esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Future Research Directions
### Landscape and Regional C and Water Fluxes Estimation: An Upscaling Framework  
Figure 5 schematically shows a synthetic upscaling framework for estimating landscape and regional C and water budgets with a reasonable accuracy. Using a data-model fusion with footprint weighting method to optimize the ecohydrological model's parameters is strongly suggested because the land surface is normally heterogeneous. This is a key component in the upscaling framework. Model parameters are optimized by minimizing the 'cost' function [240]:  
\[\mathbf{J(x)=\frac{1}{2}\left[\left(\mathbf{O-Y(x)}\right)^{T}C_{o}^{-1}\left( \mathbf{O-Y(x)}\right)+\left(\mathbf{x-x_{b}}\right)^{T}\mathbf{P}_{b}^{-1} \left(\mathbf{x-x_{b}}\right)\right]} \tag{4}\]  
Figure 5: An upscaling framework synthetically integrating ecohydrological and footprint modeling, remote sensing and land surface measurements.  
where **x** is the vector of unknown parameters and **x\({}_{\textbf{b}}\)** is a vector of _a priori_ values of **x**; \(O\) is the vector of observations and **Y** is the nonlinear model results, **C\({}_{\textbf{o}}\)** is the covariance matrix of observations and _P\({}_{\textbf{b}}\)_ is the covariance matrix of _a priori_ parameters. Different from general data-fusion method, **Y** is weighting averaged by footprint _f\({}_{i}\)_ for each pixel (_j_) within the footprint domain as **Y\({}=\sum\)Y\({}_{i}\)**_f\({}_{i}\)_.  
We also note that the dynamics of the PBL is tightly coupled with the land surface processes (e.g., energy / water / C fluxes). But for simplification, we proposed off-line modeling instead of the on-line coupling with regional climate model or the GCMs. The meteorological driving inputs for the ecohydrological model, therefore, are required for model runs. These inputs can either acquire from spatially extrapolation from climate station (including EC flux towers) measurements, or from the reanalysis data (e.g., the data provided by National Centers for Environmental Prediction (NCEP)), or from the regional climate modeling outputs.
## 4 Multi-Module Measurement Validation of MANTiS-32  
As shown in Figure 17, the MANTiS-32 system is designed to transmit measurement data from four independently operating modules, each interfaced with eight MANTiS-6050 sensors to a centralized PBM system via a common wireless AP.  
Each MANTiS-32 unit is equipped with an integrated NEO-6M GPS module, enabling precise time synchronization across all four modules and their respective sets of eight connected sensors. The synchronized acceleration data from all 32 sensor nodes are transmitted in real time to the main PBM computer via a wireless AP, where they are sequentially received and stored. These time-aligned measurements are then used to perform real-time modal analysis of a scaled cable-stayed bridge model, facilitating the extraction of critical modal parameters, such as natural frequencies, mode shapes, and damping ratios. The analysis results support performance-based structural health monitoring and inform maintenance decision-making. This section validates the measurement performance of the MANTiS-32 wireless sensing system by demonstrating its capability to reliably acquire synchronized data from multiple distributed modules and to perform accurate, real-time modal identification of the test structure.
# Reliability-Aware Cooperative Node Sleeping and Clustering in Duty-Cycled Sensors Networks
## 3 Network Model
### Cooperative Nodes Search Region  
**Definition 1** (Cooperative nodes search region).: _For a certain reference node \(m\), \(C_{m}=\{c_{m}^{1},c_{m}^{2},\cdots,c_{m}^{k-1}\}\) represents its associated \(k-1\) cooperative nodes. The search region to find the next-hop cooperative nodes is represented by \(\textbf{R}_{RCR}\), which is defined as the overlapping area of the \(k\) circle regions with the centers of \(m\), \(c_{m}^{1}\), \(c_{m}^{2}\), \(\cdots\), \(c_{m}^{k-1}\), whose radius is \(R\). The radius of the circle region with the center of sink \(t\) is represented as \(r_{t}(m)\), where \(|mt|-R<r_{t}(m)<|mt|\), while the radius of the circle region with the center of the middle node between \(m\) and \(f_{m}\) is \(\frac{R}{2}\), where \(f_{m}\) represents the strategic location as shown in Figure 1._  
We use \(V_{RCR}\) to represent the set of cooperative nodes in RCR as shown in Figure 2. Thus, \(V_{RCR}\) is equivalent to the combination of \(m\) and \(C_{m}\), i.e., \(V_{RCR}=\{m,c_{m}^{1},c_{m}^{2},\cdots,c_{m}^{k-1}\}\). In order to keep the consistency of the algorithm, we also represent the reference node \(m\) as \(c_{m}^{0}\).  
