esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Review of Sensor Requirements for A3R
### Decrease Separation to Astern Proximity  
As the separation between the aircraft decreases, the requirement for more precise location and time data increases. This is analogous to the requirement that commercial airliners have an increased need for accuracy as they approach landing: airliner accuracy requirements are 2.0 nautical mile (NM) enroute, decreasing to 1.0 NM for the terminal approach, and further decreasing to 0.3 NM for the final approach [81]. A similar cone of required precision exists as the receiver UA approaches the tanker aircraft.  
As the receiver UA decreases the range separation to the tanker, DGPS can be used to support the increased accuracy for the UA. DPGS has a multitude of applications for air navigation [82], including aircraft position [83], reference positioning between vehicles for both air breathing [84] and space-based vehicles [85], and approach capabilities for both shipborne [86] and land base recovery [87]. Modern day DGPS systems have positional accuracy less than 1M and near-nanosecond time accuracy [26]. The SWaP-C associated with installing an aperture on the drogue to obtain GPS information of the drogue is prohibitive though, and therefore, the GPS receiver must be installed on both the receiver UA and the tanker aircraft. To obtain a carrier phase-based solution with regards to the relative position of the receiver UA, the tanker aircraft will need to transmit GPS information to the receiver UA [26]. By transmitting the raw GPS over a data link, such as Link-16, to the UA, a precision relative navigation (P-RELNAV) solution becomes available, which enables faster initialization, a robust operation due to aperture obscuration, and an insensitivity to carrier lock loss [88].  
Transitioning from a possible contested environment (for example, an environment in which GPS is denied) to a more permissive environment to conduct A3R, the UA must be able to transmit and receive position information both (1) to the GCS over BLOS or LOS data links and (2) to the tanker aircraft to create a P-RELNAV solution. To complete the P-RELNAV solution, this requires GPS receivers on both the tanker and UA in L-Band [49] and a data link between the tanker and UA, such as Link-16 [57].
# Dynamic Performance of Soil-Tunnel System under Transverse Sinusoidal Excitations
## 5 Responses of Segmental Tunnel
### Extension/Closure of Longitudinal Joints of Model Tunnel  
Time histories of extension/closure responses of longitudinal joints are shown in Figure 21.  
The extension/closure of longitudinal joints are relatively large at 45\({}^{\circ}\), 129\({}^{\circ}\), 231\({}^{\circ}\), and 315\({}^{\circ}\), while the extension/closure responses of longitudinal joints are small near the top and bottom of the tunnel. The extension/closure responses of symmetrical monitoring positions show almost 180 degrees out-of-phase. Moreover, the extension/closure responses on the internal and external side of each longitudinal joint show almost 180 degrees out-of-phase.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **A-Top (g)** & **A-Bottom (g)** & **Difference (\%)** & **Phase Difference** \\ \hline Sin-2 & 0.120 & 0.119 & 0.83 & \(1/64\pi\) \\ \hline Sin-3 & 0.210 & 0.158 & 24.76 & \(3/64\pi\) \\ \hline Sin-6 & 0.117 & 0.054 & 53.85 & \(3/5\pi\) \\ \hline Sin-9 & 0.057 & 0.093 & \(-63.16\) & \(1\pi\) \\ \hline Sin-11 & 0.065 & 0.099 & \(-5 In the case of the Sin-2, response waveforms of longitudinal joints are approximately sine function. In the cases of the Sin-3 and Sin-6, the response waveforms of longitudinal joints are irregular, and there exists residual deformation. In the cases of the Sin-9, Sin-11, Sin-14, and Sin-17, the response waveforms become more complex.  
According to the distribution of residual deformations, it can be concluded that the model tunnel produces a slight ovaling deformation. The seismic settlement of model ground will occur under stronger earthquake interaction, consequently resulting in the permanent ovaling deformation of the segmental lining in the direction of gravity.  
As the frequency of input motions approaches the fundamental frequency of the model ground, residual deformations of longitudinal joints are appearing, and the extension/closure deformations reach maximum. This is probably because the interaction between the soil and the tunnel is much stronger. The response waveforms become more complex with the increase of the excitation frequency, which may be influenced by high-order vibration modes.
