esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
However, this result might be due to the relatively low number of patients included in these GWAS compared with the MDD study and the small number of genome-wide significant variants reported in the original datasets. The identification of the molecular mechanisms underlying the association between psychiatric disorders and known comorbidities such as cardiometabolic and sleep disorders can improve our still limited understanding of the pathogenesis of psychiatric disorders. Moreover, it can also be of help to design treatment strategies aimed at reducing the impact that comorbidities exert on the clinical course of psychiatric disorders. Indeed, persistent insomnia has been shown to predict depression relapse and may contribute to poor clinical outcome [57]. The bidirectional causal association observed between MDD and insomnia using MR underlines the need of a more integrated assessment of sleep-related symptoms in patients with MDD as well as of mood symptoms in patients with sleep disorders. Indeed, based on the observed interplay between MDD, insomnia and metabolic traits, personalized treatment strategies addressing cardiometabolic and sleep disorders might benefit subgroups of patients with increased genetic predisposition to these phenotypes. Our results should be interpreted in light of some limitations. Firstly, all identified genetic variants have small effect sizes and only explain a small part of the observed variability of the investigated traits. Secondly, we used publicly available datasets from different GWAS studies, some of which have included participants from the UK Biobank, and are thus characterized by sample overlap. While some of the methods we used are robust to sample overlap (e.g., the local genetic correlation analysis conducted with LVA, in which all analyses were adjusted based on the computed sample overlap matrix), it cannot be excluded that this factor might have at least partly affected our results. In conclusion, using the k-means clustering machine learning algorithm based on cross-trait genetic correlation, we identified a cluster including insomnia and the psychiatric disorders MDD, PTSD and ADHD. We confirmed the existence of a bidirectional association between MDD and insomnia using MR and outlined loci characterized by a significant local genetic correlation which was either found to be specific for MDD and insomnia or mediated by increased genetically predicted BMI and CRP levels. The identified loci, some of which were found to affect the brain expression of different RNAs in brain regions, might be further investigated as regards to their potential role as drug targets or to develop improved treatment approaches.
## 3 Proposed Schemes
### Proposed Scheme 1: Intar
#### 3.1.1 Network Setup Phase  
In the setup phase of Intar, every node broadcasts a message to find its neighbors and hop count, which is computed through beacon generated from the sink and Euclidean distance from the sink. This information is exchanged with neighbors of every node with the help of HELLO packet, as shown in Figure 2. In Intar, hello packet consists of four fields i.e., node ID, number of neighbors, distance to sink and hops from the sink, whereas, in Re- Intar, hello packet consists of one more field, i.e., depth.  
Figure 1: Network architecture of the proposed Re-Intar, illustrating void hole problem and collision avoidances.  
When sensor node receives hello packet, it stores this information in the neighbor table. In Intar, neighbor table of each sensor node consists of four fields, i.e., NeighID, NumNeighbor, HopSink, and DistNeighbor. In addition, neighbor table of Re-Intar consists of two more fields, i.e., Depth and Timestamp. The neighbor table is illustrated in Table 2, where NeighID is the unique id (address) of a sensor node, NumNeighbor is the total neighbors available in the transmission range, HopSink denotes the hop-count using Euclidean distance from the sink, DistNeighbor is the Euclidean distance towards that neighbor, Depth denotes the depth difference with that neighbor, and Timestamp represents the time to update neighbor entry in the table. Furthermore, we also exploited piggy-backing mechanism to lower neighbor requests. When sender node sends data packet, it also includes hello packet information in the data packet. Upon receiving the data packet from sender node, receiver node updates the neighbor entry in the neighbor table if its depth is greater than the sender node.
# A Sensitivity Analysis of the Damage Behavior of a Leading-Edge Subject to Bird Strike
## 5 Conclusions  
In this work, a numerical study for increasing the structural performance of a leading-edge subject to bird strike is presented. The component is derived from a vaticat tail of a general aviation aircraft and is made of aluminum parts (ribs) and a metal sandwich (skin).  
A sensitivity analysis was carried out on the geometric parameters of the skin, i.e., the thicknesses of the two faces of the sandwich and the core's one. The material systems and the remaining parts were not subject to any changes. The parametric model is obtained by means of a python macro that is the core of a sensitivity analysis workflow developed in the ModeFrontier environment. The main parameter for controlling the performance of the component is the maximum displacement of the impact point placed on the skin (center of the bay). Therefore, the sensitivity analysis aims to identify the configuration that maximizes structural performance and keeps the total weight within a reduced range of variability.  
