y}^{i}=\left[\hat{y}^{i}_{1},\ \hat{y}^{i}_{2},\ \ldots,\ \hat{y}^{i}_{n}\right]^{ \mathrm{T}}\) is the forecast for the \(i_{th}\) model, the linear combination of \(n\) forecasts is produced by [15]:  
\[\hat{y}_{k}=w_{1}\hat{y}^{(1)}_{k}+w_{2}\hat{y}^{(2)}_{k}+\ldots+\ w_{n}\hat{y }^{(n)}_{k}=\sum_{i=1}^{n}w_{i}\hat{y}^{(i)}_{k},\ \ \forall k=1,\ 2,\ \ldots,\ N \tag{15}\]  
Here, our analysis is based on these most popular ensemble methods. A brief discussion follows for each one.  
* The _simple average (AVG)_ method [82] is an unambiguous technique, which assigns the same weight to every single forecast. Based on empirical studies in the literature, it has been observed that the AVG method is robust and able to generate reliable predictions, while it can be characterized as remarkably accurate and impartial. Being applied in several models, with respect to effectiveness, the AVG improved the average accuracy when increasing the number of combined single methods [82]. Comparing the referent method with the weighted combination techniques, in terms of forecasting performance, the researchers in [84] concluded that a simple average combination might be more robust than weighted average combinations. In the simple average combination, the weights can be specified as follows:\[w_{i}=\frac{1}{n},\ \ \forall i=1,\ 2,\ \ldots,\ n \tag{16}\]
* The _error-based (EB)_ method [16] consists of component forecasts, which are given weights that are inversely proportional to their in-sample forecasting errors. For instance, researchers may give a higher weight to a model with lower error, while they may assign a less weight value to a model that presents more error, respectively. In most of the cases, the forecasting error is calculated using total absolute error statistic, such as the sum of squared error (SSE) [80; 83]. The combining weight for individual prediction is mathematically given by: \[w_{i}=e_{i}^{-1}/\sum_{i=1}^{n}e_{i}^{-1},\ \ \forall i=1,\ 2,\ \ldots,\ n\] (17)
# A Truly Second-Order and Unconditionally Stable Thermal Lattice Boltzmann Method
###### Abstract  
An unconditionally stable thermal lattice Boltzmann method (USTLBM) is proposed in this paper for simulating incompressible thermal flows. In USTLBM, solutions to the macroscopic governing equations that are recovered from lattice Boltzmann equation (LBE) through Chapman-Enskog (C-E) expansion analysis are resolved in a predictor-corrector scheme and reconstructed within lattice Boltzmann framework. The development of USTLBM is inspired by the recently proposed simplified thermal lattice Boltzmann method (STLBM). Comparing with STLBM which can only achieve the first-order of accuracy in time, the present USTLBM ensures the second-order of accuracy both in space and in time. Meanwhile, all merits of STLBM are maintained by USTLBM. Specifically, USTLBM directly updates macroscopic variables rather than distribution functions, which greatly saves virtual memories and facilitates implementation of physical boundary conditions. Through von Neumann stability analysis, it can be theoretically proven that USTLBM is unconditionally stable. It is also shown in numerical tests that, comparing to STLBM, lower numerical error can be expected in USTLBM at the same mesh resolution. Four typical numerical examples are presented to demonstrate the robustness of USTLBM and its flexibility on non-uniform and body-fitted meshes.  