# Best Practices Evidenced for Software Development Based on DevOps and Scrum: A Literature Review
## 3 Results
### Challenges and Mitigation Strategies for Integrating Scrum and DevOps in VSEs
#### 3.4.2 Technical Limitations  
Some authors, such as those of [90; 99; 107; 110], propose changing how development and operations teams work based on the significant automation of repetitive tasks. When combined with limited financial resources, the skills gap makes it difficult for VSEs to invest in the robust DevOps tooling and infrastructure required for seamless automation, continuous integration, and continuous delivery [14; 79]. Implementing and managing these tools can quickly become overwhelming, especially for small teams lacking specialized expertise. As a viable solution to address this, VSEs must prioritize what they require to implement in a staggered manner and then implement cloud-based services to overcome the automation problems [36]. According to the authors of [37], by leveraging cloud-based DevOps platforms, VSEs can access a wide range of pre-configured tools and managed services that significantly reduce the burden of infrastructure management and maintenance and implement the main practices found in the literature as continuous integration (CI), versioning, continuous deployment (CD), cloud computing, IaC, automated testing, unit testing, static code analysis (SCA), continuous monitoring, and continuous improvement.  
Cloud solutions also offer the flexibility to scale resources up or down as required, aligning costs with actual usage and reducing upfront investment requirements. Therefore, cloud-based services can empower VSEs to overcome technical limitations and implement essential DevOps practices without straining their limited resources [36]. Another possible solution is to use infrastructure as code (IaC), according to [72; 73; 74; 75; 76; 77; 78; 79; 80; 99; 107; 103].  
Instead of managing infrastructure manually, VSEs can define servers, networks, and other components as code to automate provisioning, simplify management, and ensure consistency. Thus, IaC tools facilitate rapid deployment, scaling, and cost optimization to adapt quickly to changing project requirements. Storing IaC configurations in versioncontrol systems improves collaboration, reduces errors, and allows for easy rollback to previous configurations. Additionally, IaC empowers VSEs to streamline their infrastructure operations, improve reliability, and reduce the requirement for specialized infrastructure skills, making it an option for a successful Scrum and DevOps implementation. This presents a powerful solution to overcome technical limitations and resource constraints.
## 3 Results and Discussions  
### Flows Characterization  
#### 3.1.1 Physico-Chemical Parameters of Flows  
The physico-chemical parameters obtained for wheat, buckwheat and sprouted buckwheat flours are presented in Table 3. The results indicated that the levels of ash, total fat, protein and fibers were significantly higher in sprouted buckwheat flours compared to buckwheat and wheat flours, respectively.  
In the present study, the SF protein content (18.75%) is in line with the values presented by [PERSON] et al. [44] (19%) and [PERSON] and [PERSON] [45] (19.35 \(\pm\) 2.14%), respectively. [PERSON] et al. [44] reported also higher levels for ash (2.82%) and fibers (4.39%) of sprouts compared to seeds (2.80% ash and 3.82% fiber contents). In this light, [PERSON] and [PERSON] [45] reported a value of 3.51% ash for sprouts and 1.78% for ungerminated seeds. With respect to total lipid amount, a higher level of total lipids in sprouts compared to seeds (2.59% and 3.02%, respectively) were reported [44;45].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Moisture Content** & **Ash** & **Total Fat** & **Protein** & **Fibes** & **TTA** \\  & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **TTA** \\ \hline
**WF** & 10.55 \(\pm\) 0.23  & 1.14 \(\pm\) 0.11  & 1.14 \(\pm\) 0.11  & 1.14 \(\pm\) 0.11 [FOOTNOTE:]Footnote : WF: wheat flour; BW flour: buckwheat flour; 10 BW, 20 BW, 30 BW: composite flour with 10%, 20%, 30% buckwheat flour addition; SF flour: sprouted buckwheat flour; 10 SF, 20 SF, 30 SF: composite flour with 10%, 20%, 30% sprouted buckwheat flour. Different lowercase letters within a column indicate significant differences between the means of the same parameter analyzed at \(p<0.05\) and (n = 3) according to [PERSON]’s multiple range test. All results are expressed to fresh weight (f.w.) product.[ENDFOOTNOTE] & 1.93 \(\pm\) 0.10 [FOOTNOTE:]Footnote : WF: wheat flour; BW flour: buckwheat flour; 10 BW, 20 BW, 30 BW: composite flour with 10%, 20%, 30% buckwheat flour addition; SF flour: sprouted buckwheat flour; 10 SF, 20 SF, 30 SF: composite flour with 10%, 20%, 30% sprouted buckwheat flour.  
Regarding the composite flows, the partial addition of these unconventional flours to wheat flour, led to an enrichment of samples from a physico-chemical point of view (Table 3), where buckwheat sprouts flour had a greater impact on these parameters.
