esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Comparison with Existing Methods  
When comparing the method used in this work with other existing ones, one must take into account that the studies conducted to date [8; 10; 11; 12; 50; 53] focus on mapping the situation of the studied wetlands. Although this is important, it should be noted that they leave aside future simulations, through which, alongside big data, the most probable occurrence scenarios can be inferred.  
Regardless of the above, the authors used a patented methodology [29], allowing them to obtain soil deposition predictions, which are an extremely useful tool for managing all types of wetlands subjected to both climate change events and human-induced climate change processes.  
It is necessary to emphasize that this methodology can be used with data of any type, including data from LIDAR, optical and radar remote sensing, and UAV images, as long as the latter cover a period of time which is broad and significant enough.
# A Study on the Normalized Delineation of Airspace Sectors Based on Flight Conflict Dynamics
## 4 Sector-Delineation Method Based on Mean Shift Clustering Algorithm
### Clustering Method Based on Mean Shift Algorithm  
The mean shift algorithm [23] is the application of kernel density estimation in the feature space of the climbing algorithm. Its algorithmic idea is to assume different clusters of datasets in line with different probability density distributions (to find the fastest direction of any sample point density increase); the sample density of the region corresponds to the center of the cluster class, so that the sample point will ultimately converge in the local density of the largest place, and the convergence of the same local maximum value of the sample point is considered to be a member of the same cluster, based on this principle. The mean shift algorithm can be used for clustering, image segmentation, tracking, and so on.  
When the mean shift algorithm is applied to the sector division, the center of the regional node obtained in the previous section is \(x\), and the kernel function is \(G(x)\). The basic mean shift vector is of the form:  
\[M_{h}(x)=m_{h}(x)-x_{i} \tag{27}\]  
Adding kernel functions and sample weights to the basic mean shift vector form yields the following improved mean shift vector form:  
\[M_{h}(x)=\frac{\sum\limits_{i=1}^{n}G\Big{(}\|\frac{x_{i}-x}{h}\|^{2}\Big{)}w (x_{i})x_{i}}{\sum\limits_{i=1}^{n}G\Big{(}\frac{x_{i}-x}{h}\Big{)}w(x_{i})}-x \tag{28}\]  
\(w(x_{i})\) is the weight of each regional center, and for any regional center in the graph, its weight value \(w(x_{i})\) is defined as the sum of the point strengths of the aerial vehicle nodes contained in that regional center:\[w_{i}=\sum\limits_{j=1}^{n}s_{ij} \tag{29}\]  
The center of the region \(x\) is selected as the seed point, \(\varepsilon\) is the tolerance error, and the mean shift algorithm implements the clustering of the center of the region according to the following steps:  
Step 1: Calculate \(x_{0}\) from the initial region center \(m_{h}(x)\) and assign \(m_{h}(x_{0})\) to \(x_{0}\).  
Step 2: Determine the move step and compute \(m_{h}(x)\) for the next region center.  
Step 3: If \(\|m_{h}(x)-x\|<\varepsilon\), mark the center of the region and end the loop by assigning all the passed region centers from the start point with the same marking of the marked region center; otherwise, continue with steps 1 and 2 until all the nodes have been marked.  
Step 4: Merge the homogeneous regions to obtain the clustering center.
## 2 Literature Review
### Identification of PFSC Enablers
#### 2.1.1 Perishable Supply Chain Related to RFID and IoT Technologies (Digitalization)
1.4 Perishable Food Supply Chain Related to Global Warming, Carbon Emissions, and Energy Utilization (Sustainability)  
Perishable products require cold storage in SC to maintain freshness due to their limited shelf life. [PERSON] et al. (2020) [27] studied a coordination method in a three-echelon SC considering the freshness-keeping effort by 3 PL service providers, where SC decisions on carbon trading mechanisms were investigated under two alternative systems. The findings demonstrated that total carbon emissions are reduced with an increase in the eco-friendliness effort. [PERSON] et al. (2020) [28] proposed a multi-objective hyper-heuristic approach for a real problem of location-routing concerning low-carbon CC, involving minimizing the fuel usage prices, product freshness, and carbon emissions. [PERSON] (2021) [29] integrated environmental regulations and credit risk for the inventory models by developing an algorithm to find the trade-off value for green technology investment. [PERSON] et al. (2015) [30] used a model to simulate a real-world SC in which a distribution center delivers fresh tomatoes to stores, and key performance indicators were provided to optimize various metrics (such as total cost, carbon emissions). [PERSON] et al. (2016) [31] examined the distribution of different types of fruits and vegetables grown by Italian farmers by utilizing three different means of transportation, and the food distribution planner was a proposed system that efficiently controlled product perishability while limiting CO\({}_{2}\) emissions. [PERSON] and [PERSON] (2021) [32] presented an optimization model for the integrated distribution and production activity scheduling that considers perishability and changeover times to reduce expenses and energy consumption. The literature review summary for enablers of PFSC is provided in Table 2. The numbers 1 to 15 (Table 1) are the enablers obtained from Section 2.1.