The results show that the increase in the thickness of each considered part (outer/inner face and core) leads to a reduction in the maximum displacement of the target point, as was to be expected. However, on the other hand, by associating this increase in thickness with the increases in mass, it is possible to see that minimal increases in the thickness of the outer/inner face (0.4/0.6 mm) lead to significant increases in weight, high increases (6.35 mm) in the thickness of the core lead to minimal mass increases and to maximization of structural performance.  
Furthermore, the results allow identifying the configurations that degrade the performances significantly. In fact, the reduction in the thickness of the outer face quickly leads to a catastrophic failure and thus to a complete penetration of the bird.  
In particular, three solutions were compared: the starting configuration, the optimal and the worst one. The best solution shows that with a minimum mass increase of about 5%, it is possible to obtain a clear structural performance improvement and, therefore, a reduction of about 50% of the maximum displacement. Obviously, intermediate solutions can preserve the functional requirements and keep the total mass even more reduced.  
The presented work also aims to identify the key parameters that could be implemented in an optimization analysis for maximizing the structural performance (compliance with functional requirements at minimum weight). For example, the results highlighted that minimizing the damage energy does not avoid certainly penetration possibility. In contrast, it can be quite large, but it must be well distributed and localized in the model.  
All authors equally contribute to this work. All authors have read and agreed to the published version of the manuscript.  
Figure 17: Deformed configurations.  
**Funding:** This research received no external funding.
## 4 Coordinate Transformation Model and Calibration System
### Decoupling Calibration Procedure  
The decoupling calibration procedure for the 3D electric field sensor is illustrated in Figure 7 and consists of the following steps.  
(1) Initial Calibration: Mount the sensor on the calibration fixture such that the LCS coincides with the GCS (\(\mathbf{R}_{1}=\mathbf{I}\)). Apply a standard electric field ranging from 0 to 50 kV/m, calculate the electric field \(\mathbf{E}_{1}\) in the LCS, and record the sensor output \(\mathbf{U}_{1}\).  
(2) Rotation Calibration around the \(OZ\prime\) axis: Rotate the sensor counterclockwise by 90\({}^{\circ}\) around the \(OZ\prime\) axis (obtain \(\mathbf{R}_{2}\)). Repeat the application of the standard electric field ranging from 0 to 50 kV/m, calculate the electric field \(\mathbf{E}_{2}\) in the LCS, and record the second sensor output \(\mathbf{U}_{2}\).  
(3) Rotation Calibration around the \(OX\prime\) axis: Based on the previous step, rotate the sensor counterclockwise by 90\({}^{\circ}\) around the \(OX\prime\) axis (obtain \(\mathbf{R}_{3}\)). Repeat the application of the standard electric field ranging from 0 to 50 kV/m, calculate the electric field \(\mathbf{E}_{3}\) in the LCS, and record the third sensor output \(\mathbf{U}_{3}\).  
(4) Decoupling Calculation: Use the three sets of calibration data to calculate the sensitivity matrix \(\mathbf{K}\) and the decoupled calibration matrix \(\mathbf{C}\), completing the sensor decoupling calibration.  
Figure 6: Three-dimensional electric field decoupling calibration system.  
This decoupling process ensures the uniqueness of the sensitivity matrix **K** and enables the accurate decoupling of the 3D electric field.
## 2 Related Work  
The growing interest in BSNs and the continual emergence of new techniques have inspired some efforts to study the reliability and quality-of-service (QoS) of BSNs. [PERSON] _et al._[6,12,13] proposed a novel QoS cross-layer scheduling mechanism based on fuzzy-logic rules for body sensor networks. An energy-saving distributed queuing MAC protocol is adopted. It can guarantee that all packets are served with a specific BER and within particular latency limit while keeping low power consumption. However, it neglects different reliability requirements of different types of biosensors. In [11], the challenges brought about by BSN applications are presented and a statistical bandwidth strategy, _i.e._, BodyQoS, is proposed to guarantee reliable data communication. However, the dynamic change in reliability requirements of sensors is not considered in BodyQoS. [PERSON] _et al._ highlighted the existence of the inter-user interference effect in BSN from the perspective of network architectures in [14], and make a preliminary investigation of the impact of inter-user interference and implement an instance with a fixed WSN infrastructure to reduce the interference between users in [15]. [PERSON] _et al._[16, 17] modeled probabilistic connectivity in multi-hop body sensor networks to determine ways to improve reliability, which can guarantee k-connectivity between nodes. [PERSON] _et al._[18] proposed a multi-homed body sensor network framework and investigate handover strategies during sensor nodes' movement to increase data reliability for BSNs. In [19] a novel packaging technology for BSN based on non-conductive thermoplastic polyurethane adhesive is presented to connect electronic modules with textile circuits in a cost efficient and reliable way.  