Figure 1: Illustration of the cooperative nodes search region.  
Figure 2: Cooperation between adjacent groups of cooperative nodes.
# Applicability of Infrared Thermography for the Detection of Phase Transitions in Metal Alloys
## 2 Materials and Methods  
### Materials  
The phase transition detection capability of the IRT method for metal alloys is assessed for two samples: pure Ga and a Ga-In mixture. The CAS number and general information of the two pure used materials are presented in Table 1.  
The choice of materials for the proof of applicability is motivated by three main factors. First, the materials to be studied and the phase diagram for their binary system must be well-known and reliably documented. Then, the type of phase diagram and the nature of occurring transitions must have been encountered and successfully depicted with the IRT method in previous studies. Finally, all occurring transitions must occur within the temperature range which the IRT method has been validated for.  
Indium and Gallium, being commonly used for the calibration of DSC, are well-known materials, and the phase diagram of their binary system is well described [17]. Their phase diagram is identified as a eutectic phase diagram [7] whose form is similar to the one retrieved with IRT for binary system of sugar alcohols [8]. In addition, all occurring transitions are within the 0-200 \({}^{\circ}\)C range which the IRT method has already been validated for. In consequence, to prove the applicability of the IRT method for the transition detection in metals alloys, the goal is to retrieve phase transition data of a simple pure Ga sample and for a Ga-In mixture presenting at least the two types of transitions reliably documented for this system (namely, the liquidus transition and the eutectic transition). The molar fraction for the mixture of Gallium and Indium was selected within a range between 50 mol% and 60 mol% of In. This composition range is chosen both (i) because transitions are occurring in a temperature range that does not challenge the validity domain of the method (0-200 \({}^{\circ}\)C) and are far enough apart to ease the data interpretation in this context of proof of applicability and (ii) because the molar fraction of both compounds are high enough to be considered a multi-component mixture. After weighing both compounds using a Mettler Toledo weighing scale (Mettler Toledo, Bordeaux, France) with a 0.03 mg uncertainty, the Ga-In mixture obtained has been established at 54.9 mol%In-45.1 mol%Ga.
# Stable Evaluation of 3D Zernike Moments for Surface Meshes
## 3 Algorithms for Computing the 3D Zernike Moments for a Surface Triangular Mesh
### Two Algorithms for Computing Zernike Moments  
The previous subsections provide the elements for computing the contribution of one triangle of a surface mesh to the 3D Zernike moments of the shape enclosed with this mesh. We summarize those elements in Algorithm 1.  
```
procedureTRIANGLE(\(N\), \(A\), \(B\), \(C\), \(R\)) Input:\(N\): The maximum order for the 3D Zernike moments. \(A\), \(B\), \(C\): The three vertices defining the triangle. \(R\): The 2D quadrature rule Initialize:\(N(R)\) number of points in \(R\). Initialize \(c_{nl}^{m}(T)=0\). Compute \(V\), the signed volume of the tetrahedron \((O,A,B,C)\) for\(i=1,\ldots,N(R)\)do (1) Define \((r_{i},\theta_{i},\phi_{i})\) for point \(\mathbf{P}_{i}\) in the quadrature rule. (2) Evaluate \(Q_{nl}(r_{i})\) over all \(n\in[0,N]\), \(l\) with \(0\leq l\leq n\) and \(n\) and \(l\) of same parity (3) Evaluate \(\tilde{Y}_{nl}^{m}(\theta_{i},\phi_{i})\) for all \(l\) with \(0\leq l\leq N\) and all \(m\) with \(0\leq m\leq l\) (4) Compute all \(g_{nl}^{m}(\mathbf{P}_{i})\) based on Equation (31) (5) Update \(c_{nl}^{m}(T)=c_{nl}^{m}(T)+3 Vw_{l}g_{nl}^{m}(\mathbf{P}_{i})\) endfor Output: The Zernike moments \(c_{nl}^{m}(T)\) associated with triangle \((A,B,C)\). endprocedure
```  
**Algorithm 1**_Zernike moments associated with one triangle of a surface mesh_  
Briefly, given a quadrature rule \(R\) defined by a set of weighted points \(\mathbf{P}_{i}\), the algorithm proceeds by computing the functions \(g_{nl}^{m}(\mathbf{P}_{i})\) over all those weighted points, and then accumulating the results based on the quadrature rule given by Equation (32). The functions \(g_{nl}^{m}(\mathbf{P}_{i})\) are computed from the \(Q_{nl}^{m}(r_{i})\) at \(r_{i}\), the radial distance of \(\mathbf{P}_{i}\), and from the \(Y_{l}^{m}(\theta_{i},\phi_{i})\), at the inclination angle \(\theta_{i}\) and azimuthal angle \(\phi_{i}\) of \(\mathbf{P}_{i}\). The corresponding procedure is defined as TRIANGLE. Details on its implementation are provided in the section below.  