# Effect of Chemical Composition of Bio- and Petroleum-Based Modifiers on Asphalt Binder Rheology
## 3 Results
### Chemical Characterization of Modifiers
#### 3.1.3 Gel Permeation Chromatography  
The average molecular weights (M\({}_{n}\) and M\({}_{w}\)) for modifiers A, C, D, and E ranged from 3700 to 4700, and for G it is around 2500 with polydispersity index (PDI) in a range of 1.03-1.13 as shown in Table 3. Interestingly, modifier K has a high M\({}_{n}\) of 8933 and significantly higher M\({}_{w}\) of 48,784 which is the reason for high PDI of 5.46. The plot showing retention times (Figure 3) suggests that even though modifiers A, C, D, and E have similar range of molecular weights, M\({}_{n}\) and M\({}_{w}\) (Table 3), their molecular size distribution varies. Following are the observations from GPC analysis as per Figure 3:  
* The molecular weight distribution in modifier A and D showed two peaks at similar retention times but with different intensities. The larger peak occurred at lower retention times than the smaller peak, which indicates presence of two different sized molecules with higher proportion of larger size particles.
* Modifier C has a similar two peak distribution to A and D. However, the two peaks occur at longer retention times, indicating that the respective weights are smaller than A and D.
* Modifiers E and G have one sharp peak indicating the presence of single weight molecule. However, the peaks occur at different retention times which represent different particles.
* Modifier G has the largest retention time suggesting lower average molecular weight.
* Modifier K has the largest molecular weights among the modifiers. The molecular size distribution is wider compared to all other modifiers with two peaks. The wider distribution indicates a wide range of molecular size present in modifier K, resulting in a high PDI.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Modifier**} & \multicolumn{2}{c}{**Number-Average**} & \multicolumn{2}{c}{**Weight Average**} & \multirow{2}{*}{**Polydispersity Index**} \\  & **Molecular Weight (M\({}_{n}\))** & **Molecular Weight (M\({}_{w}\))** & **(PDI)** \\ \hline A & 4450 & 4616 & 1.04 \\ C & 3709 & 3971 & 1.07 \\ D & 4345 & 4909 & 1.13 \\ E & 4684 & 4805 & 1.03 \\ G & 2349 & 2596 & 1.11 \\ K & 8933 & 48,784 & 5.46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number-average molecular weight, weight-average molecular weight, and polydispersity index of modifiers.
# Parametric Selection of Optimized Epicyclic Gearbox Layouts for Wind Power Plant Applications
## 4 Results
### Calculation Input Data  
The following input data have been used to calculate the two-carrier gear trains for two-stage and three-stage setups:  
* Interval of the required transmission ratios: \(\left\lvert 0.0196\right\rvert\leq i\leq\left\lvert 1.0205\right\rvert\) for two-stage setup, \(\left\lvert 0.079\right\rvert\leq i\leq\left\lvert 1.081\right\rvert\) for three-stage setup;
* Minimal durability requirement of the planet bearings: \(L=20,000\) h;
* Planet bearing type: spherical rolling;
* Input torque \(T_{\text{A}}\), \(=208,891\) Nm;
* Input shaft speed, \(n_{\text{A}}\), \(32\) min\({}^{-1}\);  
Figure 8: Sample kinematic scheme representing the S55 NE(W) PGT from Figure 6. The power flow direction is from input A (sun gear I) to output B (planet carrier II).  
