esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Prototype Description and Implementation
### Assembly of the Prototype System
Once this procedure is complete, the pairing process (binding) between the receiver and the transmitter must be completed. This will be done by adjusting any required parameters on the Radio Controller (maximum power output, frequency, etc.). 3. When the connection process between the transmitter and the receiver has been successfully established, the flight mode will be configured. These flight modes are configured through the Mission Planner software from the CGS and are activated and deactivated from the transmitter. 4. It will also be necessary to make the correct configuration of the radio control channels to carry out proper handling of the UAV. To do so, it is necessary to connect the battery to the aircraft and connect to the Raspberry Pi through the SSH protocol. 5. Afterwards, the different components of the UAV can be calibrated. In this regard, we proceed with the calibration of the accelerometer. This component is integrated in the controller and is responsible for providing information on the forces that work against the UAV flight (wind, gravity, etc.) to keep it always stabilized. It must be connected to the CGS and place the aircraft in the positions indicated by the Mission Planner software. 6. The UAV compass must be calibrated right afterwards. This component is also integrated in the controller and allows the aircraft to know in which direction it is pointing. To carry out this process it is necessary to take the UAV and rotate it 360 degrees around all axes. 7. Next, the radio transmitter is calibrated. It is necessary to ensure that all the transmitter controls respond correctly and that their transmitted signal level corresponds exactly to that received by the UAV. It is needed to configure throttle, pitch, yaw, roll maneuvers and a switch to select flight modes. 8. The final step required in the UAV setup is to select the flight modes to use. The Mission Planner software has 25 flight modes, of which 6 are used. The flight modes are assigned to one or more of the channels that the transmitter has and are selected through the position of the switch. The flight modes used are as follows:  
1. Stabilize: This flight mode is used most of the time. It allows the operator to fly over a surface keeping the UAV in a stable position during flight. RTL (Return to Launch): When this flight mode is activated, the UAV automatically returns to the point where it took off and lands. It is very useful for when the operator wants the UAV to return to the base. 3. Auto: used to perform predefined missions through the software (usually, autonomous missions). 4. Land: It reduces progressively the UAV altitude to ground level. It is used to land the UAV. 5. Brake: used to perform an emergency stop. In the event of an unexpected event or accident, it stops the UAV motors. Once the configuration of the components and parameters of the aircraft has been completed, the tests and missions can be undertaken.
## 3 Case Study Application  
### Industrial Context  
Because of the potentially catastrophic consequences for the environment and life itself involved in the wastewater treatment process, every human settlement is forced to address the problem of returning sick water into the environment in a manner that is both responsible and as efficient as possible. In this regard, several studies have been conducted aiming to optimize wastewater treatment processes or even critical subprocesses, such as the anaerobic digestion [24], addressing the mentioned criticality.  
It is known that failure is a dormant threat constantly waiting to arise randomly; this is also true for wastewater treatment and is even more critical because of the mentioned catastrophic consequences. Furthermore, raw materials for this process differ from any other industrial process in the fact this raw material is human residue, and because of this, it is extremely hard to predict what kind of material will arrive at the plant. In this regard, every system and subsystem embedded in the wastewater treatment process is exposed to different and unpredictable types of materials, which can damage and produce failures along the process, producing a negative effect on reliability and availability levels.  
A generic layout for a Wastewater Treatment Facility (WWTF) is usually divided into two stages: a primary stage, which is mainly focused on disposing of large and medium-size elements extracted by physical means, and a secondary stage, where wastewater and biosolids undergo chemical and physical processes in order to return \"safe\" water and solids to the environment [25].
