esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Improved A* Algorithm
### Adaptive Step Size Adjustment Algorithm  
One of the crucial factors influencing mobile robots in the A* algorithm is the step size. As a result, our work suggests an adaptive step size adjustment mechanism. When there are many obstacles in the surrounding region, lowering the step size will increase the number of nodes evaluated each time and make the search path safer and more thorough. When there are fewer obstacles in the immediate vicinity, increasing the step size will speed up and improve the effectiveness of the search. The step size is automatically adjusted according to the distribution of obstacles in order to enhance the robot's adaptability. When step size-influencing aspects were considered, the distribution of obstacles in the surrounding environment was separated into two categories based on whether the obstacles were dynamic obstacles and the distribution of obstacles' quantity and placement within a certain range.  
Figure 3 shows the robot model with eight motion directions in lieu of the mobile robot, and the robot adopts an eight-neighborhood search. The graphic shows that there are d dynamic obstacles in the moving direction. The dark yellow section has \(x_{1}\) static obstacles, whereas the bright yellow area has \(x_{2}\) static obstacles. The closer the static obstacle is to the mobile robot, the higher the threat to the robot. The threat function \(f(x_{1},x_{2})\) is defined as:  
\[f(x_{1},x_{2})=\begin{cases}\frac{1}{k_{1}x_{1}+k_{2}x_{2}+c}&,d=0\\ 1&,d\
eq 0\end{cases} \tag{3}\]  
The formula uses c as the self-regulating constant and \(k_{1}\) and \(k_{2}\) as the static obstacle's danger factors, and the number of dynamic obstacles in the direction of motion is \(d\). Since \(x_{1}\) is more dangerous to the car, the value \(k_{1}\) set in this paper is twice that of \(k_{2}\), \(k_{1}\in(1,2)\), \(k_{2}\in(0.5,1)\), \(c\in(0,1)\). Then the adaptive adjustment steps are:  
\[l=\begin{cases}f(x_{1},x_{2})\cdot l_{max},d=0\\ f(x_{1},x_{2})\cdot l_{min},\ d\
eq 0\end{cases} \tag{4}\]  
where \(l_{min}\leq l\leq l_{max}\), \(l_{min}=0.1\) m, \(l_{max}=0.2\) m, and \(l\) is the safe distance between the mobile robot and the obstacle.  
The route planning outcomes employing the adaptive adjustment step size method and the conventional A* algorithm are shown in Figures 4 and 5, respectively. The variations in planning time, route node count, and planning time reduction rate are shown in Table 1,  
Figure 3: Threat weight graph.  
as well as the path node number reduction rate before and after the improvement of the method. The two figures demonstrate how the improved A* algorithm considerably lowers the number of search nodes and confirms the adaptive adjustment step size method's optimization impact.  
The performance metrics of the adaptive step size adjustment method and the conventional A* algorithm are contrasted in Table 1. By cutting the running time by 14.06% and the number of nodes by 34.78%, the improved approach increases operational efficiency.
## 3 Multi-Source Fusion Structure of Wi-Fi/Acoustic/MEMS Sensors  
This paper presents a hybrid fusion structure based on Wi-Fi/Acoustic/MEMS sensors for the indoor positioning system (H-IPS). The developed H-IPS considers the advantages of each location source in order to provide indoor location information with different precision coverage.
# Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by Copulas
## 6 Numerical Illustrations
### Example 3: Papillion River Basin Slope  
#### 6.3.1 Soil Profile  
To validate the application of the proposed method, a set of on-site observations of soil parameters in the Papillion River Basin slope, the United States, is adopted [39]. Figure 25 shows the scatters and corresponding empirical distributions of in-situ data from the Papillion River Basin of Brassica. The statistics are shown in Table 9. The density is assumed as a constant of 17 kN/m\({}^{3}\). The effect of the water table is ignored here. To model the Papillion River Basin slope with spatially variable \(c\) and \(\phi\), the same simplified slope geometry as that in Section 6 is used here.  
