esa-sceva/satcom-chunk-collection huggingface dataset:
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 3 Intersection-Based Link-Adaptive Beaconless Forwarding for City Scenarios
### Routing Protocol Design
#### 3.2.4 Winner Relay Management  
Another issue with the most of receiver-based scheme is the high ETE delay, which is due to the waiting time required for the relay selection procedure for every data packet. In ILBFC, once the relay node wins the contention right, it changes its state to \"winner\". The relay node in this state does not participate in forwarding zone and waiting time criteria, and directly forwards all incoming data packets for a duration of \(\delta_{eff}\) s. In addition, the relay node stays in this state as long as its distance to the destination (\(d_{c}\)) is less than the distance between the previous relay node and the destination (\(d_{p}\)). However, if \(d_{c}\geq d_{p}\), the relay node leaves its \"winner\" state and discards the data packet. This condition improves the routing efficiency, especially in the city scenarios where the speed and direction changes in a vehicle are relatively high when compared to highway scenarios.
# Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images
## 3 Method
### Point Cloud Refinement  
Through the semantic fusion method, the 3D point cloud is classified into point-wise semantic labels. However, there are still few scattered points with error labels due to incorrect semantics or depths of source images. To remove these unexpected semantic errors, we explore both local and global refinement strategies for point cloud refinement. The _KD-Tree_ data structure is employed to accelerate the query speed of the point cloud from O(n) to O(log(n)).  
Generally, adjacent point clouds often have some correlation and are more likely to be segmented into the same class. Hence, we utilize the local refinement method for each point by combining the hypothesizes with the neighbor points. Given a 3D point \(X\) from the dense semantic model, through the _KD-Tree_ structure established by the whole point cloud, the k-nearest neighbor of \(X\) could be  
Figure 3: Illustration of our semantic fusion method: the 3D semantic labels are determined by multi-view information; the 3D point’s label is decided by the correspondence accumulated probability of 2D pixels in each image.  
determined in a short time. \(P_{i}(l_{j}),i=1,\cdots,k\) represents the probability for neighbor point \(i\) labeling by \(l_{j}\); the new semantic label \(l^{\prime}(X)\) is updated by:  
\[l^{\prime}(X)=\underset{l_{j}}{Argmax}(\frac{1}{k}\sum_{i=1}^{k}P_{i}(l_{j})),l _{j}\in\mathcal{L}. \tag{6}\]  
However, the local refinement method only takes the local adjacency into consideration with the global information ignored. For overall optimization, we further apply a graph-based global refinement method by minimizing an energy function. For every 3D point in the point cloud \(\mathbf{V}\), a graph \(\mathbf{G}\) is established by connecting it with its k-nearest neighbor. Then the energy function is defined as:  
\[E(L)=\sum_{<X_{p},X_{q}>\in\mathbf{D}}B(l(X_{p}),l(X_{q}))+\lambda\cdot\sum_{X\in \mathbf{V}}R(l(X)), \tag{7}\]  
where \(\mathbf{L}=\{l(X)|X\in\mathbf{V}\}\) are the semantics of \(\mathbf{V}\) and \(\mathbf{D}\) is the set of all neighbor pairs. Similarly to [30], \(B(l(X_{p}),l(X_{q}))=1\) and \(R(l(X))=\frac{1}{k}\sum_{i=1}^{k}P_{i}(l_{j})\) are the boundary term and inner region term respectively, while \(\lambda\geq 0\) is a constant. Finally, the energy \(E(\mathbf{L})\) is minimized by a max-flow algorithm, as implemented in [31]. The refined point cloud is illustrated in Figure 4. Compared with the coarse result, our method wipes out semantic outliers and noises.
# A Research Platform for Autonomous Vehicles Technologies Research in the Insurance Sector
## 4 Use Cases
### Multi-Sensor Localization in Urban Environments  
Considering localization as one of the most basic needs of an autonomous vehicle, it is of great importance to analyze and compare the multiple methods that are commonly used to solve this problem. The wide selection of sensors equipped in the vehicle makes it a great platform for research in localization techniques. In addition to local odometry methods, such as wheel or LiDARodometries, the vehicle can also localize itself globally (in the world and/or inside a map) by making use of the GNSS data and other global localization methods such as Monte Carlo localization (AMCL).  
