esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modelling and Parameterisation
### Multiscale Geographically Weighted Regression (MGWR) Model
#### Gwr  
According to the first law of geography: everything is spatially related, and closer things are more related. When analysing each sample, it is necessary to include samples within a certain spatial range around it [17]. In GWR regression analysis, the fixed bandwidth is mainly used to limit the scope of the regression model analysis.  
The GWR model, as shown in Figure 1, builds on the OLS model by considering the spatial heterogeneity of ridership distribution at urban rail transit stations. The regression coefficients for each station vary due to geographical differences, and the model expression is as shown in Equation (2) below:  
\[y_{i}=\beta_{0}(u_{i},v_{i})+\sum_{t=1}^{m}\beta_{it}(u_{i},v_{i})x_{it}+ \epsilon_{i} \tag{2}\]  
where (\(u_{i}\),\(v_{i}\)) are the latitude and longitude coordinates of site \(i\), \(\beta_{ij}(u_{i},v_{i})\) represents the regression coefficient for the \(j\)-th independent variable at station i. It can be seen that, when the regression coefficients for the same independent variable are equal, i.e., \(\beta_{1j}=\beta_{2j}=\beta_{3j}=\cdots=\beta_{nj}\), the GWR model can be transformed into the OLS model. The GWR model is a manifestation of the spatial heterogeneity of the OLS model.  
The first law of geography states that everything is spatially related, with closer things being more related. When analysing each sample, it is necessary to include samples within a certain spatial range around it [18]. In GWR regression analysis, the fixed bandwidth is mainly used to limit the scope of the regression model analysis.
# Hierarchical Concept Learning by Fuzzy Semantic Cells
## 3 Method
### Hierarchical Structure of Concept Leaning  
When people describe an abstract concept corresponding to a specific object, they may use one or more familiar prototypes in the explanation. These prototypes are highly relevant, and the boundaries between them are somewhat vague. Within a certain range, each prototype can explain the meaning of the object. Then, starting with these prototypes, we can achieve a higher level of abstraction to determine the concept's meaning. We may only need one prototype for relatively clear and simple data; however, some more ambiguous and broad concepts may necessitate multiple prototypes to cover all interpretable rangesadequately. Considering account the ambiguous nature of abstract concepts, we develop a hierarchical concept learning model based on the fuzzy semantic cell. As shown in Figure 2, the hierarchical structure is composed of three layers. The bottom is the data layer, and the middle is the fuzzy semantic cell layer, the top is the conceptual layer. The membership degrees \(\mu\) characterize the relationship between the samples in the data layer and the fuzzy semantic cells in the middle layer. In particular, \(\mu_{L_{i}}(x)\) indicates how well the semantic cell \(L_{i}\) can interpret the sample \(x\). Conversely, each fuzzy semantic cell can explain a range of different radii, covering a different number of samples in the data layer. Each concept is partitioned at the concept level by one or more FSCs. Because of the uncertainty and overlap of the boundaries between FSCs, this partition is ambiguous.  
Formally, suppose we have a dataset \(DB\subset\Omega\) with category information. This category information is a series of the abstract concepts. The \(DB\) can be partitioned into \(K\) subsets by category information:  
\[DB=\{x\in D_{j}\mid j=1,\ldots,K\}\]  
where \(K\) is the number of categories, and we mark this partition way as \(S\). In other words, \(D_{j}\) is a subset of \(DB\), and all of the elements \(x\) in \(D_{j}\) belong to the same category.  
At the same time, assume that \(\Omega\) can also be partitioned into \(LA=\{L_{1},\ldots,L_{M}\}\), where \(L_{i}=(P_{i},d,\delta_{i})\) is a fuzzy semantic cell with a prototype \(P_{i}\in\Omega\), a density function \(\delta_{i}\) defined on \([0,+\infty)\), and a distance metric \(d\) on \(\Omega\). Then, we will obtain the appropriate fuzzy semantic cell partition using the method described in Section 3.3.
