esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Neuromorphic Processor Architecture
### Features Comparison of FBGVNP and TrueNorth  
FBGVNP and TrueNorth both serve as a neuromorphic processor but have their respective features. The comparison of FBGVNP and TrueNorth can be seen in Table 3.  
The main difference between FBGVNP and TrueNorth is from their corresponding structure, since FBGVNP is developed based on a reprogrammable FPGA, while TrueNorth uses a relatively fixed crossbar structure and is developed via ASIC-based approach. As mentioned above, ASIC-based TrueNorth has the highest energy efficiency.  
Further, an execution comparison between the two processors based on a fully-connected, proportional-integral-derivative (PID) neural network (FCPIDNN) is presented as follows. The structure of the reference FCPIDNN is shown in Figure 5.  
Figure 5: Structure diagram of the reference fully-connected, proportional–integral–derivative neural network (FCPIDNN).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Processors** & **Neuron Number** & **Computing and Addressing** & **Scalability** & **Power Consumption** \\ \hline FBGVNP & variable & integrated & internal \& external & high \\ TrueNorth & fixed & separated & external & low \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of FBGVNP and TrueNorth.  
The FCPIDNN is a structure suitable for resolving multi-input, multi-output (MIMO), real-time control problems [27; 28]. It inputs reference and sampled real signals, and outputs control signals to the actuators. The scale of the FCPIDNN depends on the number of input and output channels. The presented FCPIDNN in Figure 5 adopts a 300 inputs and 6 outputs configuration. The execution time comparison of FBGVNP and TrueNorth, based on the reference FCPIDNN, is presented in Table 4.  
It is obvious to see that, based on the FCPIDNN, the execution time of the TrueNorth lasts longer than the FBGVNP. The main influencing factor lies in the dendrite extension operation. Because the neuron number is fixed in one TrueNorth core, when there more synapses need to be accumulated dendrite extension has to be applied, though that takes additional time and hardware resources.  
On the other hand, when the number of the neurons connected in one TrueNorth core is less than 256, it will create waste in the unconnected crossbar (memory resources for saving synaptic weights). For instance, Figure 6 shows the connection condition of the second layer of the reference FCPIDNN. The valid connection is less than 1% (1/128). That is, over 99% of the crossbar resource is wasted.  
Relatively, FBGVNP employs the methodologies of granularity variability and integrated computing and addressing. That makes the number of neurons in one core capably vary as needed without additional hardware resources, which helps to improve the utilization of the hardware resources and reduce time delays.
## 3 Multisolution Selection of Joint Angle Based on the Key Order of the Joint
### Multisolution Selection
Namely, the possible solutions for the jth key joint can be selected from the possible solutions for the (j\(-\)1)th key joint, and all possible groups of solutions obtained in Section 2 can be seen as the possible solutions for the 0 th key joint. The flow chart of the multisolution selection method based on the key order of joints for the appropriate solution of joint angles at one point in the trajectory of the robot is presented in Figure 4. First, all possible groups of joint angles of point \(\mathrm{P_{i}}\) are obtained based on Section 2 to serve as the possible solutions of the 0 th key joint. Secondly, the Jacobian matrix is obtained by substituting in joint angles of point \(\mathrm{P_{i-1}}\). The sensitivities of all joints are calculated with the Jacobian matrix to sort the joints and obtain the key order of all joints. The nominal increments and the nominal joint angles of all joints are calculated based on Equations (16) and (17), with the Jacobian matrix. Thirdly, the possiblesolutions of all joints are selected according to the key order of all joints, applying the principle based on smallest differences of the nominal angles. When key order is j, the joint with the key order j is found by comparing the key orders of all joints with j in order to obtain its nominal joint angles. The differences between this nominal joint angle and the corresponding joint angles of all possible solutions for the (j\(-\)1)th key joint are calculated. The possible solutions for the jth key joint are the solutions with the smallest differences of the joint angles of this joint. When j is larger than the number of joints of the robot, the possible solutions for all joints are obtained. The possible solutions for the joint with largest key order are the appropriate solutions for point P\({}_{\text{i}}\) of the robot.