In [8], a method which includes GNSS information in the AMCL algorithm is proposed, which allows the improvement of the distribution of particles in the filter thanks to the GNSS measurements. Furthermore, this method also uses the local odometry generated from the LiDAR sensor to accurately propagate the particles. Figure 9 shows how the LiDAR point cloud (orange) and the map feature (black) matching improves the GNSS localization (green arrows) and makes it converge into a more accurate localization (red arrows).  
Figure 10 compares the proposed method with the localization sources that it combines (GNSS and original AMCL), plotting the error along a recorded sequence. The method that combines LiDAR and GNSS information results in a more stable localization, avoiding peaks on localization error or the kidnapped robot problem shown at the end of the sequence by AMCL algorithm, while improving GNSS accuracy (0.25 m of mean error compared to 0.38 m for GNSS localization) due to the use of the LiDAR and map information.  
Figure 10: Comparison of the localization accuracy of the proposed method, original adaptive Monte Carlo localization (AMCL) and GNSS.  
Figure 9: Example of localization using GNSS and LiDAR data.
applied sciences  
Mapping of Hydrothermal Alteration Zones in the Kelaat M'Gouna Region Using Airborne Gamma-Ray Spectrometry and Remote Sensing Data: Mining Implications (Eastern Anti-Atlas, Morocco)  
The mapping of hydrothermal alteration zones associated with mineralization is of paramount importance in searching for metal deposits. For this purpose, targeting alteration zones by analyzing airborne geophysical and satellite imagery provides accurate and reliable results. In the Kelaat M'Gouna inlier, located in the Saghro Massi of the Moroccan Anti Atlas, natural gamma-ray spectrometry and ASTER satellite data were used to map hydrothermal alteration zones. Natural gamma-ray spectrometry data were processed to produce maps of Potassium (K in %), Uranium (eU in ppm), Thorium (eTh in ppm) and ratios of K/eTh and K/eU. In addition, four-band ratios were computed, on ASTER data, to map the distribution of clay minerals, phylmitic minerals, propylitic minerals, and iron oxides. The combined results obtained from geophysical and satellite data were further exploited by fuzzy logic modelling using the Geographic Information System (GIS) to generate a mineral prospectivity map. Seven hydrothermal alteration zones likely to be favorable for mineralization have been identified. They show a spatial correlation with (i) known surface prospects and mineral occurrences, (ii) the granite-encasing contact zone, and (iii) the fault zones (Sidi Flah and Tagmout faults). This research therefore provides important information on the prospecting of mineral potential in the study area.  
mineral exploration; natural gamma-ray spectrometry; ASTER; fuzzy logic modelling; Kelaat M'Gouna inlier; Eastern Anti-Atlas; Morocco
# Self-Tuning Algorithm for Tuneable Clamping Table for Chatter Suppression in Blade Recontouring
## 6 Validation of the Concept  
The introduced self-tuning algorithm has theoretically demonstrated a higher performance compared to other wide-bandwidth tuning methods. It has been noted that ZOA leads to accurate stability predictions as long as the milling process is not interrupted or only lightly interrupted. Nonetheless, blade recontouring may involve low radial immersion operations performed with cutters featuring few teeth and negligible smoothing effect of the tool helix. This would lead to an interrupted cutting that could favour the occurrence of both flip instabilities and machining mode coupling effects that might compromise the effectiveness of the algorithm. For this reason, the effectiveness of the algorithm presented in this paper is validated in this section by means of the semidiscretization and initial value time domain simulations for the milling case in Table 2, which resembles an interrupted recontouring operation.  
Figure 7: Implemented algorithm for chatterless blade recontouring based on the real-time measured chatter frequency.
# Sustainable Plant-Based Diets and Food Allergies: A Scoping Review Inspired by EAT-Lancet
## 4 Discussion
### Clinical and Public Health Implications  
Although this review does not aim to provide formal clinical recommendations, it includes several elements that may inform professional practice. These include the potential need for nutritional supervision in patients with restrictive plant-based diets, attention to hidden allergens in processed products, and emerging evidence on the immunomodulatory effects of dietary components. Given the heterogeneity and preliminary nature of the current evidence base, we believe that practical implementation strategies should be developed cautiously and only within a clinically supervised framework. This aligns with the purpose and methodological scope of a scoping review.  
