esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 AMTB Filter without Need for Clutter Density
### Formation of Existing Objects  
The surviving objects are acquired by picking the object with \(r_{i,k}^{e}>\tau_{r}\) from the updated set of existing objects in (24) where \(\tau_{r}\) is a given picking probability. We suggest that its value range is from 0.001 to 0.009. The set of acquired survival objects is:  
\[\left\{r_{i,k}^{e},N(\mathbf{x}_{i,k};\mathbf{m}_{i,k}^{e},\mathbf{P}_{i,k}^{e}),\ell_{i,k} ^{e}\right\}_{i=1}^{N_{k}^{e}} \tag{46}\]  
We use the mean vectors of survival objects and their track labels to form set \(\mathbf{X}_{k}^{s}\) as:  
\[\mathbf{X}_{k}^{s}=\left\{\mathbf{m}_{i,k}^{e},\ell_{i,k}^{e}\right\}_{i=1}^{N_{k}^{s}} \tag{47}\]  
where \(N_{k}^{s}\) is the number of survival objects.  
The existing objects include the survival objects and the newborn objects. The set of existing objects at time step \(k\) can be acquired by combining the set of the newborn objects in (37) and the set of survival objects in (46) as:  
\[\begin{split}&\left\{r_{i,k}^{e},N(\mathbf{x}_{i,k};\mathbf{m}_{i,k}^{e}, \mathbf{P}_{i,k}^{e}),\ell_{i,k}^{e}\right\}_{i=1}^{N_{k}^{e}}\\ &=\left\{r_{i,k}^{e},N(\mathbf{x}_{i,k};\mathbf{m}_{i,k}^{e},\mathbf{P}_{i,k} ^{e}),\ell_{i,k}^{e}\right\}_{i=1}^{N_{k}^{e}}\cup\left\{r_{h,k},N(\mathbf{x}_{h,k };\mathbf{m}_{h,k},\mathbf{P}_{h,k}),\ell_{h,k}\right\}_{h=1}^{N_{k}^{e}}\end{split} \tag{48}\]  
where \(N_{k}^{e}=N_{k}^{s}+N_{k}^{a}\) is the number of existing objects at time step \(k\). The set of existing objects in (48) and the set of potential birth objects in (45) along with unused measurement sets \(\mathbf{Z}_{k}^{u}\) are delivered to the next time step.  
Combining set \(\mathbf{X}_{k}^{s}\) in (47) with the set in (38), we acquire set \(\mathbf{X}_{k}\) as:  
\[\mathbf{X}_{k}=\mathbf{X}_{k}^{s}\cup\mathbf{X}_{k}^{b} \tag{49}\]  
Set \(\mathbf{X}_{k}\) is regarded as the output of the filter at time step \(k\), and sets \(\mathbf{X}_{k-2}^{b}\) and \(\mathbf{X}_{k-1}^{b}\) in (38) are used to supply the output of the filter at steps \(k-2\) and \(k-1\) as:  
\[\mathbf{X}_{k-2}=\mathbf{X}_{k-2}\cup\mathbf{X}_{k-2}^{b},\ \mathbf{X}_{k-1}=\mathbf{X}_{k-1}\cup\mathbf{X}_{k-1} ^{b} \tag{50}\]  
Algorithm 4 gives the pseudo-code for the formation of existing objects.  
According to the implementation steps of the AMTB filter, the clutter density and survival probability of the target needed in the adaptive RFS-based filters are obviated in the proposed filter. Therefore, it has an important application value in the situation where obtaining the clutter density and survival probability is difficult. In addition, the pruning threshold and merging threshold required in the adaptive RFS-based filters are also avoided in the proposed filter.
## 5 Conclusions  
In previous studies, the authors have shown that through the application of FEM combined with advanced fatigue criteria based on the critical plane concept is possible to have a better estimation of \(f_{korr}\) for any specific gear design (instead of considering \(f_{korr}\) always equal to 0.9).  
In the present work, the effect of the gear design parameters on the translation of STBF results into MGs' strength was investigated. Specifically:  
* Three design parameters were included in the analysis: the profile shift coefficient \(x^{*}\), the normal pressure angle \(\alpha_{n}\), and the normal module \(m_{n}\).
* For each of those parameters, three different levels were considered, and a full factorial DOE (27 designs) was studied.
* The 27 gear geometries were numerically simulated via FEM both in the RG and in the STBF conditions. The calculated stress states for the 2 configurations (for each gear design) were analyzed exploiting a fatigue criterion based on the critical plane concept, i.e., the Findley criterion.
