esa-sceva/satcom-chunk-collection huggingface dataset:
# Discussion on the Design of Sprayed Eco-Protection for Near-Slope Roads Along Multi-Level Slopes
## 2 Key Design Parameters and Principles for Near-Slope Roads  
The near-slope roads of multi-level slopes primarily consist of temporary unpaved roads, which exclude pavement design and transition curves. However, to ensure traffic safety, the design must comprehensively address three dimensions: Plane layout, longitudinal profile design, and cross-sectional design. Vertical profile: Prioritizes maximum gradient design. Vertical profile design prioritizes maximum gradient specifications. Horizontal design focuses on optimizing the spatial layout of near-slope road systems, with proposed design formulas establishing minimum curve radii for turnaround bends, access ramps, and berms. Longitudinal profile design governs maximum gradient requirements to balance vehicle maneuverability and slope stability. Cross-sectional design incorporates critical parameters including turnaround area widths, berm widths, ramp widths, and drainage arrangements. When designing these parameters, the following overarching principles must be observed:  
1. Temporality requires a simplified design approach that aligns with the transient nature of near-slope roads.
2. Safety provides fundamental guarantees that must be maintained despite the implementation of design simplifications.  
Cost-effectiveness is achieved by accommodating concurrent construction equipment operations, thereby reducing project timelines and costs.
* Integrated design holistically adapts to the on-site slope gradients and elevations.
## 5 Results
### Experimental Results and Analysis  
#### 5.2.1 Sample Size Selection  
For algorithms based on the PF scheme with fixed sample size, expanding the sample size will improve tracking accuracy. However, once the sample size reaches a certain degree, due to the limitations in algorithms and application scenario, the tracking performance will gradually stabilize, i.e., the expansion of sample size will not lead to significant improvement in accuracy as before, and the tracking error will approach the minimum error threshold. In this section, assuming that both the process noises and measurement noises obey the Gaussian distribution with the mean of 0, the standard deviation of measurement noises is set to 100 m for experiments to find the required sample size of PF, PS-PF, UPF and PS-UPF with the optimal accuracy. Furthermore, the simulation of PS-AUPF is also carried out in this section. By changing the initial sample size, the adaptive strategy in Section 3 is further validated.  
Figure 6 shows the relationship between the sample size and tracking performance of algorithms, where the tracking performance is defined by the root mean square error (RMSE) of the target position, reflecting the deviation between the estimated position and the real state.  
For general PF, when the sample size is less than 1000, increasing particles can effectively reduce the tracking error. After 1000, the trend of improvement slowed down, and after 3000, it approached the minimum error threshold. For PS-PF, the tracking error can be effectively reduced by increasing particles when the sample size is not more than 500, and the minimum error threshold can be approached as long as the number of particles exceeds 1000. Clearly, the accuracy of PS-PF is improved faster  
Figure 5: Real trajectory of a high-maneuvering target.  
by increasing samples compared to PF, and only needing fewer samples will it achieve the optimal accuracy. Besides, the minimum error threshold of PS-PF is lower than PF. Both UPF and PS-UPF can obtain more accurate results than PF, because the importance sampling density established by UKF is closer to the real posterior probability distribution, which can reduce the required sample size to some extent. Therefore, expanding sample size could not significantly improve the performance of UPF and PS-UPF. Among these five algorithms, the tracking error of PS-AUPF is the lowest and most stable. Since its sample size is adaptively adjusted according to the estimated variance at each time step, the initial sample size has little effect on the accuracy of PS-AUPF, so the optimal accuracy can be obtained under a different initial sample size. In subsequent experiments, the sample size of PF, PS-PF, UPF and PS-UPF are set to 3000, 1000, 30, and 30, respectively, where all of these algorithms could achieve their optimal accuracy, ensuring the reliability of experimental results. The initial sample size of PS-AUPF is also set to 30 to hold the same initial condition as UPF.
# Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control
## 2 Compartmental Model of the Spread of the COVID-19 Epidemic in Hungary
### Available Measurements  
It is commonly stated in the literature [24; 25] that the daily number of infected people is not well observable, as the measurement relies on aggressive and exhaustive contact tracing and testing strategies [64; 65]. Though it is reasonable to assume that testing is wide-spread and quick enough in the hospitals, the registered numbers of hospitalized patients [66] are still influenced by practical considerations. The limited healthcare capacity on weekends and holidays usually results in a lower number of performed and documented tests, as well as in a delayed hospital discharge. Therefore, following the common engineering practice, we applied a 7 d-long moving average filter to the published data (\(\mathbf{H}_{k}^{\text{Off,raw}}\)) [66] to avoid biased estimates caused by these administrative inaccuracies. The smoothed time series \(\mathbf{H}_{k}^{\text{Off}}\) is formally calculated as:  
\[\mathbf{H}_{k}^{\text{Off}}=\frac{1}{\min(3,T-k)+\min(3,k)+1}\sum_{i=-\min(3,k )}^{\min(3,T-k)}\mathbf{H}_{k+i}^{\text{Off,raw}}. \tag{5}\]  
Obviously, the 7 d-long sliding window must be truncated at both ends of the series. Finally, the filtered hospitalization data were considered as the single available processed measurement, which reveal relevant information about the time-evolution of the process.
###### Abstract  
A landslide inventory, after an intense rainfall event in 1998, Southwestern Korea, was collected by digitizing aerial photographs. This left high uncertainty in the inventoried features to be verified by ground truths. To reduce the uncertainty, the photographs were reexamined, supported by the time slider in Google Earth. We observed 77 deformed slopes, which were similar in shape and texture, to the inventoried landslides. We then sought to label the observed formations based on their spatial relationship with surrounding conditions. A three-phase methodology was developed. First, an inventory of landslide, no landslide, vulnerable slopes, and unlabeled features was analyzed based on spatial cluster patterns, and then the dimension was reduced using the t-distributed stochastic neighbor embedding (t-SNE). Second, the Apriori algorithm, based on association rule mining, was used to identify common relations in the inventory using landslide antecedent factors (derived from topographic and landcover maps) that are linked to areas of unlabeled features. Third, the findings were validated using Landsat TM (Thematic mapper) and ETM+(Enhanced thematic mapper) images acquired before and after the original inventory. Current research offers practical and economical solutions (reduced reliance on paid remote sensing sensors and field survey) to labeling and classification of missing or outdated spatial attributed information.  
automatic change detection; machine learning; landslide inventory; uncertainty; distribution pattern +
Footnote †: journal: Applied Sciences  
0021
# CA-CGNet: Component-Aware Capsule Graph Neural Network for Non-Rigid Shape Correspondence
## 3 Proposed Method
### Component-Aware Graph Routing  
Considering the CapsNet can capture the part-whole relationship to cope with ambiguity [25], we introduce the capsule network to enhance feature expression ability. The features extracted by GNN are replicated in eight copies, the primary capsules are obtained after Conv1d+BN+ReLU, and the pose features of the mesh are further extracted to compose the latent capsules. Among them, routing is the basis of the capsule network, which transmits the information from the previous capsule layer to the next one through the protocol. To further improve the quality of feature acquisition, we propose a component-aware graph routing, which learns the relationship between capsules in the same layer through CA-GNN.
# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 6 Future Work  
In future work, we will aim to further improve the adaptability and robustness of the proposed 5G signal ranging framework by extending it in three key directions. First, we plan to incorporate additional multimodal sensing data (e.g., inertial sensors, barometric pressure) to enhance trajectory continuity and vertical floor discrimination in complex indoor environments. Second, we will explore adaptive federated learning strategies at MEC nodes to enable real-time model personalization for different users and scenarios, while preserving data privacy. Third, large-scale field deployments across diverse urban regions will be conducted to validate scalability, cross-region transferability, and long-term stability under realistic operational constraints. These enhancements are crucial for moving toward truly autonomous and privacy-preserving location services.
