esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain-Based Quality Assurance System for Academic Programs
## 4 Experimental Results and Discussion
### Time Delay and Cost of Blockchain Transactions  
This section presents the results in terms of the estimated Gas fees and costs of Ethereum transactions. Additionally, the estimated time delay for block confirmation is calculated. We assume that the input size of the transaction data ranges from 200 bytes to 1600 bytes. Equations (1) and (2) are used to calculate the cost and time delay, respectively. The results are presented in Table 2.  
\[\text{Estimated cost (USD)}=\text{((Gas Unit $\times$ ETH)}/100)\times\text{EX} \tag{1}\]  
where ETH is the Ethereum Gas price and is generally denoted in Gwei, and EX is the real-time exchange rate for ETH to USD.  
\[\text{Estimated Time delay (in seconds)}=\text{No. Blocks $\times$ BlockTime} \tag{2}\]  
We assumed that the average block time is 15 s.  
Figure 9 presents a comparison between the time delay (secs) and cost (USD) for blockchain transactions on quality files of different sizes. By observing the experimental results, it can be noticed that there is a direct relationship between the costs and time delays for blockchain transactions. Moreover, both the time delay and cost values increase as the file size increases. Consequently, the incorporation of IPFS technology to downsize the data that are kept in the blockchain becomes crucial in reducing the time delay, saving transactions costs, and boosting the overall performance of blockchain transactions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Size** & \multicolumn{3}{c}{**Time Consumption of Cryptographic Schemes with Blockchain**} \\ \cline{2-4} (Kilobytes) & **AES\_ECCDSA** & **AES\_RSADSA** & **ECIES** \\ \hline
400 KB & 30.036 & 30.42 & 30.150 \\
800 KB & 60.107 & 60.124 & 60.282 \\
1200 KB & 90.188 & 90.231 & 90.739 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time consumption results for the cryptographic operations with blockchain transactions.  
Figure 9: Time delay and cost of blockchain transactions on quality files of different key sizes.  
The next section presents a deep analysis of the time consumption results and comparisons between the three cryptographic schemes. This aims to highlight the scheme that achieves the best performance level.
# Drone-Based Parcel Delivery Using the Rooftops of City Buildings: Model and Solution
## 2 Related Works
### Drone-Based Parcel Delivery  
In recent years, drones have shown great potential for parcel delivery. Drone delivery is thought to be faster, less expensive, and more eco-friendly than traditional delivery modes such as trucks. Commercial drones require small batteries. According to the existing prototypes, logistical drones (i.e., for parcel delivery services) tend to be small battery-powered rotary-blade copters with a payload capacity of 2 kg (K  
fixed depots in conjunction with hybrid vehicle-drone routes and vehicle-only delivery. [PERSON] et al. [27] investigated a flying sidekick TSP. An integer programming formulation was proposed, and several heuristics based on local search and dynamic programming were developed. These heuristics begin by forming a delivery and then partitioning the route into locations delivered by truck and locations delivered by drone. [PERSON] et al. [28] proposed a TSP with drones in which the objective is to minimize the total transportation cost, comprising vehicle and drone costs. [PERSON] et al. [29] proposed a mathematical model for the operation of a truck-drone hybrid delivery system using the truck as a station for drones and considered the effect of both parcel weight on drone energy and restricted flying areas. More recently, [PERSON] et al. [30] designed a drone-based parcel delivery system considering the impact of payload-induced drone battery consumption rates (BCRs). First, they showed the linear relationship between the BCR and the payload amount. Based on a linear regression model, they proposed strategic and operational planning for a given region. The results showed that 3 out of 5 flight paths were not feasible if the BCR was not considered.  
Although relevant topics on urban logistics and drone-based parcel delivery have been surveyed or studied in recent years, to the best of our knowledge, there is a lack of studies regarding drone operation planning using the rooftops of buildings within cities. Most of the studies have focused on truck-drone hybrid delivery systems, which seem to be much more difficult than rooftop-based drone delivery systems in terms of operability and adaptability. A truck-drone hybrid delivery system might require both an expert who can drive and operate drones together and appropriate spaces for truck parking and drone operation in the center of cities that are full of buildings.  