Figure 24: Average errors of the statistics with respect to the three clay layers deduced by different strategies.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Copulas** & **Soil Layers** & \(\mu_{c}\) & **COV\({}_{c}\)** & \(\mu_{\phi}\) & **COV\({}_{\phi}\)** & \(\rho\) \\ \hline \multirow{3}{*}{Strategy 1} & Layer 2 & 57.8521 & 0.3629 & 4.8705 & 0.2021 & \(-\)0.5091 \\  & Layer 3 & 41.5108 & 0.1901 & 8.6453 & 0.2109 & \(-\)0.4383 \\  & Layer 4 & 59.4741 & 0.1931 & 15.3436 & 0.2321 & \(-\)0.5080 \\ \hline \multirow{3}{*}{Strategy 2} & Layer 2 & 57.8521 & 0.3629 & 4.8747 & 0.2073 & \(-\)0.4639 \\  & Layer 3 & 41.5108 & 0.1901 & 8.6081 & 0.2112 & \(-\)0.5181 \\  & Layer 4 & 59.4741 & 0.1931 & 15.0663 & 0.2375 & \(-\)0.5602 \\ \hline \multirow{3}{*}{Strategy 3} & Layer 2 & 57.8521 & 0.3629 & 4.9316 & 0.1971 & \(-\)0.0244 \\  & Layer 3 & 41.5108 & 0.1901 & 8.6091 & 0.2115 & \(-\)0.4735 \\  & Layer 4 & 59.4741 & 0.1931 & 15.0663 & 0.2375 & \(-\)0.5602 \\ \hline \multirow{3}{*}{Strategy 4} & Layer 2 & 57.8521 & 0.3629 & 4.9316 & 0.1971 & \(-\)0.0244 \\  & Layer 3 & 41.5108 & 0.1901 & 8.7082 & 0.2040 & \(-\)0.0098 \\  & Layer 4 & 59.4741 & 0.1931 & 16.1749 & 0.2279 & 0.0080 \\ \hline \multirow{3}{*}{Strategy 5} & Layer 2 & 58.0809 & 0.3468 & 4.8671 & 0.2055 & \(-\)0.5254 \\  & Layer 3 & 41.4723 & 0.1970 & 8.4923 & 0.1738 & \(-\)0.4457 \\  & Layer 4 & 59.4304 & 0.1885 & 15.3504 & 0.2365 & \(-\)0.5116 \\ \hline \multirow{3}{*}{Strategy 6} & Layer 2 & 57.8589 & 0.3628 & 4.8763 & 0.1994 & \(-\)0.4954 \\  & Layer 3 & 41.5646 & 0.1848 & 8.7449 & 0.2400 & \(-\)0.4179 \\  & Layer 4 & 59.4177 & 0.1955 & 15.3251 & 0.2299 & \(-\)0.4889 \\ \hline \multirow{3}{*}{Strategy 7} & Layer 2 & 57.9215 & 0.3550 & 4.8668 & 0.2071 & \(-\)0.5243 \\  & Layer 3 & 41.4660 & 0.2018 & 8.3971 & 0.1535 & \(-\)0.4396 \\ \cline{1-1}  & Layer 4 & 59.3558 & 0.1849 & 15.3588 & 0.2365 & \(-\)0.5031 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Statistics of CCRF variables associated with different strategies.
## 1 Introduction  
In response to the increasing demand for solar cells as the source of renewable energy, photovoltaic solar panels based on perovskite semiconductors have been gaining wide attention [1; 2; 3; 4; 5]. Perovskite is a calcium titanium oxide mineral, discovered in the Ural Mountains of Russia, that exhibited superior transmission of solar energy compared to silicon or selenium, as high as 27% [6; 7; 8]. However, the instability of lead within perovskite minerals has introduced new environmental concerns, as temperature or humidity fluctuations can cause toxic lead-based substances to leak into the environment [9; 10; 11; 12; 13].  
For applications in semiconductors for solar cells, perovskites require strong electronic properties which can be understood from their band structures and bandgap values. Although cubic perovskites possess bandgaps that are suitable for many applications [14; 15], the bandgap values are often inadequate for solar cells, and may poorly bond with other components during use. To resolve issues in stability and transmission efficiency, researchers have looked towards halide perovskites, hybrid perovskites, and double perovskites, yet the presence of lead or other toxic components generates environmental concerns [16; 17; 18; 19; 20; 21; 22]. Thus, the search for lead free, nontoxic, and inorganic perovskite materials is an urgent and ongoing research field [23; 24].  
