esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Printed electronics is a new way of electronics, in which electronic system and components are directly produced on a rigid, flexible or stretchable substrate by low-cost coating and printing processes rather than traditional silicon-based processes. Not only have common printing technologies such as inkjet, flexography, gravure, screen printing and offset printing been utilized for printed electronics, but newly emerging additive manufacturing can also be considered a new platform for their applications. There are many advantages of using printing processes: low-cost manufacturing is achievable with low-temperature fabrication near ambient condition, and less wastes are generated. Since the advent of printed electronics in the early 2000's, it has attracted a great attention from research and industrial fields, and it is still considered one of the promising novel fabrication technologies for new functional electronic devices and systems such as flexible displays, smart labels, batteries and wearable electronics.  
The purpose of this special issue was to disseminate the platform by highlighting challenges and recent advances in all the areas of printed electronics. All types of research articles with fundamental and applied nature were invited, and the invitation was open to all the related areas including, but not limited to, substrates, materials, printing and processing, equipment, fabrication of devices or systems, measurements and evaluations, and various applications. A total of 15 manuscripts were submitted to this special issue, and only 10 papers were finally published after rigorous peer review processes of _Applied Sciences_. The papers covers a wide range of topics including inkjet-printing, electrohydrodynamic (EHD) printing, nanoparticle production, plastic substrate, and applications for sensors, organic light emitting diode (OLED), and printed circuit boards (PCBs). This editorial briefly summarizes the primary contributions of the published papers.
## 6 Results and Discussion
### Validation
As further validation, we compare our vibration results with finite element predictions as performed in COMSOL, for a square plate with constant thickness \(h=10\) mm, length 250 mm, and different GPL distribution profiles. As the GPLRC plate features an inhomogeneous behavior, the material properties are assumed to be graded in the thickness direction. In Tables 5-7, we compare the vibration results for the first six modes, and for three different reinforcement distributions, namely, a GPL-UD, -X, and -O profile, respectively, It is worth noticing the very good matching of results compared to finite elements, with a limited percentage error (lower than 1.04% in each case), despite the reduced computational effort. For a GPL-O distribution we also represent the related mode shapes in Figure 6, while keeping \(\Lambda_{\text{GPL}}=0.1\) (wt%), with a reasonable kinematic response with the selected simply-supported boundary condition. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S. No** & **Mode** & \(\mathbf{\omega}\) **(Hz)** & **FEM (Hz)** & **Relative Error** \\ \hline S1 & 1 & 468.1 & 470.1 & 0.42\% \\ S2 & 2 & 944.0 & 947.3 & 0.34\% \\ S3 & 3 & 1377.3 & 1381.4 & 0.29\% \\ S4 & 4 & 1665.5 & 1670.2 & 0.28\% \\ S5 & 5 & 2069.4 & 2074.0 & 0.22\% \\ S6 & 6 & 2615.0 & 2620.4 & 0.20\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison between FEM-based predictions and results from our formulation (GPL-O). Figure 6: The first six mode shapes of a GPLRC rectangular plate with a GPL-O distribution pattern.
# Onboard Centralized ISL-Building Planning for LEO Satellite Constellation Networks
## 2 Onboard Centralized ISL-Building Planning Scheme  
Studies in the existing literature have conducted ISL-building planning at the ground station. Managing ISL-building planning at the ground station has the advantages of strong computing power and easy information collection; however, it also has obvious shortcomings such as poor constellation management autonomy and weak anti-destruction ability. In this paper, in order to adapt to future management autonomy needs for large-scale constellations, an onboard centralized ISL-building planning scheme is proposed for the first time, taking into account constraints such as limited onboard computing power and constrained information collection channels. As shown in Figure 1, the proposed satellite onboard centralized ISL-building planning scheme includes the following steps.  