## 4 Proposed Techniques
### Dummy Uniform Distribution  
Injecting a dummy packet in every interval for each node during the lifetime of the network is going to consume a large amount of power and resources. Therefore, the motivation behind DUD is to have the same transmitting constant rate for both real and fake traffic. It works as follows: Each node will throw a random number (\(num_{random}\)) between 0 and 1. If the number is smaller than the constant rate i.e., \(num_{random}<rate_{constant}\), send the real packet, but if there is no real packet in the node's buffer, send a dummy packet instead. By applying this technique, the adversary cannot recognize if the transmitted packet is real or a dummy. Real packets are only created if a real event is detected, and then the node that detected the real event uses the selected transmission rate, for example 0.1, to try to send the real packet every interval. For instance, if a real event was detected between interval 5 and 6, the node will throw a random number between 0 and 1. If this number was less than the selected threshold, which is in our case 0.1, the real packet will be transmitted in interval 6. However, if the thrown number was larger than the threshold, the real packet will not be transmitted, and the node will try to send it in the following interval (interval 7). This process is repeated until all real packets are sent. Dummy packets are only generated if the node does not have a real event. In addition, dummy packets are sent based on a probability. If the thrown number was less than the threshold, a dummy packet is generated and transmitted by the node. Otherwise, the node will not transmit any packets. However, this scheme does not always guarantee the arrival of events' packets, because the real packets can take a long time to be delivered, or if there is a specific maximum delay requirement for the application. It is obvious that increasing the transmission rate reduces the delay and increases the overhead and, vice versa.
# Topology Duration Optimization for UAV Swarm Network under the System Performance Constraint
## 4 The Proposed GTSC Method  
The clustering based topology construction method is well suitable for the UAV swarm networking applications, especially in the large scale swarm scenarios where the number of nodes \(N\) is large [11]. In this paper, a clustering scheme is employed for solving the model in Equations (13)-(19). The construction process of swarm topology is divided into three steps, i.e., clustering, cluster heads selection and topology generation.The flowchart of the proposed GTSC method is presented in Figure 3.
# Molecular Imaging with \({}^{68}\)Ga Radio-Nanomaterials: Shedding Light on Nanoparticles
## 4 Biomedical Applications of \({}^{68}\)Ga-Labeled Nanoparticles
### Other Nanoparticles
Fifteen minutes post-injection, one can mainly observe liver, spleen and heart accumulation. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Nanomaterial** & **Radialabeling Method** & **Imaging Modalities** & **Biomedical Application** & **Reference** \\ \hline Fe\({}_{3}\)O\({}_{4}\)-PEG & Surface labeling, Chehatter free & PET/MRI & Biodistribution studies & [101] \\ \(\gamma\)-Fe\({}_{3}\)O\({}_{4}\)-FLAG-PEG & Surface labeling, Chehatter approach (NDOAGA) & PET/MRI & Biodistribution studies & [98] \\ Fe\({}_{3}\)O\({}_{4}\)-O\({}_{5}\) & Surface labeling, Chehatter approach (DPD) & PET/MRI & Biodistribution studies & [105] \\ \(\gamma\)-Fe\({}_{3}\)O\({}_{4}\)-FLFLFL & Core doping & PET/MRI & Neutrophil investigation in lung & [73] \\ \(\gamma\)-Fe\({}_{3}\)O\({}_{4}\)-FLFLFLFL & Surface labeling, Chehatter approach (DOTA) & PET/MRI & & [73] \\ Fe\({}_{3}\)O\({}_{4}\)-FLFLFLFL & Core doping & PET/MRI & Prostate tumor imaging & [77] \\ Fe\({}_{3}\)O\({}_{4}\)-FLFLFLFLFL & Core doping & PET/MRI & \(\alpha\), \(\beta_{5}\) integrin expression imaging in melanoma & [72] \\ Fe\({}_{
# Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories
###### Abstract  
Terrestrial laser scanning (TLS) has been successfully used for three-dimensional (3D) data capture in forests for almost two decades. Beyond the plot-based data capturing capabilities of TLS, vehicle-based mobile laser scanning (MLS) systems have the clear advantage of fast and precise corridor-like 3D data capture, thus providing a much larger coverage within shorter acquisition time. This paper compares and discusses advantages and disadvantages of multi-temporal MLS data acquisition compared to established TLS data recording schemes. In this pilot study on integrated TLS and MLS data processing in a forest, it could be shown that existing TLS data evaluation routines can be used for MLS data processing. Methods of automatic laser scanner data processing for forest inventory parameter determination and quantitative structure model (QSM) generation were tested in two sample plots using data from both scanning methods and from different seasons. TLS in a multi-scan configuration delivers very high-density 3D point clouds, which form a valuable basis for generating high-quality QSMs. The pilot study shows that MLS is able to provide high-quality data for an equivalent determination of relevant forest inventory parameters compared to TLS. Parameters such as tree position, diameter at breast height (DBH) or tree height can be determined from MLS data with an accuracy similar to the accuracy of the parameter derived from TLS data. Results for instance in DBH determination by cylinder fitting yielded a standard deviation of 1.1 cm for trees in TLS data and 3.7 cm in MLS data. However, the resolution of MLS scans was found insufficient for successful QSM generation. The registration of MLS data in forests furthermore requires additional effort in considering effects caused by poor GNSS signal.  