Although existing schemes [6,11-19] provide some solutions to improve fault-tolerant performance of BSN, designing fault tolerant BSNs to deal with channel impairments is still a challenging issue. In this paper, an adaptive and flexible fault tolerant communication scheme (AFTCS) for BSN is proposed. Major differences between this work and the aforementioned schemes include:  
(1) In order to fulfill the reliability requirement of critical sensors, fault-tolerant priority and queue are employed to adaptively adjust the channel resource allocation. Thus it can adaptively provide the reliability assurance for the sensors with high demand of reliability.  
(2) A resource reservation method based on dynamic priority queue is presented, including bandwidth measurement, bandwidth requirements calculation and bandwidth allocation methods. In AFTCS, the fault-tolerant priority dynamically changes according to the fault-related information, which can reflect the dynamic change in reliability requirements of sensors. In case of channel impairments, the packet loss rates for critical sensors will be decreased after channel reservation, and the times of retransmission will be reduced, thus lowering the average transmission latency.
## 2 Theoretical Model
### The Prediction Method of the Droplet: Pinned or Sliding  
When the droplet stays on an inclined, hydrophilic rough surface, we can predict the front end of the three-phase contact line, moving or not, by the comparison between \(\theta_{1-0}/\theta_{1-*}\) and \(\theta_{a}\), and predict the rear end of the three-phase contact line moving or not by the comparison between \(\theta_{2-0}/\theta_{2-*}\) and \(\theta_{t}\). According to the motion state of the front and the rear end of the three-phase contact line, we finally predict the droplet being pinned or sliding. The prediction procedure is divided into five steps, as follows (for the flow chart, see Section S7 in Supplementary Materials):  
* Step 1: We used Model 1 to calculate the advancing angle \(\theta_{a}\) and the receding angle \(\theta_{t}\);
* Step 2: We used Model 2 to calculate the initial droplet profile \(\Omega_{0}\), the initial droplet front contact angle \(\theta_{1-0}\), and the initial droplet rear contact angle \(\theta_{2-0}\);
* Step 3: We used \(\theta_{1-0}\), \(\theta_{2-0}\), \(\theta_{a}\), and \(\theta_{t}\) to first judge the motion state of the droplet: (1) If \(\theta_{1-0}<\theta_{a}\) and \(\theta_{2-0}>\theta_{t}\), the droplet is pinned; (2) If \(\theta_{1-0}\geq\theta_{a}\) and \(\theta_{2-0}\leq\theta_{t}\), the droplet is sliding; (3) If \(\theta_{1-0}<\theta_{a}\) and \(\theta_{2-0}\leq\theta_{t}\), the droplet is contracting--we then went to Step 4 and further judged the motion state of the droplet; (4) If \(\theta_{1-0}\geq\theta_{a}\) and \(\theta_{2-0}>\theta_{t}\), the droplet is stretching--we then went to Step 5 and further judged the motion state of the droplet.
* Step 4: For the contracting droplet, we used Model 3 to calculate out \(\theta_{1-*}\) and \(\theta_{2-*}\). By constraining the contact line length \(l_{\text{given}}=l_{0}-j\delta l\) (\(j=1,2,3\dots\)), we calculated every \(\theta_{1-*}\) and \(\theta_{2-*}\) corresponding to every contact line length during the droplet contracting period. Then, we calculated \(\theta_{1-*}\) when \(\theta_{2-*}=\theta_{t}\). Subsequently, we made the judgment that the droplet is contracting-to-pinned if \(\theta_{1-*}<\theta_{a}\), and the droplet is contracting-to-sliding if \(\theta_{1-*}\geq\theta_{a}\);
* Step 5: For the stretching droplet, we used Model 3 to calculate out \(\theta_{1-*}\) and \(\theta_{2-*}\). By constraining the contact line length \(l_{\text{given}}=l_{0}+i\delta l\) (\(i=1,2,3\dots\)), we calculated every \(\theta_{1-*}\) and \(\theta_{2-*}\) corresponding to every contact line length during the droplet stretching period. Then, we calculated \(\theta_{2-*}\) when \(\theta_{1-*}=\theta_{a}\). Subsequently, we made the judgment as to that the droplet is stretching-to-pinned if \(\theta_{2-*}>\theta_{t}\), and the droplet is stretching-to-sliding if \(\theta_{2-*}\leq\theta_{t}\).