# Improved Detection and Response via Optimized Alerts: Usability Study
## 2 Material and Methods
### Data Analysis  
The data collected for analysis from the results of a SurveyMonkey online questionnaire were processed with IBM SPSS software and R, a programming language for statistical computing, machine learning, and graphics. The analysis focused on data exploration of dependent and independent variables. The main dependent variable was the level of acceptance of the security alert output and was based on the four individual TAM components: PU, PEU, AU, and IU. Each component was derived from responses to groups of Likert-style statements (scored 1 through to 7, with 7 representing the most favorable response). PU and PEU had a total of six statements, and AU and IU had three statements. The level of acceptance of the alert output was calculated by adding all 18 scores together, with a maximum score of 126 and a minimum score of 18. The sub-scores for PU, PEU, AU, and IU represent secondary dependent variables. The within-subjects independent variable was scenario. It had three levels, Scenario 1, Scenario 2, and Scenario 3, with all participants being subject to all scenarios. The between-subjects independent variable was Maximum Visual. This had two levels: a preference for VAO in all three scenarios, and a preference for TAO in at least one of the scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**RQ** & **Type of Analysis** & **Variable** & **Scale** & **Data** \\ \hline RQ1 & Variance & IV-DV & Likert & H1, H2 \\ SQ1 & Variance & IV-DV & Likert & H1, H2 \\ SQ2 & Variance & IV-DV & Likert & H1, H2 \\ \hline \hline \end{tabular} Note. RQ = research question; SQ = sub-question; DV = dependent variable; IV = independent variable; H1 = null hypothesis; H2 = alternative hypothesis.  
\end{table}
Table 2: Research question and hypotheses testing.  
Both parametric and non-parametric tests were performed. Mixed ANOVA tested whether the level of acceptance of alert outputs is influenced by the within-subjects variable Scenario and the between-subjects variable Maximum Visual. Mixed ANOVA was also repeated for the four sub-scales of PU, PEU, AS, and IU, with Bonferroni corrections for multiple comparisons. Additionally, a Mann-Whitney U test was performed, comparing the level of acceptance of alert outputs of the two levels of Maximum Visual, and a Friedman test compared the level of acceptance across the three scenarios.
## 2 Related Work
### Wireless Signals of UAVs  
Most UAVs use wireless signals for communication (e.g., Phantom, Parrot, Hubsan, Xiro and so on [29; 30]). Current 802.11 standards specify frame types for use in transmission of data as well as management and control of wireless links, and frames are divided into very specific and standardized sections. Each frame consists of a MAC header, payload, and frame check sequence (FCS) [31]. The antenna orientation in relation to the frame of the UAV (single and multiple antennas) influences the link quality and range of communication significantly. Using a three antenna extension to IEEE 802.11 devices, air-to-ground links can support high throughput at distances up to 500 m. Therefore, we can use the wireless signals to detect UAVs, despite the size, line of sight, protocol standardization and forensic tool support (hardware and software) of the UAVs. Almost all UAVs use the standard Institute of Electrical and Electronics Engineers (IEEE) 802.11a/b/g/n/w protocol to pair with controller devices, and the working frequency is between 2.4 and 2.5 GHz. All standards have the same preamble that signifies the start-point of a signal, and the center frequency of the transmission. At the 2.4 GHz band, WiFi is allocated throughput 11 wireless channels ranging from 1 to 11. However, only three of these channels (1, 6, and 11) are used, as each channel is only 5 MHz wide. Since transmissions on the 2.4 GHz band are 20 MHz wide, the utilized channels are spaced to mitigate co-channel interference [32]. Table 2 provides a summary of the frequency, modulation, and technology systems used by most of the UAVs, including DJI phantom, Parrot, Spektrun, JR, Futaba and so on.  
In this paper, we use the USRP software defined radios, oscilloscope, and antennas to collect UAVs wireless signals. Different UAVs have different wireless signals. For example, the center frequency  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Frequency** & **Modulation** & **Technology** \\ \hline Control & 2.4 GHz/5.8 GHz & FHSS/DSSS/OFDM & FAST/Lightbridge/DSMX /DMSS/AFHSS/HOTT \\ \hline Telemetry & 868 MHz/433 MHz/ 2.4 GHz/4 GHz & Divers/DSSS/OFDM & ZigBee \\ \hline Video & 2.4 GHz/5.8 GHz & OFDM/FM & Lightbridge/WiFi \\ \hline \hline \end{tabular}
\end{table}
Table 2: Popular technology systems used by UAVs.  
of Phantom2 operation is 2.447 GHz, and the center frequency of Phantom3 operation is 2.457 GHz, as shown in Figure 2. Thus, when we collect the UAVs signal, the center frequency and bandwidth of the acquisition system should be accurately set in the acquisition. At the same time, by comparing the wireless signals of different UAVs, it is evident that the signals of different UAVs have different features.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 2 Literature Review
### Decentralized Trust Management Algorithms  
Decentralized trust management algorithms address the challenges of scalability, flexibility, and robustness in IoT systems by eliminating the need for a central authority.  
