esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermomechanical Analysis of Steel-to-Timber Connections under Fire and the Material Density Effect
## 2 Materials and Methods
### Finite Elements  
A three-dimensional (3D) numerical model was created using ANSYS(r) [11] to carry out the thermomechanical analysis and consider the nonlinear properties of wood and steel materials. For the numerical model, the principle of symmetry is used, reducing the computational costs and analysis time. Due to the study of half of the connection, symmetrical boundary conditions (S) were applied on the left side of the connection on the steel elements' sides, as shown in Figure 13. The solution method is incremental and iterative, based on displacement. The software uses the Newton-Raphson method to determine the new equilibrium position. Furthermore, the numerical model simulates the contact between all wood and steel components.  
Figure 13 presents the mesh used for one half of the numerical model and a representative plane under study, where different points (K1, K2, and K3) and the length of the dowel are considered for further analysis.  
To simulate the thermomechanical response, the connection is discretized using different types of elements available in the ANSYS(r) [11] internal library, as described in the flowchart of Figure 12.  
To represent the connection mesh for thermal analysis, the finite element SOLID 278 with a 3-D thermal conduction capability was chosen. This element has eight nodes with one degree of freedom, temperature, for each node. The element is applicable to a 3-D thermal analysis, in steady state or transient.  
To represent the connection mesh for structural analysis, the SOLID 185 finite element was used for the 3-D modeling of solid structures. It is defined by eight nodes with three degrees of freedom at each node: translations in the x, y, and z nodal directions.  
All contact interactions between different components were simulated using standard ANSYS(r) Contact174 and Target170 finite elements. These elements were only used to attach the connection between the contacting surfaces and between the dowels to transfer mechanical forces.  
CONTA174 is used to represent contact and sliding between 3-D target surfaces and a deformable surface defined by this element. The element is applicable to 3-D structural and coupled-field contact analyses. It can be used for both pair-based contact and general contact. TARGE170 is used to represent various 3-D \"target\" surfaces for the associated contact elements. The contact modeling requires the specification of the static friction coefficient for sliding surfaces, here considered a value of 0.3 [22]. In the study by [PERSON] [22], the static coefficients of friction for laminated veneer lumber on steel surfaces were experimentally determined. The authors concluded that the values were obtained between 0.1 and 0.3 for a smooth steel surface.
## 8 Current Challenges in the Development of 3D-Printed Prostheses
### Durability and Functionality of Prostheses  
Durability is a key concern in the design of robotic prostheses, especially those used in intensive physical activities or extreme conditions. Prostheses must be able to withstand daily wear and tear and continue to function optimally for long periods of time without the need for constant maintenance. This problem is amplified in robotic prosthetics, where the integration of electronic components and motors increases the risk of failure due to continuous use.  
In addition, functionality is also a challenge. Although modern robotic prosthetics have advanced in terms of control and movement, there are still limitations in terms of the accuracy, speed, and force they can exert. Current control systems, based on myoelectric signals or sensors, do not yet allow for completely natural and precise control, making it difficult to perform complex tasks such as manipulating small objects or performing fine movements [15]. The development of more advanced control algorithms, such as those based on artificial intelligence, is helping to mitigate this problem, but implementing these systems in accessible prostheses remains a challenge.
# Deduplication-Aware Healthcare Data Distribution in IoMT
## 5 Results and Analysis
### Discussion  
The proposed scheme, DICA, was compared to the RAM, CWCA, DSW, and WCA-BF + ASE schemes under several important evaluation metrics, focusing on data chunking, the average chunk size, the energy consumption, and the cut-point identification factor. The DICA dynamically adjusts the chunk size based on the healthcare data, resulting in a better number and size of chunks. The RAM shows some sensitivity to the data readings as the fluctuations rise, resulting in slightly smaller chunk sizes and numbers. The CWCA and WCA-BF + ASE schemes achieve better chunk numbers and sizes. In terms of cut-point identification, most of the schemes perform well. The DICA minimizes the number of cut-point identification failures by dynamically adjusting the chunk size based on the  
Figure 10: Energy consumption at sensing devices.  