# Walkability Compass--A Space Syntax Solution for Comparative Studies
## 2 Method
### The Research Method
#### 2.2.1 Open Geospatial Data Management Method (Steps 1-3)  
The first stage aims to download the open geospatial data on road street networks: building footprint, road network (cars and pedestrian road axis), points of interest (POI) and public transport bus stops. The core data were obtained from the openstreetmap.org [47] project, specifically the geofabric.de [53] server, which sorts the city databases into ready-to-download packages, thus imposing no restrictions on the amount of downloaded data. Next, the city administration boundary was downloaded from Living Atlas (2018) [48] database. Data were retrieved in Esri Shapefile format and transformed into the following EPSG coordinate system: 2180 (PL), 3346 (LT), 3059 (LV), 3301 (ES) and 3006 (SE). Since the Space Syntax technique requires a slightly wider extent of spatial datasets relating to the core research area, the data were downloaded with a buffer zone of no less than 4 km from the cities' administrative borders. However, no equal buffer zones were used; the input data were clipped to the geometry of the main roads (city ring roads) surrounding the city's administrative borders rather than an equal buffer zone. Such an approach results from the specificity of the space syntax method, which is based on the geometric properties of the streets network, which we had to maintain concerning analysed area borders.  
In the second step (Figure 3, the following source layers for space syntax calculations were extracted: road axes, building footprints with perimeter and area attributes, POI and population density 1 km\({}^{2}\) grid. Additionally, the building's footprint geometrical parameters were calculated too. Any errors in the urban street network geometry are a potential cause of space syntax biases. Therefore, the third step aims at spatial data topology control and geometry simplification. In order to reduce the street network vertex number, the generalisation procedure was the 3 m radius parameter was set up. The task includes deleting excessive roads from the street network, e.g., sidewalks, duplicated central street lines, and some secondary roads; reconnecting isolated street lines; identification of bridges and tunnels and marking two-level crossroads. The topology rule, as well as geometry simplification (3 m radius), were executed using ArcGIS Pro (v2.8) software (Environmental Systems Research Institute, Redlands, CA, USA) [54]. The case study cities' buildings of  
Figure 3: The method workflow includes spatial data download and management (step 1–3), space syntax calculations (step 4–6), walkability compass and walkability patterns analysis (step 7–8) and research outputs sharing via WebMap (step 9); stages that are subject to results reporting are marked with an asterisk.  
active industrial production and buildings with a storage function were eliminated since they do not support street culture and walkability. The quality control-passed database was used for further calculations (the 4 th step).