We herein present a new approach to the composition of lead-free perovskites via mixing of halide and oxide perovskites, enabling tuning of structural stability and bandgap values to design effective photovoltaic materials [25]. Specifically, we mixed, combined, stretched, and compressed various cubic perovskites symmetrically to modify the structural and electronic properties, furthering our understanding of mixed perovskite compounds and improving transmission efficiency [26; 27]. To form the mixed perovskite compound, we mixed halide or oxide perovskites that share the same ABX3 structure, where A is a cation with different groups of valence electron configurations, B is a cation chosen from transition metals, and X is either a halide or an oxide [28]. In particular, we selected CsSnCl\({}_{3}\), CsSnI\({}_{3}\), SrRhO\({}_{3}\), and SrTiO\({}_{3}\) as exceptional halide or oxide perovskites as they exhibit similar electronic properties. Moreover, halide perovskites can bond strongly to other perovskites within thin films [29]. However, this combination approach provided challenges as perfect cubic symmetry is rather uncommon [30; 31; 32; 33], while the actual bandgap produced by a single cubic perovskite is not theoretically applicable. Lastly, we constructed the mixed perovskite compounds in diverse arrangements and orders to tune the computational bandgap to values ideal for solar cell applications.
# Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines
## 5 Experiment and Evaluation
### Performance of the Proposed Method
#### 5.2.7 The Recognition of Transmission Line Icing  
At this stage, the contours of the transmission line icing area are identified. The top and bottom lines of the iced transmission line were determined in advance for further analysis of the binocular vision scheme using 3D measurements. Thus, labeling is needed to recognize independent objects. In this study, connected component labeling is used to distinguish between upper and lower boundary areas, or right and left. Figure 26 shows the edge-extracted images and the images identified at the top and bottom of the line icing.
# A Study on the Design of Fog Computing Architecture Using Sensor Networks
## 4 Experiment and Analysis  
### Performance of Proposed Fog Computing  
The fog network was tested by the iFogSim Toolkit. iFogSim provides functions to simulate all network nodes and print the simulation results. For comparison of all fog computing data, the cloud computing and the computing system adopting fog layer were compared. The number of sensors was total 50, 100, 150 or 200. A proxy server was configured, combining four segments. The CPU of each sensor was 1.0 GHz. The size of data to be transferred was set to 20 KB. The average transmission time between network devices was 5 ms.  
Compared to the event that uses cloud computing only, it is shown that the system using the fog layer has reduced waiting time and network usage, which is caused by communication control between a fog computer and sensors. Figure 13 demonstrates comparison of energy consumption at an end point, an edge device and a data center between cases when only cloud is used or fog calculation is used. Figure 13 also indicates power consumption of fog computing architecture that uses a cloud. For the fog layer, power was mainly consumed at sensors that perform most processing works while a data center or a cloud consumed most energy in the cloud computing.
# Computational Analysis of PDE-Based Shape Analysis Models by Exploring the Damped Wave Equation
## 2 Theoretical Background
### PDE-Based Models
#### 2.3.2 Initial Conditions  
Both considered PDEs need to be supplemented by an initial function and an initial velocity to solve them properly. In all our scenarios, we will set the initial velocity \(\partial_{t}u(x,0)=0\). Let us now comment on the chosen initial functions.  
First, we use the Dirac delta function  
\[u(x,0)=u^{\delta}(x)=\delta_{x_{i}}(x)=\begin{cases}1,&x=x_{i}\\ 0,&x\
eq x_{i}\end{cases} \tag{8}\]  
centred at the point \(x_{i}\in\mathcal{M}\); cf. [4] for use of this function in the context of feature computation with PDE.  
The second initial function we use is a highly peaked Gaussian distribution  
\[u(x,0)=u^{G}(x)=\exp\Biggl{(}-\frac{1}{2}\frac{d_{\mathcal{M}}^{2}(x,x_{i})}{ \sigma^{2}}\Biggr{)} \tag{9}\]  
around the centre \(x_{i}\in\mathcal{M}\) and with \(\sigma^{2}\) as the width parameter. Letting \(\sigma\) converge towards zero would lead to the Delta distribution:  
\[\lim_{\sigma\to 0}\exp\Biggl{(}-\frac{1}{2}\frac{d_{\mathcal{M}}^{2}(x,x_{i})}{ \sigma^{2}}\Biggr{)}=\delta_{x_{i}}(x) \tag{10}\]
## 4 The Dual-Radio Interface Routing Protocol for LLNs
### Provisions for Multi-Interfaced Operation  
To prevent ambiguity, the most basic provisions for DRiPLOF are:  
* Physical nodes, not their interfaces, are the RPL neighbors.