* For each gear design, the applied load was selected according to the ISO 6336 standard. The comparison between the results of the FEM for the RG and the STBF configurations has led the determination of the correction factor \(f_{korr}\).
* The systematic approach used in the present work and the amount of data collected, allowed to better understand the possible relations between the gear design parameters and \(f_{korr}\).
* An ANOVA has shown that among the three considered parameters (\(x^{*}\), \(\alpha_{n}\) and \(m_{n}\)), only the profile shift coefficient have a statistically significant effect on \(f_{korr}\).
* It has been translated in terms of linear regression in Equation (10). To validate the goodness and reliability of this relation, 6 additional gear designs which parameters did not fall in the ranges used for the derivation of the relation between \(f_{korr}\) and \(x^{*}\) were investigated with the same procedure.
* The results are aligned with the predictions of the linear model with a maximum error of 4%.  
Figure 11: Date used for the DOE and for the validation.  
* The relation could be possibly used to improve the reliability of the material parameters obtained via STBF tests.
* Future studies will be aimed at further investigating the effect of material strength parameters (e.g., \(\sigma_{f}\) and \(\tau_{f}\), critical to [PERSON]'s implementation) on the relationship between \(f_{korr}\) and \(x^{*}\) obtained in this study.  
Conceptualization, F.C.; methodology, F.C. and L.M.; software, L.F., L.M., C.C. and F.C.; writing--original draft preparation, L.M.; writing--review and editing, F.C. and C.C.; supervision, F.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Open Access Publishing Fund of the Free University of Bozen-Bolzano.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Performance
### Variation of Time Synchronization
#### 4.3.2 Transmission duration  
The precisely synchronized transmission of images from several sensor nodes can increase congestion, which can in turn lead to an increase in transmission duration. Figure 15 shows the image transmission duration with variations in the time synchronization. With RAIT, it took about 15 s for an image to be delivered from a source node to a sink node, and this duration decreased drastically as the  
Figure 15: Image transmission duration with variations in time synchronization.  
clock differences were increased. A long delay in the case of precise synchronization might present a problem in some applications, such as emergency programs, while certain delays might be tolerable in other applications. Consequently, we could say that RAIT is suitable for applications that are not sensitive to delay but that require the reliable transfer of images.
## 3 Health Benefits
### Hypoglycemic Action  
Diabetes mellitus (DM) is a complex metabolic disorder characterized by hyperglycemia, a physiologically abnormal condition represented by continued elevated blood glucose levels. Approximately 95% of patients with DM present type 2 diabetes, which is characterized by insulin resistance and \(\beta\)-cell dysfunction [147].  
Many studies have evidenced the ability of artichoke to show beneficial effects against this condition due to its hypoglycemic action [148,149]. [PERSON] et al. [107] evaluated the effects of artichoke in alloxan-induced diabetic rats, obtaining positive results with a reduction in plasma glucose levels. Treatment with the extract also significantly prevented serum fructosamine levels and had significantly lower serum TC and TG levels than untreated animals. A study in overweight patients with impaired fasting glucose (IFG) treated with two daily oral doses of artichoke extract for 8 weeks showed similar results, reducing blood glucose and insulin concentrations. Other diabetes markers such as homeostasis model assessment (HOMA) and A1c-derived average glucose (ADAG) were also reduced [108].  
The possible mechanisms responsible for the hypoglycemic effects of artichoke are diverse, and several remain unknown. However, it has been evidenced that some bioactive compounds present in artichoke, such as chlorogenic acid, cynarin and luteolin, can decrease glucose uptake through the intestine [150]. In addition, chlorogenic acid is known to be a potent inhibitor of glucose-6-phosphate translocase, and dicaffeoylquinic acid derivatives can modulate alpha-glucosidase activity [109]. Both enzymes are involved in gluconeogenesis and glycogenolysis and therefore in the homeostatic regulation of blood glucose levels. On the other hand, the presence of fiber is known to stabilize the blood glucose level by allowing glucose to be slowly absorbed into the blood [151]. Inulin, the main component of artichoke fiber, can regulate glucagon-like peptide-1, promote \(\beta\)-cell proliferation and reduce \(\beta\)-cell apoptosis [152]. Due to this relationship with the production of SCFA, this compound may also have a role in maintaining the insulin signaling pathway and energy homeostasis [153].