1. **Link Status Collection by the Dynamic Primary Satellite** The ground station designates a satellite as the dynamic primary satellite. Alternatively, when the ground station is unreachable, the satellites in the constellation take turns as the dynamic primary satellite. The dynamic primary satellite collects link status information such as the orbital elements of each satellite in the constellation, the health status of the ISL terminals, and so on, as input parameters for the visibility calculation and link-building planning;
2. **Visibility Calculation** Based on the acquired orbital elements and the health status of all ISL terminals in the constellation, the dynamic primary satellite performs orbit extrapolation and calculates the visible time segments between each two ISL terminals;
3. **Link-building Planning** According to the calculated visible time segments between the ISL terminals, the dynamic primary satellite plans the network's link connections and disconnection plan for the next period;
4. **Planning Result Distribution and Link-building Implementation** The dynamic primary satellite distributes the planned network topology to all other satellites. According to the network topology table, each satellite implements link-building with other satellites at a specified time point;
5. **Link Status Change Collection** In this period, the dynamic primary satellite collects the link status changes in real-time. If the changes happen, the primary satellite starts planning immediately and returns to the first step.  
As shown in Figure 2, before the constellation enters the next period, it should return to the first step to plan the network topology for the next period.  
Among the five steps, the first, fourth and fifth steps can be implemented among satellites through inter- and intra-satellite communication using currently mature techniques. The visibility calculation and link-building planning procedures are detailed in Sections 3 and 4.  
Figure 1: Satellite onboard centralized ISL-building planning scheme.
# Digital Twin in the Maritime Domain: A Review and Emerging Trends
## 3 Adoption of Digital Twin Technology for the Marine Sector
### Automation of Marine Vehicles
Recently, in the Russia-Ukraine conflict, Unmanned Surface Vessels (USVs) have been used as self-destructing attack drones capable of destroying surface vessels or strategic land infrastructures such as piers, bridges, and port facilities, which has brought up a new dimension in the modern battlefield. These drones can also be swiftly deployed for the protection of high-valued assets including the major fleet units, port facilities, offshore platforms, coastal power generation stations, etc. The commercial applications can be stated as commercial shipping (cargo/passenger), marine research and survey, search and rescue (SAR), meteorology services, environmental protection, etc. The functioning of these unmanned automated vessels including the integrated Guidance, Navigation and Control (GNC) will be monitored and governed by the shore control stations with the capacity to intervene where it is necessary depending on the situational shift based on the imposed level of autonomy protocol. In the case of a harbor, there will be various other drones, ROVs, and auxiliaries that will also be controlled by this station supporting the Smart Port functions. For example, the Norwegian University of Science and Technology (NTNU) Shore Control Lab has implemented a similar station to control the autonomous passenger transport ferries at Trombheim River [42]. A higher level of autonomy in the operation of any marine system will ensure a smooth and straightforward approach to the implementation of DT-induced functionalities in processes such as design, control, management, etc.
## 2 Materials and Methods
### In Situ X-ray Fluorescence (XRF) Measurements  
A portable XRF \"Alpha 4000\" (Innov-X systems, Inc., Woburn, MA, USA) analyzer was used. The equipment consisted of a Ta anode X-ray tube excitation source, and a Si PIN diode detector with an active area of 170 mm\({}^{2}\) (full width at half maximum \(<\) 220 eV at 5.95 keV for Mn K\({}_{\alpha}\) line). The analyzer allowed the detection of elements ranging from phosphorus (Z = 15) to lead (Z = 82). The instrument operated in \"soil\" mode, a Compton normalization algorithm designed in order to achieve a limit of detection (LOD) as low as possible, i.e., trace concentrations (ppm levels) for soil and bulk samples. The \"soil\" mode was employed together with the \"Environmental\" elements suite, using the following element list: P, S, Cl, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Rb, Sr, Zr, Mo, Ag, Cd, Sn, Sb, I, Ba, Au, Hg, and Pb. Each area was investigated by two sequential tests, using operating conditions of 40 kV and 7 \(\mu\)A for the first run (60 s collection time) and 15 kV and 5 \(\mu\)A for the second one (60 s collection time). The instrument management and the data storage were guaranteed by a Hewlett-Packard iPAQ Pocket PC. The calibration was performed by means of soil LEAP (Light Element Analysis Program) II and verified using alloy certified reference materials produced by Analytical Reference Materials International. When detected, peaks at \(\sim\)8.15 keV and \(\sim\)9.34 keV have to be ascribed to L\({}_{\alpha}\) and L\({}_{\beta}\) transitions of Ta anode.
