esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Participant Demography  
The dataset offers valuable insights into the demographics of participants in the construction industry, focusing on their roles, years of experience, and knowledge of digital twins and AI technologies. A descriptive analysis of each category is presented in Figure 2.  
Figure 2: Demographic information of questionnaire participants.  
According to the analysis, architects constitute the largest group of participants, comprising 28.3% (30 out of 106), while contractors represent the smallest group, accounting for 6.6% (7 participants). Regarding participants' years of experience in the construction industry, 36.8% (39 participants) have 11-20 years of experience, and 28.3% (30 participants) have more than 20 years of experience. Together, this indicates that 65% of participants possess over 11 years of experience, reflecting a significant portion of highly experienced professionals. Additionally, approximately 18% of participants reported having between 5 and 10 years of experience. Regarding familiarity with DT technology, 81% of participants reported medium to higher levels of familiarity, while only 5.7% indicated very low familiarity. Similarly, for familiarity with AI-based DT applications, 53% of participants reported medium familiarity, and 21% reported familiarity above medium levels. In contrast, 25.5% indicated low familiarity with this concept. The data highlight that participants are predominantly experienced professionals, with nearly two-thirds having over 11 years of experience in the construction sector. Most respondents demonstrated moderate familiarity with digital twin technology and AI integration. Architects and project managers represent the majority of respondents, ensuring a balanced representation of both strategic and technical perspectives within the industry.
## 3 Materials and Methods
### TCN with LeakyRelu (L-TCN)  
The linear rectification function with leakage (LeakyReLu) [36] is more effective for time series prediction tasks than the linear rectification function (ReLu). To enhance the learning of long-term dependencies in time series data, LeakyReLU can be used in place of ReLU as the activation function in the TCN. Its structure is shown in Figure 2, which will be abbreviated as L-TCN in this paper. When the input value x is negative, the gradient of LeakyReLu is a constant \(\lambda\in(0,\ 1)\) instead of 0, allowing the neuron to update the weight on the negative input. LeakyReLu and ReLu are consistent when the input value is positive. The operational algorithms are the following Equations (1) and (2):  
\[\overset{\sim}{x}=max(0,W_{t}x+b)\qquad if\ \text{x}>0 \tag{1}\]  
\[\overset{\sim}{x}=max(0,W_{t}\lambda x+b)\qquad if\ x\leq 0 \tag{2}\]  
where \(\lambda\) is constant and \(\lambda\in(0,\ 1)\).  
Figure 1: The flowchart diagram of the research design.  
Figure 2: L-TCN structure.  
In addition, opting for LeakyReLU over ReLU can help maintain model performance while minimizing the expansion rate. Usually, the dilated rate will increase exponentially with the number of dilated convolution layers. Because of the larger dilated rate, although the receptive field is increased, the amount of computation is also increased. Therefore, different from the general TCN dilated rate size selection, too large dilated rates are not used to reduce the amount of model computation in this paper, as shown in Figure 3. The dilated causal convolution algorithms are the following Equations (3)-(5):  
Suppose that by given a one-dimensional time series \(X=x_{0},x_{1},x_{2},\ldots,x_{t}\) the corresponding output sequence is \(Y=y_{0},y_{1},y_{2},\ldots,y_{t}\), the causal convolution operation on the time series is formulated as follows:  
\[P_{(x_{t})}=\prod_{t=0}^{T}P_{(x_{t}|x_{0},x_{1},x_{2},\ldots,x_{t-1})} \tag{3}\]  
where \(P_{(x_{t})}\) is the predicted probability and \(T\) is the total moment.  
The dilated convolution \(D\) operation on the time series is formulated as follows:  
\[D(T)=(X*f_{d})(t)=\sum_{i=0}^{k-1}f(i)\cdot X_{t-2}\quad i\geq 1 \tag{4}\]  
\[D(T)=(X*f_{d})(t)=\sum_{i=0}^{k-1}f(i)\cdot X_{t-1}\quad i=0 \tag{5}\]  
where \(d\) denotes the expansion factor, \(k\) is the filter size, \(f(i)\) is the \(i\)th element in the convolution kernel, and \(*\) denotes the convolution operation.