forest inventory parameters; quantitative structure models; wood volume; diameter at breast height; crown projection area +
Footnote †: journal: Physics Letters B
## 3 Simulation Analysis
### Finite Element Model of Mirror Assembly and Modal Analysis  
Modal analysis helps to understand the mirror's structural characteristics by solving the natural frequency of the mirror assembly and its model shape. It is possible to intuitively understand the vibration form of the primary mirror and its supporting structure, which is the basis for analyzing the dynamic characteristics of the structure. This paper uses finite element software programs Hypermesh (Altair, Troy MI, USA) and MSC Patran (MSC Software, Los Angeles CA, USA) to mesh the mirror component structure model, set boundary conditions, and assign element attributes. The mirrored finite element model is shown in Figure 4, with 38,508 elements and 53,797 nodes.  
Modal analysis of the finite element model of the primary mirror is performed using MSC/NASTRAN (MSC Software, Los Angeles CA, USA). To simulate the excitation of the mirror assembly by the shaker in the mechanical vibration test, the large-mass point element method is used for analysis. The large-mass simulation method is used to simulate the shaker in the vibration test. In an on-ground aerospace vibration test, the shaker is excited by an electromagnetic coil and vibrates in only one DOF, constrained by a hydrostatic slide guide. This is simulated in MSC.Patran (MSC Software, Los Angeles CA, USA) by selecting such boundary conditions. To ensure the accuracy of the calculation, the large mass point is \(10^{3}\)-\(10^{8}\) times the structural mass. When the large mass point is set to \(10^{3}\) times the mass of the component, applying acceleration on the mass point cannot fully excite the structural response of the mirror body and a rigid body mode occurs. However, when the large mass point is set to \(10^{4}\) times the mass of the component, it can not only stimulate the structural response of the mirror body but also better imitate the excitation of the shaker to the mirror body holder. Therefore, a quality point with a quality \(10^{4}\) times the mass of the mirror body is established. The mass point and the reflector assembly are constrained in the form of  
Figure 4: Finite element model of the primary mirror assembly.  
MPC. Considering the rigid connection between the mirror body and the shaker, the RBE2 MPC is used to connect the large mass point unit to the node of the main mirror substrate mechanical installation interface. During the test, the connection relationship between the shaker and the mirror assembly remains unchanged and there is no relative movement between the two. Therefore, the boundary condition is constrained, that is, the large mass point is constrained with 6 degrees of freedom and the first 10 natural frequencies of the mirror are calculated. Figure 5 shows the first six modal shapes of the mirror assembly.  
According to the modal analysis, the stiffness of the main mirror assembly is large and the first mode is 204.35 Hz, which can avoid resonance with other structures of the star in the process of carrier reflection.
## 1 Introduction
### Application and Objective  
The development of Wireless Underground Sensor Networks (WUSNs) is a recent research axis based on sensor nodes buried at a few dozen centimeters deep to not impact the aboveground activities [19; 20; 21; 22], with as main applications the environmental monitoring and precision agriculture [23; 24; 25]. The radio communication ranges of the buried sensor nodes are however limited due to the high attenuation of the electromagnetic waves in soil. Our previous work highlighted that the communication ranges of radio devices based on LoRa technology can vary from a few meters to several hundred meters with respect to various parameters (e.g., burial depth, soil moisture, transmit power, frequency--see [26]. The idea consists to use an UAV to collect the data: that approach has the advantage to avoid the installation of numerous and costly gateway in the environment, and offer the possibility to rapidly and safely cover wide areas. To that end, the trajectory of the UAV has to be determined, entering successively within the communication ranges of each node to perform the data collection task with the minimal route length.  