## 5 Particle Detectors
### Neutron Detectors
If necessary/useful, it is also possible to add dopant elements to the fluorescent material, in order to shift the wavelength of the light output towards the maximum sensitivity range of the imaging system. The typical scintillator screen, providing the best performances, can be composed using the following:  
* A neutron-absorbing component: LiF (\({}^{6}\)Li is a high neutron absorber, 940 barn of absorption cross section);
* A scintillation component: ZnS;
* A dopant: Ag, Cu (moving maximum light output wavelength to approximately 550 nm);
* A composition ratio of LiF:ZnS ranging from 1:1 to 1:2;
* A scintillator thickness range of 50-250 \(\upmu\)m;
* The substrate material and thickness: aluminium 0.5-1 mm. Imaging applications sometimes need higher spatial resolutions than those provided by LiF/ZnS scintillators; it is thus necessary to reduce the thickness. As such, it is possible to use gadolinium oxide, both as a neutron absorber and scintillator material. By decreasing the thickness down to 15 \(\upmu\)m, a 3-fold increase in terms of resolution with respect to the thinnest LiF/ZnS scintillators can be obtained. In general, the neutron-absorbing efficiency per unit volume is also increased with Gd, but light conversion is heavily hindered, so that the light output per absorbed neutron is 2-3-fold lower than the LiF-ZnS scintillator (at the same thickness). Consequently, Gadox is only used when high resolution is the most critical parameter. It is important to underline that spatial resolution in neutron imaging is limited not only by the detectors, but it often depends also on the limited neutron flux and especially by the fact that neutron optics is virtually non-existent, so that image blurring of the beam limits the resolution of neutron imaging. Another issue is the presence of gamma radiation in almost all neutron imaging facilities, making it difficult to obtain very good contrast images at very high spatial resolution. In the reference frame of INFN-CHNet, the NICHE experiment is developing a neutron imaging station at the gate B of the TRIGA reactor of the LENA Laboratory in Pavia. NICHE aims at creating the first imaging system (radiography and neutron tomography) in Italy, specifically designed for the diagnostics of CH, to provide important information in many case studies, in particular in the archaeological and artistic fields. Detector development in neutron imaging applications, thanks to the increased intensity of the new neutron sources, goes into the direction of spatial resolution improvement and energy/wavelength discrimination for microstructure analysis of crystalline systems. For example, Gadolinium oxysulfide (GOS) is indicated as a novel scintillator for ultra-high spatial resolution detectors in neutron imaging, with the perspective of achieving spatial resolution down to the micron level. The drawback of this system is an extremely reduced light output (\(\sim\)100 times lower than that of ZnS/LiF:Ag scintillators), which requires highly increasing the neutron flux to reduce the exposure time to reasonable levels [246].
# Reverse Engineering and Topology Optimization for Weight-Reduction of a Bell-Crank
## 2 Theory of Weight Reduction and Stiffness Matrices  
### Modelling Approach  
In a static structural analysis, for an arbitrary structure that is subjected to external forces and under the prescribed boundary conditions, the FE matrix equation can be expressed as [13; 14; 15]:  
\[[\mathbf{F}]=[\mathbf{K}][\mathbf{u}] \tag{1}\]  
where \([\mathbf{F}]\) the force vector, \([\mathbf{K}]\) the stiffness matrix, and \(\{\mathbf{u}\}\) the displacement vector [16].  