## 3 Results
### Genomic Analysis
\begin{table}
\begin{tabular}{c c c} \hline
**No** & **Microorganism** & **Size Approx.** \\ \hline
1 & _Escherichia_ & 1–1.5 \(\times\) 2–6 \(\upmu\)m \\
2 & _Salmonella_ & 0.7–1.5 \(\times\) 2–5 \(\upmu\)m \\
3 & _Sligella_ & 0.4–0.6 \(\times\) 1–3 \(\upmu\)m \\
4 & _Halomonas_ & 0.6–0.8 \(\times\) 1.6–1.9 \(\upmu\)m \\
5 & _Acinetobacter_ & 0.9–1.6 \(\times\) 1.5–2.5 \(\upmu\)m \\
6 & _Moraxella_ & 1–1.5 \(\times\) 1.5–2.5 \(\upmu\)m \\
7 & _Pseudomonas_ & 0.5–1.0 \(\times\) 1.5–3 \(\upmu\)m \\
8 & _Oligotropha_ & N/A \\
9 & _Sinotrohizobium_ & 0.8 \(\times\) 2 \(\upmu\)m \\
10 & _Celerbiacker_ & 0.18–1.2 \(\times\) 1.2–3.4 \(\upmu\)m \\
11 & _Paraccoccus_ & 0.4–0.9 \(\times\) 2 \(\upmu\)m \\
12 & _Magnetospirillum_ & 0.5 \(\times\) 6–7 \(\upmu\)m \\
13 & _Erythrobacter_ & 0.5–0.7 \(\times\) 1–4 \(\upmu\)m \\
14 & _Noyosphingobium_ & 0.5–0.8 \(\times\) 0.8–2 \(\upmu\)m \\
15 & _Parasphingopxis_ & N/A \\
16 & _Splingomonas_ & 0.3–0.8 \(\times\) 1–2.7 \(\upmu\)m \\
17 & _Varivoorax_ & 0.3–0.6 \(\times\) 0.7–3 \(\upmu\)m \\
18 & _Janthinobacterium_ & 2.5 \(\times\) 6 \(\upmu\)m \\
19 & _Massilia_ & 0.5–0.75 \(\times\) 2.5–9.2 \(\upmu\)m \\
20 & _Sorangium_ & 0.8–1.3 \(\times\) 3.7–7.7 \(\upmu\)m \\
21 & _Luteitala_ & 0.6–0.75 \(\times\) 0.9–1.4 \(\upmu\)m \\
22 & _Hymenopteraobacter_ & 0.3 \(\times\) 4 \(\upmu\)m \\
23 & _Aequiorivita_ & 0.4–0.5 \(\times\) 1.1–1.7 \(\upmu\)m \\
24 & _Flavobacterium_ & 0.4–0.5 \(\times\) 1–5 \(\upmu\)m \\
25 & _Luteolibacter_ & 0.5–0.7 \(\times\) 0.9–1.5 \(\upmu\)m \\
26 & _Mycobacterium_ & 0.2–0.6 \(\times\) 1.0–10 \(\upmu\)m \\
27 & _Mycolicibacterium_ & N/A \\
28 & _Rlodococcus_ & 0.4 \(\times\) 1.6 \(\upmu\)m \\
29 & _Geodermatophilus_ & 1.0–2.8 \(\times\) 1.0–3 \(\upmu\)m \\
30 & _Brevibacterium_ & 0.6–1.2 \(\times\) 1.5–6 \(\upmu\)m \\
31 & _Cellulomonas_ & 0.75–1.5 \(\times\) 1.5–3.75 \(\upmu\)m \\
32 & _Braclybacterium_ & 0.5 \(\times\) 0.5 \(\upmu\)m \\
33 & _Dermabacter_ & 0.6–0.8 \(\times\) 0.9–1.1 \(\upmu\)m \\
34 & _Dermacoccus_ & 0.9 \(\times\) 1.6 \(\upmu\)m \\
35 & _Kytococcus_ & 0.8 \(\times\) 1.1 \(\upmu\)m \\
36 & _Herbiconiux_ & N/A \\
37 & _Microococcus_ & 0.5 \(\times\) 2 \(\upmu\)m \\
38 & _Isoptericola_ & N/A \\
39 & _Micromonospora_ & N/A \\
40 & _Nocardioides_ & 0.35 \(\times\) 1.5 \(\upmu\)m \\
41 & _Ctibacterium_ & 0.4–0.7 \(\times\) 3–5 \(\upmu\)m \\
42 & _Pseudonocardiia_ & N/A \\
43 & _Streptomyces_ & 0.5 \(\times\) 1 \(\upmu\)m \\
44 & _Actinomadura_ & 3–20 \(\times\) 6–30 \(\upmu\)m \\
45 & _Bacillus_ spp. & 0.5–1.0 \(\times\) 1.0–4 \(\upmu\)m \\ \end{tabular}
\end{table} 
Table 4: The list of microorganisms detected in the forest/lake site with their approx. sizes.
