esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Environmental Assumptions in Shaping Requirements Technical Debt
## 4 Results and Analysis
### Impact of Wind on Stopping Distance (DR3)  
This requirement states that drones must account for wind effects when computing stopping distances and is associated with four environmental assumptions, i.e., A11-A14. Because wind conditions are inherently variable and can change rapidly, requirements grounded in these assumptions must be continuously validated and updated to remain accurate.  
The first assumption (A11) states that operators do not launch drones in unsafe wind conditions. If this assumption is omitted or turns out to be incorrect, drones may be deployed in high winds, leading to unstable flight dynamics that violate safe separation requirements. To address this, developers must either equip drones with onboard wind sensors capable of detecting unsafe conditions in real-time or implement strict pre-flight checks to enforce launch constraints.  
Assumptions A12 and A13 concern the drone's lateral drift due to wind. These assumptions imply that any sideways displacement will remain within controllable margins. However, if actual drift exceeds these thresholds, due to stronger-than-expected crosswinds or aerodynamic instability, the separation requirements must be revised to prevent mid-air collisions. For instance, if wind patterns grow more extreme over time due to broader environmental changes, original assumptions may no longer hold, and the requirements may become outdated.  
Assumption A14 states that the Gazebo simulation environment accurately reflects drone performance under windy conditions. If this assumption is either invalid or missing, then any requirement derived from simulation data becomes unreliable. A mismatch between simulated and real-world drone behavior introduces significant RTD, as the requirements will not reflect actual performance.  
Because wind conditions, drift tolerances, and stopping distances can evolve due to environmental variability, the requirements informed by A11-A14 are inherently unstable. Without regular reassessment and validation of these assumptions, drones may behave unpredictably in the field, which requires urgent and costly design revisions.  
**RTD:** This type of debt is classified as _outdated assumption debt_. It refers to requirements that become obsolete or misaligned due to changes in the environment over time. If these assumptions are not actively monitored and updated, the system may fail to adapt to real-world operating conditions, leading to late-stage corrections and increased maintenance overhead.
## 3 Results and Discussion
### Marshall Stability Analysis  
Marshall stability analysis was extensively carried out on both types of bitumen (plain and modified) for comparative evaluation. For each percentage of the proportions of modified bitumen,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Composition** & **Penetration** & **Ductility** & **Flash Point** & **Softening Point** \\ \hline \multicolumn{5}{c}{(25 \({}^{\circ}\)C, 100 g, 5 s)} \\ \hline \multirow{2}{*}{**Test Method**} & **ASTM:** & **ASTM:** & **ASTM:** & **ASTM:** \\  & **D5-97** & **D113** & **D92-16b** & **D36** \\ \hline \multirow{4}{*}{M 1} & Units & 0.1 mm & 0.1 cm & 1\({}^{\circ}\)C & 1\({}^{\circ}\)C \\  & 100\%B + 0\%M & 67.0 & 99 & 266 & 55 \\ \cline{1-1} \cline{2-  
there were three sets of samples prepared. The average stability value of all three samples for each tested proportion was taken for detailed analysis, with reference to the previously followed procedures [17; 24; 27; 28; 29; 30; 31] listed in Table 4. These results showed that modified bitumen fell in the better range of stability in comparison to the conventional bituminous mixes.  
Usually, decision-making can be done based on the stability value, which indicated that with the application of silicon waste up to 40%, satisfactory performance of asphalt mix can be achieved. Figure 3 shows the progressive pattern of the relationship of stability and flow, where up to a 40% modification was suitable. However, after the top point, the flow ranges crossed the standard limit.
## 5 Multi-Physics FEM  
The preliminary multiphysics simulation work was conducted using numerical analysis with ANSYS software, specifically ANSYS Workbench. This involved creating a simplified model that maintains the overall envelope and mass of the system while reducing geometric complexity and non\(-\)essential details. A simplified model is crucial at this stage to ensure reasonable computation times without sacrificing simulation accuracy. The model includes all major structural and thermal characteristics, ensuring reliable analysis of multiphysical loads.  
A key aspect of this work was setting up a fine mesh, Figure 4, a detailed mesh is essential for accurately capturing physical phenomena such as mechanical deformations and temperature distributions. The fine mesh provides high\(-\)resolution numerical solutions, ensuring precise and reliable results. The mesh setup involved selecting the element type, distributing mesh density in critical regions, and optimizing mesh quality to avoid distortions. The combination of a simplified yet accurate model and a fine mesh enabled effective preliminary analysis, providing a solid foundation for further detailed studies and design improvements.  
