esa-sceva/satcom-chunk-collection huggingface dataset:
# Crasworthiness Analysis to Evaluate the Performance of TDM-Shielded Street Poles Using FEA
## 4 Results
### Parameterisation of TDM Thickness on Impact Energy Absorption on Street Pole and Vehicle
Therefore, it is imperative that optimization of these thicknesses is done to ensure that the passengers are mitigated by these absorptions as much as possible. Furthermore, after additional observations and comparisons between the impact energy absorbed by the street pole and the vehicle (especially between 0.2 and 0.4 s) as in the previous study [1] and the current one, it is shown in Figure 8 that the impact energy of the street pole is reduced throughout the simulation time. This might suggest that the vehicle is initially absorbing more energy when impacting a TDM-shielded pole. However, in fact, this only occurs because the distance between the vehicle is maintained for both simulations (in comparison to the work in [1]), so the contact between the reinforcement and vehicle happens first, and the energy absorption happens sooner than with the original unreinforced collision scenario. When analyzing the impact energies of the corner collision scenario, as seen in Figure 8, it can be observed that the reduction of the impact energy on the street pole is significantly lesser, with a thickness of 35 mm, than with the frontal collision scenario. While the frontal collision showed a mitigation of 5% on impact energy absorbed by the street pole when TDM reinforcement was applied, the impact energy by the corner collision was mitigated to about 30%. Since the corner collision scenarios are usually the more common forms of accidents between a car and a street pole, it can be suggested that TDM-based reinforcement can greatly decrease the damage caused on the street pole when corner collisions occur, and this was reflected by the significant reduction in deformation to the street pole. Furthermore, these significant mitigation of impact energies were also evident on the vehicle, with Figure 9 showing that the vehicle's impact energies were significantly reduced due to the presence of the TDM material. The reduction varies between 28% and 43% depending on the thickness of the TDM reinforcement, suggesting that corner collisions on street poles shielded with recycled shielded tires can greatly reduce the risk of harm and injury to a passenger. Figure 7: FEA results of energy absorptions of a vehicle after colliding on a street pole (frontal collision scenario) using five different thicknesses of TDM shielding. However, when comparing Figures 8 and 9, a peculiar observation was found that calls for further study on the optimization of these TDM-based reinforcements. When the thickness of the reinforcements was increased, the impact energy of the vehicle was seen to be higher than when the vehicle collided with the street pole using a lesser thickness. Therefore, while it is important that reinforcement using recycled tires need to be added to the pole, it should be noted that the thickness should be optimized, or passengers actually may suffer an increased risk of injury.
## 4 Phytochemical Content
### Phenolic Compounds
#### 4.1.2 Flavonoids  
Flavonoids are subclassified as flavanols, flavonols, flavanones, flavonones and anthocyanins. Total flavonoid concentration in lesser-consumed fruits has been reported as 87,140 mg of quercetin equivalents (QE)/100 g dw, 227 mg QE/100 g dw and 162 mg QE/100 g dw in jackfruit peel, pulp, and seed, respectively. Some specific flavonoids have been reported, such as 158.037 mg of rutin equivalents (RE)/L fw in passion fruit pulp, 65.24 mg QE/100 g fw in mamey pulp, and 7.0 mg RE/100 g fw in acai pulp [10; 28; 48; 62].  
Flavanols  
Catechin and epicatechin can be found in mamey, acgaI and lychee pulps as representative compounds. Reported values of catechin in mamey and lychee are 11.31 and 0.486 mg/100 g fw, respectively. In acgaI pulp, catechin values of 5.07 mg/100 g dw have been reported. For epicatechin, values of 0.58 mg/100 g fw in mamey pulp and 0.498 mg/100 g fw in lychee pulp have been reported. In acgaI pulp, 2.09 mg/100 g dw of epicatechin was reported. Gallocatechin-3-gallate is found in mamey and acgaI pulp, while gallocatechin and catechin-3-\(O\)-gallate is found in mamey pulp. A total flavonol content of 50.65 mg/100 g dw have been reported in acgaI pulp [56; 57; 58; 60; 63; 64].  