Keywords:lattice Boltzmann method; unconditionally stable; thermal flows; second-order of accuracy +
Footnote †: journal: Applied Sciences
## 3 Transient Voltage Stability Boundary
### Safe Sag Duration  
Assuming that \(U_{sqg}\) is zero during voltage sag, then the electromagnetic torque of the induction motor is zero. In this case, the increasing speed of the slip is the highest. When \(U_{sqg}\) is zero, the sag duration that causes a critical instability of the induction motor is called the safe sag duration \(t_{safe}\)[3]. As Figure 3b shows, the induction motor initially operates at point B. When the bus voltage drops to zero, the slip of the induction motor increases rapidly. If the voltage sag duration is \(t_{1}\), the induction motor operates at point C1 at the moment of voltage recovery. Then, the slip will decrease, and the motor finally operates at point B1 stably. If the voltage sag duration is \(t_{2}\), the induction motor operates at point C2 at the moment of voltage recovery. Then, the slip will continue to increase, and the motor will stall. If the voltage sag duration is \(t_{safe}\), the induction motor is critically stable. The safe sag duration \(t_{safe}\) is the critical sag duration when \(U_{sqg}\) is zero. As long as \(t_{sqg}\) is less than \(t_{safe}\), the induction motor can remain stable, independently of the value of \(U_{sqg}\). Equation (10) can be obtained according to Equation (2). The meaning of Equation (10) is that when the voltage drops to \(U_{sqg}\), the time required for the slip to increase from \(s_{0}\) to \(s_{n}\) is \(t_{n}\). Equation (11) can be obtained according to Equation (10):  
\[t_{n}=\int_{s_{0}}^{s_{n}}\frac{2H}{T_{l}(s)-T_{e}(U_{sqg},s)}ds \tag{10}\]  
\[t_{safe}=\int_{s_{0}}^{s_{crit}}\frac{2H}{T_{l}(s)}ds \tag{11}\]  
In Figure 2, assuming that \(U_{tr}\) is equal to \(U_{0}\), \(s_{crit}\) can be obtained by equalizing the load torque and the electromagnetic torque (Equation (12)):  
\[T_{l}(s)=T_{e}(U_{0},s) \tag{12}\]  
In Equation (12), only the slip \(s\) is unknown. Two solutions can be obtained by solving Equation (12): the larger value corresponds to \(s_{crit}\), and the smaller one is \(s_{0}\). Combining Equations (11)-(12), the expression of \(t_{safe}\) is obtained as Equation (13).  
\[t_{safe}=\frac{2 HR_{r}}{T_{0}^{2}}\frac{\sqrt{U_{0}^{4}-4R_{t}U_{0}^{2}T_{0}- 4X_{t}^{2}T_{0}^{2}}}{\left(R_{t}^{2}+X_{t}^{2}\right)} \tag{13}\]  
It can be concluded from Equation (13) that the increase of stator impedance, branch impedance, rotor reactance, or load torque will reduce \(t_{safe}\), which is detrimental to the transient voltage stability. The increase of the rotor resistance or the rotor inertia constant will increase the safe sag duration, which is beneficial to the transient voltage stability.
## 2 Renewable Energy Source Trends for Micro-Grids
### Power Electronic Converters Used in Micro-Grids
Interlinking converters play a key role in hybrid micro-grids regarding the grid-tied and islanding operations and the seamless transition and resilience to main grid perturbation (LVRT-Low Voltage Ride Through capability), active harmonic filtering capability and power-flow control. Each converter type requires a proper control strategy/algorithm that is selected among numerous methods, based on specific requirements and applications. The control algorithms could be classified according to their loop either, in feedback or in feed-forward structure. For instance, the controller of DC/AC switch-mode inverters can contain six parts: Maximum power controller, ESS charge controller, voltage controller, harmonic controller, RMS voltage controller, and current waveform generator. Many DER components provide or generate a DC current, which is part of an internal DC micro-grid to avoid losses and permits plug-and-play capabilities. The hybrid energy systems involve a well-optimized energy control and management infrastructure to achieve the highest possible energy level. Moreover, control strategies and monitoring capabilities, automatic control, and configuration of the grid, and the active involvement of the consumers in energy production extend the importance of the micro/smart grids. All the advantages of smart grids can be achieved by power electronic converter integration and ICT technologies with the grid [10; 26; 27].
