esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Architecture of the Proposed SDAMs
### Software Architecture  
The software architecture is shown in Figure 4. There are two main processes, the ARM process and the DSP process, which are run in the ARM cores and the DSP cores, respectively. The DSP process responds to the physical-layer algorithms and the driver of the transmitting/receiving hardware. The ARM process responds to the network protocols and the applications. The ARM core and DSP core communicate via the interprocess communication (IPC) scheme provide by TI AM5728. The ARM process can be further divided into two subprocesses--the networking process and the ARM-DSP interaction process. When a data packet is generated by an application and its transmission request is triggered, the packet is sent to the networking process, where the packet header is inserted into the packet. Then, the packet passes through the ARM-DSP process and reaches the DSP process, where the data packet is segmented into frames and standard physical-layer processing, including channel coding. Modulation is applied to each frame to produce an acoustic waveform. The waveform is amplified and sent out by a transducer. The complete flow of packet processing is shown in Figure 5.  
Figure 4: The software architecture of the proposed SDAM modem.  
Figure 3: The physical picture of the receiving/transmitting board for the proposed SDAM (yellow— the coreboard, blue—the transmitting board, red—the receiving board).
## 2 Methodology  
This section presents the key elements of the satellite's design and its intended operating environment and demonstrates the feasibility of using a lower inclination orbit for the satellite's mission as follows:  
1. Orbital Parameters: This section describes the parameters that define the orbit of the satellite. This information is crucial for determining the satellite's position and movement in space.
2. Comparison between SSO and Lower Inclination Orbit: This section presents the advantages and disadvantages of both orbits which at the end demonstrates the feasibility of using a lower inclination orbit for the satellite.
3. Lower Inclination Orbit Mission Definition: This section defines the mission scenario used for analysis and verification with all the necessary parameters defined.
4. LoRa-IoT Design Requirements: This section lists the design requirements of both the IoT ground terminal and the satellite payload receiver.
5. IoT terminal design: This section describes the design of the IoT terminal. The terminal is responsible for transmitting data to the satellite, as well as communicating with ground-based sensors and other IoT devices.
6. Link Budget Analysis: This section lists the requirements for the link budget and calculates the minimum power required for a successful link margin of transmission.
# S.O.V.O.R.A.: A Distributed Wireless Operating System
## 4 Experiment and Results
### Proposed Testbed  
To characterize the energy consumption, an experimental validation was carried out with the MiBench benchmarking suite, with the algorithms listed in Table 3 to measure the energy consumption at different CPU rates, which were controlled through processor frequency. The linearization of the output function can be seen in Figure 7. This generated output function is the basis of the OR policy to select the destination node in terms of the task to be performed on the network or application selected by the user.  
In the same way, communication in the ad hoc mode was validated through the proactive routing protocol B.A.T.M.A.N. With the transmission of data (14 MB for test) validating the routes and the TQ value--Transmit Quality--as an additional factor for the selection of the destination node, taking the measurement of power consumed by each transmission carried out; the results can be seen in Table 4 for network protocol and in Table 5 for energy on a RPI 3.  
The OR monitors the network nodes' status and its resources, creating a virtual abstraction of the resources mapping on each node and the amount of resources free for use on the distributed wireless OS and managing it. The test policy is quite simple but it can be customized, for the purposes of the experiment, when mapping the available resources from the information provided by each local agent, and it is stored in the orchestrator which compares it with the information referring to the energy consumption model, the TQ value and the state of charge of the processors of each node. Based on this information, it creates a workload and dispatches it depending on the state of resources and the available route for delivery of the task.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**RPi\_3 bactl** & **14 MB Rpi\_Zero** & **14 MB Odroid** \\ \hline \%CPU & 100.00 & 100.00 \\ Time (s) & 10.19 & 10.54 \\ Power (mW) & 2211.88 & 2230 \\ Energy (J) & 2211.88 & 2230 \\ Throughput (MB/s) & 0.8893 & 1.7 \\ MB Send & 7.605 & 18.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test Ad hoc-bactl/batman-adv RPi 3.  
Figure 6: Experiment Model.