Each component of the model is assigned a specific material, defined by its mechanical properties. The materials are either selected directly from the ANSYS library or custom-defined by manually specifying the relevant mechanical characteristics. Each element within the module is subjected to distinct mechanical and thermal loads, depending on its location and functional role within the overall experiment. Table 1 summarizes the material database established for this study.  
Figure 4: Detailed mesh on payload.  
Figure 3: Preliminary payload towards and improved study design.  
A series of numerical analyses were performed to assess the structural integrity and operational performance of the payload under various expected conditions. These analyses covered multiple physical domains, including:  
* Quasi-static analysis, applying loads of 15 g along each principal axis to simulate extreme mechanical stress scenarios representative of launch conditions.
* Random vibration analysis, based on an interpolated Power Spectral Density (PSD) curve derived from both European (ECSS) and American (GEVS) standards, ensuring compliance with applicable guidelines and qualification requirements.
* Thermo-elastic analysis, incorporating thermal loads extracted from orbital environment simulations conducted using Thermica, to evaluate the structural response to in-orbit thermal gradients.
* Modal analysis, conducted to ensure that the first natural frequency remains above 120 Hz, meeting the critical mission requirement (100 Hz plus the required safety margin).  
The combination of these analyses provided a comprehensive understanding of the payload's mechanical and thermal behavior, supporting design validation and compliance with mission specifications.
## 5 BN-MAC Protocol Design
### Selection of the BN
\"\(L_{e}\)\" is the residual energy level of each sensor node after performing the event, so we apply the following derivation:  
\[S_{e}=-\ \frac{1}{2}\ V\ \frac{z^{2}}{h_{n}} \tag{6}\]  
where \(S_{e}\) is the current energy level of the sensor, V is the voltage level of the sensor node before the event, \(z^{2}\) is the number of contention window slots, which is equal to \(2^{8}\), and \(d_{1}\) is the number of hops required to travel the data, and \(h_{n}=n^{2}*d_{1}\), where \(d_{1}\) is the distance between each hop of the WSN. If the sensor node completes the event, then the sensor node decreases its energy level. Therefore, the energy used in the event is equal to the difference between the sensor node's final and initial energy levels:  
\[R_{e}=S_{e1}-\ S_{e2}=\ \frac{1}{2}\ V\ \frac{z^{e}}{d_{1}}\ \Big{[}\ \frac{1}{{e_{1}}^{2}}-\ \frac{1}{{e_{2}}^{2}}\ \Big{]} \tag{7}\]  
where \(R_{e}\) is the remaining sensor energy after the event, \(S_{e1}\) is the initial energy of the sensor, \(S_{e2}\) is the final energy of the sensor, and \(e_{1}\) and \(e_{2}\) are the initial and final energy levels, respectively. If part or all of the consumed energy in the sensor node is renewable \(Rn_{e}\), then the new energy of the sensor node \(\frac{c}{Rn_{e}}\) can be found as follows:  
\[\frac{1}{Rn_{e}}=\frac{1}{2}\ V\ \frac{z^{e}}{cd_{1}}\ \Big{[}\ \frac{1}{{e_{1}}^{2}}-\ \frac{1}{{e_{2}}^{2}}\ \Big{]} \tag{8}\]  
where:  
\[\Big{[}\ \frac{1}{{e_{1}}^{2}}-\ \frac{1}{{e_{2}}^{2}}\ \Big{]} =\ L_{e} \tag{9}\]  
We obtain the energy of the sensor node by substituting for \(\frac{1}{2}\ V\ \frac{z^{e}}{cd_{1}}\) as follows:  
\[\frac{1}{Rn_{e}}=L_{e}(\frac{1}{2}\ V\ \frac{z^{e}}{cd_{1}}\ ) \tag{10}\]  
Therefore, the level of energy can be expressed as:  
\[L_{e}=(\frac{Vz^{e}}{2 cd_{1}}\ ) \tag{11}\]The current BN also sends a signal for a new election when the battery is running down. After the election process, the new BN resumes its duty and the current BN terminates its function. In case of BN failure, the remaining nodes wait for four consecutive BNISs, and the BN is subsequently considered a malfunction. The new BN is automatically selected, allowing network disturbances to be avoided. We illustrate the complete mechanism of BN-MAC in Figure 12.