Given the procedure TRIANGLE, there are two possible algorithms that can then be used to compute the Zernike moments of a shape defined by a surface triangle mesh, one for an exact computation, and one for finite precision. We summarize them in Algorithm 2 and Algorithm 3, respectively.  
```
Input: The triangular mesh with \(M\) facets supposed to fit inside the unit ball. The maximum order \(N\) for the 3D Zernike moments. Initialize: Given \(N\), choose the 2D quadrature rule \(R\) to be applied on all triangles. Initialize \(c_{nl}^{m}=0\) for\(k=1,\ldots,M\)do (1) Define \((A,B,C)\) the three vertices of triangle \(k\). (2) Compute \(c_{nl}^{m}(T)=\text{TRIANGLE}(N,A,B,C,R)\) (3) Update \(c_{nl}^{m}=c_{nl}^{m}+c_{nl}^{m}(T)\). endfor Output: The exact Zernike moments \(c_{ll}^{m}\) of the shape.
```  
**Algorithm 2**_Exact Zernike moments for surface triangular meshes_
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 5 Framework Evaluation
### Generalized Framework  
The CMS framework is designed to be mission-agnostic, serving as a general and transversal tool for all SNOs. To achieve this, it employs task scheduling as its operational approach, incorporates standard interfaces, and features a modular and flexible architecture. Additionally, it utilizes a constraint satisfaction programming engine to model diverse scenarios, ensuring adaptability across different missions and operational contexts. From the perspective of infrastructure and architecture challenges, this approach effectively addresses system and scenario heterogeneity while supporting a hybrid architecture that maximizes operational efficiency. Regarding interoperability and standardization challenges, the framework directly tackles the mission-specific nature of existing tools and the absence of a common standard for integrated operations. For integrated management challenges, the framework facilitates end-to-end scheduling by accommodating the operational requirements of various heterogeneous system agents and ensuring coordinated task execution across them. In addressing operational dynamics challenges, the CMS framework effectively supports the multi-customer nature of NTNs by integrating multiple operators within a unified scheduling system. Finally, in the context of technology maturation and deployment challenges, the framework overcomes the lack of a general-purpose solution for integrated NTN operations, providing a standardized approach to coordinated network and satellite management.
# Distributed Load-Balancing Method for CCA Parallel Component Applications
## 1 Introduction
The static strategy cannot cope well with load changes during runtime. The centralized dynamic strategy imposes a significant burden on the management node, potentially causing it to become a bottleneck in the system and negatively impacting overall performance. This paper investigates the operational mechanisms of CCA parallel components and introduces a dynamic, distributed method for load-balancing in parallel component applications, termed Parallel Component Dynamic and Distributed Balance (PCDDB). We have developed a class library of computing nodes utilizing an object-oriented mechanism and defined the relevant interfaces within this library for load-balancing. These interfaces are compiled to create local computing-resource proxies for each computing node. In our proposed approach, the business component is generated by compiling the business interface and then deployed by the resource-management node within a sub-cluster. The sub-cluster is formed using an aggregation algorithm and represents a group of computing nodes with favorable communication conditions. We determine the dependencies between different component calls through data flow analysis. During application runtime, parallel component instances are treated as tasks to be assigned. We maintain the load information of the relevant nodes on the execution platform using a distributed table-update algorithm. When new tasks are identified, the task allocation request is circulated among the computing nodes within a sub-cluster. Each node can opt to receive tasks from the task allocation request based on its current load. We then locally generate and execute these component instances. By capturing the dynamic load information of the computing nodes at runtime and implementing the load-balancing strategy in a distributed manner, we achieve superior load-balancing results with reduced overhead. This approach outperforms existing methods in terms of performance and scalability.