# BPMNE4 IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes +
## 1 Introduction
### Contributions  
This work proposes a sophisticated framework for the modeling, execution and monitoring of IoT-driven BPs that address problems discussed in Section 1.1. Starting with these problems, we have derived research questions RQ1-RQ3 and then refined them by defining research objectives O1-O8. To model IoT-driven BPs, we propose the use of the defacto standard modeling language BPMN 2.0, as many of its modeling elements can be found in the context of IoT-driven BPs as well (cf. Section 2). To make the involvement of IoT devices in BPs explicit, we extend the BPMN 2.0 metamodel. Our main goal is to provide an intuitive and user-friendly modeling notation for IoT-driven BPs, based on which IoT-driven BPs can be executed, monitored and recorded. Moreover, we propose an appropriate software architecture that enables the integration of IoT devices as well as the flexibility to evolve and maintain the IoT-enhanced BPMS.  
This article extend the work we published in [11], as follows:  
1. We add a backgrounds section that summarizes basic concepts necessary for the understanding of this article.
2. We systematically compare the framework with recent works.
3. We present more detailed insights into the implementation of the framework.
4. We extend the evaluation section by adding a second case study from the smart home domain.  
We present the results of a comprehensive user study.
* We carefully revise the architecture (middleware, IoT services and recording component) according to the feedback obtained from experts.
## 2 Research Background
### Approaches to IFC Information Extraction and Storage  
At present, the architecture, engineering, and construction (AEC) industry mainly employs IFC to support building information exchange [32]. IFC represents a conceptual data schema, and it contains an exchange file format of BIMs according to the ISO 16739-1:2024 standard, inside which the rigid and complex hierarchical structure of the IFC schema prevents simple extraction of building information manually and requires a detailed understanding of the IFC data model [33].  
To parse the information in the IFC file efficiently, some researchers have investigated the field of IFC file analysis, processing, and storage [34; 35; 36; 37]. [PERSON] and [PERSON] [38] developed a graph data model (GDM) to employ semantic information, to extract, analyze, and present the topological relationships among 3D objects in 3D space, and to perform topological queries faster. [PERSON] et al. [39] realized the extraction and transformation of geometry information from IFC to ShapeFile format. In order to better support network transmission, [PERSON] et al. [40] developed a JavaScript Object Notation (JSON) representation of IFC specification. Some studies were also carried out on BIM database design to improve information processing and retrieval efficiency. For example, [PERSON] et al. [41] developed a cloud-based BIM information storage and visualization system using a Hadoop-distributed file system. [PERSON] et al. [42] designed a BIM database to store IFC component attributes, spatial relations, and geometric information, and their research mainly focused on the complete storage of IFC files. In addition to these methods, the concept of ifcOWL is also crucial in managing BIM data. ifcOWL converts the IFC schema into Web Ontology Language (OWL), enabling the application of Semantic Web technologies [43]. This transformation promotes the linked data approach, enhancing data interoperability and reducing redundancy. By using ifcOWL, data points within the BIM model can be more effectively connected, allowing for advanced querying and better cross-system data integration. However, IFC files usually contain complex and redundant information, which does not need to be stored entirely during the construction stages of steel structures. Moreover, redundant information burdens the system's performance. Therefore, the extraction and storage of IFC information for steel structural models need to be customized.
