esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### New Procedure for Beam-Steering Correction
As a consequence, the presented modification method should be applied in spray droplet size measurements when beam steering occurs and the largest droplets of a distribution have an over-proportional impact on the stability and performance of a subsequent process step. This is the case in complex processes, like spray drying, spray painting, or combustion. In turn, the method of detector deactivation shows excellent agreement when the real and apparent parts of the measured droplet size distribution can be clearly separated from each other. Hence, this method can still be used safely for beam-steering correction in the case of separated distribution parts. Figure 9: Characteristic values (x\({}_{10,3}\), x\({}_{50,3}\), x\({}_{90,3}\), \(\overline{x}_{1,2}\)) of droplet size distributions, corrected by the method of detector deactivation (Det. off, empty symbols) and the new modification procedure (Mod., filled symbols) at different viscosities (0.12, 0.39, 0.69 Pa-s) and gas pressures of 0.6 MPa (**left**) and 0.8 MPa (**right**).
# Solid-State Transformer-Based DC Power Distribution Network for Shipboard Applications
## 3 Overall System Control
### Dual Active Bridge Converter
The V/f control can provide an acceptable dynamic response via maintaining the rated flux for most of the operating region of the induction motor. The applied terminal voltage, \(V_{s}\), can be expressed as in Equation (6):  
\[V_{s}=\frac{V_{s,rated}}{\omega_{e,rated}}\omega_{e} \tag{6}\]  
where \(V_{s,rated}\), and \(\omega_{e,rated}\), are the rated per-phase terminal voltage and frequency, respectively. Figure 5: Multiphase electric propulsion system. Typically, in medium voltage high-power induction motor drives, V/f control is used to control the speed of the asymmetrical six-phase (ASP) machines, as shown in Figure 6[51]. The speed of ASP, \(\omega_{m}\), is fed back to the PI regulator to maintain the speed reference, \(\omega_{m}^{*}\), of the ASP machine. Meanwhile, the slip regulator calculates the slip frequency, \(\omega_{sl}^{*}\), based on the speed controller output. The reference synchronous speed, \(\omega_{ms}\), is then obtained from the summation of the rotor speed, \(\omega_{m}\), and the slip frequency, \(\omega_{sl}^{*}\). The V/f characteristic is then used to obtain the reference voltage, \(V^{*}\), as depicted in Figure 6.
## 4 Results and Discussion
### Qualitative Classification of Risk Degrees in Maqin-Maqin Segments  
Based on the fact that the average recurrence intervals of earthquakes calculated in this paper are long, we conduct a qualitative classification on the degree of earthquake risk in the Maqin-Maqin segment of the Eastern Kunlun fault zone. We usually use \(E=t/R\) to describe the time urgency of earthquake occurrence on the faults, E represents the quantitative parameter of describing the time urgency of earthquake occurrence, t represents the elapsed time since the last earthquake, R represents the average recurrence interval, while, to some extent, we ignore the contribution of \(R\) to the time urgency of earthquake occurrence. Even the value of \(E\) is the same. In two segments with different \(R\) values, the degrees of earthquake risk are different. Generally, the smaller the value of \(R\), the higher is the degree of risk. Therefore, we can use two parameters to divide the degree of risk: (1) it is class C if \(E<0.5\) and \(R>2500\) years, which indicates that there is no risk in this section within one thousand years; (2) it is class B if \(0.5<E<0.8\) and \(R<2500\) years, which indicates that there is a relatively small risk in this segment during the coming several hundred years; and (3) it is class A for \(0.8<E<1\) and \(R<2500\) years, which indicates that the risk is relatively  
Figure 9: (**A**) Distribution map of the Maqin segment and adjacent segments of the Eastern Kunlun fault. (**B**) The occurrence ages of paleoearthquakes since 12 ka are compiled [25; 26; 27].  
large in this segment during the coming several hundred years. \(E\) takes 0.5, 0.8, and 1.0 as the critical values, which are determined according to the distribution characteristics of earthquake recurrence \(T/T_{ave}\), and \(R\) is 2500 years because the recurrence intervals along the two segments are smaller than 2500 years. As the prediction time increases, the increase of risk is also relatively rapid [28]. We divide the segments according to this principle as follows: the Maqin segments belong to class B, and the Maqin segment belongs to class A.
# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
###### Abstract  
Human-induced climate change has highlighted the need for community-based adaptation (CBA) to build resilience in vulnerable communities. CBA empowers communities to leverage their resources and skills in shaping effective adaptation strategies. Agent-based modelling (ABM) is a suitable tool to develop tailored strategies that account for local capacities, priorities, and cultural contexts. This study assesses ABM tools for their suitability to model CBA, focusing on key criteria such as agent definition, sensitivity analysis, scalability, and experiment design. A comprehensive review of available ABM tools identifies NetLogo as the most fitting tool by its features, due to its flexibility in handling complex community-environment interactions. GAMA and Envision are nearly as suitable, offering robust support for modelling socio-economic and environmental dynamics. This article provides guidance for researchers and practitioners in choosing an appropriate ABM tool aligning with the specific needs of CBA, ensuring contextually relevant and sustainable adaptation solutions.  
community-based adaptation; climate change adaptation; agent-based modelling; simulations; tool assessment +
Footnote †: journal: Applied sciences
# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
## 3 System Model
### MECS Energy Consumption  
In [23], measurements have revealed that the power consumption of a server is composed of idle power and active power that varies according to the workload of the server. Based on these results, an energy consumption model was proposed in [5]. By following this load-dependent energy consumption model, we express the energy consumed by an active MECS as  
\[e_{i}^{p}(t)=\alpha P_{m}+(1-\alpha)P_{m}\mu_{i}(t), \tag{3}\]  
where \(P_{m}\) is the maximum power that a MECS \(i\) consumes when it is fully utilized, \(\alpha\) is the proportion of the power that determines the energy consumption of a MECS \(i\) in an idle state, and \(\mu_{i}(t)=\min\{Q_{i}(t)+a_{i}(t),\tau b_{i}\}/\tau b_{i}\) is the utilization of a MECS \(i\) during a time slot \(t\).  
When a MECS \(i\) decides to sleep, it has to distribute the tasks in \(Q_{i}(t)\) to its neighbors. If we denote by \(\varepsilon_{tr}\) the energy consumed to transfer a task from a MECS to its neighbor, the energy consumed by a MECS \(i\) to transfer the tasks in its task queue becomes  
\[e_{i}^{tr}(t)=\varepsilon_{tr}Q_{i}(t). \tag{4}\]  
Then, the energy consumed by a MECS \(i\) during a time slot \(t\) is given as  
\[e_{i}(t)=\beta_{i}(t)e_{i}^{p}(t)+(1-\beta_{i}(t))e_{i}^{tr}(t), \tag{5}\]  
where \(\beta_{i}(t)\) is an indicator function that represents the operational mode of a MECS \(i\) during a time slot \(t\). More specifically, \(\beta_{i}(t)=1\) indicates that a MECS \(i\) is in an active state during a time slot \(t\). By contrast, \(\beta_{i}(t)=0\) indicates that a MECS \(i\) is in a sleep state during a time slot \(t\).
## 5 Discussion and Limitations  
In this study, we proposed a Fundus-DANet model. Although our model showed promising outcomes in capturing subtle interclass differences and enhancing overall classification accuracy, implementing it in real-world scenarios still has several limitations and challenges. First, the quality of fundus images was inconsistent, and the number of samples across different classes was severely imbalanced, making model training challenging. The limited data reduced the model's robustness, making it difficult to maintain consistent performance across various application scenarios. Second, deploying Fundus-DANet requires substantial computational resources due to the complexity of the model and its extensive  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **ACC** & **AUC** & **F1\_Score** & **Final\_Score** & \(\kappa\) & **HL** \\ \hline EfficientNetB3 [29] & 92.00 & 74.00 & 89.00 & 72.00 & 52.00 & – \\ MCGL-Net [30] & 94.46 & 93.50 & 91.60 & – & 64.30 & – \\ MCGS-Net [31] & – & 78.16 & 89.66 & – & 57.65 & – \\ DCNet [32] & – & 93.00 & 91.30 & 82.70 & 63.70 & – \\ CNN-Trans [33] & 80.68 & 95.9 & – & – & – & – \\ Our & 93.00 & 95.12 & 83.74 & 82.13 & 67.52 & 7.00 \\ \hline \end{tabular}
\end{table}
Table 6: Comparative experiments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **ACC** & **AUC** & **F1\_Score** & **Final\_Score** & \(\kappa\) & **HL** \\ \hline Self-attention mechanism [25] & 92.39 & 94.58 & 82.59 & 80.79 & 65.20 & 7.61 \\ Spatial attention mechanism [26] & 92.40 & 94.81 & 82.14 & 80.42 & 64.33 & 7.60 \\ ECA-Net [27] & 92.68 & 94.92 & 83.62 & 81.93 & 67.25 & 7.32 \\ SGE [28] & 92.55 & 94.39 & 82.62 & 80.76 & 65.28 & 7.45 \\ \hline \end{tabular}
\end{table}
Table 5: Comparative results of attentional mechanisms.  