A trust evaluation model has been proposed in [33] for IoT networks to address challenges posed by centralized trust mechanisms. This scheme leverages mobile edge nodes to collect, calculate, and relay trust information between sensor nodes and cloud platforms using trust chains and an improved Dijkstra's algorithm. The model introduces compositional trust chain evaluation (atomic, tandem, parallel) and incorporates multiple trust components like interaction trust, energy trust, and recommendation trust.  
In [27], the authors combine blockchain with trust evaluation to address vulnerabilities in access control. The proposed system forms blockchain-based IIoT groups (B-IIoT-G), where IIoT devices maintain access control lists (ACLs) and vote on authorization decisions using trust-weighted voting mechanisms rather than majority rule alone. The system introduces a lightweight blockchain structure to reduce storage and processing demands and updates trust values dynamically based on historical behaviors and voting outcomes.  
A framework called BC-Trust has been introduced in [34]. It is a blockchain-based trust evaluation method that leverages fog computing. This system offloads trust information storage and computation to fog nodes, ensuring scalable and secure trust assessments across IoT devices. A decentralized Attribute-Based Access Control (ABAC) system integratedwith a Trust and Reputation System (TRS) has been proposed in [35]. Trust and reputation scores are calculated via smart contracts on a public blockchain, supporting dynamic access control and enhanced security in IoT networks.  
[PERSON] et al. [36] developed a decentralized trust management system (DTMS) that combines blockchain technology with a consensus-based model. The system utilizes a hierarchical design to perform trust evaluations locally, ensuring transparency and tamper resistance while rewarding honest participation. The authors of [29] proposed the BD-VTE scheme, a decentralized trust evaluation method for smart network systems. The scheme uses Mobile Vehicles (MVs) and UAVs for active data verification, enhancing the reliability and efficiency of data collection through distributed trust assessment.  
These decentralized approaches offer innovative solutions for managing trust in IoT systems, enhancing scalability, flexibility, and security. Nevertheless, these proposed algorithms may introduce latency, resource demands, and implementation complexity [37; 38].
# Analysis of a Preliminary Design Approach for Conformal Lattice Structures
## 3 Results and Discussions
### Conformal Wireframe Modeling  
The front and side views of the wireframes obtained by the NURBS-FFD method are presented in Figure 5. The method allows obtaining wireframe lattice structures that conform to the boundaries in the same environment but, as a drawback, it can be complicated to reach the aimed deformation of the captive parts depending on the number of control points; more, in Rhinoceros software, the NURBS-FFD control volume is rectangular or cubical leading to a trial-and-error approach when moving the points to follow the external shape of the object.  
In all the configurations, the cell aspect ratio remains similar to the undeformed structure in the central zone, whereas the cells are more stretched along the design space borders to conform to the boundary. The types of the structure resulting from the application of [PERSON]'s rule to the lattices are reported in Table 3, together with the number of nodes and beams and the total length of the beams of the models. The SC and DA structures are bending-dominated, whereas the BCC and WG structures are stretching-dominated. Even if the SC is a bending-dominated structure, considering the load direction, it behaves as a stretching-dominated, as explained below. The DA structure presents the lowest number of elements due to the bigger size of the unit cell. The WG unit cell is the most populated, so the lattice structure presents the highest number of elements. More, being the BCC an evolution of the SC unit cell, the number of elements increases as expected.
# Whole-Body Motion Planning for a Six-Legged Robot Walking on Rugged Terrain
###### Abstract  
Whole-body motion planning is a key ability for legged robots, which allows for the generation of terrain adaptive behaviors and thereby improved mobility in complex environment. To this end, this paper addresses the issue of terrain geometry based whole-body motion planning for a six-legged robot over a rugged terrain. The whole-body planning is decomposed into two sub-tasks: leg support and swing. For leg support planning, the target pose of the robot torso in a walking step is first found by maximizing the stability margin at the moment of support-swing transition and matching the orientation of the support polygon formed by target footholds. Then, the torso and thereby the leg support trajectories are generated using cubic spline interpolation and transferred into joint space through inverse kinematics. In terms of leg swing planning, the trajectories in a walking step are generated by solving an optimal problem that satisfies three constraints and a bioinspired objective function. The proposed whole-body motion planning strategies are implemented with a simulation and a real-world six-legged robot, and the results show that stable and collision-free motions can be produced for the robot over rugged terrains.  