# CFD Design Performance Analysis for a High-Speed Propeller
## 1 Introduction
The SST model combines the advantages of the k-omega model in the boundary layer with the k-epsilon model in the free stream, making it ideal for accurately capturing the behavior of the bounded layer and flow separation phenomena at the propeller surface. The use of specialized software licenses for marine engineering allows the quality control of the designed product, without the risk of material losses and unjustified financial expenses. In a virtual space identical to the one in which the analyzed object will be placed, it is possible to detect errors, identify and solve problems before it is sent to production. Of course, the authors recognize the need to validate the simulations (on replica-models) in hydrodynamic testing laboratories and sea trials (the latter being possible only after the construction of the prototype). With their paper, the authors focused exclusively on the preliminary part of the design process of a high-speed propeller. The work proposes the computerized analysis (using Computational Fluid Dynamics (CFD) modeling with ANSYS CFX module used standard parameters) of a high-speed propeller, in open water, from the perspective of velocity and pressure manifested on the propeller blades. The scope of the study is to determine the ideal performance of the design propeller. For that the author aims to determine using numerical methods the \"Thrust-Propeller RPM\" diagrams and \"Torque-propeller RPM\" diagrams, which are most important in choosing a propeller for a given boat. The \"Thrust-Propeller RPM\" diagrams help the design team to choose the proper propeller for the desired boat speed and the \"Torque-propeller RPM\" allows the design team to choose the proper engine. The simulations were targeted on analyses for velocity and pressure on propeller blades and the working method; results and conclusions are briefly presented below. The paper addresses the gaps in the literature by performing an advanced CFD analysis on the high-speed marine propeller, before the manufacturing stage, providing validated thrust and torque curves depending on the rotation regime, an aspect rarely addressed in the preliminary design phase. The novelty consists of simulating real naval operating conditions, identifying areas with cavitation risk and optimizing the geometry, facilitating propeller-engine correlation without the need for expensive physical tests in the preliminary stages of design.
# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 4 Remote Sensing-Based Proxies for DRM in Urban Areas
### Built-Up RS-Based Proxies
#### Others  
One of the unique features of tsunami inundation zones important during and after an event is the level of sea water penetration on land. Thus, RS studies have focused on extracting water bodies from the post-event images as the first step to use it as a proxy for damage assessment [98]. Similarly, surface water areas (level of flood water coverage) serves for damage [99; 100] and recovery assessments [46]. Furthermore, debris line acts as a proxy to identify the impact of water related disasters, such as tsunami [102] and hurricane [101]. The debris line demonstrates how far water reached inland after a disaster, and it has also been used to identify debris zone [81].  
In a different study, impervious surface ratio is estimated as a proxy to compute the vulnerability of urban areas because the increase of impervious surface can lead to an increase of volume, intensity,and duration of urban water run-off, i.e., the duration that water remains on the surface after a flood [95]. In addition, [PERSON] and [PERSON] [49] used impervious surface as a measure for recovery assessment.  
As a unique recovery situation, after a disaster people that are located in a temporary shelter during the reconstruction processes move back to their permanent houses when the reconstructions is completed. Consequently, change in the ratio of permanent buildings to temporary accommodations serves as a proxy for movement of the population from temporary accommodation to permanent houses and as a successful recovery process [32; 46; 48].  
After a flood event, drainage plays an important role to evacuate flood waters, with an increase in drainage density reducing the vulnerability from the affected area; therefore, it has been used as a proxy for urban flood vulnerability assessment [103; 104; 105; 106]. In contrast, a higher river network density ratio indicates a high vulnerability due to the relatively larger amount of flood-prone regions during a flood event [95].