## 3 System Model and Problem Formulation
### Calculation Model  
Each M-IoT device has a computation-intensive task. For M-IoT device \(i\), the task information can be represented as {\(D_{i}\), \(C_{i}\), \(T_{i}\)}, where \(D_{i}\) denotes the input data size of the task for device \(i\), \(C_{i}\) represents the number of CPU cycles required to process \(1\) bit of data, and \(T_{i}\) indicates the maximum delay tolerated for task processing. Considering that each M-IoT device has a large computing task that must be completed within the maximum tolerable delay, the following offloading scheme can be adopted. Due to the limited computational capacity of M-IoT devices, part of the task is processed locally, while another part needs to be offloaded to the UAV nodes for auxiliary computation. If the UAV nodes still cannot fully process the M-IoT device's task, further offloading to the cloud servers equipped on the LEO satellites is considered.  
Specifically, the total task size of M-IoT device \(i\) can be divided into three parts, represented as \(D_{i}^{l},D_{i}^{u}\) and \(D_{i}^{s}\), where \(D_{i}^{l}\) represents the portion of the task processed on the M-IoT device, \(D_{i}^{u}\) denotes the portion processed on the UAV, and \(D_{i}^{s}\) represents the portion processed on the LEO satellite. Similar to [9], it is assumed that task partitioning does not introduce additional computational input data, i.e.,  
\[D_{i}^{l}+D_{i}^{u}+D_{i}^{s}=D_{i} \tag{5}\]  
For example, when \(D_{i}^{l}=D_{i}^{s}=0\), the entire task will be offloaded to the UAV, and so on. In this system, three methods can be used to process tasks, including local execution, offloading to the UAV, and offloading to the LEO satellite.
# A Green Computing Business Aggregation Strategy for Low Earth Orbit Satellite Networks
## 4 Problem Solution
### Determining the Set of Sleeping Satellites  
For Sub-problem 1, this paper adopts the Capacity-Driven Sleep Satellite Quantity (CDSSQ) mechanism to determine the number of sleeping satellites. The design is as follows:  
Figure 3: Business aggregation flow chart.  
Sorting the satellites: First, sort the business satellites in ascending order based on their business volume to obtain the list \(\mathbb{S}^{\prime}=\{S_{1},S_{2},\ldots,S_{v},\ldots,S_{M}\}\).  
Calculating the number of working satellites: Determine the number of working satellites \(V\) using the relationship between the current business volume \(B^{c}\) and the system's maximum capacity \(B^{\text{max}}\) as per Equation (22):  
\[V=\left\lceil\frac{B^{c}}{B^{\text{max}}}\cdot M\right\rceil \tag{22}\]  
where \(B^{c}\) represents the current total business volume of \(\mathbb{S}^{\prime}\); \(B^{\text{max}}\) is the maximum computable business volume per satellite (measured in bits); and \(\lceil\cdot\rceil\) denotes the ceiling function.  
Determining satellite sets: Based on the calculated number \(V\) and the sorted list \(\mathbb{S}^{\prime}\), identify the set of working satellites \(\mathbb{S}^{W}\) and the set of sleeping satellites \(\mathbb{S}^{S}\).
###### Abstract  
Fragile X syndrome (FXS) is caused by pathologic expansions of the CGG repeat polymorphic region of the FMR1 gene. There are two main categories of FMR1 mutations, \"premutation\" and \"full mutation\", that are associated with different clinical phenotypes, and somatic mosaicism can represent a strong FXS phenotype modulator. FXS is the leading cause of inherited intellectual disability and autism, and it is characterized by musculoskeletal manifestations such as flexible flat feet, joint laxity and hypotonia. The former have been associated with altered joint kinematics and muscle activity during gait. The aim of this study was to use gait analysis parameters to classify FXS children from healthy controls and, within FXS children with full mutation, to classify children with mosaicism. Seven supervised machine learning algorithms were applied to a dataset of joint kinematics and surface electromyographic signals collected on twenty FXS children and sixteen controls. Results showed that the k-NN algorithm outperformed in terms of accuracy (100%) in classifying FXS children from controls, while CN2 rule induction obtained the best accuracy (97%) in classifying FXS children with mosaicism. The proposed pipeline might be used for developing assisted decision-making systems aiming at identifying and treating the musculoskeletal alterations associated with FXS.  