* Target intervals of planetary gear set torque ratios: \(2\leq t_{\rm I}\leq 8\) and \(2\leq t_{\rm II}\leq 8\);
* Tooth number of the first and second gearset sun gears: \(z_{\rm II}=18\) and \(z_{\rm III}=18\);
* Data for calculation of the relative losses \(\psi_{0}=0.02\);
* Width-to-diameter ratio \(\psi_{\rm b}=0.8\);
* Gear material: 16 MnCr5, tooth flank strength \(\sigma_{\rm Hlim}=1470\) N/mm\({}^{2}\), and tooth root strength \(\sigma_{\rm Film}=860\) N/mm\({}^{2}\). Additionally, the third-stage single-carrier gear train has been calculated with the following input data for three-stage setups:
* Power flow: H1(3), input to carrier H, exit to sun gear 1, and ring gear 3 locked;
* Required transmission ratio: \(i=0.25\);
* Minimal durability requirement of the planet bearings: \(L=20{,}000\) h;
* Planet bearing type: spherical rolling;
* Input torque \(T_{\rm A}=16{,}711\) Nm;
* Input shaft speed, \(n_{\rm A}\), 400 min\({}^{-1}\);
* Target planetary gear set torque ratio: \(t_{\rm III}=3\);
* Sun gear tooth number: \(z_{\rm III}=18\);
* Data for calculation of the relative losses \(\psi_{0}=0.02\);
* Width-to-diameter ratio \(\psi_{\rm b}=0.8\);
* Gear material: 16 MnCr5, tooth flank strength \(\sigma_{\rm Hlim}=1470\) N/mm\({}^{2}\), and tooth root strength \(\sigma_{\rm Film}=860\) N/mm\({}^{2}\).  
16 MnCr5 was selected as a material in common use, and the width-to-diameter ratio was selected for the same reasons. A relative loss below 2% is generally acceptable for a planetary gear train [23].  
The third stage is a simple single-carrier multiplier, and it will be manufactured using the same materials, preferably in the same casing as the primary two-stage gear train. It was found to require a gear module of \(m_{\rm n}=8\) mm, and it will have a mass of approximately 300 kg. As the calculation of single-carrier gear trains is well known, this will not be elaborated further.
# A Multi-Task Dynamic Weight Optimization Framework Based on Deep Reinforcement Learning
## 3 Methodology
### Multitasking Classification Network  
When solving multi-task classification problems, it is crucial to construct an efficient multi-task classification network. A multi-task learning network structure using hard parameter sharing can reduce the number of model parameters and facilitate information sharing between tasks. The network consists of a shared feature extraction module and multiple task-specific classifiers. The shared feature extraction modules usually include convolutional layers, a pooling layer, a batch normalization layer, and a fully connected layer for extracting generic features from the input image. All tasks share this module. Subsequently, each task has an independent linear classifier for mapping the shared features to the task's corresponding category space, enabling independent prediction for each task. The schematic is shown in Figure 3.  
Specifically, the input image \(x\in\mathbb{R}^{3\times H\times W}\) is mapped into a low-dimensional feature vector \(f=\phi(x)\in\mathbb{R}^{d}\) by a shared feature extraction function \(\phi(x)\). For each task \(t\in\{1,2,\ldots,T\}\), the output \(y^{(t)}\in\mathbb{R}^{C_{t}}\) is obtained by the task-specific linear classifier \(\psi^{(t)}(f)=W^{(t)}f+b^{(t)}\) where \(C_{t}\) is the number of categories for task \(t\). category number. The overall output of the network is \(F(x)=(y^{(1)},y^{(2)},\ldots,y^{(T)})\). During training, the loss contribution of each task is dynamically balanced by introducing a task weight vector \(\alpha\)  
Figure 2: Schematic diagram of a multi-task learning framework based on deep reinforcement learning.  
with the total loss function defined as \(\mathcal{L}=\sum_{t=1}^{T}\alpha_{t}\mathcal{L}^{(t)}\). By minimizing this loss function, collaborative learning and performance improvement of multiple tasks can be achieved.
## 2 Materials and Methods
### Design of the Positioning System: Modification of a 3D Printer  
The positioning system is designed to move the optical system in a three-dimensional workspace, a task that can be performed by a Cartesian robot (Figure 3). For convenience, to set a proper Cartesian positioning system, a suitable 3D printer can be used and properly modified for the specific task. In fact, a 3D printer is a kind of Cartesian robot used in the manufacturing field. It has only prismatic joints for the main movements, and therefore, it is able to move the end-effector only along straight lines on the three axes. Therefore, a 3D printer represents an adequate solution to build a specific Cartesian robot for the automated spectral experimental acquisitions proposed in this paper. Furthermore, a 3D printer normally has a planar worktable for the arrangement of the samples and an end-effector normally used for the extrusion of filaments, which can be modified for mounting the handling system and the collimator to receive reflected data from the desired point in the workspace.  