## 4 Materials and Methods
### Ancestor Family Attribution and Multi-Descent Fusion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Traits**} & \multirow{2}{*}{**Type**} & \multicolumn{10}{c}{**Ransomware Families**} \\ \cline{3-14}  & & & & & & & & & & & & & & & \\ \hline Windows API calls & Dynamic & ✗ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\ \hline Windows Cryptographic APIs & Dynamic & ✗ & ✓ & ✓ & ✓ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\ \hline Registry Key & Dynamic & ✗ & ✓ & ✓ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\ \hline System File Process & Dynamic & ✗ & ✗ & ✓ & ✗ & ✗ & ✗ & ✓ & ✓ & ✓ & ✗ & ✗ & ✗ \\ \hline Directory Actions & Dynamic & ✓ & ✓ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ & ✓ & ✓ & ✓ & ✓ \\ \hline Application Folders & Dynamic & ✓ & ✓ & ✓ & ✗ & ✗ & ✗ & ✗ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline Control Panel Settings & Dynamic & ✗ & ✓ & ✓ & ✗ & ✗ & ✗ & ✗ & ✓ & ✗ & ✗ & ✗ \\ \hline System File Locations & Dynamic & ✗ & ✗ & ✓ & ✗ & ✗ & ✓ & ✓ & ✓ & ✗ & ✗ & ✗ \\ \hline Pay-loaders/Downloaders & Dynamic & ✗ & ✓ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\ \hline Command and Control Server & Dynamic & ✓ & ✓ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\ \hline Windows Volume Shadow & & & & & & & & & & & & & & & \\ (vssadmin.exe and WMIC.ex) & Dynamic & ✗ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\ \hline File Fingerprint & Dynamic & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\ \hline Directory Listing Queries & Dynamic & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\ \hline Windows Safe Mode Booting & & & & & & & & & & & & & & & \\ (bcdcid.ex) & Dynamic & ✗ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✓ & ✓ \\ \hline File Extensions & Static & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✓ & ✓ & ✓ & ✓ \\ \hline Files Names & Static & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✓ & ✓ & ✓ & ✓ \\ \hline Portable Executable Header & Static & ✗ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✓ & ✓ & ✓ & ✓ \\ \hline Embedded Resources & Static & ✗ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✓ & ✓ \\ \hline Packers & Static & ✗ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\ \hline Shannon’s Entropy & Static & ✗ & ✗ & ✓ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\ \hline Cryptors & Static & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\ \hline Portable Executable Signature & Static & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✓ & ✓ & � \\ \hline Embedded Scripts & Static & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & � & ✗ & ✓ & � & ✓ \\ \hline Fuzzy Hashing & Static & ✗ & � & � ✓ & ✓ & � & ✗ & � & ✗ & ✗ & ✗ & ✗ & � & ✗ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Potential traits exploited by 0-Day versions of generic ransomware families. ransomware versions that may belong to multi-descent families of ransomware with respect to their relative redundancy in malware and good-ware families on the learned trait vectors (\(T\)). \[P_{r}(T_{i}\mid h_{i})=\frac{N_{h_{i}\to R}}{N_{h_{i}\to R}+N_{h_{i}\to R^{ }}} \tag{2}\]  
\[MDR(T_{i})=\frac{\sum_{i=1}^{|K|}\Pr(T_{i}\mid h_{i})}{|T_{i}|} \tag{3}\]
## 3 Results and Discussion
### Molecule Docking  
The bindings of essential protein targets to the eight key cannabinoid ligands were further confirmed by molecular docking simulation using Autodock Vina. The binding affinity of a ligand-target complex was evaluated by the binding energy (\(\Delta\)G, binding free energy change), where a more negative binding energy value indicates a stronger binding affinity or a greater binding constant for the formation of the ligand-target complex. In this work, the binding energies calculated were based on the complex conformation with the lowest docking energy. Table 23 showed the binding energies of the eight key cannabinoid ligands to the 18 essential protein targets, which had values ranging from \(-\)10.5 kcal/mol to \(-\)4.7 kcal/mol. The binding energy of \(\leq\)\(-\)5.0 kcal/mol indicates the strong binding between a ligand and its target [70; 71; 72]. The strongest binding was observed between CBDA and TNF with a binding energy of \(-\)10.5 kcal/mol. Figure 8 illustrated the binding of CBDA to TNF at the binding pocket where hydrophobic interactions were observed between amino acid residues at GLY-121, LEU-120, LEU-57, SER-60, TYR-119, TYR-151, and TYR-59; and H-bonds were formed at GLY-121, LEU-120, and SER-60 with distances of 3.2 A, 3.4 A and 2.5 A, respectively. Since the stronger the binding affinity of a ligand to its protein target, the higher the potency of the ligand, the binding affinity data can guide us to select the proper ligand-targets pairs from each functional module for experimental validation of the efficacy of cannabinoid for the aimed illnesses and therapeutic outcomes.  