## 3 Experimental Results and Discussion
### Crack Pattern and Failure Mode
#### 3.3.4 Effect of Adopting TMS Plates on the Failure Mode  
The implementation of TMS plates to replace conventional shear links in deep beams altered the failure mode, shifting it from predominantly shear diagonal tension failure to a combination of flexural failure and shear deformation. Despite reinforcing bars having a higher yield strength than TMS plates, substituting the former with the latter as shear reinforcement could provide an overall greater amount of bidirectional shear reinforcement [23]. This bi-directional property can significantly increase shear capacity without raising any steel congestion issues that unidirectional shear links would raise. Nonetheless, the main concern in beams using TMS plates as shear reinforcement is the adequacy of the bonding to prevent slippage between the TMS plates and longitudinal bars [20]. This is addressed by fabricating different number of holes in the TMS plates, ensuring a gradual progression in diagonal tension failure in the HP series specimens.
# Research and Application of Slag-Nanosilica Stabilizer for Silt Subgrade
## 4 Results and Discussion
### Stabilizer Mix Ratio Design
#### 4.1.4 Range Analysis of Orthogonal Test  
According to the comprehensive evaluation value of grey correlation analysis, the comprehensive evaluation value of each factor and level was calculated, and range analysis was conducted. The results are given in Table 10.  
In the above table, \(k_{i}\), whose optimal level of column factor can be judged by size, represents the average value of \(K_{i}\). \(R\) represents the range of \(K_{i}\) of this column factor. The greater the \(R\), the greater the effect of this factor on the test indicators. Therefore, the primary and secondary order of each factor can be determined according to the size of \(R\).  
As can be seen from Table 10, among all the influencing factors, the content of quicklime had the greatest influence on stabilized soil, followed by the content of nanosilica, while the content of slag had the least influence. Among all factors, the optimal mix ratio was composed of the level with the highest average comprehensive evaluation value, that is, with quicklime, nanosilica, slag content of 3%, 0.4%, and 5%, respectively. Compared with the optimal mix ratio determined by the grey correlation analysis method, the optimal mix ratio of stabilizer was quicklime (A): nanosilica (B): slag (C) = 32:3:65.
# Optimal Placement of Multiple Feeder Terminal Units Using Intelligent Algorithms
## 3 Mathematical Model
### Objective Function
#### 3.2.1 Initial Total Investment Cost  
The \(C_{inv}\) of the distribution FTU configuration scheme is the sum of the purchase and installation costs of the FTU types determined by decision variables as follows:  
\[C_{inv}=n_{1}(pc_{1}+ic_{1})+n_{2}(pc_{2}+ic_{2})+n_{3}(pc_{3}+ic_{3}) \tag{8}\]  
where \(n_{1}\), \(n_{2}\) and \(n_{3}\) are the configuration number of \"one remote\" terminal units, \"two remote\" terminal units and \"three remote\" terminal units, respectively. \(pc\) and \(ic\) are the purchase and installation costs of FTU respectively. Their subscripts correspond to the type of FTU as the same as the variable \(n\) mentioned above.
# Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition
## 3 Applications Across Domains
### Astrophysics  
Astrophysical research has benefited from ML methods for event reconstruction in Imaging Atmospheric Cherenkov Telescopes (IACTs). Ensemble methods have enhanced the accuracy of Cherenkov event classification in the ASTRI Mini-Array project [8], enabling more precise studies of high-energy cosmic phenomena.
# ALPINE: Active Link Prediction Using Network Embedding
## 3 The ALPINE Framework
### Network Embedding for Partially Observed Networks  
Network embedding for partially observed networks differs from general network embedding in the way it treats the unknown link statuses. It uses only the observed links and non-links to train the model, where the unobserved part does not participate. We defined an (undirected) Partially Observed Network (PON) as follows:  
**Definition 1**.: _A Partially Observed Network (PON) is a tuple \(\mathcal{G}=(V,E,D)\) where \(V\) is a set of \(n=|V|\) nodes and \(E\subseteq\binom{V}{2}\) and \(D\subseteq\binom{V}{2}\) the sets of node pairs with observed linked and observed unlinked status, respectively, where \(E\cap D=\emptyset\). Thus, \(K\triangleq E\cup D\) represents the observed (known) part, and \(U\triangleq\binom{V}{2}\setminus K\) is the set of node pairs for which the link status is unobserved._  
For convenience, we may also represent a PON by means of its adjacency matrix \(\mathbf{A}\), with \(\mathbf{A}\in\{0,1,\text{null}\}^{n\times n}\) and \(a_{ij}\) at row \(i\) and column \(j\) equal to null if \(\{i,j\}\in U\), to one if \(\{i,j\}\in E\), and to zero if \(\{i,j\}\in D\).  