The low cost, easy availability, and adaptability allowed identifying a Fused Deposition Modeling (FDM) 3D printer as the Cartesian robot to be modified and used as the positioning system of the newly proposed test rig. In particular, the chosen 3D printer was an Artillery Sidwinder X1 (Artillery, Hong Kong, China), which is a medium-sized printer with 400 mm of stroke along the z-axis and a worktable of 300 mm \(\times\) 300 mm.  
Figure 3: Positioning system: The Cartesian robot.  
Figure 2: Collimator: Gershun Tube and apertures with different angles.  
This means that the available workspace for the automated spectral acquisitions was \(300\times 300\times 400\) mm\({}^{3}\).  
The axes move thanks to rubber wheels running on V-slot tracks. The structure has a main base case where the control boards and cables are laid out. As can be observed in Figure 3, the worktable can move along the y-axis through proper guides, and the vertical beams act as supports for the movements along the z-axis of the horizontal rods, which, in turn, work as the support for the slider with the end-effector mounted, which slides along the x-axis. For motions along z, there are two motors and a belt transmission with pulleys that connect the two upper ends of the threaded rods. Therefore, the considered Cartesian robot has three native sliding axes, but in this work, it was modified by adding a fourth axis, which allows the rotation of the handling system, mounted on the end-effector, to obtain spectral acquisitions at different incidence angles, as will be disclosed in the next paragraph. The stroke-end switches are inductive. In addition, the worktable is equipped with a heating system, and this is a very useful function that could allow studying the variation of the spectral data with temperature.
## 3 System Model
### OppNets Environment  
Let mobile nodes in the network have access to the Global Positioning System (GPS) to obtain their respective real-time geographic information, including moving direction and current location. Mobile nodes move independently in an area with \(K\times K\) m\({}^{2}\), and each node has a fixed transmission range of \(r\) meters. A contact is established when two nodes move in a mutual transmission range. If two nodes contact each other, the routing decision is made based on the relative opportunity of delivering a message to the target node. A relay node in a neighboring area can be appointed to carry a message if its moving direction is toward the destination and its weighted utility value, e.g., the estimate of delivery probability, DP, exceeds a specific threshold value, or its centrality is higher than the current message-carrying node.
# Monitoring of Structures and Infrastructures by Low-Cost GNSS Receivers
## 2 Concepts
### Warnings and Alarms
One can also observe that, when deemed useful, the latency can be reduced by switching from a solution over the time interval \(T\) to one over a shorter interval; for instance, if a daily solution raises a warning, we can switch to hourly solutions to verify whether the alarm is confirmed or not. We can now answer the two initial questions:  
1. In this case, the hypothesis \(H\) is \[H:a(\overline{t}+T)=0\] (17) and we want to verify whether there is evidence in the data that (17) is not correct. In this case, the critical threshold is simply \[c=Z_{\alpha}\sigma_{\
u}\.\] (18) It is clear that if we want to go with two tails, the test must verify whether \[\sigma_{u}^{2}=\sigma_{v}^{2}+\sigma_{pr}^{2} \tag{10}\]0 (19) to accept \(H\) with the significance level \(\alpha\). 2. In this case, the hypothesis \(H\) we want to test is \[\sigma_{u}^{2}=\sigma_{v}^{2}+\sigma_{pr}^{2} \tag{10}\]1 (20) where \(\overline{S}\) is the threshold established by a structural engineer by considering that if (20) is verified, there is a risk to the stability of the structure. We then have to fix a critical value \(c<\overline{S}\) (when \(\overline{S}>0\)), such that if \[\sigma_{u}^{2}=\sigma_{v}^{2}+\sigma_{pr}^{2} \tag{10}\]2 (21) we have a high probability of confirming \(H\) when this is true. Or, equivalently, we must have \[\sigma_{u}^{2}=\sigma_{v}^{2}+\sigma_{pr}^{2} \tag{10}\]3 (22) Let us start with a simple hypothesis \(H\) \[\sigma_{u}^{2}=\sigma_{v}^{2}+\sigma_{pr}^{2} \tag{10}\]4 (23) in this case, we have to impose \[\sigma_{u}^{2}=\sigma_{v}^{2}+\sigma_{pr}^{2} \tag{10}\]5 (24) leading to \[\sigma_{u}^{2}=\sigma_{v}^{2}+\sigma_{pr}^{2} \tag{10}\]6 (25) given the symmetry of the distribution of \(Z\) with respect to zero. Now it is clear that if we change \(H\) from (23) to \[\sigma_{u}^{2}=\sigma_{v}^{2}+\sigma_{pr}^{2} \tag{10}\]7 (26) we will have, with the same \(\alpha\) and \(c\) as in (24), \[\sigma_{u}^{2}=\sigma_{v}^{2}+\sigma_{pr}^{2} \tag{10}\]8 namely, the choice of \(c\) is conservative in the sense that the probability of declaring \(H\) false because \(\widehat{u}(\overline{t}+T)<c\) when, on the contrary, \(H\) is true, is even smaller than \(\alpha\). Just to make a small example with realistic numbers, assume there is a series of hourly solutions with \(\sigma_{\