* Physical nodes are the addressable entities, meaning: (1) all IPv6 addresses are node-scope; and (2) each node has one link-layer address, common to all interfaces.
* Each node may have at most one interface of a given type, allowing it to uniquely identify from which of its neighbor's interfaces a packet originated.
* Each node owns the same set of interfaces or is otherwise aware of the interfaces it should possess (through a mechanism that is out of this paper's scope).  
IPv6 ND is optional. The provisions for its use are laid out in Appendix A. If it is not used, some other mechanism is required to keep inferred metrics updated (Appendix B). In any case, RPL control traffic should also accommodate optimal multi-interfaced operation. Especially if ND is not supported, a proper rule-set is required to keep interface metrics updated with minimal overhead. As such, we propose to use the following rules for RPL management traffic (adapted from [PERSON] et al. [26]):  
* Broadcast DIOs must be sent on all interfaces.
* Unicast DIOs, when sent in response to a DIS, must be sent on the same interface as the incoming DIS.
* Unicast DIOs used for freshness probing (Appendix B) must be sent on all interfaces.
* Otherwise, unicast DIOs must be sent via the preferred interface towards a neighbor.
* Unicast DAOs must be sent to DAO parents via the preferred interface towards those parents.
* DAO Acknowledgments (ACKs), which are always unicast in response to a unicast DAO, must be sent on the same interface as the incoming DAO.
* Broadcast DISs must be sent on all interfaces.
* Unicast DISs used for freshness probing (Appendix B) must be sent on all interfaces.
* Otherwise, unicast DISs must be sent via the preferred interface towards a neighbor.  
Other RPL control messages are not considered (for now). RPL control messages sent over multiple interfaces are not duplicates, i.e., they have different MAC sequence numbers [20] (p. 100). Note that, there is often only one DAO parent, which is also the preferred parent as determined by the OF.
# Detection of Volatile Organic Compounds (VOCs) in Livestock Houses Based on Electronic Nose
###### Abstract
To analyze the characteristics of VOCs in livestock farms comprehensively and quickly, first, GC-MS was used to obtain the types and relative contents of VOCs in nursery piggeries and chicken houses at different time and locations. At the same time, the developed electronic nose system detected the target gas. The determination of relative content will provide support and verification for the electronic nose test. Then, according to the response of the sensor array of the electronic nose system, three pattern recognition methods--linear discriminant analysis (LDA), K nearest neighbor (KNN), and support vector machine (SVM)--were used to identify and classify the gas samples. Finally, the distribution and content of volatile substances were further analyzed combined with the results of GC-MS analysis, which provided theoretical and technical support for the characterization of livestock farm odor.
# A Source Anonymity-Based Lightweight Secure AODV Protocol for Fog-Based MANET
## 3 Preliminary Knowledge and Analysis
### Aodv
#### 3.1.1 Route Discovery  
In the mobile ad hoc networks (MANET), when the source node must send a packet to the destination node, but there is no valid routing message about the destination node in its routing table, the source node starts a route discovery process to establish a route towards the destination node. Therefore, the source node sends its neighbors a route request message (_RREQ_). The neighbor nodes receive the request and forward it to their neighbors, so that the _RREQ_s are broadcasted using the flooding approach. Each node in MANET maintains two counters independently: the broadcast ID counter and the node sequence number counter. The _RREQ_ message contains the broadcast ID and the source node address, and a _RREQ_ message is thus uniquely identified.  
A neighbor node receiving a _RREQ_ message has three potential roles: (1) the neighbor node is the destination node. In this case, the _RREQ_ message reaches the destination node and the neighbor node reacts with a route reply (_RREP_) message. Then, the _RREP_ message is sent as a unicast towards the source node, using the path established by the \(RREQ\). This process is similar to what happens with \(RREQ\); (2) the neighbor node is not the destination node, but the neighbor node knows a route towards the destination. In this case, the neighbor node generates a \(RREP\) message and sends it to the source node, but the source node will deliver the \(RREQ\) message to the destination node if needed; (3) the source node is not the destination node and has no valid route to the destination node. The source node increases the hop count and forwards the message to its neighbors. It should be noted that a node may receive multiple \(RREQ\) messages with the same IP address and broadcast ID. Thus, when a node receives the \(RREQ\) message, it first checks whether the \(RREQ\) message has previously been received, and discards the \(RREQ\) message if it has. If a node is not the destination node, it has to take part of the \(RREQ\) message to set the reverse routing information, so that the other nodes can identify a route (the \"reverse route\") toward the source node. If a node is not the destination node, it has to track part of the \(RREQ\) message to set the reverse routing information, so that the other nodes can identify a route (the \"reverse route\") toward the source node.