Figure 9: Energy consumption at collector devices (CDs).  
window size criteria, while the RAM uses a fixed-size window to find breakpoints based on extreme data values. CWCA and WCA-BF + ASE show better cut-point identification results, further contributing to their efficiency. Overall, the DICA is suitable when the data readings exhibit many fluctuations and lower energy and computational costs are a priority. DSW is ideal for scenarios where maximizing the deduplication efficiency is crucial.  
The RAM scheme is better to use when the data exhibit less variation. In scenarios where the number of fluctuations increases, this results in a degradation in performance, so it is not an optimal choice for data with frequent changes. Although WCA-BF + ASE maintains good performance, it has relatively high energy and computational overheads. This scheme utilizes the Bloom filter for deduplication, minimizing the likelihood of false positives and false negatives, and a biased sampling approach is used. Afterward, ASE encryption is performed, which, despite enhancing the security, also increases the energy utilization and computational overhead. Thus, this scheme is not appropriate to use where resources are rare. The CWCA scheme preserves a larger number of chunks and is suitable to use when a stable approach to chunking is required, considering both minimum and maximum thresholds for better data processing.
## 4 Discussion
### Advantages and Limitations of the Proposed PB Modelling Approach  
The advantages of the proposed _DIMONA_ PB modelling approach are mainly its simplicity and the applicability of the plant water control equations for photosynthesis, plant growth and productivity, root/rhizoid respiration, and nutrient uptake, i.e., Equations (1)-(11), referred to hereafter as representative for their general versions (Section S4 in the Supplementary Materials). Together with the simple hydrology equations for peat introduced earlier within the _DIMONA_ platform [26; 27; 56], Equations (1)-(11) make process-based modelling with _DIMONA_ tractable to most researchers, regardless of their previous modelling experience. Equations (1)-(11) are simple yet process-oriented, circumventing the computationally complex, data-intensive, and parameter-expensive plant-water relation algorithms usually coupled to 1D or 3D soil hydrology in complex ecosystem models [22; 57; 58]. Thus, the _DIMONA_ PB model produces reasonable modelling outcomes while circumventing explicit but complicated simulations of water fluxes based on explicit plant physiological considerations.  
Modelling of plant photosynthesis, growth, root respiration, and nutrient uptake--and hence GPP and NPP--using the proposed plant water control equations would allow the _DIMONA_ PB model to be coupled to other hydrological models capable of producing the required moisture-related terms in Equations (1)-(11), thus further enhancing the applicability of _DIMONA_'s PB modelling approach. In addition, the proposed plant water control equations could be coupled to continuous field observations of \(\theta\) at various depths in the soil profiles, from which the terms in Equations (1)-(11) could be dynamically computed, resulting in a hybrid data-fusion-like approach for modelling plant and ecosystem productivities. This makes DIMONA applicable to many other ecosystems.  
Further to the above, there are several inherent advantages of the _DIMONA_ PB model as part of the _DIMONA_ online platform. The _DIMONA_ platform is able to ingest globally available meteorological forcing, near-surface \(\theta\), and soil properties extracted from satellite imagery and/or provided via Internet data repositories or API websites, in addition to detailed, but scarce, field near-surface \(\theta\) observations [27]. As such, the _DIMONA_ PB model is globally applicable at the site level, as well as at the regional level, where simultaneous simulations at many sites over any polygons of interest are possible. Another advantage of the _DIMONA_ PB model is its reduced computational time, e.g., the 14 years of simulations in each model run of this study (Table 2) were completed in less than 2 min via the _DIMONA_ web application's GUI. The inherent limitations of the proposed approach are the scarcity of WT observations and hydrological parameters, which for other sites may differ from those reported for Mer Bleue.
# BFE-Net: Bidirectional Multi-Scale Feature Enhancement for Small Object Detection
## 4 Experiments
### Main Results  
In this section, we evaluate the BFE-Net on the COCO test-dev and compare it with other state-of-the-art one-stage and two-stage detectors. Original settings of RetinaNet, such as hyper-parameters for anchors and focal loss, were implemented to ensure a fair comparison. For all studies, we used an image scale of 500 pixels unless noted for training and testing. All the results are shown in Table 1.  