## 3 Results and Discussion
### Analysis of Lithium Distribution
#### 3.3.2 Lithium Distribution within Structured and Unstructured Electrodes
Figure 7: **(a)** Original cross-section of a micro-pillar structure, **(b)** Li-concentration depth profile of a micro-pillar structure, and **(c)** cross-sectional lithium concentration at layer 6. The electrode was lithiated at 0.01 V by applying 1 C.  
Moreover, laser-generated line structures could not only promote the lithium-ion insertion but also accelerate Li extraction from the bulk material. Artificial pathways on the electrode reduced efficiently the tortuosity for lithium-ion transport. A lower amount of lithium within the entire structured electrode delithiated by 1 C could be detected in comparison to the unstructured electrode by LIBS measurements (not shown here). For the unstructured electrode, only a part of lithium-ions close to the separator side could be extracted from the anode. Trapped lithium-ions become electrochemically inactive and do not attend the next cycling steps. In this work, it is determined that the increasing amount of inactive lithium causes capacity fading at high C-rates. Laser-generated new pathways could improve the lithium-ion diffusion ability and reduce the lithium-ion tortuosity within thick film anodes.
## 3 Results and Discussion
### Docking-Based Interaction Simulations
The hydrogen bond between the ligand and the HIS-524 amino acid residue is essential because it determines whether a ligand acts as an agonist or antagonist. ER\(\alpha\) has a ligand binding domain (LBD) dominated by hydrophobic region and composed of helices 3, 6, 7, 8, 11, and 12. If a ligand forms a hydrogen bond with the HIS-524 residue and helix 12, it results in antagonist activity [81]. To further validate the interaction profiles and assess the dynamic behavior of the ligand-receptor complexes, molecular dynamics simulations were subsequently performed for both PBD-17 and PBD-20 compounds. Figure 4: The 3D representations of the molecular interactions between ER\(\alpha\) and five ligands: (**a**) 4-OHT, (**b**) raloxifene, (**c**) lasofoxifene, (**d**) PBD-17, and (**e**) PBD-20. The interactions of hydrogen bonds, alkyl & pi-alkyl, amide-pi stacked, and pi-sigma are represented as green, pink, magenta, and purple dashed lines, respectively.
## 3 System Model and QoT Estimation  
This part centers on the network model of C+L-EONs, including the method for calculating the weight of each link, selecting request modulation formats, and estimating physical layer impairments.
## 3 Results and Discussion
### Effect of Coil Pitch
This attributes to the high ice formation rate for small twin helical coil pitches. The impact of coil pitch on the average discharging rate is exemplified in Figure 6. It can be observed that the average discharging rate declines rapidly with time (first 60% of the discharging process) for all studied coil pitches. Afterward, it continues nearly constant for all studied pitches. In addition, as the coil pitch improves, the average discharging rate decreases for the fixed tube diameter. The discharging rates are observed to be small when matched to the charging process, indicating that higher discharging times are accomplished. The ice movement is slow due to the pitch effect described previously. Figure 4: Percentage of energy regained with time for different coil pitches with coil tube diameters during discharging process. Figure 5: Average charging rate with time for different coil pitches with coil tube diameters during the charging process. Figure 6: Average discharging rate with time for different coil pitches with coil tube diameters during the discharging process.
## 3 Results
### Comparative Analysis of HT-qPCR with Shotgun Metagenomic Sequencing
#### 3.2.4 Taxonomic Profiling via Shotgun Metagenomic Sequencing  
Unlike the HT-qPCR assay, the taxonomic binning of the predicted ORFs in the SMS method captured the presence of all the microbial domains (\(\sim\)87%). Some other communities (12%), most likely the higher organisms, were also found in the present samples. Among the microbial population, overwhelming abundances of bacteria (86%) were recorded whereas only 1% were Archaea (Figure 6a) and \(<\)0.001% were viruses as well as Eukaryota. The number of sequences affiliated with each bacterial phyla in the sediment samples exhibited a dominance of Proteobacteria (Min 42% at S4 and Max 59% at S8) followed by Bacteroidetes (Min 6% at S12 and Max 18% at S3), Cyanobacteria (Min 1% at S5, S12, S1, S6, S10, and S8 and Max 10% at S2), Actinobacteria (Min 0.3 at S1 and Max 6.5% at S10), and Firmicutes (0.0-4%) (Figure 6b). The selective primers set in the HT-qPCR only amplified Bacteroidetes and Firmicutes DNA. The dominant genera were _Woseia_ (Min 0.2% at S1 and Max 10.6% at S11), _Marinobacter_ (Min 0.1% at S5, S8, S7, S3, S11, and S10 and Max 12% at S1 and S6), _Pseudomonas_ (Min 0.1% at S5, S7, and S11 and Max. \(-\)6.5% at S6), _Vibrio_ (Max 4.7% at S2), _Loktanella_ (Max. 3.8% at S6), and _Rubrivirg_ (Max. 3.6% at S4), (Figure 6c). Among the prevalent genera, species of _Vibrio_ and _Pseudomonas_ are pathogenic. In addition to these ESKAPE (E-_Enterobacter_ sps.; _S-_Staphylococcus aureus_; K-_Klebsiella pneumonia_; A-_Acinetobacter baumannii_; P-_Pseudomonas aeruginosa_; and E-_Enterococcus faecium_) pathogens were found in very low abundances (Figure 6d). None of these pathogenic forms or ESKAPE genera were amplified with the HT-qPCR primers. ESKAPE are the WHO-listed priority genera that call for immediate attention while monitoring AMR.  