use of convolutional and attention mechanisms, which may result in longer inference times and higher hardware requirements. This limits its applicability in clinical settings requiring rapid diagnosis or real-time analysis. Lastly, the continuous updating and maintenance of the model are crucial for adapting to new data and evolving medical standards. This necessitates ongoing efforts in both technology and human resources.  
To address these issues, we aim to collaborate with hospitals to acquire more data for model training, enhancing the model's generalization capability and robustness. In addition, we will strive to reduce the model's computational cost and inference time without compromising its accuracy to better suit the needs of real-time clinical applications.
# Burst Detection in Water Distribution Systems:
## 2 Methodology  
The proposed stochastic-hydraulic methodology, called SHtsG, consists of two phases: the first refers to the stochastic generation of a water request time series and the second regards the WDS simulation. Specifically, the stochastic part superimposes daily, weekly, and yearly water request patterns jointly with a random component in order to produce suitable water request time series. These are subsequently employed in the second part, where the hydraulic solver computes a WDN water consumption time series through an extended period simulation.  
For the sake of clarity, Figure 1 illustrates the SHtsG. Specifically, the first part provides the water consumption time series for each district/group of users of the WDS (district is defined as a group of neighboring users with uniform characteristics) by means of a superimposition of deterministic and random trends. This SHtsG stage contemplates multiple time series according to different district typologies as well as various user aggregation. Given the water request distribution along the network, the WDS hydraulic model is built in the second part of the SHtsG. The hydraulic model exploits a fully pressure driven approach, called DFPDM, where water demands, background leakages, and water bursts are properly embedded in order to ensure extended period simulations accuracy and fidelity.  
The final output is the consumption time series of the WDS inlet flow rate (or district metered areas flow rates) considering both hydraulic and mechanical reliability.
## 3 Results
### Land Cover Types in the Footprint  
Studies have shown that the composition of the underlying vegetation and its spatial distribution around the eddy covariance observation systems certainly has an influence on the footprint [(52; 53; 4)]. The dominant land cover type within the footprints has the highest footprint-weighted percentage. To illustrate the contribution of different land cover types to the footprint, we analyzed the proportions of each land cover types in the footprint based on the 30 m land cover data, and the percentage of land cover types in the footprint is shown in Figure 7. The land cover type in the footprint was homogeneous at Huazhaizi, Bajitan, Dashalong and Arou. Specifically, bare land accounted for 100% of the footprint at Huazhaizi and Bajitan, grass land accounted for 100% in the footprint at Dashalong and Arou from May to September in 2014. However, multiple land cover types were located in the footprints of the other sites (Daman, Shidi, Huyanglin, Hunhelin, Sidaoqiao). The area of the dominant land cover type accounted for more than 90% of the footprint at Daman (cropland), and more than 80% at Shidi (wetland). At Hunhelin, Huyanglin and Sidaoqiao, the dominant land cover types only accounted for less than 50% of the footprint. This demonstrated that the underlying surface was related to the distance of the footprint.  
Figure 5: Climate footprint of field GPP from May to September in 2014. The gray level describes the cumulative weight contribution to the footprint.  
Figure 6: Validation of MODIS GPP and downscaled GPP. (**a**) Validation of MODIS GPP at field scale; (**b**) validation of MODIS GPP at footprint scale; (**c**) validation of downscaled GPP at field scale; (**d**) validation of downscaled GPP at footprint scale.  
Figure 7: Area ratio of land cover types in the footprint.