## 4 Experimental Setup and Results Analysis
### Experimental Results
#### 4.5.3 The Effect of Number of Substructure Markers  
In our experiments, we varied the number of substructures, denoted as K, to study its impact on the results. Specifically, we set \(K\) to 100, 200, 500, and 1000. As shown in Figure 3c, we found that the model performed best when \(K\) was set to 200. As the number of substructures increased beyond this point, the performance did not improve but rather gradually decreased. Consequently, if the value of \(K\) is excessively diminutive, leading to underfitting, or overly expansive, causing overfitting, there is potential for the precision to diminish. Generally, the optimal performance can be achieved when \(K\) is approximately its median value.  
Specifically, for the accuracy (Acc) metric, DDI-SSL demonstrated an improvement of around 5% compared to the latest method. For the F1-score metric, DDI-SSL exhibited an improvement ranging from 2% to 5% compared to the latest method. As for the AUC, DDI-SSL showed an improvement of approximately 2% compared to the latest method.
# Profit-Driven Methodology for Servo Press Motion Selection under Material Variability
## 4 Use Cases  
### Expected Profits  
As an example of using the Smart Forming Algorithm with expected utility, consider the following problem. Suppose that \(\alpha\), the profit from a good part, equals USD 2, and that \(\gamma\), the loss from a defective part, equals USD 3. Given an hour to run, AD15 can yield 1538 parts, while Crank and LowHigh can yield 2000 parts. Now suppose a batch of metal comes in with a yield strength of 255 MPa and an elongation of 30%. The expected profit/loss for each press motion within that hour can be estimated with the help of expected utility.  
The expected utility of each motion can be calculated as follows:  
\[EU(m)=\alpha*N_{m}*P(o=1|m,\ 255,\ 30)-\gamma*N_{m}*P(o=0|m,\ 255,\ 30) \tag{2}\]In the equation, \(m\) refers to the press motion, \(N_{m}\) refers to the number of parts produced by motion \(m\) in an hour, \(P(o=1|m,\,255,\,30)\) refers to the probability of the part being good given that motion \(m\) was used on a metal with yield strength 255 MPa and elongation 30%, and \(P(o=0|m,\,255,\,30)\) refers to the probability of the part being defective under the same conditions.  
Because the Smart Forming Algorithm returns a distribution of probabilities for a good part being produced, upper and lower bound probabilities can be easily obtained. The Smart Forming Algorithm's expected probability of good part production is estimated by taking the median of the probabilities sampled by MCMC. (Technically, the expected value of an empirical distribution created from the MCMC samples should be the mean of the sampled values. However, because MCMC samples converge to the correct distribution after an infinite number of samples, values that are highly unlikely may still get into the finite sample collection, skewing the mean. To avoid this problem, the median was chosen as an estimation of the expected probability of a good part being produced. This also ensured that the expected probability lay within the lower and upper bounds computed by the continuous HDI. Using this process, the following numbers can be obtained, where the expected return is given first and the parenthesized values show the lower bound expected return and the upper bound expected return:  
* AD15: +USD 844.62 (+USD 205.55, +USD 1847.48);
* Crank: \(-\)USD 5999.99 (\(-\)USD 5999.99, \(-\)USD 5999.98);
* LowHigh: \(-\)USD 1294.92 (\(-\)USD 5999.27, +USD 1360.05).  
The expected return bounds for Crank are quite narrow, indicating that the model is quite certain about defective parts from Crank. The expected return bounds for LowHigh, however, are far apart. This indicates that the model is uncertain about how probable LowHigh is to produce good parts, though it expects that the parts are more likely to be defective. AD15 manages to remain positive not only in expected returns but also in both bounds.
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 5 Industrial Application Perspective
### Smart Farming and Agriculture  
Long battery lives are needed for sensor equipment in the agricultural sector. In a conventional farming environment, short coverage is sufficient. According to [29; 97; 98], utilizing temperature and humidity sensors in the irrigation industry might drastically cut water use while increasing productivity. Considering that a farming environment generally remains the same, devices only update sensor data a few times per hour. Sigfox and LoRaWAN are perfect for this sort of technology due to their low power consumption [29; 99; 100]. [PERSON] et al. [101] presented a low-cost, modular LoRaWAN-based IoT platform for improving the management of generic farms. The suggested platform was tested on farmland in Italy, gathering environmental data (air/soil temperature and humidity) relevant to the growth of agricultural goods (namely, grapes and greenhouse vegetables) over three months. According to the result, LoRaWAN performed optimally regarding data transmission and energy efficiency observed in indoor and outdoor areas. Also, many agricultural businesses still need legacy LTE-cellphone-based application connectivity due to financial and technological constraints. As a result, the NB-IoT cloud might not be a viable solution for agriculture in the future [29].