According to [PERSON] [13], the vector \(\{\mathbf{u}\}\) can be partition into boundary degrees of freedom (DOF) and internal DOF, and they are denoted with subscript \(\mathbf{b}\) and \(\mathbf{i}\), respectively. Equation (1) is fully general and is applicable for two-dimensional and three-dimensional problems, with appropriate boundary conditions. The entire system can be partitioned to [14]:  
\[\left\{\begin{array}{c}\mathbf{F}_{i}\\ \mathbf{F}_{\mathbf{b}}\end{array}\right\}=\left[\begin{array}{cc}\mathbf{k}_{ii}&\mathbf{k }_{\mathbf{b}b}\\ \mathbf{k}_{\mathbf{b}i}&\mathbf{k}_{\mathbf{b}b}\end{array}\right]\left\{\begin{array}{c} \mathbf{u}_{i}\\ \mathbf{u}_{\mathbf{b}}\end{array}\right\} \tag{2}\]  
Once the global stiffness matrix \([\mathbf{K}]\) is assembled, the unknown nodal displacement, \(\{\mathbf{u}_{i}\}\) and unknown reaction forces, \([\mathbf{F}_{\mathbf{b}}]\) can be obtained by solving Equation (2). From the first line of equation, we first solve for \(\{\mathbf{u}_{i}\}\), i.e., \(\mathbf{u}_{i}=\mathbf{k}_{ii}^{-1}(\mathbf{F}_{i}-\mathbf{k}_{\mathbf{b}i}\mathbf{u}_{\mathbf{b}})\); subsequently, substitute \(\mathbf{u}_{i}\) into the second line of equations, and thus \(\mathbf{F}_{\mathbf{b}}\) can be solved as \(\mathbf{F}_{\mathbf{b}}=\mathbf{k}_{\mathbf{b}i}\mathbf{u}_{i}+\mathbf{k}_{\mathbf{b}b}\mathbf{u}_{\mathbf{b}}\).
## 4 Discussion
### Nep  
Under the baseline climate conditions, the simulated NEP (Table 5) in our study plots was always positive (i.e., stands act as C-sinks), in agreement with other field-based and modeling studies in Mediterranean ecosystems [74; 91; 92; 93; 94]. [PERSON] et al. [92] using inventory data estimated an average NEP for _P. bruttia_ stands in Turkey of around 320 g C m\({}^{-2}\) year\({}^{-1}\), a value slightly higher than the one simulated with our model.  
According to our simulations under the two climate change scenarios, changes in NEP are primarily driven by variations in R\({}_{\text{het}}\), which exhibit a larger percentage shift (Tables 3 and 4) compared to GPP, except under the RCP4.5 scenario in the 2060 period. Overall, NEP is projected to decrease under all scenarios and study periods. Similar findings were reported for the Iberian Peninsula, where approximately 53% of the negative trends in NEP were attributed to negative trends in NPP and positive trends in R\({}_{\text{het}}\)[95].  
Theoretically, NEP is expected to be negative during the early years following a disturbance [96], due to the greater release of carbon through respiration than the photosynthetic C uptake. Following this initial phase, ecosystem productivity begins to exceed respiration, leading to positive NEP. Eventually, productivity and respiration reach a steady state, balancing NEP. In our case, the simulated NEP of post-fire plots was always positive but still followed this general trend. The younger (13 years old) plot appeared to have overcome the initial negative NEP phase. The oldest plot (92 years old) seemed to be approaching maximum productivity based on the shape of the curve during the baseline period (Figure 7). The parabolic trajectory was disrupted in the 72-year-old plot where NEP was lower than expected. Interestingly, a new trend emerged concerning the severity of climate scenario with. the 72-year-old plot showed stroger decrease in NEP compared to all other plots. This decline seemed to be primarily driven by the significant rise in R\({}_{\text{het}}\), probably due to the contribution of several factors. Some of these may be the high soil moisture values (\(\theta\) values) maintained by the \"Very Fine\" soil texture (Appendix A, Figure A1), the relatively warmer conditions in this low elevation plot and the relatively higher LAI. All these parameters can favor higher R\({}_{\text{het}}\). Notably, under the extreme RCP8.5 2100 scenario and study period, all of our chronosequence plots shifted from C sinks to C sources and the overall trend of NEP seemed to reverse.