# Nematic Liquid Crystal Composite Materials for DC and RF Switching
## 5 Performance Comparison among Different Composite Systems
As 5 CB/CNT shows a near-room-temperature operation (both in the nematic and isotropic phases) with more than three orders of magnitude switching. For these reason, 5 CB/CNT is a good candidate for switching application. We choose this material of our RF simulations. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Composite**} & **Concentration** & **Operating** & **Resistivity** & **Mechanism of** & **Reference** \\  & **of NPs** & **Temperature/Phase** & **Ratio** & **Conductivity Modulation** & **R**el \\ \hline E7/CNT & 0.05 wt.\% & Near RT, Nematic phase & 10\({}^{3}\) to 10\({}^{4}\) & Elastic interaction, reorientation & [PERSON] et al. [20] \\
5 CB/CNT & 0.005 wt.\% & 40 \({}^{\circ}\)C, Isotropic phase & 5\(\times\) 10\({}^{3}\) & \(\pi\)-\(\pi\) staking, PND reorientation & [PERSON] et al. [57] \\ PCPBB/CNT & 0.00036 vol\% & 42 \({}^{\circ}\)C Nematic phase & 170 & Dual frequency switching & [PERSON] et al. [84] \\ HAT6/GNP & 1.0 wt.\% & 105 \({}^{\circ}\)C & 10\({}^{3}\) to 10\({}^{4}\) & Electrophoresis of GNPs & [PERSON] et al. [93] \\
5 CB/Ag NPs & 0.5 wt.\% & Room temperature & \textgreater{}10\({}^{2}\) & Self-assembly, reorientation & [PERSON] et al. [99] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A switching performance comparison among various composite systems.
## 4 The Proposed 3-Tier AKA Mutual Authentication and Key Agreement (AKA) Protocol
### Edge Device Handover Authentication Phase  
During this phase, when an edge device \(\delta_{d}\) relocates from one fog node \(\zeta_{1}\) to another fog node \(\zeta_{2}\), a handover process is initiated. The edge device \(\delta_{d}\) undergoes mutual authentication with the new fog node \(\zeta_{2}\). The next steps explain the details of this Authentication and Key Agreement protocol during the handover process. They set up mutual authentication between the target fog nodes and edge devices and created a shared session key for future security services, as shown in Figure 9.  
**Step 1:**: The fog node \(\zeta_{1}\) performs the fog-to-fog node mutual authentication protocol and generates a secure session key \(SK_{\zeta_{1}\zeta_{2}}\), as specified in line 1 in Figure 9.
**Step 2:**: Fog node \(\zeta_{1}\) generates a secure random key, denoted as \(SK_{\delta_{d}\zeta_{2}}\), intended for utilization by the target fog node \(\zeta_{2}\) and edge device \(\delta_{d}\). It then proceeds to generate two handover messages, namely, \(M_{\zeta_{1}\zeta_{2}}\) and \(M_{\zeta_{1}\delta_{d}}\). The content of message \(M_{\zeta_{1}\zeta_{2}}\) consists of the encryption of the present timestamp \(T\) along with a string literal \"\(\zeta_{1}\)\(t_{0}\)\(\zeta_{2}\)\" and \(SK_{\delta_{d}\zeta_{2}}\) using the secret key \(K_{\zeta_{1}\zeta_{2}}\), \(M_{\zeta_{1}\zeta_{2}}=E(T\parallel``\zeta_{1}t_{0}\zeta_{2}\"\parallel SK_{ \delta_{d}\zeta_{2}},SK_{\zeta_{1}\zeta_{2}})\). That is, the message \(M_{\zeta_{1}\delta_{d}}\) includes the encrypted timestamp \(T\), the string literal \"\(\zeta_{1}\)\(to\)\(\delta_{d}\)\", and the secret key \(SK_{\delta_{d}\zeta_{2}}\). It is written as \(M_{\zeta_{1}\delta_{d}}=E(T\parallel``\zeta_{1}\)\(to\)\(delta_{d}\)\" \(\parallel SK_{\delta_{d}\zeta_{2}},SK_{\zeta_{1}\delta_{d}})\). Subsequently, \(\zeta_{1}\) transmits \(M_{\zeta_{1}\zeta_{2}}\) to \(\zeta_{2}\) and \(M_{\zeta_{1}\delta_{d}}\) to \(\delta_{d}\). Then, \(\zeta_{1}\) forgets \(SK_{\delta_{d}\zeta_{2}}\). See lines 2, 3, 4, and 5 in Figure 9.
**Step 3:**: Edge device \(\delta_{d}\) and the target fog node \(\zeta_{2}\) receive the handover message from fog node \(\zeta_{1}\). Then, both \(\delta_{d}\) and \(\zeta_{2}\) possess a shared session key \(SK_{\delta_{d}\zeta_{2}}\) to establish a secure channel.  