## 2 Materials and Methods
### Fabrication of PDs  
Firstly, the Ga\({}_{2}\)O\({}_{3}\) sample was cut into 2 cm \(\times\) 2 cm pieces and cleaned again. A 100 nm IZO layer was deposited onto the sample by a sputter system with a shadow mask. The PbBr\({}_{2}\) precursor was obtained by dissolving 367 mg of PbBr\({}_{2}\) into DMF at 80 \({}^{\circ}\)C with sufficient stirring. Then, 80 \(\mu\)L of PbBr\({}_{2}\) solution was spin-coated onto the sample with the IZO electrode at 2000 rpm for 30 s. This was followed by a thermal treatment at 90 \({}^{\circ}\)C for 30 min. Next, 110 \(\upmu\)L of a CsCl/H\({}_{2}\)O solution with a concentration of 1 M was spin-coated onto the PbBr\({}_{2}\) film at 2000 rpm for 30 s. Finally, the sample was annealed at 250 \({}^{\circ}\)C to fulfill the crystallization of the CsPbBr\({}_{3}\) film, and thus, the fabrication of the CsPbBr\({}_{3}\)/Ga\({}_{2}\)O\({}_{3}\) UV PDs was finished.
## 2 Materials and Methods
### Igniter  
The igniter (Figure 2b) belongs to the Advanced Corona Ignition System (ACIS) family and was a preproduction specimen supplied by Federal-Mogul Powertrain Italy, a Tenneco Group Company (FM from now on). It is operated by means of a dedicated control unit, which provides energy to the igniter assembly. The latter is composed of a coil and a firing end, linked through an extension (Figure 2b). In a lumped-element model, these three parts are idealized as a pure inductor, a pure capacitance (with respect to engine ground), and a pure resistance, respectively [20]. The natural frequency of such a circuit is about 1.04 MHz, and the assembly is fed with alternating current (AC) power at the same frequency, in order to exploit the resonant conditions to obtain streamer discharge [35] between the four-tip electrode igniter and the combustion chamber, which acts as grounded counter-electrode [20]. The ACIS control unit, upon receiving the ignition signal from the conventional ECU, sends RF power to the igniter assembly to start the corona discharge. There are two main electrical parameters which can affect the corona behavior: driving voltage V\({}_{\text{d}}\) and discharge duration T\({}_{\text{d}}\)[32]. Such parameters, stored in the ACIS control unit, can be adjusted by means of a dedicated user interface. The driving voltage is proportional to the max voltage at the electrode [21]; the latter can vary up to 70 kV peak-to-peak. The corona discharge is extremely sensitive to V\({}_{\text{d}}\). Depending on the chamber pressure at ignition timing, too low V\({}_{\text{d}}\) values can reduce the number of the streamers generated by the ACIS tip or even completely inhibit the discharge [32]. Too high V\({}_{\text{d}}\) values, instead, can result in streamer-to-arc transition [45; 36]; breakdown occurs, as in a conventional spark igniter, and the LTP benefits are lost.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Unit** & **Value** \\ \hline Image resolution & pixel & \(256\times 256\) \\ Sampling rate & kHz & 79.0 \\ Exposure time & \(\upmu\)s & 12.3 \\ Bit depth & bit & 8 \\ Spatial resolution & \(\upmu\)m\({}^{2}\)/pixel & \(125\times 125\) \\ Temporal resolution @ 1000 rpm & CAD/frame & 0.0759 \\ Number of consecutive events recorded & - & 63 \\ \hline \hline \end{tabular}
\end{table}
Table 3: High-speed camera settings.  
T\({}_{\rm d}\) is the time the corona discharge is active; it was found that an increase in such duration can reduce the cycle-to-cycle variability [32]. After a certain threshold, a further increase is no longer beneficial; an optimal duration can be found to contemporarily have a stable combustion and limit the ACIS energy consumption [32].