Most network embedding methods (and methods for link prediction more generally) do not treat the known unlinked status differently from the unknown status, such that the networks are embedded with possibly wrong link labels. This appears almost inevitable in methods based on random walks (indeed, it is unclear how one could, in a principled manner, distinguish unlinked from unknown statuses in random walks), but also many other methods, such as those based on matrix decompositions, suffer from this shortcoming. We now proceed to show how CNE, on the other hand, can be quite straightforwardly modified to elegantly distinguish unlinked from unknown status, by maximizing the probability only for the observed part \(K\) of the node pairs:  
\[P(\mathcal{G}|\mathbf{X})=\prod_{\{i,j\}\in E}P(a_{ij}=1|\mathbf{X})\cdot\prod_{\{k,j\} \in D}P(a_{kl}=0|\mathbf{X}). \tag{3}\]  
In this way, we do not have to assume that the unobserved links are absent, as state-of-the-art methods do. Furthermore, the link probability in CNE is formed analytically because the embedding is found by solving a Maximum Likelihood Estimation (MLE) problem: \(\mathbf{X}^{*}=\text{argmax}_{\mathbf{X}}\;P(\mathcal{G}|\mathbf{X})\). Based on this, later in Section 4, we will show how it also allows us to quantify the utility of an unknown link status for active learning.
## 4 Simulation Results
### Efficiency of Energy Conservation  
We first evaluate the efficiency of energy conservation by comparing the HNA of the two algorithms (LEACH and 2 TC-cor). Simply, the LEACH protocol is a hierarchical protocol in which nodes transmit their data readings to cluster heads. The mechanism of cluster head rotation leads to a balanced energy consumption and hence to a longer lifetime of the network. Obviously, the denser networks have the longer lifetime as shown in Figure 4. But the cluster members are continuously sensing the environment and reporting to cluster head. It leads to unnecessary energy consumption while the reported data are highly temporal and/or spatial correlated.  
Our 2 TC-cor algorithm uses prediction model to reduce the transmission in which the reporting data are highly temporal correlated. Also, some nodes can turn off their radio units (_i.e._, avoid idle  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Network size & 1000 m \(\times\) 1000 m \\ Number of nodes & 500, 750, 1000, and 1250 \\ Signal range & 150 m \\ Sensing interval (\(t^{*}\)) & 5 (s) \\ Ratio of \(\Delta\) to \(k\) (\(k=72\)) & 1/6 \\ Pre-specified basis function (\(F\)) & \(F=\{1,t,t^{2}\}\) \\ Error threshold \(\epsilon\) (\(|\hat{R}_{s}(t)-R_{s}(t)|\)) & \(0.1*R_{s}(t)\) \\ Upper bound of spatial correlated weight (\(scw^{ub}\)) & 0.8 \\ Lower bound of spatial correlated weight (\(scw^{lb}\)) & 0.2 \\ \hline \end{tabular}
\end{table}
Table 1: Simulation environment.  
listening) for more energy saving based on the prediction mechanism. Furthermore, the nodes with highly spatial correlated data are grouped into virtual clusters. In the context, only the representative node is working and the neighboring nodes within a virtual cluster rest instead. It is rational that the representative mechanism results in the reduction of energy consumption, especially under a denser network. However, the overhead of constructing virtual clusters is non-negligible. In the experiments of sparser network, the expended energy for constructing virtual clusters exceeds the conserved energy by the predicting and representative mechanism. Therefore, the performance of our algorithm is not as good as LEACH in the metric of energy efficiency under the sparser network condition (number of nodes are 500 and 750). But, the benefits of energy conservation reveal finally under the denser network condition (number of nodes are 1000 and 1250) as depicted in Figure 4.
## 3 Methodology  
The proposed methodologywas structured around three core components designed to enhance positioning accuracy. The first component, represented by the proposed MDDA algorithm, addresses the GNSS receiver level by optimizing satellite selection through an appropriate weighting matrix and applying satellite exclusion strategies when necessary.  
The second component focuses on the inertial navigation system (INS), where accuracy is improved through precise calibration techniques aimed at minimizing inertial sensor errors.  
The third component involves the integration of refined GNSS and INS data using two fusion strategies: loosely coupled (LC) and tightly coupled (TC), as illustrated in Figure 1. Each of these components is discussed in detail in the following sections.  