From a clinical perspective, the integration of sustainable plant-based diets into allergy management holds promise, especially when such diets are personalized and supervised by qualified healthcare professionals. Dietary interventions rich in fermentable fibers, polyphenols, and probiotics may reinforce gut barrier function, promote microbial diversity, and support immunoregulatory pathways. However, clinicians must remain alert to potential nutritional deficiencies (e.g., B12, zinc, and iron) and inadvertent allergen exposures--particularly in patients with complex dietary restrictions or in those consuming ultra-processed plant-based products.  
Personalized strategies, including component-resolved diagnostics, structured food reintroduction, and microbiota assessment, may enhance the safety and efficacy of dietary interventions in allergic individuals. Particular attention should be given to pediatric populations, pregnant women, and patients with multiple food sensitivities. At the public health level, this review supports the alignment of allergy prevention frameworks with sustainable nutrition policies. Promoting microbiota-supportive diets within national guidelines, enhancing food literacy regarding allergen risks in plant-based products, and incentivizing the consumption of minimally processed, seasonal foods may reduce both allergy burden and environmental impact. Future research should prioritize randomized controlled trials assessing plant-based dietary interventions in diverse allergic cohorts. Particular emphasis should be placed on clinical endpoints (e.g., IgE and oral food challenges), biomolecular markers (e.g., SCFA levels and cytokine profiles), and long-term outcomes, including tolerance induction and quality of life. These studies will be essential to substantiate the translational potential of sustainable dietary patterns in allergy prevention and management.
## 3 Results  
### Periodic Term Composition Analysis  
#### 3.1.1 GNSS Data  
Considering the SCPZ station as an example, we used the Morlet wavelet transform and Fourier transform to examine the main periodic components of the three components of GNSS time series NS, EW and UD in the time-frequency domain and the frequency domain, respectively (Figure 2). We found 0.5 years and 1.0 year periods for the three components of the SCPZ time series. Other stations in the area showed that the semi-annual cycle of the horizontal component was stronger than that of the annual cycle, and the N component was stronger than the E component, indicating that the semi-annual cycle seasonal term should not be overlooked. In addition, other significant periodic components, such as 1.5 years, 2.0 years, and 3.5 years, were observed. Figure 3 shows the main frequency components of the 24 stations in three directions extracted using Fourier transform. As the effective observation time of most stations is approximately 10 years, based on the Nyquist sampling theorem, the longest period that can be accurately determined is five years; therefore, we only focused on periodic components of less than five years. The N and E periodic components were scattered and complex, with the main periods being 0.5 years and 1.0 year; additionally, a 1.1 years periodic term was observed. However, every station did not have an annual (e.g., N component: SCMN; E component: SCMN, YNYL) or semi-annual (for example, N component: XIAG, YNMZ) component. Compared with the horizontal component, the annual and semi-annual components of the U component were more evident. Furthermore, all 24 stations had periodic terms of 0.5 years and 1.0 years. In addition, a 0.9 years periodic component was observed and the three components of some stations had 1.1 years or longer periodic components. However, the present study focused on the semi-annual and annual components.
# Analytic Model for U-Nb Liquidus and U-6 Nb Melting Curve
## 4 Generalization of the Theoretical Model to Nonzero \(P\): Melting Curve of U-6 Nb as an Example  
Formula (8), applied above to the U-Nb system, represents the particular \(P=0\) case of the more general Formula (7) that predicts the liquidus of the mixture at any \(P\
eq 0\) if both \(P_{\ell,1}\) and \(P_{\ell,2}\) (as functions of \(T\)), as well as the value of \(C\) as a function of \(P\), are all available. Note that if the melting curve of a constituent is given by the SG form \(T(P)=T(0)(1+P/a)^{b}\), \(P_{\ell}\) is the analytic function of \(T:P_{\ell}=a\Big{(}[T/T(0)]^{1/b}-1\Big{)}\). As regards \(C\), we assume that \(C\) is a very weak function of \(P\). In other words, \(C\) can be (approximately) considered as a constant over some interval of \(P\). In general, this interval of \(P\) should not be expected to be very wide; its (effective) width must be determined by the differences in the values of the EOS parameters of the constituents such as \(\alpha\), \(B_{T}\), etc.  