Keywords:six-legged robot; whole-body motion planning; rugged terrain; support; swing +
Footnote †: journal: Applied Sciences
## 4 Computational Experiments and Results Analysis
### Comparison Between Models
#### 4.4.4 Robustness by Changing Data in Stochastic Solutions  
Similar to the previous section, in this case, the feasibility of the sizing provided by each stochastic model is tested by introducing changes in demand and forecasts. However, in this scenario, the variations are applied with a level of uncertainty. This involves making random adjustments to the data, increasing or decreasing their original values, as illustrated in Table 15.  
For a clearer understanding of the concept, Figure 14 illustrates the changes in uncertainty in Variation 2. The blue line is the original demand data, and the orange line is the new demand, including uncertainty.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variation** & **Demand** & **Forecast** \\ \hline Variation 1 & Uncertainty 1\% & Uncertainty 1\% \\ Variation 2 & Uncertainty 1.5\% & \\ Variation 3 & & Uncertainty 1.5\% \\ Variation 4 & Uncertainty 2\% & Uncertainty 2\% \\ Variation 5 & Uncertainty 5\% & Uncertainty 5\% \\ Variation 6 & Uncertainty 10\% & Uncertainty 10\% \\ \hline \hline \end{tabular}
\end{table}
Table 15: Stochastic variations in demand and forecast.  
Figure 13: Variation 4—demand rises 10%.  
The results, when applying uncertainty, as shown in Table 16, indicate that the multi-year model is more sensitive to changes in the data. Particularly, extreme changes such as in variation 6 have a notable impact. For instance, in the one-year model, the LCOE increased significantly, while in the multi-year model, it rendered the solution infeasible. This demonstrates that without reliable data during the sizing process, and in cases of drastic changes in demand or environmental conditions, the sizing may not be adequate. Therefore, incorporating stochasticity helps mitigate this issue compared to deterministic models.  
These diverse results highlight the significance of implementing stochasticity. Moreover, the option of incorporating a multi-year model enables comparisons with the one-year model, assisting in determining the most suitable solution for the given problem.  
It is also noteworthy that the dispatch strategy algorithm models the real-time operation of the network.
# A Method for the Rapid Propagation of Emergency Event Notifications in a Long Vehicle Convoy
### Communication Channels  
Generally, the wireless channel in vehicular environments can be characterized using a combination of log-distance power loss and Nakagami fast fading. As a brief summary, log-distance states that receive power drops as a function of distance to the power of \(n\), and [PERSON] said that received power is randomly distributed more widely as the value of \(m\) decreases. Thus, a high probability of high received power over distance means a low log-distance \(n\) and high Nakagami \(m\) values. [PERSON] et al. stated that for light traffic on a highway, the log-distance characterization parameter \(n\) generally falls between 1.8 and 1.9, with crowded highways generally having higher values [13]. [PERSON] also stated that the fast-fading Nakagami \(m\) factor was usually 3-4 for short distances and less than 1 for long distances. [PERSON] et al. made similar findings, saying that the log-distance \(n\) could vary between 1.6 and 2.9 on the highway, 2.3 and 3.5 in suburban environments, and 1.8 to 3.4 in urban environments [14]. [PERSON] et al. focused on the 5.85-5.925 GHz range for their study and found similar results to those of [PERSON] with regards to the Nakagami \(m\) factor [15]. They found that \(m\) would typically be around 3 to 4 over a 0 to 5 m distance, while dropping below 1 at 70 to 90 m. Together, these findings imply that longer distance direct communications can be more challenging and much less predictable than shorter-range communications.