# Improving Waste Management System Efficiency and Mobility with Efficient Path MANET
## 3 Intersection GA-Based Routing Algorithm
### Individual Path Selection and Population Generation  
From the initial population, which is considered as the expected paths that can be used for the service applied for, one path from the source node to the destination node is presented by each chromosome in the population on a single linkage map. All the chromosomes are evaluated with Equation (2), which calculates the intersection activation value for each path. Then, the chromosomes are ranked according to the calculated activation value.  
Chromosomes with low activation values are chosen as the best candidates. In the worst-case scenario, chromosomes with values that are either outside the range of the constraints or very close to the boundary values are considered problematic. Removing undesirable chromosomes and replacing them with new chromosomes becomes possible using the proposed chromosome intersection method. This finding means that the most efficient path based on the constraint values is found on the best chromosomes, which have the most favorable values. The top-ranked chromosome is recombined with one of the next highest-ranked chromosomes through floor-off division. The next highest-ranked chromosomes are chosen based on the chromosome's intersection point and presented in Equation (3).  
\[\text{Intersection point}=\text{floor\_off}(\text{Chromosome\_length}/2). \tag{3}\]
## 2 Materials and Methods
### Image Quality Metrics and Statistical Analysis  
We used SSIM [39] to measure the accuracy of our deep-learning models. In computer vision tasks, like image classification or object detection, the model's accuracy is often evaluated using metrics like precision, recall, and F1 score. However, these metrics do not provide information about the visual quality of the output images or the degree to which the model's predictions match the ground truth. _SSIM_ can help fill this gap by measuring the structural similarity between the output and ground truth images. The output images from CNN can be compared to the ground truth images using _SSIM_, and the resulting value can be used to evaluate the model's accuracy. The range of _SSIM_ is between 0 and 1, where a value of 1 indicates perfect similarity between two images, while a value of 0 indicates no similarity. The measure between two images \(x\) and \(y\) of common size N \(\times\) N is:  
\[SSIM(x,\ y)=\frac{\left(2\mu_{x}\mu_{y}+c_{1}\right)\left(2\sigma_{xy}+c_{2} \right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\right)\left(\sigma_{x}^{2}+\sigma _{y}^{2}+c_{2}\right)} \tag{4}\]  
where \(x\) and \(y\) are the two images being compared; \(\mu_{x}\) and \(\mu_{y}\) are the mean pixel intensities of images \(x\) and \(y\); \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are the variances of pixel intensities in images \(x\) and \(y\); \(\sigma_{xy}\) is the covariance of pixel intensities between \(x\) and \(y\); and \(c_{1}\) and \(c_{2}\) are constants used to stabilize the division, typically \(c_{1}=\left(k_{1}L\right)^{2}\) and \(c_{1}=\left(k_{2}L\right)^{2}\), where \(L\) is the dynamic range of the pixel values (for example, 255 for 8-bit grayscale images) and \(k_{1}\) and \(k_{2}\) are constants that control the relative contribution of the luminance and contrast terms to the final SSIM score. The SSIM formula considers the compared images' luminance, contrast, and structure, producing a value between 0 and 1.