Flavonols  
Flavonols are found in most of these less-consumed fruits, except for jackfruit, and are significant in acgaI and lychee. For example, rutin (3.89 mg/100 g dw), quercetin (13,566 mg/100 g dw), isorhamethetin rutinoside (1.7 mg/100 g dw) and kaempfer (0.521 mg/100 g dw) are found in acgaI pulp, along with others. In mamey pulp, dihydromyicetin (200.77 ppm fw) was reported, as well as myricitin (25.48 ppm fw) [10; 59; 60; 63].  
Flavones  
Flavones have been identified in açai pulp, where orientin and isovitexin stand out, with 15.0 and 12.0 mg/100 g dw, respectively. Passion fruit peel also contains isoorientin, with 19.63 mg/100 g dw and its pulp has 16.226 mg/L fw [10; 11; 62]. This phenolic species is widely spread in the plant kingdom and has been reported in several fruits, vegetables, cereals, legumes, and wines [65]. Nevertheless, no studies have been addressed to identify and quantify them in lesser-consumed tropical fruits such as lychee, mamey, jackfruit or their by-products.
# Imputing Covariance for Meta-Analysis in the Presence of Interaction
## 4 Covariance in Meta-Analysis  
### Relationship Between Covariance and Correlation Coefficient \(r\)  
The overall main effect, \(\beta_{1,\text{overall}}\), and the overall interaction effect, \(\beta_{2,\text{overall}}\), can be calculated using popular R packages or SAS macros. Consequently, the overall effect at different ages can be determined. In this paper, we propose a method for calculating the overall covariance in meta-analysis in the presence of interactions, as required by Equation (3). This method leverages the relationship between the correlation coefficient, variance, and covariance, as expressed in the following equation [13]:  
\[r=\frac{cov(X,Y)}{\sigma_{1}\sigma_{2}} \tag{12}\]  
Here, \(cov(X,Y)\) represents the covariance between two random variables \(X\) and \(Y\), while \(\sigma_{1}\) and \(\sigma_{2}\) are their standard deviations. Specifically, \(X\) and \(Y\) can be replaced with the estimated coefficients \(\hat{\beta}_{1}\) and \(\hat{\beta}_{2}\), and the standard deviations with their corresponding standard errors. Based on this, the overall covariance can be calculated as  
\[cov(\hat{\beta}_{1},\hat{\beta}_{2})_{overall}=r_{overall}\times\sqrt{V_{\hat{ \beta}_{1,\text{overall}}}V_{\hat{\beta}_{2,\text{overall}}}} \tag{13}\]  
In Equation (13), \(cov(\hat{\beta}_{1},\hat{\beta}_{2})_{overall}\) represents the overall covariance between \(\hat{\beta}_{1}\) and \(\hat{\beta}_{2}\), and \(r_{overall}\) is the overall correlation coefficient. This overall covariance is derived from either a fixed-effect or random-effects meta-analysis. However, a key step involves calculating an overall correlation coefficient \(r_{\text{overall}}\), which is not straightforward in meta-analysis.  
To calculate the overall correlation coefficient, \(r_{\text{overall}}\), the following steps can be followed [9]:  
1. For each cohort, extract the standard errors of the two coefficients and their covariance from the regression model.
2. Calculate the correlation coefficient \(r_{i}\) for each cohort using Equation (12) \[r_{i}=\frac{cov(\hat{\beta}_{1i},\hat{\beta}_{2i})}{\sigma_{1i}\sigma_{2i}}\] (14) where \(cov(\hat{\beta}_{1i},\hat{\beta}_{2i})\) represents the covariance between the two coefficients, and \(\sigma_{1i}\), \(\sigma_{2i}\) are the standard errors of \(\hat{\beta}_{1i}\) and \(\hat{\beta}_{2i}\), respectively.
3. Perform a random-effects meta-analysis (or fixed-effect meta-analysis) on \(r_{i}\) values to estimate \(r_{\text{overall}}\)Once \(r_{\text{overall}}\) is determined, it can be substituted into Equation (13) to calculate the overall covariance \(cov(\hat{\beta}_{1},\hat{\beta}_{2})_{overall}\).