Figure 1: Flowchart of the main methodological components.
# Evolution Mechanism of Interlayer Properties of CRTS III Slab Track during Construction
## 2 Methods  
### Interface Constitutive  
The interface damage between the track slab and SCC manifests as brittle cracking at the interface between old and new concrete. To simulate such interfacial cracking, a bilinear CZM was employed. The constitutive model is shown in Figure 2.  
Figure 1: Interface damage between the track slab and SCC.  
To describe the constitutive characteristics before the occurrence of damage, the crack initiation factor \"\(D_{I}\)\" was defined according to the quadratic nominal stress criterion [15], which can be expressed as follows:  
\[D_{I}=\left\{\frac{<t_{n}>}{t_{n}^{0}}\right\}^{2}+\left\{\frac{t_{s}}{t_{s}^{0} }\right\}^{2}+\left\{\frac{t_{t}}{t_{t}^{0}}\right\}^{2} \tag{1}\]  
where symbol \(<\) > indicates that the calculation is performed only when the normal tension is non-negative, and \(t_{n}\), \(t_{s}\), \(t_{t}\) represent the stress in the normal direction and the two tangential directions, respectively. It is evident from Figure 2 and the provided formulas that \(\left(\delta_{n,s,t}^{0}\), \(t_{n,s,t}^{0}\right)\) acts as the demarcation point, delineating the conditions before and after the occurrence of interface damage. This signifies that interface damage initiation commences when the interfacial tension reaches the critical value \(t_{n,s,t}^{0}\). Furthermore, the \(D_{I}\) value progressively rises from 0 before the interfacial tension attains the interfacial strength \(t_{n,s,t}^{0}\). Subsequently, \(D_{I}\) increases to 1 precisely when the interfacial tension equals \(t_{n,s,t}^{0}\), indicating the initiation of interface damage.  
The constitutive parameters of the bilinear CZM encompass interface stiffness \(K\), interface strength \(t_{n,t,s}^{0}\), and interface fracture energy \(G_{n,t,s}^{0}\). Following the casting of self-consolidating concrete, a contact interface emerges between the new and aged concrete. The CZM parameters \(K\), \(t_{n,t,s}^{0}\), and \(G_{n,t,s}^{0}\) for this interface exhibit an increment with the concrete's age [28; 29], as shown in Figure 3.  
Figure 3: The definition of the time-varying coefficient.  
Figure 2: Bilinear cohesive zone model.  
From Figure 3, the CZM parameters \(K\), \(t_{n,t,s}^{0}\), and \(G_{n,t,s}^{0}\) all exhibit logarithmic curve characteristics, and the three patterns are relatively similar. In order to facilitate the calculation of the changes in interfacial strength, interfacial stiffness, and interfacial fracture energy with age, a time-varying coefficient \(\xi\) by fitting the logarithmic function curve is proposed, ranging from 0 to 1. By multiplying \(\xi\) with the original fixed value of the parameters (Table 1), the time-varying interface constitutive parameters that change with the age of SCC can be obtained. This extension enables the generalization of the bilinear CZM to the early-age period of slab track.
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 2 Methods
### Algorithm Flow  
The algorithm is divided into three steps: calculating initial data, obtaining the range of interest (ROI) of the pallet, and estimating pallet pose parameters. After running step 1 once, the system loops through steps 2 and 3 to update the pose parameters of the pallet, as shown in Figure 2.  
Figure 2: Flow chart of the proposed algorithm.  
In step 1, the algorithm obtains information in respect of pallets and goods and then selects the classifier model that classifies each pixel of the color image. The initial distance is a statistic from the distance data of pallet category pixels, which is the initial parameter for the next step.  
In step 2, the algorithm obtains a new RGB and depth image from the sensors. If the target distance is in the effective range, then the compression unit size is calculated based on the projective principle. After classifying each pixel, a category matrix records the label information. A template is created based on the target information. To accelerate the matching process, both the category matrix and template are compressed. The labeled template is matched to the category matrix, and the match score of pixels is calculated. If the match score is higher than the threshold score, then the pallet ROI is confirmed.  
In step 3, the foot coordinates of the pallet are obtained from the ROI using the pallet geometry information. In sequence, the pallet center coordinate and the pallet angle are calculated. A sliding average filter is constructed to filter the pose parameters. Finally, the distance of the target is updated to step 2 to calculate the template size.  
This algorithm achieves real-time parameter updates. All the details are described in following sections.