\begin{tabular}{c c c c c c c c c} \hline \hline
**Target (PDB-ID)** & **Ligands** & **Ligands** & **Ligands** & **Ligands-THC** & **Ligands-THC** & **CBC** & **CBD** & **CBDA** & **CBG** & **CBGA** & **CBN** \\ \hline AKT1 (659W) & / & / & / & -8.6 & -8.8 & / & / & / \\ CASP9 (4 RHW) & / & / & / & -5.8 & -5.9 & / & / & / \\ CAT (1 DGF) & / & / & / & -7.5 & -7.9 & / & / & / \\ COMT (4 PYI) & / & -8.0 & / & / & / & / & / & -8.0 \\ CREB1 (5 ZKO) & / & -5.8 & / & / & / & / & / & -6.6 \\ CYCS (5 TY3) & / & / & / & / & / & -4.7 & / & / \\ CYP17A1 (6 WR1) & / & / & / & -7.4 & -7.0 & / & / & / \\ GRIN2B (5 EWM) & -7.6 & -8.0 & / & / & / & / & / & / \\ GSTA2 (5 LD0) & / & -7.0 & -5.4 & / & / & / & / & -6.5 \\ GSTM3 (3 GTU) & / & -7.7 & -6.7 & / & / & / & / & -7.6 \\ GSTP1 (5J41) & / & -8.7 & -7.6 & / & / & / & / & -8.1 \\ HMOX1 (1N45) & / & -8.0 & -7.4 & / & / & / & / & -7.7 \\ PLCG1 (3 GQI) & / & / & / & / & / & / & / & -5.6 & / \\ PRKCA (4 RA4) & / & -7.5 & -7.8 & -6.7 & -7.4 & / & / & -8.2 \\ PRKCB (2 IOE) & / & -8.1 & -8.1 & / & / & / & / & -8.5 \\ TNF (6 OOY) & -10.4 & / & / & -6.0 & -10.5 & / & / & / \\ CNR1 (5U09) & -7.9 & -7.6 & -7.0 & -7.3 & -7.1 & -7.9 & -6.9 & -9.4 \\ CNR2 (6 KPC) & -8.0 & -8.2 & -7.7 & -9.2 & -7.3 & -6.9 & -7.0 & -8.5 \\ \hline \hline \end{tabular}  
Symbol, / means that there is no predicted binding between a cannabinoid ligand and a protein target.
## 4 Simulation Results and Discussion
### Two-Turbine Case  
In this case, the wind farm of two turbines discussed in Section 2.2 is considered. The diameter of the wind turbine is 126.4 m and the simulation is performed at 8 m/s [26]. In this paper, the wind farm of two turbines is only developed to investigate the impact of varying the spacing between turbines on the wind farm power production with and without incorporating the WRC. The results are included in Table 1. When the spacing between the turbines is increased, the power production increases as expected. However, because of space constraints and other economic considerations, increasing the distance between turbines beyond a certain point is not feasible. The percentage gain in power can be improved while the WRC is in action.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Control Method** & **3D** & **4D** & **5D** & **6D** \\ \hline Without WRC & 1.7507 MW & 1.7504 MW & 1.7591 MW & 1.7589 MW \\ With WRC & 1.7605 MW & 1.7737 MW & 1.7920 MW & 1.8259 MW \\ \hline
**Percentage Increase** & **0.56\%** & **1.33\%** & **1.87\%** & **3.81\%** \\ \hline
**Control Method** & **7D** & **8D** & **9D** & **10D** \\ \hline Without WRC & 1.7711 MW & 1.7730 MW & 1.7873 MW & 1.7979 MW \\ With WRC & 1.8488 MW & 1.8856 MW & 1.9101 MW & 1.9539 MW \\ \hline
**Percentage Increase** & **4.39\%** & **6.35\%** & **6.87\%** & **8.68\%** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Wind farm power for 3D to 10D spacing between the turbines.  
In Figure 12, dynamic simulation results for two-turbine wind farm with 4D spacing under normal operating conditions and when the WRC strategy is used are compared. When WRC is used, the wind speed approaching the downstream turbine increases, as seen in the diagram. Figure 13 shows the flow field diagram and wind speed curve for the two-turbine wind farm with and without WRC for 8D spacing. According to the figure, the mean wind speed is reduced between the turbines because of the wake effect, and under WRC the mean wind speed is improved.  
Figure 12: Two-turbine case with and without WRC for 4D spacing.