# Safety Effects of Freeway Hard Shoulder Running
### Safety Effects of HSR in South Korea  
There are several reasons why HSR might lead to an increased crash risk. This study will focus on three possible driver-geometric explanations that are more relevant to South Korea. The first reason is drivers' low familiarity with HSR. Since many freeway motorists in South Korea do not often experience hard shoulder running, they may not be familiar with driving within this new cross-sectional design. The general look and feel of a road and roadside environment have significant effects on road users' attention, the routes they choose, and the speeds at which they choose to drive [25; 26; 27]. Therefore, freeway motorists' lack of familiarity with HSR design may contribute to more driving errors [19].  
A second reason is the poor lane positioning of vehicles in HSR [28; 29; 30; 31]. A reduction or elimination of shoulders will potentially have an impact on the lane positioning of vehicles. Based on the results of his earlier study, one researcher recommended a two-foot minimum shoulder width for a concrete median barrier [32]. In one of the few studies conducted in South Korea, it was found that the design criteria for satisfying the minimum 1.25 m shoulder width had a significant impact on crash occurrences [33]. Since hard shoulder running is built with a shoulder width of less than 0.5 m in South Korea, drivers may have difficulty keeping proper lane positioning and thus, presumably, experience more crashes.  
A third possible reason is the unpredictability of drivers' speed before and after the implementation of HSR. One study found that the cues drivers used to select their driving speed on highways were derived mostly from peripheral vision [34]. In HSR, the peripheral vision may be different from that on normal freeway sections, because the lateral clearance on the right side of the freeway travel lane is greatly reduced or no longer exists. Clearly, any deterioration in drivers' ability to estimate the correct speed will increase their crash risks.  
In summary, we have sufficient reasons to hypothesize that the vehicle crashes occurring in HSR in South Korea may be associated with inappropriate driver performance due to low familiarity, the elimination of shoulder width, and changed peripheral vision.
# A Numerical Study on Computational Time Reversal for Structural Health Monitoring
## 4 Applications with Numerical Examples
### Source Localization
#### 4.2.2 Two-Dimensional Frame Structure  
In order to demonstrate the applicability of the method to more complicated geometries, a 2D frame structure made of an assembly of beams is studied (see Figure 5). The structure is assumed to be of a total length \(L=30\) mm while the height is \(H=10\) m. The modulus of elasticity was chosen \(E=1 Pa\), the mass density \(\rho=1\) kg/m\({}^{3}\) and the Poisson's ratio \(\
u=0.25\). The total duration of the numerical experiment for \(T=t_{o}+\frac{2L}{c^{2}}=63\) s, where the initial time is \(t_{o}=3\) s when the source is imposed as a pulse, the number of discrete time steps is chosen as a power of two \(2^{11}\) and the number of elements for every beam between the main nodes is 200, which means a total number of 2800 finite elements.  
During the forward procedure, six receivers are used to collect the response data (displacements and velocities) for every DOF (two translational and one rot  
Figure 4: Imaging using the absolute value of the axial (**left column**) and the norm of the transverse and rotational (**right column**) displacement (m) degrees of freedom (DOFs); the green line (with a dot on the horizontal axis) is the location of the receiver while red line (with an asterisk) is the location (m) of the source.  
Figure 5: Geometry of 2D frame structure, axes in meters (m). The bright red point is the location of the source, arrow icons show the direction of each receiver while the numbers give the ID of main nodes.  
possible positions of sensors are indicated together with the respective recording directions for every receiver. There is one single excitation point on the node of ID = 2 at the bottom horizontal part of the assembly. The leftmost and rightmost nodes are restrained in zero horizontal and vertical displacement.  
For the backward step, the time when the wave refocuses on the excitation point is \(T-t_{0}=60\) s, that is the \(t_{r}=1949\) discrete time step. In the plots of Figure 6, the Euclidean norm of displacements, with respective units, is shown for the \(t_{r}\) time step. While in the plot of Figure 6b, one may observe that ghost refocused signals are not so strong, in the rest of the plots of Figure 6, there is an obvious appearance of a symmetric-like ghost on node 4 with the value of the same order. Furthermore, we considered a 10% level of Gaussian noise in the recorded signals of displacement, and observed that the location of the source can be similarly well reconstructed.