supervised learning; fragile X syndrome; gait analysis; surface electromyography; kinematics +
Footnote †: journal: Applied Sciences
# Modification and Optimization of Cycloidal Gear Tooth Profile Based on Machining Error Compensation
## 3 Monte Carlo Simulation
### Case Analysis  
In the Monte Carlo method, specific differences can be observed in the results of each simulation sampling, indicating the random characteristics of actual machining errors. In order to ensure the accuracy of the Monte Carlo simulation of tooth profile variation caused by machining errors, it is necessary to perform multiple simulations and statistical analyses on the obtained data. A total of 100,000 cycloidal gears were simulated and processed in MATLAB using the Monte Carlo simulation method, and the distribution of tooth profile variation caused by machining errors was obtained, as shown in Figure 10. The average tooth profile variation of 100,000 randomly-produced cycloidal gears is 0.012067 mm, and the variance is 0.000145 mm, which shows that the variation distribution is relatively concentrated, and the model is reasonable. The confidence interval of the tooth profile variation simulation is \([0.011953,0.012103]\), which shows that 99.7% of the tooth profile variation caused by machining errors is distributed within the confidence interval in the 100,000 simulation statistics, demonstrating the accuracy of the statistical results.  
The Kolmogorov-Smirnov method is utilized for normality testing, and the results of H = 0, \(p\)-value = 0.7383 \(>\) 0.05 are obtained, showing that the distribution in Figure 10b follows a normal distribution.
## 4 Simulation Setup and Parameters
### Mobility and User Behaviour Models  
User trajectories in the simulation environment are designed to capture both linear and non-linear mobility patterns that are representative of realistic urban communication scenarios. Linear mobility models simulate vehicular movement with constant velocity and direction, while non-linear models emulate pedestrian or urban motion, including curves, zigzag patterns, and city-block-style routes, as shown in Figure 2. Each user begins at a defined offset from the base station and follows an independent, randomly assigned path with updates processed at every simulation step. The user positions are refreshed every 1 ms to align with standard CSI reporting intervals, which may range from 1 ms to 160 ms based on user mobility and network configuration (as specified in 3 GPP TS 38.214, Clause 5.2.1.4.1, and in TS 38.331).  
For clarity, and to focus on the core predictive beamforming mechanisms, the simulation results presented in this paper primarily consider a single-user scenario. This controlled setup allows us to isolate and demonstrate the core innovations, such as adaptive AoA prediction, clustered regression, SVR fusion, and fallback refinement, without introducing the added complexity of inter-user interference. However, the underlying architecture is explicitly designed for multi-user support, as detailed in Section 3.4.2. The same predictive and precoding mechanisms, including UEID-indexed steering and Zero-Forcing beamforming, are readily applicable in multi-user deployments. Future extensions will focus on validating these principles under dense interference-rich scenarios.
# Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?
## 5 Test and Results
### Simulated Environment
Observing the first row, for the LS estimates, it becomes clear how the bias on satellite \(G17\) is \"dragging\" the estimate towards the wrong direction. On the second row, the M-Huber estimate manages to discriminate the effect of the outlier and the solution becomes unbiased. Moreover, the minimization constitutes a convex problem, for which a single minimum exists and the uniqueness and stability is guaranteed. Finally, the third row shows the M-Tukey estimate. While the solution remains unaffected by the outlier, it is clear that the minimization of the nonconvex problem leads to the appearance of multiple minima. Therefore, if the initial point estimate is defect, the final estimate might not be found (due to jumps between close minima) or be spoiled (due to a local minimum).
## 1 Introduction  
### Motivation  
Growing product variance stemming from an increase in customer requirements raises the demands on both the flexibility of production systems and the skills of employees on the shop floor [1]. Where approaches of Industry 4.0 are focused on the efficient automation and interconnection of systems providing the technical foundations for adaptive production, its successor, Industry 5.0, has evolved around notions that place the human at the center. The envisioned result of both trends are highly connected and human-oriented factories, in which the technologies are tailored to the needs of the people who work in them, and the latter, as the central decision-maker, can control production in unison with a multitude of autonomous systems [2].  