# An Algorithm of Acoustic Emission Location for Complex Composite Structure
## 3 The Algorithm Testing  
### Theoretical Verification  
In an ideal condition, a 3D model should be built to test the algorithm, where a detector is fixed on the model surface. Then, the travel time from the detector to all nodes is calculated and compared with the theoretical travel time to obtain the maximum relative error among all travel times. However, it is difficult to obtain the theoretical travel time of each node for complex structures. In this paper, we carry out this part of the work under a 1D wave velocity structure as a special case of the 3D case. This is because the 1D theoretical time is easy to calculate and can also satisfy the purpose of testing our algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Total Nodes** & **Time Consumption** & **Memory Usage** \\
**n** & **t (s)** & **(MB)** \\ \hline
1,000,000 & 6 & 94 \\
8,000,000 & 65 & 385 \\
27,000,000 & 292 & 1169 \\
64,000,000 & 786 & 2647 \\
125,000,000 & 1745 & 5126 \\
216,000,000 & 3139 & 8826 \\
343,000,000 & 5231 & 13,863 \\
512,000,000 & 7377 & 20,699 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time consumption and memory usage under various number of nodes.  
Figure 3: The relationship between time consumption and number of nodes.  
We build a test model, which is divided into five layers (five blocks). the thickness of each layer is 20 mm and the P-wave velocity from top to bottom layers is 3000 m/s, 3500 m/s, 4000 m/s, 4500 m/s, and 5000 m/s, respectively. The sensor is set in the center of the upper model surface (Figure 4).  
Errors of arrival time are mainly caused by discrete meshing and the limitation of adjacency radius. Reducing the mesh spacing can reduce the errors caused by discrete meshing, but cannot reduce the ones produced by the adjacency radius. To reduce the error caused by adjacency radius, we need to increase adjacency radius or to perform a straight path correction. Table 3 shows that the errors are mainly caused by the influence of the grid spacing when the grid spacing is 2 mm, while the errors are mainly caused by adjacency radius when the grid spacing is less than or equal to 1 mm. Straight-line path correction at block interfaces can significantly reduce errors stemmed from adjacency radius.
# Multiscale Sieve for Smart Prime Generation and Application in Info-Security, IoT and Blockchain
## 5 Benchmark Setup
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Size of Input** & \begin{tabular}{c} **Sieve of** \\ **Sundaram** \\ \end{tabular} & \begin{tabular}{c} **Sieve of Atkin-** \\ **Bernstein** \\ \end{tabular} & \
\begin{tabular}{c} **Linear Sieve** \\ **Linear Sieve** \\ **Wheel Factorization** \\ \end{tabular} &
\begin{tabular}{c} **Segmented Sieve of** \\ **Eratosthenes with** \\ **Wheel Factorization** \\ \end{tabular} \\ \hline \(10^{6}\) & 234.24\% & 408.09\% & 584.60\% & 409.67\% \\ \(10^{7}\) & 218.60\% & 293.64\% & 266.09\% & 257.21\% \\ \(10^{8}\) & 1077.77\% & 914.33\% & 460.12\% & 391.04\% \\ \(10^{9}\) & 3172.77\% & 1918.99\% & 755.19\% & 582.30\% \\ \(10^{10}\) & 3565.80\% & 1641.19\% & 675.48\% & 473.43\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of execution times in percentages. Figure 5: Growth in execution time in relation to input size. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Size of Input** & \begin{tabular}{c} **Sieve of** \\ **Sundaram** \\ \end{tabular}  & \begin{tabular}{c} **Sieve of** \\ **Atkin-** \\ **Bernstein** \\ \end{tabular}  & \begin{tabular}{c} **Linear Sieve** \\ **Eratosthenes with Wheel Factorization** \\ \end{tabular}  & \ \begin{tabular}{c} **Estimed Sieve of** \\ **Eratosthenes with Wheel** \\ **Sieve** \\ \end{tabular}  & \begin{tabular}{c} **Multiscale** \\ **Generated** \\ \end{tabular}  \\ \hline \(10^{6}\) & 1.66 & 2.19 & 2.80 & 1.30 & 3.38 & 78,498 \\ \(10^{7}\) & 6.13 & 10.75 & 15.83 & 1.38 & 16.00 & 664,579 \\ \(10^{8}\) & 48.89 & 96.58 & 140.55 & 1.34 & 143.67 & 5,761,455  
We can state that, apart from the segmented sieve, which is always the most efficient in terms of memory consumption, the multiscale sieve ranks, on average, close to the modern sieving algorithms such as Atkin or the linear sieve, with almost similar consumption.