## 2 Methods  
### Background of Water Reuse in Industrial Parks in Chonburi, Thailand  
Chonburi Province is located in the eastern region of Thailand in the EEC, along with nearby provinces, that is, Rayong and Chachoengsao. Under the EEC initiated by the Thai government, the province is now set to become a major industrial center for Thailand's eastern region, with the port of Laem Chabang serving as a major commercial port. It is also a tourist attraction with a diverse and beautiful natural landscape. Chonburi's gross provincial product (GPP) was THB 1,030,949 million, equivalent to 6.3% of Thailand's gross domestic product (GDP) in 2018, making it the third-highest GPP in the country [32]. The economic structure of Chonburi Province in 2018 was divided into three sectors: industry (64.78%), agriculture (2.44%), and tourism and services (32.78%) [33].  
Based on a study conducted by the Office of the National Water Resources [30], the water demand in Chonburi around 2017 was 469 million cubic meters (MCM), while in Rayong it was 494 MCM, and Chachoengsao recorded 1456 MCM. The major water use in Chonburi and Rayong is industrial water use, while Chachoengsao mainly uses water for irrigation. The study also projected that in the next 20 years, around 2037, the domestic and industrial water demand in the three provinces would increase from approximately 857 MCM to 1258 MCM.  
Eight industrial parks are located in Chonburi, as shown in Figure 1, with 11 central WWTPs. Most factories in the industrial parks in Chonburi produce auto parts and electronic parts. According to our 2018 survey [31], which obtained secondary data from industrial parks in Chonburi, the flow rates of wastewater in the central WWTPs in the industrial parks in Chonburi varied from 226 to 12,704 m\({}^{3}\)/day (Table S1), with a total flow rate of 51,119 m\({}^{3}\)/day or 18,658,501 m\({}^{3}\) per year. The concentrations of pollutants in WWTP effluents, that is, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), total phosphorus (TP), suspended solids (SS), total dissolved solids (TDS), and heavy metals, are shown in Table S2. A total of 70.5% of the wastewater was reused for various activities, that is, industrial water supply (49.7% of water reuse), irrigation of plants and green areas (21.1% of water reuse), power plant cooling water (28.7% of water reuse), and sale of reused water without post-treatment (0.5% of water reuse).
## 3 Result and Discussion
### Regional Scale Strain Rate
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Station**} & \multirow{2}{*}{**[PERSON] et al. [9]**} & \multicolumn{2}{c}{**[PERSON] et al. [20]**} & \multicolumn{2}{c}{**[PERSON] et al. [18]**} & \multicolumn{2}{c}{**This Study**} \\ \cline{3-10}  & & & **EW \(\pm\)\(\sigma\)** & **NS \(\pm\)\(\sigma\)** & **EW \(\pm\)\(\sigma\)** & **NS \(\pm\)\(\sigma\)** & **EW \(\pm\)\(\sigma\)** & **NS \(\pm\)\(\sigma\)** & **NS \(\pm\)\(\sigma\)** \\  & & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline
4 & CMT & N/A & N/A & \(5.11\pm 0.34\) & \(2.97\pm 0.15\) & \(6.3\pm 0.08\) & \(0.36\pm 0.06\) & \(2.78\pm 0.26\) & \(3.14\pm 0.20\) \\ \hline
5 & CNGA & N/A & N/A & \(9.22\pm 0.40\) & \(-3.32\pm 0.24\) & \(8.4\pm 0.04\) & \(-1.80\pm 0.04\) & \(3.81\pm 0.25\) & \(2.27\pm 0.17\) \\ \hline
6 & CPWD & \(2.21\pm 0.74\) & \(-0.54\pm 0.67\) & \(3.70\pm 0.25\) & \(0.27\pm 0.14\) & \(1.9\pm 0.03\) & \(-1.20\pm 0.03\) & \(1.37\pm 0.26\) & \(-0.36\pm 0.21\) \\ \hline
7 & CSBY & \(5.96\pm 0.41\) & \(0.08\pm 0.39\) & \(3.23\pm 0.66\) & \(-1.47\pm 0.25\) & \(4.3\pm 0.02\) & \(-1.40\pm 0.02\) & \(2.43\pm 0.23\) & \(-0.51\pm 0.21\) \\ \hline
8 & CSEM & \(1.64\pm 0.41\) & \(-1.45\pm 0.41\) & N/A & N/A & \(1.6\pm 0.07\) & \(-2.30\pm 0.03\) & \(0.51\pm 0.22\) & \(-1.15\pm 0.18\) \\ \hline
9 & CSLO & N/A & N/A & \(4.41\pm 0.29\) & \(0.78\pm 0.20\) & \(5.2\pm 0.03\) & \(-0.18\pm 0.03\) & \(4.01\pm 0.24\) & \(1.27\pm 0.18\) \\ \hline
10 & CTBN & \(6.66\pm 0.74\) & \(4.44\pm 0.57\) & \(3.33\pm 0.17\) & \(1.51\pm 0.12\) & \(1.9\pm 0.03\) & \(-0.87\pm 0.02\) & \(0.85\pm 0.23\) & \(-0.24\pm 0.16\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._cells consisting of 0.2 (latitudinal) by 0.25 (longitudinal) in dimension. Meanwhile, our model presents higher resolution depicting short wavelength features in the region [34].