# Object Detection-Based Video Compression
###### Abstract
In the areas of object detection and recognition, deep learning has shown a significant influence. Learning based on given features has led to a remarkable breakthrough in this field. Object-detection tools are diversified according to the types of networks, such as You Only Look Once (YOLO) [16], Feature Pyramid Networks (FPNs) [17], and Regions with Convolutional Neural Networks (R-CNNs) [18]. As a result of the rapid evolution in object detection, the combination of deep learning and video coding is now also necessary. Therefore, Video Coding for Machines (VCM), which is founded on ISO/IEC JTC1/SC29/WG11, will standardize a bitstream format [19]. As the features are extracted from deep learning or object-detection tools, the extracted features  
Figure 1: Example of a picture partitioned into tiles and slices [7]. can be used in applications supporting smart cities, intelligent industries, surveillance video, etc. Due to the rise of the Internet of Things (IoT), the aforementioned applications will be used in many specific applications, such as traffic monitoring, density detection and prediction, traffic flow prediction, and resource allocation. As a result, a method that combines video coding with object detection is proposed in this paper. The proposed method combines the VVC Test Model version 8.2 (VTM-8.2) with YOLO version 3 (YOLOv3), which showed high-speed object detection with the same accuracy as that of other algorithms [20]. The objects are detected from videos, and the information from these objects is encoded and used to generate a feature bitstream. In the proposed method, the information of objects, such as the object's name, position, width, and height, is contained in the feature bitstream. The extracted features are used by the quantization parameter (QP) control in VTM-8.2 to compress the areas in which objects are included or not; that is, the object detection-based video compression uses a low QP in the detected objects and a high QP in the background. This paper is organized as follows. Section 2 discusses the related works, Section 3 presents the proposed method, Section 4 presents and analyzes the experimental results, Section 5 presents our conclusions, and Section 6 introduces future works.
# Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service
## 4 Results
### Prediction with EEG z-Scores  
Considering that EEG profiles would be available for the individual VR users in future scenarios, we considered the overall EEG z-scores as input of the QoE and QoS predictions in the following. We illustrate the initial QoE results in Figure 5.  
We initially observed that the z-scored approach yielded a significantly high level of \(R^{2}\) scores for the subjects under consideration (\(M=0.91,SD=0.14\)). We furthermore noted that the overall variability decreased, as well, which indicated significantly less between-subject differences in prediction performance. Attempting a reduction in the number of z-scored EEG data points to be employed for the prediction approach yielded the by now anticipated reduction in the \(R^{2}\) score attained.  
Shifting the view to the prediction errors, we noted a high level of errors as indicated by the MAE (\(M=1.9\), \(SD=0.17\)). The high level of errors shows that the increased \(R^{2}\) score did not result in an accurate prediction outcome, while the small variability indicated that this was observed for this group of subjects in general. A reduction in the number of data points here had only a minor impact on the MAE results, which remained high throughout.  
For the QoS results, illustrated in Figure 5, we also observed a fairly high level of \(R^{2}\) scores on average (\(M=0.9\), \(SD=0.19\)) when considering all available data. As observed for the direct EEG value scenario, we also note that the high \(R^{2}\) scores were accompanied by a low average MAE level (\(M=0.08\), \(SD=0.15\)) with perfect predictions for several subjects. In this scenario, we note that the MAE scores for the four alternative EEG source data scenarios resulted in an additional increase of the MAE scores, rendering these alternative approaches non-favorable.  
Figure 5: Overview of the EEGz results for individual users and electrode positions for the QoE/QoS predictions based on SVR.
# Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration
## 4 Methods
### Incremental Ground Position Estimation
6:Compute the distance d from the origin to the fitted ground plane. 7:Calculate the standard deviation \(\sigma\) of the distances from the point set to the fitted ground plane. 8:If the standard deviation \(\sigma\) is less than the given threshold, assign \(\mathbf{p}_{c}^{total}\) and variance \(Var\left(S_{f}^{total}\right)\) to \(\mathbf{p}_{c}\) and \(Var\left(S_{f}\right)\), respectively, for storage, and terminate the algorithm. If \(\sigma\) is greater than the given threshold, proceed to step 7. 9:\[\mathbf{p}_{c} :=\mathbf{p}_{c}^{total}\] (9) \[Var\left(S_{f}\right) :=Var\left(S_{f}^{total}\right)\] (10)
10:Points with distances greater than \(\sigma\) from the fitted ground plane are considered outliers and are removed, while points with distances less than \(\sigma\) are considered inliers and are retained. The retained inliers are then used to reconstruct the point set \(S_{f}\). Steps 2 to 6 are repeated thereafter. ```  
**Algorithm 1** Incremental Calculation of Fitted Ground Plane Parameters  
It can be observed that, when updating the fitted ground plane parameters, the algorithm only requires incorporating the previously computed \(\mathbf{p}_{c}\), \(Var\left(S_{f}\right)\), and the newly observed ground map point set \(S_{f}^{new}\) into the calculations. The originally observed ground map point set \(S_{f}\) is not needed. This approach ensures a stable time complexity for the plane fitting update, thus improving the efficiency of the mapping system in outdoor scenarios.