## 4 Discussion
### The Object-Based Approaches in GEE  
The classification of high-resolution satellite imagery creates relevant problems related to the thematical and geometrical quality of the outputs. These issues are typically addressed using focal majority filters on the outputs of PB approaches or adopting OB classification procedures that include a preliminary step to group similar pixels. Object identification, based on advanced algorithms, is beneficial for identifying spatially continuous entities. Depending on the scale of analysis, these spatial entities may correspond from real-word objects to patches of a landscape mosaic whose textural or geometrical features are critical for their classification. In this research, in addition to measuring the improvements of the OB method on PL data, for the first time, we assessed the advantages of integrating PL imagery with Sentinel 2 and Sentinel 1 data in an OB LULC classification within GEE. Results confirmed the better performance of the OB method compared to PB methods on medium-high (S2) and high spatial resolution (PL) datasets (Table 5). The OB method, in particular, due to the textural analysis, produces a better distinction between bare soils and built-up areas, and between shrulands, croplands, and grasslands (Figure 4). The better thematic and geometrical outputs of the OB method applied to all dataset combinations become evident in the sample maps (Figure 3). This evidence was also noted in a previous, pioneer study about OB LULC classification in GEE, based on SNIC and GLCM, where three single-sensors datasets (Landsat 8, S2, and PL) were compared [21]. In this study, the OB RF classification on the single PL dataset produced worse accuracy than the S2 dataset. In contrast, in the present research, the application of an OB approach to a PL dataset, despite the lower spectral resolution, produced thematic accuracy results comparable to those obtained with the S2 dataset (Table 5), but with about double the spatial resolution. This difference is probably related to using a six-month composite instead of a single-date image. The former, resulting from a map composition performed by PlanetScope, may be more effective in the LULC classification.
# Estimation of Impact Loads Transmitted to Vibro-Ripper Housing Using Transfer Path Analysis
## 2 Vibration Experiments with a Vibro-Ripper
### Selection of Load Transfer Path  
The structure of the vibro-ripper receives the striking force through the link modules, and the eccentric gear mounted on the rotating shaft. The shank and tooth that receive the impact force hit the rock in the crushing operation. Due to the structure and operation characteristics of the vibro-ripper, the gearbox that generates the striking force (i.e., the medium that transmits the load between the vibration source and the housing) is where the link modules are mounted in four places on the left (LH side) and right (RH side). During ripper operation, the reaction force generated by the strength of the rock is transmitted to the housing through the shank and link module, so the position of the link module in the assembly was considered here as the main path of load transmission (Figure 7).  
Transfer path analysis employs calculations founded on the characteristics of operational vibrations, and the transfer function matrix [\(\mathbf{H}_{\text{MN}}\)] gained by a modal experiment of the structural path. The path is the medium that transmits the load between the gearbox (i.e., the excitation source) and the ripper's housing. Calculation of the load transmitted by this medium requires the characteristics of the principal contributing medium. Consequently, we considered all paths contributing to load transmission in the vibro-ripper (Figure 7).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Specification** & **Value** \\ \hline ULM-T100 & Capacity & 100 tonf (980.7 kN) \\  & Compensated temperature range & \(-\)10\({}^{\circ}\)C \\  & Nonlinearity & 0.05\% R.O. \\  & Hysteresis & 0.05\% R.O. \\  & Repeatability & 0.03\% R.O. \\  & Diameter & 310 mm \\  & Height & 130 mm \\  & Weight & 60 kg \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specifications of the load cell sensor.  
Figure 6: Results of striking force measurement via the load cell.  
A modal-experiment jig, identical in shape to the link part, was developed to investigate the transfer path in triaxial excitations, for each position (Figure 7). The main load was applied horizontally during vibro-ripper operation. The impact load was transmitted to each part through triaxial vibration (roll, pitch, and yaw), induced by rock crushing. To analyze impact loads transmitted on the X, Y, and Z axes [12], modal impact experiments were undertaken to provide path transfer functions reflecting the main contribution to rock crushing. Attachment positions of the acceleration sensors in the experiment are shown in Figure 8, with a total of 84 sensor channels.