Figure 6: (**a**) Major domains, (**b**) phylum, (**c**) genera, and (**d**) ESKAPE pathogens found in marine sediments collected from Kuwait.
## 3 Numerical Solution and Model Validity
### Model Validity  
In order to illustrate the validity of the ball bearing model, the contact angle results for the b218 bearing considering different load and speed conditions are compared in Figure 7 with that from [PERSON]'s work [21]. It can be seen that the contact angle obtained by the present ball bearing model agreed well with the previously published results, and the maximum relative error between them was 16% as the preload was near 8000 N and the bearing speed was 10,000 rpm; However, the error was very small for other running conditions, which validated that the ball bearing model was effective.  
Figure 6: Flowchart of solving the shaft-bearing system.  
Figure 7: The comparison of contact angles from [PERSON]’s model (markers) and from the present bearing model (lines).  
Table 1 gives the stiffness comparison of ball bearing NSK 7014A5 TYSULP4 between the NSK manual and the calculation results based on the present ball bearing model. The bearing stiffness formulas can be derived from the partial derivatives of the bearing load with respect to bearing displacement based on Equation (21). The detailed calculation process is omitted here, and a similar work can be found in [22; 23; 24]. It can be seen that the errors between the calculation results and manufacturer data were very small and less than 3%, which indicated that the ball bearing model was very valid.  
To further validate the effectiveness of the system model, a gear shaft supported by a pair of angular contact ball bearings from [PERSON]'s work [25] was analyzed considering different shaft diameters and bearing arrangements. The bearing displacements and loads are presented in Figures 8 and 9. As shown in Figures 8 and 9, the results calculated by the present system model were very consistent with those from [PERSON]'s model [25], and the difference between the results from the two different models was less than 8%, which verified the system model and the calculation procedure.
# FD-LTDA-MAC: Full-Duplex Unsynchronised Scheduling in Linear Underwater Acoustic Chain Networks
## 4 Results and Discussion
### Medium Scale Scenarios: 50 and 100 km Pipelines  
In practice, pipelines span several hundreds to thousands of kilometres, such as the Langeled pipeline in the North Sea measuring about 1200 km [6], and the 7200 km long pipelines under the gulf of Mexico [37]. For underwater oil and gas pipeline monitoring, applications such as leak detection require timely sensor readings at certain intervals and demand a high resolution of sensed data. This motivates studying the deployment of FD-LTDA-MAC protocol in medium scale scenarios, so as to understand intermediate performance improvement and to track the improvement by understanding where the optimum performance enhancement lies. The frame durations derived by FD-LTDA-MAC for 50 and 100 km pipelines with 25 and 50 hop configurations as seen in Figure 12 are of the range 104 s to 779 s, with the lower bound corresponding to the 25 hop case and the upper bound the 100 hop case. In comparison with the frame durations ranging from 218 s to 4457 s and 144 s to 1192 s derived by LTDA-MAC and LTDA-MAC over FD nodes, respectively, there is significant reduction in the frame duration. The performance improvement as a ratio can be approximated as 1:1.4:2 for the lower bound and 1:2:6 for the upper bound. Thus, LTDA-MAC performs poorly in medium scale scenarios  
Figure 11: Frame duration cdfs of LTDA-MAC with HD nodes, LTDA-MAC with FD nodes, and FD-LTDA-MAC for a 20 km pipeline.  