## 4 Discussion  
This study aimed to compare the effects of EG and CG on various parameters, including muscle activity and cervical and sagittal shoulder postures, in individuals with scapular internal rotation. In this study, the CVA and SSA significantly increased in the EG and CG when comparing the measurements taken before and after the experiment. Shortening of the levator scapulae affects the coordination of the neck muscles, as well as flexion of the neck, loss of contralateral rotation, increased shear force in the neck, and abnormal elevation of the scapula, thus affecting the neck and shoulder posture. CVA is most often used for the evaluation of cervical posture while, in the evaluation of shoulder posture, SSA is commonly used. When CVA and SSA decrease, abnormal shoulder and neck posture increases. Stretching is a common way to lengthen the shortened muscles. Stretching exercises for the levator scapulae are often recommended [21; 28]. Many previous studies have shown that complex shoulder exercise programs that improve poor posture increase CVA and SSA [1; 21; 29]. In the process of the wall-sliding exercise, the subject was asked to keep their chin pleated, which improved the shoulder posture while improving the neck posture. In this study, subjects in both groups performed the same levator scapular muscle stretching and movement exercises (wall-sliding exercises). Therefore, there were differences in both groups before and after the experiment. The reason for the lack of differences between the two groups in this study may be that the subjects in both groups are mild subjects, with both reaching a normal range after exercise. In this study, the CVA and SSA increased. Poor posture improves when the cranivertebral angle and sagittal shoulder angle increase [13; 30]. To the best of our knowledge, scapular internal rotation is also associated with excessive utilization of the upper trapezius muscle [13], compromised strength in the LT and MT muscles, imbalances in the SA muscle, and other muscular dysfunctions [31]. Strengthening the LT and SA muscles was found to be effective in correcting scapular problems [32; 33]. Therefore, in addition to stretching exercises, strengthening exercises (Bilateral external rotation, L-exercise, Y-exercises, push-up) were also performed in this study. In order to further enhance posture, this study also implemented corrective training interventions targeting postural alignment, such as the wall-slide exercise, posture exercise, thorax-stabilization exercise, and backward-rocking exercise. In previous studies, posture-correction exercises have been shown to improve scapular posture [13; 21]. When the scapular position is improved, the CVA and SSA are thus also improved.
# An Enhanced Fusion Strategy for Reliable Attitude Measurement Utilizing Vision and Inertial Sensors
## 4 Experimental Test and Comparison
### Normalization of Coordinate System  
The multi-sensor attitude measurement system is composed of inertia and vision sensors, which are expressed in different coordinate systems. To obtain the attitude from the inertial and vision data, the basic requirement is that the two different measurements are converted into a same coordinate system. There is a turntable coordinate system (\(b\) system), the camera system (\(c\) system), inertial coordinate system (\(i\) system) and target coordinate system (\(t\) system). The specific normalization among these coordinate frames is depicted in Figure 6.  
As our objective is to measure the relative angles between the body coordinate system and one stationary reference frame, such as the camera coordinate system, it can be expressed by rotation matrix \(R_{c}^{b}(t)\). The \(t\) in the parentheses indicates that the matrix is time-varying. The coordinate system normalization is to transform the inertial and visual measurements into the \(R_{c}^{b}(t)\). The mathematical model of rotation transformations from the \(c\) system to the \(b\) system by visual and inertial quantity can be expressed as follows:  
\[\left\{\begin{array}{l}R_{c}^{b}(t)=R_{t}^{b}R_{c}^{t}(t)\\ R_{c}^{b}(t)=R_{i}^{b}R_{c}^{i}(t)\end{array}\right. \tag{19}\]  
where \(R_{c}^{t}(t)\) is the rotation matrix between the \(t\) system and \(c\) system, which is measured by vision. \(R_{c}^{i}(t)\) is the rotation matrix between the \(i\) system and \(c\) system, which can be obtained by inertial measurement. \(R_{t}^{b}\) is the rotation matrix between \(t\) system and \(b\) system and \(R_{i}^{b}\) is the rotation matrix between \(i\) system and \(b\) system. As the IMU and target are both fixed onto the turntable, \(R_{t}^{b}\) and \(R_{i}^{b}\) can be obtained by calibration.  
The rotation transformations between two coordinate systems can be expressed by three times of rotation, which can be indicated by rotating around \(x\), \(y\) and \(z\) axes with corresponding \(\alpha\), \(\beta\) and \(\theta\) angles, as displayed in Figure 7. In this way, the inertial and visual measurements are converted to express the motion of the object turntable, which prepares for further data fusion.  
Figure 6: Transformation of the coordinate systems.