## 2 Detailed Analysis of the Literature
### Prosumer Relationships
#### 2.2.3 Socio-Economic-Technological Aspect
Examining both social and technological aspects led to a four-layered framework to study the interactive prosumer behaviors in the emerging distribution system [93]. Using a multi-agent based simulation, results of the study show that technical interactions and social interactions affect the operational performance of the power systems wherein social values may dominate the impact of price signals. Although a prosumer's energy generation and demand can be price-responsive, social interactions may have a significant influence on their attitudes [93]. The technical and economic feasibility of both Household Energy Storage (HES) and Community Energy Storage (CES) technologies is investigated using real data of 39 households in a pilot project in the Netherlands [28]. Results reveal that PV self-consumption significantly contributes to savings achieved by storage and influences the payback period. Using both HES and CES systems reduce costs by 22-30% while increases PV self-consumption by 23-29%. Furthermore, considering current storage costs, both HES and CES are economically infeasible. The analysis of different aspects of literature related to the effective existence of community renewable energy networks (CREN) reveals that although there is abundant information on the technology components, the social aspects have not received the same level of attention [83]. These social aspects include human infrastructure or collaboration [89], economics [85; 90; 92], and governance [94; 95]. Adopting a holistic approach is significant, and hence, both technological and social aspects should incorporate into a working solution for successful implementation of CREN [83]. An investigation on the key issues shaping ICES also reveals a similar line of thought. Attention is needed in all aspects including technical, socio-economic, environmental and institutional to support the emergence of ICES [18]. Other studies also study this holistic approach and discuss the sociological, economic and technological drivers for the energy turnaround along with the environmental and political drivers [74]. Findings reveal that although technological development promotes energy turnaround, the other four factors also drive the evolution of the energy system. Therefore, it is critical to analyze all these factors for a sustainable deployment of the smart grid [74]. Following the above, we propose the following:  
**Proposition 8**. : _(a) A holistic approach spanning social, economic and technological dimensions is essential when examining smart grids; (b) Social values are impactful in bringing a change in prosumer behavior; (c) Social interactions and cultural values influence energy exchanges between households, and attitudes on benefit and comfort._
## 3 Results
### Object Detection  
Seven different models are trained and evaluated, including the original YOLOv8s network, YOLOv11s, and lightweight models using MobileNet V4, MobileNet V3, and MobileNet V2, among others. The performance of these models was compared using the mAP50-95 metric. The mAP is a composite metric that takes into account both precision and recall and provides a comprehensive assessment of model performance.  
Table 1 shows that models with MobileNet V4 and similar backbone structures achieve comparable mAP scores on the dataset with only slight performance changes. However, there are significant differences in terms of model parameters, computational complexity, and accuracy. For example, YOLOv8s has 11,422,166 parameters and a computational complexity of 29.6 GFLOPs. In contrast, YOLOv11s achieves about 15% reduction in the number of parameters and 23% reduction in GFLOPs, while achieving the highest 99.99% accuracy. Compared with YOLOv8s, MobileNet V4 reduces the number of parameters by about 50% and GFLOPs by 32.4%. Although its accuracy is 99.87%, which is slightly lower than that of YOLOv8s, this small accuracy loss is an acceptable trade-off compared with the large parameter and computational complexity reduction. In addition, Shufflenetv1 and Shufflenetv2 also demonstrate advantages in parameters and computational complexity, but MobileNet V4 is particularly outstanding in terms of overall efficiency and deployment flexibility. Therefore, MobileNet V4 not only performs well in resource-constrained environments, but also significantly improves the running efficiency of the model while maintaining high accuracy, making it an ideal choice for mobile devices, embedded systems, and other applications that require efficient computation. The results indicate that reducing model parameters significantly enhances computational efficiency without compromising detection accuracy. Model selection should depend on specific application requirements. For tasks prioritizing precision, YOLOv11s offers a balanced solution with high accuracy and moderate computational demands. Conversely, MobileNet V4 excels in resource-constrained environments by minimizing computational demands while maintaining reliable detection accuracy.