The objective of this paper is to propose a trajectory planning algorithm enabling an UAV to successively and optimally pass through several circular areas of different radius, representing the radio communication ranges of the buried sensor nodes, see the illustration in Figure 1 and the problem statement in Figure 2.  
Figure 1: The objective is to optimally plan the trajectory of an UAV having to collect the data through radio communication of several buried sensor nodes distributed in the environment.  
It is assumed that the UAV flies at a constant altitude h, leading to targets in the form of circular areas of radius r. The UAV does not need to pass at the center of the circles but only within the disks to receive the data. The circles can intersect, offering the possibility to position the UAV at the intersections. We can also mention that it is assumed that the flying height is smaller than the smallest communication range of the nodes to access to all the data. That application necessitates clearly to address the Close Enough TSP.
## 4 Results
Essentially, the benefits of improving SR through additional APs come at the cost of increased energy expenditure, particularly due to the fixed power consumption of the backhaul infrastructure. A crucial implication of  
Figure 8: SEE performance as a function of the maximum AP transmit power for different optimization techniques. Figure 7: SEE progression across training epochs for different algorithms. MOIBBO-CNN–LSTM achieves the highest SEE with rapid convergence, while LSTM shows the slowest improvement. these findings is the need for intelligent AP management strategies to mitigate power inefficiencies. One potential solution is the dynamic selection of active APs, where redundant APs are deactivated during periods of lower traffic demand. By strategically putting under-utilized APs into sleep mode during off-peak hours, the fixed circuit power consumption can be significantly reduced, leading to improved energy efficiency without compromising system performance. This adaptive AP selection will be explored in our future research. Among the tested models, the proposed MOIBBO-CNN-LSTM consistently outperforms the baseline methods, achieving the highest SEE across all AP configurations. This further highlights the effectiveness of our approach in balancing SR improvements with energy efficiency, making it a compelling solution for optimizing large-scale CF-mMIMO networks.
# A Novel Signal Design and Performance Analysis in NavCom Based on LEO Constellation
## 4 Performance Evaluation
### Navigation Performance Analysis
These results are only inferior to the other spreading-modulated signals in terms of the tracking and narrowband merit factor and are excellent for the other three indicators. Among the other spreading-modulated signals listed, especially in terms of the demodulation and matching spectrum merit factor and the demodulation and narrowband merit factor, our scheme is significantly ahead of other spread spectrum-modulated signals.
## 4 Results and Discussions
### Applications in Occupational Health and Safety in Manufacturing
#### 4.2.2 Safety Simulations and Incident Management
In addition, AR facilitates step-by-step guidance for complex procedures and visualizes safety measures in shared workspaces to prevent collisions and improve maintenance efficiency. In the context of Industry 5.0, immersive technologies are integrated with AI and smart surveillance systems to enable intelligent factory safety management, including real-time hazard monitoring and PPE compliance alerts when workers enter hazardous areas. Collectively, these tools contribute to enhanced emergency preparedness, real-time operational guidance, error prevention, and reduced risk of accidents and injuries, while also supporting collaborative and adaptive safety strategies in modern industrial environments. Finally, sensory devices and human-machine interfaces play critical roles in providing real-time data inputs. These systems continuously monitor environmental threats and worker conditions, trigger immediate alerts, and support data-driven safety interventions. For instance, smart sensors and devices like wearables and AR devices aid in measuring KPIs, tracking objects and personnel, and monitoring bio-sensory data for improved ergonomics and OHS [15]. Ensuring a safe, healthy, and effective future workplace relies on integrating sensory devices into the work environment and processing data usingAI [16]. Likewise, virtual simulations and real-time data from wearable safety gear can aid decision-making for operator movement and emergency evacuations when chemical concentrations exceed the safe exposure limits [18]. Together, these digital technologies form a comprehensive and highly responsive safety ecosystem for modern manufacturing and Industry 5.0 settings.