# Research and Conceptual Design of Sensor Fusion for Object Detection in Dense Smoke Environments
## 1 Introduction
### Principle of 2D Radial Position Measurement Using FMCW Radar
#### 2.3.2 Angle Measurement  
As shown in Figure 7b, the angle \(\theta\), called the angle of arrival (AoA) of the reflected signal in the horizontal plane, can be estimated using at least two RX antennas in the FMCW radar system. The differential distance (\(\Delta R\)) from the target object to each RX antenna mathematically causes a phase shift (\(\Delta\varphi\)) as  
\[\Delta\varphi=\frac{2\pi\Delta R}{\lambda} \tag{14}\]  
where \(\lambda\) is the wavelength of the FMCW radar. The phase shift can also be measured from the FFT peak of two received signals. Assuming the system is constructed with a basic planar wavefront geometry, and the distance between the RX antennas is \(L\), the differential distance (\(\Delta R\)) is related to the angle \(\theta\) as  
\[\Delta R=L\sin\theta \tag{15}\]  
Thus, the angle \(\theta\) can be estimated from the measured \(\Delta\varphi\), using  
\[\theta=\sin^{-1}\biggl{(}\frac{\lambda\Delta\varphi}{2\pi L}\biggr{)} \tag{16}\]  
If there are \(n\) number of multiple objects in front of the FMCW radar system, Equation (16) can be expressed as  
\[\theta_{i}=\sin^{-1}\biggl{(}\frac{\lambda\Delta\varphi_{i}}{2\pi L}\biggr{)},\ i=1,2,3,\ldots,n \tag{17}\]  
Note that due to the nonlinear dependence of \(\Delta\varphi\) and \(\sin(\theta)\), the angles that can be estimated with the two antennas are limited by the maximum viewing angle of  
\[\theta_{\max}=\sin^{-1}\biggl{(}\frac{\lambda}{2L}\biggr{)} \tag{18}\]
# A Comparison of Segmentation Methods for Semantic OctoMap Generation
## 3 Methods
### Segmentation Models Training
#### 3.3.4 Loss Function  
In order to mitigate the class imbalance present in the dataset, we employed a squared-root scaled median-frequency inverse weighting scheme. This method assigns a weight to each semantic class that is inversely proportional to its frequency of occurrence within the dataset. These weights are calculated using the formula:  
\[w_{i}=\frac{\sqrt{\frac{f_{\text{median}}}{f_{i}}}}{\sum_{j=1}^{C}\sqrt{\frac{ f_{\text{median}}}{f_{j}}}} \tag{1}\]  
where \(f_{i}\) is the pixel frequency of class \(i\), \(f_{\text{median}}\) is the median frequency across all classes, and the square root scaling moderates weight differences while maintaining relative class priorities.  
A composite loss function combining three components was implemented. All components are presented in Table 2. The Weighted Cross-Entropy Loss is fundamental for pixel-wise classification [40]. Dice Loss is particularly beneficial for small object classes and emphasizes overlap between predicted and ground truth regions [41]. Focal Loss is introduced to reduce the contribution of simple examples and focus training on hard-to-classify pixels [42].  
The total loss (\(L_{Total}\)) for the model's optimization was computed as:  
\[L_{Total}=L_{CE}+L_{Dice}+L_{Focal} \tag{2}\]  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Loss Component** & **Formula** & **Implementation** \\ \hline Weighted Cross-Entropy & \(L_{CE}=-\sum_{i=1}^{N}w_{i}y_{i}\)\(\log(\hat{y}_{i})\) & torch.nn.CrossEntropyLoss() \\ Dice Loss & \(L_{Dice}=1-\frac{2\sum_{k=1}^{N}|p_{k}\cap G_{k}|}{\sum_{i=1}^{N}(|p_{k}|+|G_{ k}|)}\) & \(\texttt{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{ \small{\small{\small{\small{\small{\small{\small{\small{\small{\cdot}}}_{{{\small }}_{{{\cdot}}}}}}}}}}}}}}}}\) \\ Focal Loss & \(L_{Focal}=-\alpha\left(1-\hat{y}_{i}\right)^{\gamma}\log(\hat{y}_{i})\) & \(\texttt{\small{\small{\small{\small{\small{\small{\small{\small{\small{ \small{\small{\small{\small{\small{\small{\small{\cdot{\cdot{\cdot}}}}}}}}}}}}}}}}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Loss function components.