## 3 Results
### Most Studied Healthcare Settings  
When we examine where these studies are set, Intensive Care Units (ICUs) and Neonatal Intensive Care Units (NICUs) emerge as the most researched environments. This is expected, given these spaces' high noise sensitivity and their critical impact on vulnerable patient populations, such as the elderly or pre-term infants. Examples include [PERSON] et al. (2025), who documented ICU noise levels reaching 87 dBA [24], [PERSON] et al. (2024), who implemented modeling strategies for acoustic optimization [26], [PERSON] et al. (2023), who observed noise reduction through lean management interventions [34], and [PERSON] et al. (2023), who linked NICU noise to staff burnout [37]. Studies like [PERSON] et al. (2024) and [PERSON] et al. (2022) confirmed the physiological impact of noise on preterm infants [19; 42], while [PERSON] et al. (2019) and [PERSON] et al. (2022) highlighted the systemic and architectural challenges in noise control and sleep promotion [45; 61]. Dental clinics also represent a significant portion of the studies, demonstrating increasing awareness of acoustics in outpatient environments. Research in this domain often centers around stress reduction through curated music or sound design, such as in [PERSON] et al. (2022) and [PERSON] et al. (2023) [5; 44]. Other studies, like [PERSON] et al. (2024) and [PERSON] et al. (2020), explore patient and practitioner perspectives, highlighting stress, anxiety, and hearing concerns [4; 58]. Interventions include real-time monitoring systems and personalized soundscapes to mitigate negative effects. Additionally, general hospital wards, psychotherapy spaces, and healthcare environments such as smart buildings also receive attention. For example, [PERSON] et al. (2024) and [PERSON] et al. (2024) propose biophilic soundscapes using natural elements like water or birdsong to improve recovery [15; 25]. Similarly, [PERSON] et al. (2022) and [PERSON] (2024) examine sound perception in vulnerable groups [32; 40], while [PERSON] et al. (2024) offer a meta-perspective on architectural design gaps in inpatient healthcare settings [7]. [PERSON] et al. (2023) and [PERSON] (2020) illustrate how sensory design, particularly through water features and greenery, can reduce stress and promote healing [9; 57]. Overall, these studies emphasize sound's role in mental health, perceived quality of care, and patient experience in both clinical and transitional care spaces, reinforcing the need for acoustic optimization as part of sustainable and patient-centered design.
# Alpha Centauri: Disc Dynamics, Planet Stability, Detectability
## 5 Discussion and Conclusions
### Possible Orbits and Planetary Architectures in Alpha Centauri  
Given its high eccentricity, the Alpha Cen AB binary system constitutes a potentially hostile environment for the existence of S-type planets. The close stellar interactions, with distances of minimum approach of about 12 au, are likely to have had a significant influence on planetary formation, thus reducing the available mass for such processes and imposing chaotic effects on the surviving planets during each periastron approach. These factors are the primary reasons why the search for exoplanets in this system has been predominantly oriented towards rocky planets, as opposed to Jovian types, which would be significantly more unstable. In Section 2, we presented findings showing that the MEGNO chaos indicator, when correlated with the final mass and extent of the expected disc, revealed a notable trend: regions in closer proximity to the stars exhibited increased stability for rocky-type planets. This relationship between the MEGNO indicator and the characteristics of the disc provides valuable insights into the dynamics of planetary stability in Alpha Cen.  