## 2 Fundamentals of SAR for Antenna Sizing and Transmitting Power Consideration
### Desired Range and Azimuth Resolutions  
SAR systems often offer a range of imaging modes with varying resolution capabilities for different user needs and applications. The resolution depends on the specific objectives of the mission and the trade-offs between resolution and coverage. These values may vary depending on the specific imaging modes and configurations available within each mission. There are mainly three imaging modes which are Stripmap, ScanSAR and Spotlight. Interferometric SAR (InSAR) uses the phase difference between two radar echoes related to the same image pixel, but they are received by two different systems from different directions. The phase difference between images reveals changes in the Earth's surface or topography [38]. Polarimetric SAR (PolSAR) is used for target detection and classification by using polarizations (horizontal and vertical) [38; 49]. TomoSAR techniques have expanded interferometric methods to enable multidimensional imaging. By utilizing multiple SAR images captured from various angles, it allows for 3D imaging, such as mapping urban areas [50; 51]. In this paper, resolution outcomes are examined rather than explaining imaging modes and advanced techniques. The resolution data have been taken from the results of real missions. Stripmap mode in Sentinel-1 provides medium-resolution imagery suitable for a wide range of applications, including agriculture and land monitoring [15; 16; 17]. The resolution is 5 m in range and 20 m in azimuth. The parameters of 1 m in range and azimuth for TerraSAR-X Spotlight mode offer high-resolution imaging for detailed observations of specific areas, and the parameters of approximately 40 m in range and azimuth for ScanSAR mode provide wide area coverage with moderate resolution for various applications, including disaster monitoring [7; 8]. Fine Quad-Pol mode in Radarsat-2 combines high resolution (3 m in range and azimuth) with polarimetric capabilities for applications like forestry and agriculture [11]. Spotlight mode in COSMO-SkyMed has 1 m in range and azimuth which provides high-resolution imagery for applications like urban monitoring and disaster management [9]. PALSAR-2 of ALOS-2 has 1 m in range and 10 m in azimuth [18; 19]. These resolutions are the practical values for the primary imaging modes of these SAR missions.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms  
Whole-genome alignment, a foundational task in genomics, relies on a variety of sophisticated algorithms to compare entire genomes. These algorithms can be broadly classified into three categories: suffix tree-based methods, anchor-based methods, hash-based methods, and graph-based methods. Each of these categories embodies different alignment strategies, offering unique advantages and facing distinct challenges.
## 2 State of the Art
### Monitoring and Analytical Capabilities  
The idea of an integrated IoT-edge-cloud continuum, embracing autonomous and intelligent systems, is intimately connected to the independent and self-reliant features of its network elements [33]. In this context, monitoring emerges as the primary instrument necessary for endowing the system with enhanced self-diagnostic and self-awareness capabilities. Following this, AI-driven analytical resources play a crucial role in fostering the system's self-learning and self-optimization processes.  
Monitoring tools play a crucial role in maintaining system health by gathering a range of metrics at both the infrastructure and service levels that will be later employed by the orchestration engine. The specific metrics that need to be monitored are determined by the resource requirements of the workload and the ability of the worker nodes to maintain resource isolation especially when faced with disruptions [34].  
While monitoring in monolithic applications tends to be relatively straightforward and deterministic, the situation is markedly more complex when dealing with microservices. In fact, their intricate web of interconnections significantly complicates the process of gaining meaningful insights into a system's internal state solely through the analysis of its external outputs. This complexity underscores the need for advanced monitoring strategies that can effectively navigate and interpret the multifaceted interactions within these systems [35].  
Some of the most advanced and used tools for multi-cluster monitoring include the use of federated time-series databases such as Prometheus [36], whose federation capabilities allow for multiple instances, where metrics for each cluster are independently scraped to be later aggregated into a centralized monitoring system. OpenTelemetry [37] is an observability framework that provides a standardized way to collect telemetry data in a multi-cluster manner, enabling consistent instrumentation across different environments such as those present in the IoT-edge-cloud continuum.