## 3 The Hybrid Distributed Topology Control Scheme (HDTC)
### Phase I: Forming the Communication Group
#### 3.1.2 Initial Selection (Group 2): Proposed Time-Slot Method  
Based on the distance measurement and directional information of its neighboring vehicle \(w\), vehicle \(u\) can project whether, after \(t_{0}\) seconds, the neighboring vehicle \(w\) may be \(d_{0}\) meters away or not. If not, add the node \(w\) to Group 2; otherwise, it is excluded. Figure 3 (right) shows the distance between the vehicle \(u\) and the vehicle \(w\). The distance information at time \(t_{0}\) is applied to select the group members. Observe that the vehicle \(w\) may be away from \(u\), because it is faster than \(u\) (just like the black line), or as described in the red line, \(u\) is faster, so that \(u\) overtakes \(w\). In contrast, we prefer the vehicle \(w\) steadily close to vehicle \(u\), as depicted in the green line.
## 7 Conclusions  
This paper proposes a novel topology abstraction-based routing scheme, which can abstract the dynamic physical topology into a quasi-static abstracted topology for achieving a high success probability of requests. We present three heuristic algorithms based on the topology abstraction scheme, namely GF-TAR, LF-TAR, and MS-TAR. Simulation results indicate that our proposed algorithms have a better performance in terms of the success probability, the max traffic supported, the average number of relaying hops, and the total number of keys produced. The LF-TAR algorithm stands out in the hybrid GEO/LEO quantum satellite network scenario, which exhibits the best performance by collaborating GEOs with LEOs relying on the proposed routing scheme. In future work, we will conduct more research on a hybrid GEO/LEO quantum satellites network to facilitate their construction in the real world.  
**Author Contributions:** Conceptualization, M.G. and Y.C.; Methodology, M.G. and J.Z. ([PERSON]); Software, M.G., X.H. and X.Y.; Validation, M.G., X.Z. and C.Z.; Resources, Y.C., Y.Z., J.Z. ([PERSON])  
Figure 7: Total number of secret keys produced versus number of requests per minute on the (**a**) 6-plane Star topology, (**b**) 6-plane Delta topology, (**c**) 7-plane Star topology and (**d**) 7-plane Delta topology.  
and Q.W.; Writing--Original Draft Preparation, M.G.; Writing--Review and Editing, Y.C., X.Z., C.Z., X.H. and X.Y.; Supervision, Y.C. and Q.W.; Funding Acquisition, Y.C., Y.Z., J.Z. ([PERSON]) and Q.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (62201276, 62101285, 12104240, 12074194, 62021005), the Natural Science Foundation of Jiangsu Province (BK20192001, BE2022071, BK20210582), the Natural Science Research Project of Jiangsu Higher Education Institutions (21 KJB140014, 22 KJB510007), the Open Fund of IPOC (BUPT), and the Natural Science Research Start-Up Foundation of Recruiting Talents of NUPT (NY221114).  
Not applicable.
# Inverse Kinematics of Large Hydraulic Manipulator Arm Based on ASWO Optimized BP Neural Network
###### Abstract  
In order to solve the problem of insufficient end positioning accuracy due to factors such as gravity and material strength during the inverse solution process of a large hydraulic robotic arm, this paper proposes an inverse solution algorithm based on an adaptive spider wasp optimization (ASWO) optimized back propagation (BP) neural network. Firstly, the adaptability of the SWO algorithm is enhanced by analyzing the phase change in population fitness and dynamically adjusting the trade-off rate, crossover rate, and population size in real time. Then, the ASWO algorithm is used to optimize the initial weights and biases of the BP neural network, effectively addressing the problem of the BP neural network falling into local optima. Finally, a neural network mapping relationship between the actual position of the robotic arm's end-effector and the corresponding joint values is established to reduce the influence of forward kinematic errors on the accuracy of the inverse solution. Experimental results show that the average positioning error of the robotic arm in the XYZ direction is reduced from (91.3, 87.38, 117.31) mm to (18.16, 24.67, 27.21) mm, significantly improving positioning accuracy by 80.11%, 71.78%, and 76.81%, meeting project requirements.  
spider wasp optimization; BP neural network; inverse kinematics; error compensation; adaptive regulation +
Footnote †: journal: Applied sciences