u}=3\) mm, and \(\overline{S}\) is fixed to \(\overline{S}=4\) cm; then \(H_{0}(a(\overline{t}+T)=0)\) is refused if \[\sigma_{u}^{2}=\sigma_{v}^{2}+\sigma_{pr}^{2} \tag{10}\]9 (27)and \(H_{1}:\widehat{a}(\overline{t}+T)\geq 4\) cm is accepted if  
\[\widehat{u}(t)\sim\mathcal{N}[a(t),\sigma_{v}^{2}]\Rightarrow\frac{\widehat{ u}(t)-a(t)}{\sigma_{v}}=\mathcal{Z}\, \tag{11}\]0
## 3 The Proposed Fault Diagnosis Methodology
### Alarm Data Preprocessing
#### 3.1.2 Removing Redundant Alarms  
First, the repeated alarm records are removed. As mentioned above, an alarm has a unique code and two flags: start and end. Hence, although one alarm only happens once, it has two records in one alarm sequence. We merge the repeated alarm records and only remain the record of the start. Second, the chattering alarms are removed. Chattering alarms repeat with a high frequency within a short period and constitute the most common family of nuisance alarms. An alarm that is activated three or more times within one minute is often considered as belonging to the class of worst chattering alarms [25]. The chattering alarms are often redundant. Consequently, alarms that repeat three or more times in one minute are merged into one.
# Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
###### Abstract  
Photo-Acoustic Tomography (PAT) is an emerging non-invasive hybrid modality driven by a constant yearning for superior imaging performance. The image quality, however, hinges on the acoustic reflection, which may compromise the diagnostic performance. To address this challenge, we propose to incorporate a deep neural network into conventional iterative algorithms to accelerate and improve the correction of reflection artifacts. Based on the simulated PAT dataset from computed tomography (CT) scans, this network-accelerated reconstruction approach is shown to outperform two state-of-the-art iterative algorithms in terms of the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) in the presence of noise. The proposed network also demonstrates considerably higher computational efficiency than conventional iterative algorithms, which are time-consuming and cumbersome.  
photo-acoustic tomography; reflection artifacts; deep learning; convolutional neural network; time reversal; Landweber algorithm; U-net +
Footnote †: journal: Medical Imaging
# Exploiting Lunar Navigation Constellation for GNC Enhancement in Landing Missions
###### Abstract  
To support the increasing number of planned lunar missions, a collaborative international initiative is underway to conceptualise and establish a lunar satellite constellation for communication and navigation. In this context, the goal of the current paper is to analyse what the obtainable performance is for a lunar lander that executes state estimation employing one-way ranging signals from such a Lunar Navigation Service (LNS). In particular, a small-sized optimised navigation constellation is considered as the main source of measurements, which, coupled with an accelerometer and an altimeter, is used to estimate the lander absolute trajectory during the main braking phase. The guidance is extracted on board by interpolation of a ground-optimised trajectory, followed by a reference-tracking regulator. Two alternative control tuning cases are presented, one targeting high performance, the other targeting low propellant mass. Nominal performance and associated sensitivity analyses assessed the feasibility of supporting such a critical phase with a reduced LNS constellation, reaching final control errors below 500 m, with the better performing one going down to 56 m. Among the two proposed alternatives, the one targeting low fuel expenditure has proven, however, to also be more robust against time and state uncertainty, providing much larger success rates.  
M 2023 10, 850. [[https://doi.org/10.3390/aerospace10100850](https://doi.org/10.3390/aerospace10100850)]([https://doi.org/10.3390/aerospace10100850](https://doi.org/10.3390/aerospace10100850))