By analyzing the experimental results, it can be found that when ResNet101 is used as the backbone network with an image short size of 500 pixels, the standard RetinaNet [11] achieves excellent results in detecting large targets, with an \(\text{AP}_{\text{L}}\) of 49.1%; however, when detecting small targets, it is different from the two-stage target detection method, with an \(\text{AP}_{\text{S}}\) of 14.7%. Compared with Faster R-CNN w FPN, it is 3.5% lower, and the detection results are not very ideal. In particular, the BFE-Net proposed in this paper has the same effect as detecting large targets when detecting small targets, and the \(\text{AP}_{\text{S}}\) reaches 17.9%, with a 3.2% improvement compared to the standard RetinaNet. At the same time, its detection accuracy is equally competitive with some two-stage object detection results. Compared with the standard RetinaNet, BFE-Net achieves higher AP of various sizes, which shows the effectiveness of the model proposed in this paper, especially for small object detection. The extra layers this work adds to the model, such as low-level feature C2 and bidirectional feature pyramid structure, introduce additional overhead, which makes our proposed model not as fast as the standard RetinaNet [11]. To compare the speed of the improved model, this work used an image scale of 500 pixels on the MS COCO dataset to compare with other object detection models. As shown in Table 2, compared with the standard RetinaNet [11], EfficientDet-D0 [12] has similar speed and accuracy, and the BFE-Net proposed in this paper has better accuracy but slightly slower speed; however, compared with Faster R-CNN w FPN [16], BFE-Net achieves competitive results in both \(\text{AP}_{\text{S}}\) and \(\text{AP}\), and is also much faster than Faster R-CNN w FPN [16]. Compared with the one-stage object detection models SSD513 [6] and DSSD513 [13], BFE-Net also has obvious advantages in speed and accuracy. The BFE-Net proposed in this paper achieves state-of-the-art accuracy compared to the standard RetinaNet, while maintaining reasonable speed compared to other models.
## 3 Numerical Simulations
### Scheme of Simulation Test  
The final failure characteristics of the specimens with different hole diameters under various impact velocities are shown in Figure 9. All the specimens were completely intact for an impact speed of 2 m/s. As the impact velocity increased to 3.3 m/s, tensile cracks appeared in the 90\({}^{\circ}\) and 270\({}^{\circ}\) directions in the specimen with a hole diameter of 8 mm, and an additional tensile crack appeared in the 180\({}^{\circ}\) direction in the specimen with a hole diameter of 16 mm. Both vertical and horizontal tensile cracks appeared in the specimen with a hole diameter of 24 mm. The failure mode of each specimen when the impact velocity increased continuously to 4.3 m/s was similar to that for an impact velocity of 3.3 m/s. Meanwhile, vertical cracks transitioned into tensile-shear mixed failure. These results indicate that the failure process of the hole was similar. The initiation positions of all the cracks in each specimen were at angles of 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\), respectively. This indicates that the dynamic stress was concentrated in these four regions, which is consistent with the experimental results. Therefore, the stress concentration and failure process of the hole by the incidence of the stress wave were studied in detail by considering a specimen with an impact velocity of 4.2 m/s and a hole diameter of 16 mm as an example.  
Figure 8: Comparison of failure modes between experiment and simulation (d = 8 mm, v = 4.2 m/s). (a) Experiment, (b) Simulation.  
Figure 7: Experimental and numerical stresses of the incident bar.
## 3 Computational Technique  
The governing dimensionless steady and unsteady equations are solved using the implicit finite difference method approach, which is exceedingly efficient. The connected non-dimensional model is translated into the primitive form using the primitive-variable formulation, resulting in a smooth algorithm. Applying central-difference along the \(y\)-axis and backward-difference along the \(x\)-axis yields numerical solutions for the converted primitive equations. We have an algebraic equation system with unknown factors \(U,\ V,\ \theta\) and \(\phi\). These variables contain coefficients in the form of a tri-diagonal matrix, and the numerical solutions to these unknown factors are found using the Gaussian elimination technique, as provided in [11; 28]. The Equation (44) is used for oscillating \(\tau_{w}\), \(q_{w}\) and \(j_{w}\) at prominent stations of a horizontal non-conducting circular cylinder.  