Additionally, it is important to acknowledge that while an analysis for 10,000 years does not guarantee dynamical stability during several Gyr, it remains a useful guide for the exoplanetary search in multiple stellar systems. For example, it is possible to identify stable regions on Gyr scales using inherently more computationally intensive integrations [30]. Nevertheless, to ensure that the identified regions truly correspond to stability zones, we extended the integration time to 100 kyr. This required a reduction in spatial resolution, thereby confining our analysis to a 20 \(\times\) 20 grid within the same _a-e_ parameter space,which is detailed in Appendix B. This approach balances the need for longer-term stability assessments with practical constraints on computational resources. Based on this approach, recent studies suggest that up to nine planets (five around A and four around B) could survive on nested prograde orbits for the current age of Alpha Cen AB [36]. This kind of methodology was also used in the context of the restricted three-body problem to explore the stability of Earth-like planets within the habitable zones of Alpha Centauri's stars A and B [37; 38]. These previous findings showed that rocky planets are capable of maintaining stable orbits, despite potential high eccentricities and especially at inclinations under 40 degrees. Our proposed method here is complementary, since it enables us to robustly identify stability and chaos regions when considering different orbital parameters within multiple stellar systems. The main advantage of MEGNO over other methods is that the cost of the integration is relatively modest. The implementation of MEGNO maps in the context of exoplanet surveys is relevant with the specific goal of narrowing down the parameter space of possible orbital and physical parameters.
# Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions
## 2 Materials and Methods
### Extending the Servo-Control Model: A PID Steering Controller  
The second controller that we investigated is based on the above-discussed PD servo-control model, extended with an additional integral term. The rationale for incorporating the integral term is that it could provide an additional adjustment to the steering of the car if the car does not return to the center of the lane (e.g., due to skidding on cornering in slippery road conditions) for a certain period of time (in our experiments, 2 s):  
\[\delta=k_{1}\;e+k^{*}_{\;2}\;e^{\prime}+k_{3}\;\int\;e\;dt. \tag{6}\]  
In Section 3, we will explain the mechanism of tuning the parameters \(k_{1},k^{*}_{\;2}\), and \(k_{3}\).
# An Inter-Comparison Study of Multi- and DBS Lidar Measurements in Complex Terrain
## 3 Results and Discussion  
### Radial Velocity Components  
In a first step, the radial components of the individual WindScanners (\(v_{r\;lidar}\)) are compared to the projection of the sonic measurement on the respective WindScanner beam (\(v_{r\;sonic}\)). This allows an evaluation of the performance of the individual instruments and the accuracy of the setup process. Moreover, some of the complexities in the inter-comparison of the second-order moments, which arise from the combination of multiple lidar beams in three-dimensional space, can be simplified when the radial components of the sonic and lidar wind speed are compared directly. In Section 3.2, we then turn to the wind statistics which are derived from the combination of multiple WindScanner devices.
## 1 Introduction
[59], which proposes two different approaches, namely ProbMODIS and ProbSentinel, for downscaling MODIS snow products using Sentinel-2 based on snow probability computation. Snow distribution, especially in mountain areas, has high heterogeneity, both spatially and temporally [60]. Therefore, it is important to have the maximum temporal and spatial resolution of snow [61], which is why Landsat and Sentinel-2 imagery are being increasingly used. Sentinel-2, compared to Landsat, has the advantage of benefitting from a stronger temporal coverage when gap-filling procedures must be applied to obtain a daily temporal series, from which snow phenology metrics can be drawn. To fulfill these requirements (high spatial and temporal resolution), the goal of this work is to develop a workflow to retrieve a gap-free time series of snow cover maps at high resolution, to investigate the snow dynamics on a fine scale. The proposed method, based on a machine learning approach, involves the data fusion of MODIS snow data [62], consisting of daily images but at low spatial resolution, and the Sentinel 2 constellation data [63], which, conversely, achieve high spatial resolution but are acquired every 5 days. We compared three different approaches, differing in the type of input data used for training random forests, and experimented on a mountain area in the northwest Alps. Validation was performed both to investigate temporal and spatial accuracy through, respectively, a comparison with ground truth data collected by meteorological stations, and a comparison with the Sentinel-2 almost cloud-free images themselves. This document is divided into five sections. Section 2 describes the study area, the data used, and explains the workflow implemented, as well as the validation methodology. Section 3 reports the results of our double validation approach. Section 4 discusses the experiment and, finally, Section 5 concludes this paper.