## 3 Results
### Overheating Risk
#### 3.2.2 Effects of Different Measures to Reduce Overheating in Current and Future Climate
In the case with cooling ventilation, the cooled supply air comes directly to the bedroom but with the split cooling unit, it is installed in the living room. Figure 9 shows the annual hourly indoor temperature profile of the cases with the split cooling unit and the cases with ventilation cooling and ventilation boost in the new building under the current and future climate. In comparison to the base cases, the usage of the cooling of ventilation and ventilation boost reduces the maximum indoor air temperature to below 30 \({}^{\circ}\)C and 29 \({}^{\circ}\)C in the current and future climate, respectively. The split cooling in the living room installed decreases the maximum indoor air temperature of the warmest bedroom to around 25.5 \({}^{\circ}\)C in both current and future conditions. The simulations indicate that the rough rule-of-thumb design of cooling power of split units works well in both current and future climate. Table 9 shows the degree hours above 27 \({}^{\circ}\)C and 32 \({}^{\circ}\)C in the warmest bedroom of the new building for the cases with the split cooling unit installed in the living room and the cases with the ventilation cooling and ventilation boost in the new building under the current and future climate. The ventilation cooling and ventilation boost can reduce the degree hours above 32 \({}^{\circ}\)C to zero in both current and future climates. However, the degree hours above 27 \({}^{\circ}\)C is significantly high in both current and future climate. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Cases** & **Degree Hours** & **Degree Hours** \\
**above 27 \({}^{\circ}\)C** & **above 32 \({}^{\circ}\)C** \\ \hline New-Base without cooling, TRY2020 & 4848 & 1 \\ New-Base without cooling, TRY2050 & 7600 & 39 \\ New-Base with cooling, TRY2020 & 0 & 0 \\ New-Base with cooling, TRY2050 & 0 & 0 \\ New-Cooling of ventilation and ventilation boost, TRY2020 & 429 & 0 \\ New-Cooling of ventilation and ventilation boost, TRY2050 & 1156 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The annual degree hours above 27 \({}^{\circ}\)C and 32 \({}^{\circ}\)C, Comparison of the cases with different cooling systems under the current and future climate. Figure 9: The annual hourly indoor air temperature profile of the warmest bedroom, base cases, new and old building, current and future climate. (The temperature levels of 27 \({}^{\circ}\)C and 32 \({}^{\circ}\)C are shown as black dashed lines. On the other hand, the split cooling unit in the living room can reduce the degree hours of the warmest bedroom above 27 \({}^{\circ}\)C and 32 \({}^{\circ}\)C to zero in both climate conditions. According to Tables 5 and 6, electricity consumption is slightly lower when using the split cooling unit than the cooling of ventilation with ventilation boost.
## 3 Materials and Methods
### Description of the Measurement Campaign
#### 3.1.2 Equipment Setup and Configuration  
For the data collection, three sets of LoRa Dragino shields were used. The shields were coupled to an arduino and used the chip sx1276 from SEMTECH and monopole and omnidirectional antennas [32]. At transmission, a 9 V battery is used, and at reception two smartphones are used to power the circuit and record the transmission log. Table 1 shows the parameters used in measurements and Figure 3 shows the equipment.  
Figure 3: Transmission and reception equipment: (**a**) Arduino Uno module + Dragino 915 MHz LoRa module + omnidirectional antenna. (**b**) Arduino Uno module + Dragino 915 MHz LoRa module + omnidirectional antenna + GPS + smartphone.  
Figure 2: Measurement scenario Camilo Vianna forest (UFPA): (**a**) Aerial view of the Amazon rainforest grove. (**b**) Path of the tree-lined corridor traversed for data collection within the grove.  
The equipment settings shown in Table 1 were chosen for the following reason. The lowest feasible transmit power of 5 dBm was chosen to preserve the device energy, allowing for more measurements to be taken before battery recharging. Moreover, this approach minimizes potential interference with the animals' natural habitat and aligns with our sustainability goals.  
In addition, the bandwidth of 125 kHz and the coding rate of 4/5 were selected because they are the standard for LoRa devices in the AUS915-928 region [33]. Moreover, the 915 MHz frequency was chosen for our study because it is part of the ISM (Industrial, Scientific, and Medical) band allocated for such unlicensed communication purposes in Brazil.  
Finally, the SF determines the transmission rate and the sensitivity of the LoRa receiver. For densely wooded environments, SF12 is an ideal option for several important reasons. Firstly, trees and other types of dense vegetation act as physical obstacles that can weaken and attenuate the radio signal. A high SF offers a greater communication range. In addition, the high SF spreads the LoRa signal over a wider bandwidth, which increases the signal's resilience and extends the transmission range [34].