## 4 Discussion
### Implementation of Different Assessment Approaches into the WFD  
While former studies gave first insights into the potential usage of ESA for the implementation of water quality improvement measures and their use within the integrated coastal management processes [25; 39; 48], they lacked concrete information about the application within the WFD implementation cycle itself. Based on our results and the experience we created, we see the following possible applications of ESA and PM in the selection and implementation of possible measures as exemplary for the German WFD implementation cycle (Figure 5).  
Depending on the different pathways, the selection of measures (1), or the implementation of measures (2) (Figure 5), we propose different assessment approaches. During the selection of measures, we suggest an expert-based ESA. In Germany, the selection of measures is organized on a federal level. Therefore, experts which have a profound understanding of the concerned aquatic systems and the potential effects of the measures are needed. The position is different when it comes to the implementation of sea-based measures and the participation of governmental, non-governmental, industry, or other local stakeholders. During the screening process, a stakeholder-based PM approach could be used to identify potential application and conflict areas, as well as the general acceptance of different potential sea-based measures. In this context, the simplified ESA could help to identify a stakeholder's perception of sea-based measures and to identify misunderstandings. Furthermore, the holistic view on this measure can also visualize other advantages aside from nutrient reduction (e.g., benefits for biodiversity or economic benefits).  
Public participation is not only a key element and a major challenge in the implementation of WFD in coastal waters but also in the other target water resources of the WFD [53]. [PERSON] et al. 2017 [54], for example, showed that the engagement of local stakeholders during the planning of a river basin management plan resulted in better river  
Figure 5: Flow chart of the planning and implementation process of the WFD in Germany and indication where ESA and a combination of PA and ESA could be beneficial to accelerating the selection and implementation of new mitigation measures.  
basin management plan than without local stakeholders. Therefore, an adapted ESA as well as a PM can potentially be beneficial for the implementation and planning process for the other water resources targeted by the WFD.
## 2 Methodology
### Synergistic Mechanism Analysis  
The YOLO-SUMAS model proposed in this paper constructs a closed-loop framework of \"feature enhancement--lightweight inference--multi-scale fusion--loss optimization\" through the collaborative optimization of multiple modules (Figure 1), and all the modules complement each other:  
1. SCSA dynamically focuses the local details (such as missing solder joints) and global semantics (such as short circuit areas) of PCB defects through spatial-channel co-attention. Its progressive channel compression strategy reduces redundant computations and provides highly discriminative features for subsequent modules. As well, the optimized features of SCSA output are lightweight processed by the UIB module of MobileNetV4 (Figure 3) to reduce the number of parameters while retaining key information.  
Figure 6: The overall architecture of the U-Net v2 model consists of the encoder, the SDI (semantic and detail injection) module, and the decoder.  
2. The UIB module of MobileNetV4 combines with the inverted bottleneck structure to achieve efficient multi-scale feature extraction on the mobile terminal. Its MobileMQA attention reduces the amount of computation through spatial downsampling, and forms a two-stage optimization of \"coarse sift-fining\" with SCSA. In addition, the features of MobileNetV4 after lightweight are input into ASF-SDI Neck, and the small target details are enhanced by weighted bidirectional pyramid to avoid information loss caused by lightweight.
3. Through the semantic-detail injection module, ASF-SDI fuses high-level semantics (such as defect categories) with low-level geometric features (such as edge shapes) to solve the problem of large differences in PCB defect sizes. In addition, after multi-scale feature optimization, ASF-SDI and Unified-IoU refine the localization by dynamic bounding box scaling to reduce missed detection of overlapping defects.
4. Unified-IoU introduces a bidirectional weight allocation strategy, which imposes higher loss weights on low confidence prediction boxes (difficult samples), and uses cosine decay to balance training speed and accuracy. The loss optimization results of Unified-IoU reverse guide the feature extraction priorities of SCSA and MobileNetV4, forming an end-to-end optimization and closed-loop feedback to the overall framework.