# Entropy-Based Anomaly Detection for Gaussian Mixture Modeling
## 2 Materials and Methods
### Gaussian Mixtures in Model-Based Clustering and Density Estimation
For an observation \(\mathbf{x}_{i}\), the posterior conditional probability of it coming from the mixture component \(k\) is given by  
\[\widehat{z}_{ik}=\frac{\widehat{\pi}_{k}\mathbf{\phi}(\mathbf{x}_{i};\widehat{\mathbf{\mu }}_{k},\widehat{\mathbf{\Sigma}}_{k})}{\sum_{g=1}^{G}\widehat{\pi}_{g}\mathbf{\phi}( \mathbf{x};\widehat{\mathbf{\mu}}_{g},\widehat{\mathbf{\Sigma}}_{g})}. \tag{4}\]  
Then, the observation is assigned to the mixture component with the largest posterior conditional probability, i.e., \(\mathbf{x}_{i}\in\mathcal{C}_{k^{*}}\) with \(k^{*}=\arg\max_{k}\widehat{z}_{ik}\).
# The Sun and Space Weather
## 6 Solar Eruptions and SEPs
In Table 2, the last three events are observed by STEREO Behind (STB), which is better connected to the solar source, and hence the SEP intensity (Ip) is higher by 1-2 orders of magnitude. Figure 15: # means Number in Figure 15a,b. Same as Figure 13, but for CMEs associated with large SEP events: (**a**) speed, (**b**) width, and (**c**) source locations Three intensity levels of \(>\)10 MeV protons are distinguished by the size of the data points in (**c**). SEPs have several space weather consequences in the heliosphere. Interplanetary spacecraft and satellites in Earth's orbit can be directly affected by SEPs. The powerful eruption on 21 April 2002 (see Figure 16) involved a major flare and an ultrafast (\(\sim\)2400 km/s) CME that resulted in a very intense and hard-spectrum SEP event [157]. Earth was well-connected to the eruption source and hence was immersed in the particle radiation for several days. The Nozomi spacecraft, on its journey to Mars, was in the vicinity of Earth around this time and hence was impacted by the particle radiation. Nozomi's communications and power systems were damaged, causing the hydrazine to freeze in the spacecraft's attitude control system [158]. This led to a series of issues that ended the mission in December 2003, without reaching Mars. Another well-known loss to particle radiation is the Martian Radiation Environment Experiment (MARIE) on the Mars Odyssey mission. MARIE was a dedicated energetic charged particle spectrometer intended to make measurements of the particle radiation levels on the way to Mars, and in orbit it was intended to aid designers of future missions involving human explorers. When the Halloween SEP event on 28 October 2003 started, Mars Odyssey went into a safe mode. When the spacecraft came out of the safe mode, MARIE was found non-responsive, and all attempts to revive the instruments were unsuccessful; consequently, the instrument was abandoned [159].
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 4 Our Proposed Slice-Based QoS Manager for IoT
### Data Plane Networking
#### 4.4.1 LoRa Physical Interface  
In this work, the radio communication system components are not dealt with through software implementations. As seen in the Host system architecture (Figure 7), it is clear that the machine wireless interface supports BLE and can be used to fetch data from LoRa technology end devices. On the other hand, LoRa still needs a physical interface ready to receive information that will be provided by the technology devices. For that reason, an Arduino Uno [48] device along with a Dragino LoRa Shield [49] were used as the information transceiver, redirecting to the SDWI module through its serial port. In brief, the shield was chosen due to its configuration flexibility, Arduino compatibility and low power consumption, representing a good choice for the technology access point.  
It was necessary to use RH_RF95 [50], a driver that enables the reception of datagrams via a LoRa capable radio transceiver, which is also capable of configuring its signal power and operating frequency.  
Figure 7: Host system architecture.
## 3 Proposed System Methodology
### Multi-Person Tracking  
For accurate human tracking, the extraction of the true foreground, i.e., human pixels only, is a primary step. Thus, after application of the distance algorithm (mentioned in Section 3.2) for multi-person tracking, we performed the human silhouette verification step using the particles force model, and then the multi-person counting and tracking steps were executed.