Figure 10: Frame duration cdfs of LTDA-MAC with HD nodes, LTDA-MAC with FD nodes, and FD-LTDA-MAC for a 10 km pipeline.  
compared to FD-LTDA-MAC. This means FD-LTDA-MAC can achieve higher monitoring rates compared to LTDA-MAC and LTDA-MAC in FD.  
Furthermore, the 25 and 50 hop scenarios for 50 and 100 km pipelines, respectively, show that more regular sensing along a pipeline can be achieved by FD-LTDA-MAC protocol with a 2 km sensing range. The results also indicate that the greedy optimisation algorithm in FD-LTDA-MAC can achieve a better solution in longer pipelines with full-duplex capability. This is the reason why performance improvement achieved in medium scale scenarios is superior compared with small scale scenarios.
## 7 Discussion
### Error Metrics and Predictive Reliability  
The evaluation of the LSTM model relies on two widely accepted error metrics: Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The MAE provides a straightforward interpretation of average prediction error in minutes, making it intuitive for assessing delivery deviations. In this study, the final validation MAE of 3.52 min implies that, on average, predicted travel times differ from actual durations by less than four minutes--a level of precision that is typically acceptable in urban logistics, where short delays can be absorbed operationally. On the other hand, RMSE penalizes larger errors more heavily due to the squaring operation, making it sensitive to outliers such as unusually long trips or traffic anomalies. A relatively low RMSE (around 5.74 min) indicates that the model not only performs well on average but also avoids large deviations that could cause serious disruptions. Together, these metrics confirm that the LSTM predictions are both reliable and robust enough to be used in dynamic route planning.
# DR-Transformer: A Multi-Features Fusion Framework for Tropical Cyclones Intensity Estimation
## 2 Related Works  
### Tropical Cyclone Intensity Estimate  
There have been studies using CNN to estimate TC intensity [2; 3; 4; 6]. As far as we know, the application of CNN to TC intensity estimation was first introduced by [2] and they classify TC images into eight classes. However, they just obtain intensity range instead of exact intensity, and their training data and test data are related. In [3; 4], multi-channel satellite images and external information, such as latitude, longitude, and date, are used to estimate an exact intensity by a regression network. In [6], a context-aware cycleGAN was used to solve the problem of highly imbalanced TC data. In [7], GAN is used to generate PMW and VIS channel images from IR1 and WV channels for real-time TC intensity estimation.  
However, existing methods suffer from a lack of interpretability and fail to make full use of the TC intensity (label), which are continuous and important to optimize an embedding space. Differently, our proposed DR-transformer framework considers TC estimation as a nearest neighbor classification problem and utilizes the continuity of labels to construct a distance-consistency embedding space in which we can easily find the neighbors of query image by the K-nearest neighbor classifier [8].
## 6 Strengths, Limitations, and Future Research Directions
### Strengths
#### 6.1.2 Robustness  
CNNs are inherently translation-invariant, making them robust to small changes in object position or orientation within an image. The main advantage of ViTs is their ability to effectively capture global contextual information through the self-attention mechanism, enabling them to model long-range dependencies and contextual relationships, which can improve robustness in tasks that require understanding global context. ViTs can also adaptively adjust the receptive fields of the self-attention mechanism based on input data, allowing them to better capture both local and global features, making them more robust to changes in scale, rotation, or perspective of objects.  
Both architectures can be trained using data augmentation techniques, such as random cropping, flipping, and rotation, which can help improve robustness to changes in input data and reduce overfitting. Another technique is known as adversarial training, where they are trained on adversarial examples: perturbed images designed to confuse the model, to improve its ability to handle input data with adversarial perturbations. Combining models, using ensemble methods, such as bagging or boosting, can also improve robustness by exploiting the diversity of multiple models, which can help mitigate the effects of individual model weaknesses.
## 4 Towards Enhanced Global-Scale Local-Relevant Monitoring and Forecasting  
Global monitoring and forecasting for environmental, weather and climate applications needs an accurate specification of the Earth surface as boundary condition to the atmosphere. This need can be satisfied at various levels of precision and complexity. However, some properties do not receive enough attention or they are simply neglected and therefore introduce errors in the surface representation. Others are approximated, due lack of information, by drastically simplified biogeophysical processes, typically when satellite datasets are not used. To enhance global-scale forecasting that maintains high relevance at local scale, it is necessary to pay more attention to the mapping of surface properties and parameters, in association with the parameterisation schemes used. In this section, we highlight some of the important mapping efforts and their relevance for modelling capabilities.