# Surrogate-Based Robust Design for a Non-Smooth Radiation Source Detection Problem
## 2 Radiation Transport Model and Surrogate Formulations
### Numerical Model for Detector Response  
To determine the intensity of photons arriving at a given detector location \(\mathbf{r}_{d}^{i}\), the algorithm employs a simple ray-tracing scheme. Starting at the location of the source \(\mathbf{r}_{s}\), we draw a ray from \(\mathbf{r}_{s}\) to \(\mathbf{r}_{d}^{i}\). We then compute the intersection of this ray with the disjoint polygons \(P_{j},\ j=1,2,...,N_{g}\), representing the set of buildings in our domain. This yields a series of line segments expressing the path traversed in each region. We assume that a given ray intersects \(N_{\ell}\) polygons, \(N_{\ell}<N_{g}\), and let \(\mathcal{L}=\{(\ell_{j},\Sigma_{T}^{(j)})\}_{j=1}^{N_{\ell}}\) be the set of all intersecting segments, where \(\ell_{j}\) is the Euclidean length of the \(j\)-th segment and \(\Sigma_{T}^{(j)}\) is the corresponding value for the macroscopic total cross-section. With this assumption, Equation (3) takes the form  
\[\hat{F}_{i}(\boldsymbol{\theta})=S_{0}\Delta t_{i}\cdot\epsilon_{i}\cdot\frac {A_{i}}{4\pi\|\mathbf{r}_{d}^{i}-\mathbf{r}_{s}\|_{2}^{2}}\exp\left(-\sum_{j= 1}^{N_{\ell}}\ell_{j}\cdot\Sigma_{T}^{(j)}\right). \tag{5}\]  
Figure 1: Satellite image of problem domain with model geometry overlaid (Imagery ©2016 Commonwealth of Virginia, DigitalGlobe, District of Columbia (DC GIS), Sanborn, U.S. Geological Survey, Map data ©2016 Google). The figure marks indicate possible detector and source locations.  
Equation (5) provides an analytic expression estimating the expected detector response, and its computation primarily requires the intersection of lines with the model geometry. Equation (5) represents a significant simplification to the solution of (1), a nonlinear PDE with seven independent variables whose solution in complex geometries can require many hours even on a supercomputer. We implemented the numerical model (5) in a short Python code. It employs the Shapely library ([[https://pypi.python.org/pypi/Shapely](https://pypi.python.org/pypi/Shapely)]([https://pypi.python.org/pypi/Shapely](https://pypi.python.org/pypi/Shapely))) for performing the computational geometry calculations. The model takes as input a specification of polygons representing the different regions of the domain, cross-section data, detector locations, source intensity, and source location.
## 5 Conclusions  
This study proposes a plasmonic metasurface structure capable of absorbing over 90% of EM waves in a broad frequency range from 12.30 to 14.80 GHz. Simulation and experimental studies demonstrated that the plasmonic metasurface maintained a high absorption and wide bandwidth for both TE and TM waves, for incident angles up to 45\({}^{\circ}\). Notably, the broadband absorption was obtained without the use of external elements such as resistors or specialized materials. The structure was 2D and was easily fabricated using the standard PCB technology. Due to its simple and compact design, the plasmonic metasurface also exhibited high durability and was more practical for implementation compared to previously proposed structures that relied on complex fabrication techniques. Given its advantages, the plasmonic metasurface has potential for various applications, including radar stealth technology, satellite communications, surveillance radars, and many other promising fields.  
Conceptualization, T.S.P. and B.X.K.; methodology, T.S.P.; software, B.X.K.; validation, V.D.L. and Y.L.; formal analysis, T.S.P.; investigation, B.X.K.; resources, V.D.L.; data curation, B.X.K.; writing--original draft preparation, T.S.P.; writing--review and editing, Y.L., V.D.L., and L.C.; supervision, Y.L.; project administration, Y.L. and V.D.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Vietnam Academy of Science and Technology, under grant number NCPTVL.03/24/25, supported by the Shanghai Science and Technology Commission, under grant number 24110714600, and funded by the Korea Evaluation Institute of Industrial Technology (Project No. 20016179).  
Not applicable.  