As an example, consider the application of Equation (7) to U-6 Nb, where the two \(P_{\ell}\)s are determined by the corresponding SG forms (10) and (12), and \(C(\approx{\rm const})=-0.6\). With \(x=0.06\) (6 wt% Nb), we numerically solve Equation (7) at the seven \(T\)s between 1600 (close to \(T_{\ell}^{\rm U-6 Nb}(0)=1577.6\) K) and 4000 K, with an increment of 400 K. The seven \((P_{\ell},T_{\ell})\) points are listed in Table 2 along with the ambient melting point.  
The best fit of the SG form to the eight datapoints in Table 2 results in the model melting curve of U-6 Nb:  
\[T_{\ell}^{\rm U-6 Nb}(P)\,=\,1577.6\,\bigg{(}1\,+\,\frac{P}{16.73}\bigg{)}^{0. 5214}, \tag{16}\]  
for which \(T_{\ell}^{\rm U-6 Nb}(0)=1577.6\cdot 0.5214/16.73=49.167\) K/GPa, in agreement with (15).
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 2 The Principle and Theory of Rainfall Measurement by CML
### Mathematical Models
#### 2.2.1 Baseline Attenuation  
\(A_{baseline}\) is usually determined by the attenuation value in a time interval right before a rainfall event and mainly consists of free space path loss \(A_{bf}\) (dB) and atmospheric attenuation \(A_{gas}\) (dB), i.e.,  
\[A_{baseline}=A_{bf}+A_{gas}. \tag{3}\]  
* Free-space path loss \(A_{bf}\). The free-space path loss is the loss of signal strength when a signal propagates through free space. Free-space path loss \(A_{bf}\) increases as the distance between the transmitter and the receiver increases. According to Recommendation ITU-R P.525-4 [30], when the distance between antennas is much larger than the electromagnetic wavelength \(\lambda\), the path loss of electromagnetic wave in free space is only related to the frequency and distance, as shown in Equation (4): \[A_{bf}=20\ log(4\pi L/\lambda)=32.4+20\ logf+20\ logL,\] (4) where \(f\) (MHz) is carrier frequency and \(L\) (km) is the distance between the transmitter and the receiver.
* Atmospheric attenuation \(A_{gas}\). When electromagnetic waves propagate through the atmosphere, they will be attenuated by the absorption, reflection, and scattering of water vapor, fog, solid particles, oxygen, nitrogen, carbon dioxide, and other substances in the atmosphere. According to Recommendation ITU-R P.676-12 [31], the specific gaseous attenuation of a microwave link, denoted as \(k_{gas}\) (dB/km), can be estimated by \[k_{gas}=k_{o}+k_{w}=0.1820f(N^{\prime\prime}_{oxygen}(f)+N^{\prime\prime}_{ water vapor}(f)),\] (5) where \(k_{o}\) and \(k_{w}\) (dB/km) are the specific attenuation caused by dry air (oxygen, nitrogen, etc.) and water vapor, respectively, and \(N^{\prime\prime}_{oxygen}\) and \(N^{\prime\prime}_{water vapour}\) are the imaginary parts of the frequency-dependent complex refractivities. Then, the atmospheric attenuation of a microwave link can be estimated by \[A_{gas}=\int_{0}^{L}k_{gas}(l)dl.\] (6)
## 2 Thematic Review and Analysis
### The Distribution Network
#### 2.1.1 Hydraulic Balancing  
A main cause of high return temperatures in DH networks is the immature cooling of DH water due to excessively high flow rates exceeding design specifications. The primary goal of hydraulic balancing is to ensure each building in the DH system receives the necessary flow, achieved by installing differential pressure control valves to maintain a steady supply-return pressure difference. This ensures adequate flow rates to all buildings, preventing an increase in supply temperature by the DH operator to avoid thermal discomfort. Hydraulic imbalance can occur at various levels, from radiator units in individual homes to flats in multi-family buildings, and can also distort components' characteristics, such as the hydraulic response of thermostatic radiator valves to detected indoor temperatures [14; 22; 23; 47; 48].  