Figure 8: 3-Tier AKA: Authentication and Key Agreement phase for fog node and cloud.
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 2 Materials and Methods
### Traditional Image Processing Algorithms Description
#### 2.5.5 Local Threshold Approach Based on a Moving Streak Average (LTS)  
The last one, the segmentation algorithm [40], can be configured using a static or a dynamic approach [41] for the background noise estimation [38]. In this work, a dynamic approach based on a zigzag local thresholding with moving average is used [42]. In particular, a pixel is saved if its energy value is greater than the background noise, evaluated through a moving average line-by-line, plus a threshold \(\tau_{pre}\).  
The information relative to the segmented image is used for the clustering process, i.e., the combination of segments belonging to the same star streak [34; 35; 36; 37]. The most important step required by the clustering algorithm is the evaluation of the primitive clusters containing pixels which share at least one corner. A suitable technique has been developed in order to relate streaks in the image to the same star if the corresponding streak is broken into multiple streaks due, for example, to the high rate of the spacecraft. Three conditions will be satisfied related to the minimum distance, the direction and the density of the considered primitive clusters. Centroids' coordinates of each cluster are evaluated, taking into account the energies of segments merged to build the cluster itself according to Equation (6):\[\mathbf{p}_{cluster,i}=\sum_{j}\left(\frac{E_{seg,j}\cdot\mathbf{p}_{seg,j}}{E_{ cluster,i}}\right) \tag{6}\]  
where \(\mathbf{p}_{seg,j}\) are the baricenter coordinates of the j-th segment in the image plane, while \(E_{cluster,i}\) is the sum of the energies \(E_{seg,j}\) which forms the cluster i-th:  
\[E_{cluster,i}=\sum_{j}E_{seg,j} \tag{7}\]
## 1 Introduction
In this case, Raman spectroscopy experiments (not shown) reveal that the layers are dominantly in the anatase phase. The 2.2 cm-long waveguides considered in the following are obtained by I-line patterning of the UV sensitive resist AZ MiR 701. The patterned resist is then used as a protective mask during the subsequent reactive ion etching of the TiO\({}_{2}\) layer. After stripping of the residual mask, strip waveguides featuring a typical width of 1.5 \(\upmu\)m are obtained as shown by scanning electron microscope (SEM) image displayed in Figure 1a. This image shows that the fabrication process leads to asymmetric sidewalls such that the top width (\(w_{1}\) = 1.345 \(\upmu\)m) is shorter than the base width (\(w_{2}\) = 1.487 \(\upmu\)m), so that the waveguide section is close to a right trapezoid. The high contrast between the TiO\({}_{2}\) (the estimated refractive index for the anatase TiO\({}_{2}\) layer at 1.64 \(\upmu\)m is 2.35) and the silica layer (refractive index = 1.44) or the surrounding air is highly beneficial for a strong light confinement. The refractive index for anatase phase TiO\({}_{2}\) layer reported in Ref. [40] appears as a good starting point for the numerical investigation of the optical properties of the waveguides. By operating a commercial mode-solver relying on finite element method (COMSOL Multiphysics(r)), we found that the structure may sustain 4 modes. The modal intensity distributions of the two lowest orders modes are displayed for the transverse electric (TE) and transverse magnetic (TM) in Figure 1b1,b2, respectively. The TE polarized fundamental mode of interest features an effective area [41] of 0.54 \(\upmu\)m\({}^{2}\) with 85% of the energy carried out by the mode confined within the TiO\({}_{2}\) core at a wavelength of 1.64 \(\upmu\)m. In addition, as detailed in the following (Figure 4a), in the spectral region around 1.6 \(\upmu\)m, the fundamental mode features an anomalous regime of chromatic dispersion  
Figure 1: (a) Scanning Electron Microscope image of the cleaved strip TiO\({}_{2}\) waveguide (b1,b2) Corresponding mode profiles at 1640 nm and associated effective index for the first transverse electric (TE) and transverse magnetic (TM) respectively. which is a prerequisite for ultra-broad SC generation [25]. Compared to previous works [24; 33; 42], such an anomalous dispersion is obtained thanks to a thicker layer. Due to the field enhancement at the top and lower interface of the waveguide (resulting from the continuity of the normal component of the electric displacement vector), the TM\({}_{0,0}\) mode exhibits a lower confinement and thus an effective area increased by about 30% as compared to the fundamental mode.