## 4 Results  
### Yield Prediction  
Exactly 66% of the observed fields have a complete time series of Landsat NDVIs at the pixel level across the growing season. For this dataset, C-Crop performs the best with Landsat images at field (\(R^{2}=0.68\); Figure 4a), 250-m (\(R^{2}=0.85\); Figure 4d), and 25-m (\(R^{2}=0.48\); Figure 4g) pixel resolutions for yield prediction pooled for wheat, barley, and canola. MODIS-based model yields were at least 10% less in terms of the \(R^{2}\) than when using complete time-series Landsat data. The model performs almost identically when using MODIS and L-M blended data for the predictions at both field and pixel scales. However, it produces the lowest bias for field (\(MBE=-0.32\) t/ha; Figure 4c), 250-m (\(MBE=-0.23\); Figure 4f), and 25-m (\(MBE=-0.21\); Figure 4i) pixels when using the L-M blended data.  
Figure 5 shows that 210 fields have incomplete time-series Landsat pixels during the growing season. These series are incomplete due to clouds in some of the Landsat images acquired in the specific growing season when the yield data are observed. Using this set of data, the L-M blended data-based model performs the same as the MODIS-based model when aggregating to 250-m (\(R^{2}=0.86\), \(RMSE=0.52\) t/ha, \(MBE=-0.40\) t/ha; Figure 5c,d) and field (\(R^{2}=0.66\), \(RMSE=0.82\) t/ha, \(MBE=-0.49\) t/ha; Figure 5a,b) scales. The L-M blended data-based model shows its advantages at the 25-m pixel level, which explains an extra 7% of the variability in the observed yields when the Landsat time-series pixels are incomplete (Figure 5e,f).  
Figure 4: Validation of C-Crop-predicted yield pooled for wheat, barley, and canola using Landsat, MODIS, and L-M blended data where the complete Landsat time series are observed (the fraction of missing Landsat series = 0). From top to bottom, the first (**a–c**), second (**d–f**), and third (**g–i**) rows show the comparison between observed (\(x\)-axis) and model-predicted yields (\(y\)-axis) on the field scale (\(n\) = 139), 250-m pixel level (\(n\) = 2367), and 25-m pixel level (\(n\) = 113,329), respectively, where \(n\) is the sample size. From left to right, the first (**a,d,g**), second (**b,e,h**), and third (**c,f,i**) columns delineate the validation using Landsat, MODIS, and L–M blended data, respectively. The solid black line is the line of best fit, the purple and the yellow lines represent the upper and lower bounds of the prediction confidence intervals (i.e., \(p\) = 0.01 and \(p\) = 0.05), and the black dashed line is the 1:1 line.
## 3 Results and Discussion
### Characterization
#### 3.1.2 EDX Study  
EDX spectra of iron oxide, PANI, and PANI/Fe\({}_{3}\)O\({}_{4}\) composites before adsorption are shown in Figure 2a-c. These spectra show that percentages of Fe and O by weight were 65.05% and 34.95%, respectively, in Fe\({}_{3}\)O\({}_{4}\). PANI was composed of 66.17% carbon and 16.97% nitrogen by weight. Besides carbon and nitrogen some other elements like oxygen, sulfur, iron and chlorine were also present in  
Figure 1: SEM images of Fe\({}_{3}\)O\({}_{4}\), PANI (polyaniline). and PANI/Fe\({}_{3}\)O\({}_{4}\) composites (**a–c**) before adsorption, (**d–f**) after Cr(VI) adsorption, and (**g–i**) after Ni(II) adsorption.  
the PANI texture. Existence of oxygen and sulfur indicate the presence of some moiety of DBSA in PANI, while iron and chlorine may be due to ferric chloride which was used as an oxidant. In the case of composites, percent weight of iron and oxygen were increased to 9.59% and 17.92%, respectively, while those of carbon and nitrogen were decreased to 61.45% and 6.60%. Figure 2d-f represent spectra recorded after adsorption of Cr(VI) on Fe\({}_{3}\)O\({}_{4}\), PANI, and PANI/Fe\({}_{3}\)O\({}_{4}\). Appearance of Cr and Ni indicates adherence of these elements to the adsorbent materials (Figure 2g-i). On close observation one can find a higher weight percentage of Cr(VI) than Ni(II).