The basic enablers of this vision are Human-Machine Interfaces (HMIs), which allow the interweaving of humans and cyber-physical systems. Individual interfaces provide a communication channel to transmit information with the help of a specific modality [3]. The modality of information transmission is characterized by basic human abilities to absorb information, whereby visual, acoustic and haptic exchange are the most frequently used. Based on these interfaces, numerous research projects in the context of smart production systems have dealt with the support of humans in complex activities under the keyword \"Cognitive Assistance Systems\" (CASs) [4].  
The relevance of such systems increases in principle with the proportion of manual processes and with the product variance within a domain. This applies in particular to aircraft manufacturing, where the proportion of manual processes is substantial, with lot sizes within certain subsectors (e.g., cabin interior) approaching 1 [5]. Therefore, supporting manufacturing personnel in process execution is of central importance for time-efficient, robust and error-free aircraft production.  
The domain of aircraft manufacturing poses special requirements that can also be found in the manufacturing of other large-scale products--namely, the vast quantity of assembly components that constitute an aircraft, as well as the pertaining range of dimensions of said components. From this, specific requirements can be derived for the coverage of the working areas of CASs. In addition, various processes are carried out simultaneously in a large workspace, occasionally in ergonomically unfavorable positions. For projection systems, this can lead to occlusions caused by the employees and tools on the shop floor, thus hindering information transmission in the assisted processes. Overall, the working environment can be described as challenging for the use of CASs.
## 2 Characterization Technique  
Compared to other competitive material characterisation techniques operating in guided environment, free space method fits for broadband material electromagnetic properties qualification and simultaneous optical illumination implementation needs. Furthermore, thanks to a homemade three-step calibration and a genetic algorithm-based data postprocessing technique, frequency-dependent material permittivity, permeability and conductivity parameters are obtained simultaneously with accuracy [18; 19]. Schematic of the free space bistatic measurement system given in Figure 2, which consists of a pair of microwave horn antennas connected to a vectorial network analyser (VNA), acting as microwave signal transmitter and receiver.  
Figure 1: Optical image of frequency-selective surface (FSS) structures on Si substrate and unit cell physical description.  
Microwave Teflon lenses placed between antennas and the material under test (MUT), under a small signal incidence angle of 28\({}^{\circ}\) with the respect to the normal of the MUT, are added for the guarantee of microwave signal focus in a foot print at order of MUT surface dimensions and microwave signal wavelength value. These components are mandatory for occurring diffraction effects minimization at the MUT edges responsible of measurements errors.  
The focusing system has been designed for a nominal microwave frequency of 50 GHz. In this configuration, the microwave lenses must be localized at a distance \(d_{1}\) equal to 31.85 cm from transmitter or receiver delivering a microwave signal waist of \(w_{1}\) is equal to 1.24 cm, and at a distance \(d_{2}\) to the MUT equal to 18.15 cm.  
The lowest possible angle (\(\theta_{i}=28^{\circ}\)) is determined by the size of transmitter and receiver. This limitation is then governed by the MUT dimensions \(w\), the distance between antennas and MUT \(d_{1}+d_{2}\), as well as 3-dB angular beamwidths of each antenna in E-plane and H-plane [20]. In our configuration, the highest angle of 33\({}^{\circ}\) has been calculated for a 1 cm\({}^{2}\) of MUT surface and 50 cm of distance with 20 \({}^{\circ}\) of antennas angular beamwidths. S-parameters measurements are performed at the distance (\(D=d_{1}+d_{2}\)) above the far field region limit corresponding to 50 cm at the setup lowest frequency of 40 GHz.  
An optical system consisting of a LUMICS thermally-regulated cw laser diode (_model:LU0808/0980/1064D100/100/100-U30 AA_) and a set of optical lenses is inserted between microwave horn antennas in order to realize a collimated illumination of 2.5 cm\({}^{2}\) at low incident angle on the top face of the sample in order to avoid optical back reflection signal on the laser source.