[PERSON] [17] emphasizes that hydraulic balance has a greater impact on return temperature than control valve characteristics. However, partially closed or throttled control valves at some consumer sites affect the operation of other components and the overall DH return temperature. [PERSON] [49] demonstrates that a single closed thermostatic radiator valve can raise the DH return temperature by approximately 0.8 to 1 \({}^{\circ}\)C during cold periods in an 80/60 \({}^{\circ}\)C system, with detailed annual analysis provided in Figure 6.7 of the reference [49].  
[PERSON] et al. [48] highlight the drawbacks of lacking hydraulic balance, where the farthest radiator, without pressure and flow controls, becomes the dominant factor in system  
Figure 2: Schematic representation of a DH network, highlighting essential system components, alongside a substation hydraulic interface illustrated.  
operation, leading to excessive flow rates. To ensure adequate flow to this distant room, the main flow rate is increased for the entire building, resulting in a flow rate 3.2-fold higher than necessary and rates in closer rooms 7-8-fold higher. Conversely, implementing hydraulic balancing with differential pressure control and pre-set thermostatic radiator valves in a system designed for 75/50/18 \({}^{\circ}\)C (supply/return/indoor) reduces the return temperature from 63 \({}^{\circ}\)C to 52.1 \({}^{\circ}\)C, achieving a 16.2% reduction in heat consumption and a 76.3% reduction in pump electricity consumption.  
[PERSON] [50] describes the impacts of hydraulic balancing on the operating temperatures of buildings along with data for structures without balancing, all of which are claimed to be situated in the same area of the DH network (Figure 3).
## 9 Key Observations and Recommendations
### Key Recommendations  
Based on the presented observations, a set of recommendations has been developed to help future researchers and practitioners to select the optimal tools and procedures for skin image analysis. These proposals take into account both the technical aspects of model design and the requirements for experimental reliability and the interpretability of results.  
* **Selection of the appropriate architecture*
* Melanoma classification: We recommend DenseNet-121 as a base in the transfer learning approach, which provides a good compromise between network depth and limiting overfitting.
* Skin lesion segmentation: We recommend using the UNet model enriched with an attention module (Attention UNet), which enables the more precise extraction of lesion boundaries, especially with limited data.
* Large-scale lesion classification: If a large quantity of data (>10k images) and computing resources are available, consider advanced architectures like Vision Transformers (e.g., Swin Transformer) for potentially higher accuracy. Please note that these Transformer models are resource-intensive and require longer training times.
* Maximising diagnostic performance: For critical applications requiring the highest accuracy, an ensemble of models can be used. Combining multiple complementary CNN architectures (as in SkinNet, which fused MobileNet, ResNet18, and VGG11) has demonstrated improved performance (AUC \(\sim\) 0.96), albeit with increased training and deployment complexity.
* **Standardisation of experimental protocols*
* Use K-fold CV instead of a single random split, which provides a more robust assessment of model generalisation.
* Use publicly available, standardised reference sets such as ISIC or HAM10000 to increase the comparability between studies.
* **Augmentation and generative models for data enrichment*
* Use traditional techniques (rotation, scaling, brightness changes) in parallel with StyleGAN2 or CycleGAN to generate synthetic images of rare lesion types.
* Regularly verify the impact of newly generated samples on model metrics, with the goal of increasing the sensitivity with a minimal increase in false alarms.
* **Ensure interpretability and clinical confidence*
* Publish saliency maps (e.g., Grad-CAM, LIME) for each new model, which allows verification that the network focuses on symptomatic parts of the lesion.
* Consider integrating tools such as SHAP to analyse the impact of individual image features on the classifier's decision.
* **Regular bibliometric and statistical analysis*
* Update publication trend graphs every six months, taking into account new techniques (e.g., Transformers in computer vision).
* When comparing performance across architectures, use significance tests (e.g., ANOVA) to determine whether differences in accuracy are statistically significant.  
Each recommendation can be tailored to the specific project requirements and available computational resources. Implementing these practices will increase research's reliability, the comparability of results, and the broader acceptance of AI solutions in clinical applications.