# Personalized-Template-Guided Intelligent Evolutionary Algorithm
## 3 Results
### Comparison of PTG with Other Algorithms
#### 3.3.2 Analysis of Experimental Results of CEC2022 Test Function
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Function** & **Metric** & **RSA** & **FATA** & **MFO** & **ECO** & **HO** \\ \hline \multirow{4}{*}{F1} & mean & \(3.30\times 10^{4}\) & \(1.11\times 10^{4}\) & \(3.88\times 10^{4}\) & \(1.10\times 10^{4}\) & \(1.41\times 10^{4}\) \\  & std & \(9.03\times 10^{3}\) & \(4.71\times 10^{3}\) & \(2.08\times 10^{4}\) & \(6.95\times 10^{3}\) & \(7.06\times 10^{3}\) \\  & rank & 8 & 4 & 9 & 3 & 5 \\  & time & \(8.05\times 10^{-1}\) & \(1.64\times 10^{-1}\) & \(1.43\times 10^{-1}\) & \(2.64\times 10^{-1}\) & 3.78 \\ \hline \multirow{4}{*}{F2} & mean & \(2.15\times 10^{3}\) & \(4.9043\times 10^{2}\) & \(5.1656\times 10^{2}\) & \(4.9377\times 10^{2}\) & \(5.18\times 10^{2}\) \\  & std & \(7.4513\times 10^{2}\) & \(3.5924\times 10^{1}\) & \(6.3323\times 10^{1}\) & \(5.5792\times 10^{1}\) & \(5.1648\times 10^{1}\) \\  & rank & 9 & 2 & 5 & 3 & 6 \\  & time & \(8.16\times 10^{-1}\) & \(1.46\times 10^{-1}\) & \(1.30\times 10^{-1}\) & \(2.58\times 10^{-1}\) & 3.66 \\ \hline \multirow{4}{*}{F3} & mean & \(6.8129\times 10^{2}\) & \(6.5411\times 10^{2}\) & \(6.2229\times 10^{2}\) & \(6.3994\times 10^{2}\) & \(6.53\times 10^{2}\) \\  & std & \(7.5845\) & 9.1956 & 8.6708 & \(1.1516\times 10^{1}\) & 7.5945 \\ \cline{1-1}  & rank & 10 & 5 & 2 & 3 & 4 \\  & time & \(9.60\times 10^{-1}\) & \(2.43\times 10^{-1}\) & \(2.29\times 10^{-1}\) & \(3.56\times 10^{-1}\) & 4.14 \\ \hline \multirow{4}{*}{F4} & mean & \(9.6710\times 10^{2}\) & \(9.0017\times 10^{2}\) & \(9.0193\times 10^{2}\) & \(8.0836\times 10^{2}\) & \(8.74\times 10^{2}\) \\  & std & \(1.235\times 10^{1}\) & \(1.2315\times 10^{1}\) & \(2.8429\times 10^{1}\) & \(2.0832\times 10^{1}\) & \(1.1217\times 10^{1}\) \\ \cline{1-1}  & rank & 9 & 5 & 6 & 3 & 2 \\ \cline{1-1}  & time & \(8.87\times 10^{-1}\) & \(1.78\times 10^{-1}\) & \(1.67\times 10^{-1}\) & \(2.78\times 10^{-1}\) & 3.83 \\ \hline \multirow{4}{*}{F5} & mean & \(3.34\times 10^{3}\) & \(2.50\times 10^{3}\) & \(2.89\times 10^{3}\) & \(2.22\times 10^{3}\) & \(2.16\times 10^{3}\) \\  & std & \(2.8232\times 10^{2}\) & \(3.1463\times 10^{2}\) & \(1.10\times 10^{3}\) & \(4.3103\times 10^{2}\) & \(2.81\times 10^{2}\) \\ \cline{1-1}  & rank & 7 & 4 & 6 & 3 & 2 \\ \cline{1-1}  & time & \(9.38\times 10^{-1}\) & \(1.85\times 10^{-1}\) & \(1.55\times 10^{-1}\) & \(2.78\times 10^{-1}\) & 3.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance Comparison of Algorithms (RSA, FATA, MFO, ECO, HO) on CEC2022.
## 6 Advances in Analytical Strategies and Future Directions
### Sustainability in Carotenoid Analysis  
Sustainability has become a core focus in carotenoid analysis, with researchers aiming to minimize solvent use, waste generation, and energy consumption [138]. The adoption of solvent-free extractions and micro-scale chromatography systems has been a major step forward in reducing environmental impact [64]. SWE has been successfully used to extract carotenoids from fruit and vegetable by-products, aligning with green chemistry principles [139].  
Additionally, the valorization of fruit and vegetable agro-wastes has gained attention as an eco-friendly approach to carotenoid production. By utilizing peels, seeds, and other by-products, researchers are developing sustainable extraction processes that align with circular economy principles [140]. These approaches reduce food waste and enhance carotenoid bioavailability through innovative nanotechnology and encapsulation strategies.  
Another promising approach is the development of biodegradable and recyclable chromatographic materials. Bio-based sorbents and membranes derived from plant-based polymers are being explored as sustainable alternatives to synthetic stationary phases, offering renewable options for column packing [64].  
Finally, the development of automated and high-throughput methods improves efficiency while reducing the environmental footprint of carotenoid analysis. Automation minimizes solvent use, decreases energy consumption, and enhances reproducibility, aligning with modern green laboratory practices [139]. Additionally, life cycle assessments of extraction techniques provide valuable insights into their sustainability, helping to identify the most environmentally friendly and scalable methods for carotenoid analysis.