The data used and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 4 Proposed System
### Software Architecture
#### 4.3.4 Operation of the Multi-UAV System  
CntMs are sent by all the UAVs that constitute the FANET to the BS periodically. The message flow is shown in Figure 9. In this scenario, the BS receives information on the area from each VANT and whether any boat is found. At the initial time instant t\({}_{0}\), each UAV sends a CntM reporting the scanning situation, while also transmitting a new message every three seconds until the mission ends. Figure 9a represents the scanning scenario of UAV-1, where the mission was completed and no boat was found. Figure 9b shows that UAV-2 found a boat at instant t\({}_{6}\) in the coordinate X, thus informing the BS about the number of boats located so far. From this event, UAV-2 starts sending a DM to transmit the image of the detected target to the BS, while scanning continues and new CntMs are sent. When the server located at the BS receives a CntM indicating the target location, it informs the operator that a boat was found at a given geographic coordinate, as a sound alert can even be emitted if necessary.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Field** & **Size (bytes)** \\ \hline ID & Identification & 4 \\ TY & Message Type & 1 \\ DN & Address Destination Node & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confirmation messages (CnfMs) format.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Field** & **Size (bytes)** \\ \hline ID & Identification & 4 \\ TY & Message Type & 1 \\ SN & Source Node Address & 2 \\ PQ & Number of Packets & 4 \\ DN & Destination Node Address & 2 \\ PN & Packet Number & 4 \\ DA & Data & 239 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data message (DM) format.  
The image transfer process is explained in Figure 10. After locating a boat, UAV-2 starts sending the image to a BS using a type-2 DM. At instant t\({}_{6}\), UAV-2 starts the image transmission through a given number of DMs defined according to the value of PQ. It is observed that the ID field contains the position of each message. Besides, the last one will contain an ID equal to PQ. Once the receipt has been confirmed, the BS checks if all messages have been received correctly. In this case, a type-3 CntM is sent to confirm that retransmission is not necessary since ID is equal to PQ. Otherwise, the message payload is supposed to contain the message IDs required for reassembly of the original data.
# Balloon Design for Mars, Venus, and Titan Atmospheres
## 3 Software Development  
The theoretical model described in Section 2 was implemented in an integrated design and simulation environment developed in MATLAB(tm). The tool provides a platform for studying three types of balloons, namely ZP, SP, and OZP, with four different shapes, i.e., sphere, oblate, prolate, and airship. Figure 3 illustrates the top level architecture for the developed environment. The developed tool was verified and validated to identify and investigate discrepancies between simulated results and data from real flights of ZP balloons in the Earth's atmosphere.  
This verification of the developed tool was done by using GPS recorded data of different balloon flights carried out by the Swedish Space Corporation. The data used for verification are from three different types of ZP balloons, a 12,000 m\({}^{3}\) (FL-1) balloon that was flown during autumn, and a 50,000 m\({}^{3}\) (FL-2) balloon and a 1,120,546 m\({}^{3}\) (FL-3) balloon that flew in summer. Table 3 gives the details of the flight specifications for both balloons. The real and simulated flight altitude profiles of these two balloons can be seen in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **FL-1** & **FL-2** & **FL-3** \\ \hline Payload (kg) & 159.2 & 289 & 1189.6 \\ Free lift (\%) & 12 & 10 & 12 \\ Altitude (m) & 27,200 & 33,600 & 40,000 \\ Day of Year & 289 & 166 & 196 \\ Local launch time (24 h) & 8:15 & 11:15 & 01:00 \\ Solar Absorptivity & 0.024 & 0.024 & 0.024 \\ Solar Transmissivity & 0.916 & 0.916 & 0.916 \\ Solar Emissivity & 0.134 & 0.134 & 0.134 \\ Infrared Transmissivity & 0.866 & 0.866 & 0.866 \\ \hline \end{tabular}
\end{table}
Table 3: Flight parameters.  
Both the simulated and the real flights achieved the float altitude at approximately the same time. The overall flight behaviour and estimation of different parameters is fairly consistent with the real flight data. The gas mass, envelope mass, and volume obtained from the simulation tool are also similar to the real flight. The simulated flight altitudes of FL-1, FL-2, and FL-3 have average altitude  
Figure 3: Simulation architecture for balloon ascent estimation. The modules which constitute the physical simulation are shown together with their interlinked data connections as well as the databases, inputs, and outputs.  
errors of 868 m, 865 m, and 765 m, respectively, in comparison to the real flight data. This could be due to the uncertainties related to the different parameters of the ascent equations. For example, the cloud cover, albedo, and initial lift gas quantity have significant effects on the ascent rate, and there is some uncertainty in the values of these parameters corresponding to the real flight data, and hence the error. While additional systematic model errors cannot be excluded, it is not possible to claim which of these errors is larger--input errors or model errors. In [42], the author presents a detailed analysis on the effect of various input parameters on the balloon ascent.