# Authentication Securing Methods for Mobile Identity: Issues, Solutions and Challenges
## 3 Results
### Mobile Device without Fingerprint Recognition  
Like the earlier condition in the previous section, the devices without fingerprint modules also require specific privileges but use different code libraries for extracting the SIM number and IMEI depending on the mobile device type. Once the SIM number and IMEI are successfully recorded, then geo-location will be obtained using the mobile device's GPS receiver.  
The captured GPS location will be compared to the initial location defined in the AS, and if identical or within an accepted radius, the token will be issued. However, if the location is outside the accepted range, the user will be required to verify the GPS location manually from a registered device. Once verified, the transaction details and received token from AS will be encrypted using the initially defined geo-location before moving to the application server. The application server will check the validity of the token with the SIM number and IMEI, then proceed to complete the transaction once AS has verified the user identity.  
The difference in geo-location captured by GPS will be recorded and analyzed by AS from time to time. The new location will also be whitelisted, allowing for future authentication within acceptable frequency and occurrence. This will reduce the number of user interventions required for authentication.
# Improving Symbolic System-Level Synthesis by Solver Coordination and Domain-Specific Heuristics
## 5 Answer Set Programming (ASP)--Binding & Routing
### Scheduling-Aware Binding & Routing Refinement  
As mentioned before, in our _coordinated_ SMT-based synthesis approach, we add additional lexicographical optimization goals to the answer set solver such that the background theory solver can decide schedulability within a shorter amount of time--a _scheduling-aware binding and routing refinement_. In Section 8 of this paper, we will show that despite adding the optimization goals introduces an additional overhead in terms of number of constraints and variables to the answer solver, this approach can significantly increase the scalability of symbolic system synthesis. Parts of the used encoding for lexicographical optimization goals can be found in [27]. Concerning the priorities of the optimization, we encode a load balancing strategy for the PEs of the platform with the highest priority. This is followed by minimizing the total number of routed messages on the platform and the total number of hops of all messages. With the lowest priority, we discourage sharing hardware resource between different applications since the complexity of the time-triggered scheduling increases considerably if hardware resources are shared by computational tasks or packets that do not belong to the same application.
## 7 Conclusions  
EnMAP is a spaceborne imaging spectroscopy mission developed by a consortium of German Earth observation institutions with the main purpose of filling the current gap in global, high-quality and operational hyperspectral data. An overview of the mission and its current status has been provided in this contribution. EnMAP will measure in the VNIR and SWIR spectral regions of the solar spectrum with a spectral sampling of 5-10 nm, a ground sampling distance of 30 m and an image swath width of 30 km. EnMAP hyperspectral measurements will enable the monitoring of a wide span of Earth materials and environmental processes. Data products from different pre-processing levels (from calibrated at-sensor radiance to orthorectified surface reflectance data) will be delivered to scientific users under an open data policy. A series of core research topics within the EnMAP science plan, such as agriculture, forestry, water, ecosystem science, soils and geology and urban environments, is currently being addressed through a series of EnMAP-specific research programs. Other on-going preparatory activities include the simulation of EnMAP-like datasets over different environments for testing data pre-processing and application-based algorithms, the consolidation of the calibration-validation plan and the implementation of a software toolbox for the processing and exploitation of EnMAP data.