\[\tau_{s}=\bigg{(}\frac{\partial U}{\partial Y}\bigg{)}_{y=0}+\varepsilon|A_{s} |Cos(\omega t+\alpha_{s}), \tag{44}\]  
\[\tau_{t}=\bigg{(}\frac{\partial\theta}{\partial Y}\bigg{)}_{y=0}+\varepsilon|A _{t}|Cos(\omega t+\alpha_{t}),\]  
\[\tau_{m}=\bigg{(}\frac{\partial\varphi}{\partial Y}\bigg{)}_{y=0}+\varepsilon| A_{m}|Cos(\omega t+\alpha_{m})\]  
where,  
\[A_{s}=\Big{(}{u_{1}}^{2}+{u_{2}}^{2}\Big{)}^{\frac{1}{2}},\quad\ A_{t}=\Big{(} {\theta_{1}}^{2}+{\theta_{2}}^{2}\Big{)}^{\frac{1}{2}},\quad A_{m}=\Big{(}{ \varphi_{x1}}^{2}+{\varphi_{x2}}^{2}\Big{)}^{\frac{1}{2}},\]  
\[\alpha_{s}=\tan^{-1}\bigg{(}\frac{u_{2}}{u_{1}}\bigg{)},\quad\quad\alpha_{t}= \tan^{-1}\bigg{(}\frac{\theta_{2}}{\theta_{1}}\bigg{)},\quad\alpha_{m}=\tan^{ -1}\bigg{(}\frac{\varphi_{x2}}{\varphi_{x1}}\bigg{)}\]  
To obtain accurate numerical solutions for the steady and unsteady part of the flow model, the convergence criterion is  
\[max|U_{s}(i,j)|+max|V_{s}(i,j)|+max|\theta_{s}(i,j)|+max\big{|}\varphi_{ys}(i, j)\big{|}+max|\varphi_{xs}(i,j)|\leq\epsilon\]  
A grid independent test has been considered to obtain efficient and tolerable numerical results and the computation was started at \(x=0\), and marked down implicitly. Here, we have taken \(\Delta x=0.05\) and \(\Delta y=0.01\), for \(i\) and \(j\) grid points, and maximum grid point \(40\) in the following computation with tolerance \(\epsilon=0.00001\) for the convergence of the obtained numerical results.
# Comparative Impact Analysis of Cyclone Ana in the Mozambique Channel Using Satellite Data
## 2 Materials and Methods  
### Study Area  
The Mozambique Channel is comprised of all countries in coastal southeastern Africa, namely, Mozambique, Madagascar and the islands of Comoros and Mayotte (Figure 1). The chosen ROIs involved the two countries most affected by cyclones in the area: Madagascar and Mozambique. Specifically, both countries were the most affected with TC Ana, the tropical storm focused on in this study.  
Mozambique has an area of 800,000 km\({}^{2}\), with 11 regions. It shares land borders with six countries: Tanzania, Malawi, Zambia, Zimbabwe, South Africa, and Eswatini. The country is bordered by the Indian Ocean, with main coastal cities including the capital city of Maputo, Maxixe (Inhambane province), Beira (Sofala province), Quelimane (Zambezia province) [8]. The country has 11 main international rivers, including the large Zambezi and Limpopo Rivers, and about 104 river basins that feed mangrove systems.  
The neighboring island nation, Madagascar, has an area of 587,041 km\({}^{2}\) divided into 23 regions (faritras). The main coastal cities include Mahajanga (Boeny), Hell-Ville (Diana) and Maintirano (Melaky). As a result of the island's long geographical isolation from neighboring continents, Madagascar harbors a high level of biodiversity and a species endemism of more than 80% [9]. Monitoring habitat changes, particularly in resilience-enhancing habitats such as mangroves, is consequently of the utmost importance in the face of tropical cyclones.
## 4 Case Study
### Monitoring and Fault Recovery  
\"Hello\" messages are periodically sent on network links to monitor the states of the links. If a link has failed, the QuadBox that find the failure broadcasts a _link failure_ (_LFLR_) message to inform all other QuadBoxes of the failure. The QuadBoxes then update their link table and recalculate dual paths for connection pairs that include the failed link. Each QuadBox also broadcasts a _LINK_ message every 30 min even if no topology changes occur to synchronize and update the link table.
# Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks
## 3 Methods
### Selection of PHC
#### 3.3.1 Static PHC Selection  
This method is suitable for a scenario where UAVs have fixed positions (hovering UAVs) and can particularly be deployed in cellular-assisted UAVs and regions where UAVs are in a static position. Since UAVs have fixed geographic positions, a UAV having a middle position in the network is selected as a PHC node. Equation (12) calculates the middle of all nodes as a PHC-UAV. Here, we have considered absolute value to obtain an exact integer:  
\[PHC=Abs(total\ number\ of\ UAV/2) \tag{12}\]
## 4 Results  
The measurements of NO\({}_{2}\) and O\({}_{3}\) air pollutants are discussed below. Measurements at both the low-cost sensors (N1, N2, N3) and the reference sensors are conducted on an hourly basis. The data analysis implementation is performed in the MATLAB environment.  
LASSO regression is utilized on the data to identify the divergence between measurements obtained from low-cost and reference sensors. This analysis aims to determine how this divergence can be corrected by adopting and adjusting the LASSO regression, turning it into a correction factor applicable to data from low-cost air quality sensors. This study referred to one month's data (14 April 2021 to 13 May 2021), because the ageing of the sensors can impact the measurements (Kal  
Figure 4: Node 3, NO\({}_{2}\) noncorrected measurements with respect to reference measurements. (**a**) Time series of N3 (NO\({}_{2}\)) noncorrected and reference measurements; (**b**) Scatterplot of N3 (NO\({}_{2}\)) noncorrected and reference measurements.  
Figure 3: Node 2, NO\({}_{2}\) noncorrected measurements with respect to reference measurements. (**a**) Time series of N2 (NO\({}_{2}\)) noncorrected and reference measurements; (**b**) Scatterplot of N2 (NO\({}_{2}\)) noncorrected and reference measurements.  
Figure 2: Node 1, NO\({}_{2}\) noncorrected measurements with respect to reference measurements. (**a**) Time series of N1 (NO\({}_{2}\)) noncorrected and reference measurements; (**b**) Scatterplot of N1 (NO\({}_{2}\)) noncorrected and reference measurements.  
To determine the optimal value of the \(\lambda\) coefficient, cross-validation of the LASSO fit deviation, calculated for each sensor, between the low-cost sensor measurements and the reference NO\({}_{2}\) measurements was performed.  
Figures 5a, 6a and 7a show the NO\({}_{2}\) measurements' cross-validated deviance diagram of each low-cost sensor (N1, N2, N3) and reference, respectively. Figures 5b, 6b and 7b show the scatter plot between the NO\({}_{2}\) measurements of both the low-cost sensor (N1, N2, N3) and the reference. The scatter plot also shows the trend line for \(\lambda\) = 0 and for \(\lambda\) = 0.55.  
Figure 5: NO\({}_{2}\) N1, cross-validated deviance diagram and scatter plot with different values of \(\lambda\). (**a**) The cross-validated deviance diagram of NO\({}_{2}\) measurements between low-cost N1 and reference sensors; (**b**) Scatter plot of NO\({}_{2}\) measurements between low-cost N1 and reference for \(\lambda\) = 0 and \(\lambda\) = 0.55.  
Figure 6: NO\({}_{2}\) N2, cross-validated deviance diagram and scatter plot with different values of \(\lambda\). (**a**) The cross-validated deviance diagram of NO\({}_{2}\) measurements between low-cost N2 and reference sensors; (**b**) Scatter plot of NO\({}_{2}\) measurements between low-cost N2 and reference for \(\lambda\) = 0 and \(\lambda\) = 0.55.  
The aggregated results of the scatter plots of the low-cost NO\({}_{2}\) sensors before and after the application of the correction factors by means of the LASSO regression are shown in Table 2.
# Review of Remote Sensing Methods to Map Coffee Production Systems
## 3 Results  
Our systematic literature search yielded 43 peer-reviewed publications and reports. We categorized each article according to the remote sensing classification approach used in the study and identified six dominant classification approaches: spectral pixel-based, spectral sub-pixel-based, texture-based, data fusion, object-based, and hybrid. Fourteen studies applied multiple classification approaches to compare mapping outcomes. For studies that used multiple approaches in different classification models, we categorized them based on the primary approach identified in the study. Studies that incorporated multiple methods into the same classification model were considered hybrid approaches. We also captured the accuracy assessment methods used, as this represents a crucial step in understanding the validity of the resulting product. Supplementary Tables S1-S3 provide an overview of the 43 articles, including the geographic location of the study, the sensor(s) utilized, the classification approach(es), the accuracy of the product, and the validation method.