## 4 Modeling Process and Results
### Construction of the GD-SI Model
#### 4.2.2 Factor Screening Based on GD and RFE  
This study combined GeoDetector with the concept of RFE to perform factor screening for SI models. First, 18 landslide influencing factors preliminarily screened by GeoDetector were used as the initial feature set. Then, the GeoDetector \(q\)-value ranking was used as the feature importance ranking. Subsequently, the least important feature was recursively removed, and AUC values of the models under each factor subset were recorded in turn. The results are shown in Figure 6, which depicts the trend of the AUC values of the model with the number of factors. The results show that when the number of factors is 18, the model AUC value is the highest.  
Figure 5: Factor Detector results.  
Considering the adaptation between the factor importance ranking based on GeoDetector and the SI model, the concept of the RFE algorithm was improved. If the performance of the model is improved after a certain factor is eliminated in order, it indicates that the factor has a negative impact on the model to a great probability. Therefore, if the AUC value of the model increases, the related factor will be eliminated, as shown by the yellow line in Figure 6. As a result, six factors including annual average rainfall, distance from streams, NDVI, seismic intensity, distance from fault zones, and residential kernel density were eliminated. The 12 factors of distance from roads, distance from main rivers, altitude, distance from hydropower stations, soil erosion intensity, stratigraphy, land cover, aspect, slope, degree of relief, topographic wetness index, and lithology were thus retained. The model obtained after screening the factors by this hybrid method was named the GD-SI model.
# Active Noise Control over Space: A Subspace Method for Performance Analysis
## 4 Subspace Method  
In the second method, we obtain the secondary-path coefficient \(\mathbf{\eta}^{(l)}(k)\), extract the subspace spanned by secondary-path coefficients which represents the secondary sources in this environment, and only cancel the primary noise field which can be projected into this subspace.
# Traffic Simulation of Future Intelligent Vehicles in Duisburg City Inner Ring
## 3 Establishment of Simulation
### Induction Loops Traffic Demand  
The induction loops data source consists of two parts: position and amount. The position file is in pdf format and the induction loops must be manually added into the network file in SUMO one by one. The amount file is in a machine-readable format. Unlike the method mentioned above, in this section, a fixed route, with the route file and vehicle file, are used. The route file describes the edges/roads a vehicle passes along, and the vehicle file represents the departure time, position, speed, and route of each vehicle. The process of generating traffic demand from induction loop data is listed below:  
* **Loop position and type:** There are three types of induction loops in SUMO; \"source\" loops are the ones where vehicles start, \"between\" loops are in the middle position, and \"sink\" loops are the loops where vehicles vanish from the simulation. In this paper, the roads outside the ring in the entering directions are selected to be the source loops. In total, induction loops on 8 roads marked with green arrows in Figure 2b are set as the source loops.
* **Traffic flow:** In order to be recognized by the program, the flow file in SUMO should be in csv format with fixed form. However, the vehicle amount of each induction loop is saved by intersection names. MATLAB is used here to transform the data into the SUMO-required format.
* **Generate route files and vehicles:** Using the network file and flow file, the route file and vehicle file required in the simulation are generated by a sub-program in SUMO called _flowrouter_. In this step, the departure lanes are also modified to avoid conflicts by generating routes.
* **Change source loops position:** The positions of induction loops are always close to an intersection. Sometimes the distance between the induction loops is less than the length of a vehicle. This causes unwanted traffic jams upon vehicle insertion, which in turn affects the entire simulation. Therefore, all the positions of source loops are moved to the side, far from the intersection.  
The induction loop data of a coherent week in 2019 are provided. However, some intersections have no data in the given period, some intersections have no installed induction loops, and data from one intersection are not usable due to a malfunction. Therefore, the induction loops used as source loops are adjusted according to the data situation. Finally, the induction loops used for source are marked with green arrows in Figure 2b. In total, 24 induction loops on 12 roads are selected to be the source loops. Each road has 1-3 lanes and each lane has one source induction loop. A total of 76 induction loops with valid data are used as traffic volume sources.