The unique contributions of this paper are as follows.  
* The optimal operation planning of a drone-based parcel delivery using the rooftops of city buildings is suggested.
* A heuristic to quickly and effectively generate solutions (i.e., delivery schedules) is proposed, even for large problems that can commonly occur in dense urban areas.
* Our proposed optimization model and heuristic are validated by conducting experiments with realistic settings.
## 4 Discussion  
Using MENs to non-destructively evaluate adhesives using a magnetic signal can aid with the widespread implementation of adhesive bonding. It has been shown how this method detects magnetic changes that are affected by variations in the sample's microenvironment, which allows one to measure changes in the material at a localized level. Correlating the curing process of the doped polymer adhesive with the strength of the output magnetic signal, and corroborating it with thermal analysis and tensile testing, shows the potential of this method to non-destructively evaluate materials not only during their lifetime but also during their manufacturing process. In addition, being able to measure changes in the surface charge density of MENs allows for the analysis of materials that undergo harsh environmental conditions, which causes the ingression of water molecules that polarize the surface of MENs.  
Ideally, for each specific material, a baseline calibration curve needs to be obtained. Knowing how MENs behave in each material and how the magnetic signal evolves during the curing process will help to know when a specific sample is not curing ideally. This would help to observe whether a sample is incorrectly manufactured or whether the curing  
Figure 9: Magnetic signatures before and after environmental exposure of (**a**) 0 vol% concentration, (**b**) 5 vol% concentration, (**c**) 10 vol% concentration, and (**d**) 15 vol% concentration.  
parameters are not correct. Future studies will focus on obtaining calibration curves for common adhesives used in the aerospace industry and then determining when specific samples undergo a curing process deviated from the baseline.  
It is important to highlight that the tensile, lap shear, and end notch flexure testing shows enhancement in the mechanical properties of the adhesive bond when dispersing MENs into the adhesive. This means that the addition of these nanoparticles to the adhesive does not negatively influence the integrity of the adhesive bond when a low volume percentage is added. This is necessary so that MENs can successfully be used in industries, such as aerospace and automotive, where the integrity of bonds is of primary importance.
# Effect of LNR-g-MMA on the Mechanical Properties and Lifetime Estimation of PLA/PP Blends
## 3 Results and Discussion
### Mechanical Properties
#### 3.3.2 Izod Impact Test  
The impact strength of pure PLA and PP and PLA/PP (70:30 wt%) blend without and with LNR\(-\)g\(-\)MMA compatibilizer is shown in Figure 5d. The impact strength of the pure PLA and PP show an impact strength of 2.75 and 3.64 kJ/m\({}^{2}\), respectively, indicating that pure PLA is much more brittle compared to virgin PP. In the PLA/PP (70:30 wt%) blend without LNR\(-\)g\(-\)MMA compatibilizer, the value decreased slightly to 3.09 kJ/m\({}^{2}\). PLA and PP have a polar difference, in which polymer blends occur a phase separation. However, impact strength of the polymer blends was slightly increased from  
Figure 5: Physical properties of PLA, PP, and PLA/PP (70:30 wt%) blends with LNR\(-\)g\(-\)MMA of 0.0, 2.5, 5.0, and 10.0 phr: (**a**) stress–strain curves, (**b**) tensile strength, (**c**) elongation at break, (**d**) impact strength, and (**e**) water absorption. The error bars indicate the standard deviation.  
3.14 to 3.57 kJ/m\({}^{2}\) with an increase of the LNR\(-\)g\(-\)MMA content from 2.5 to 10.0 phr, as shown in Figure 5d. This can be concluded by the presence of LNR\(-\)g\(-\)MMA, which acted as an impact modifier in the PLA/PP blends due to the elastomeric nature of the LNR component as a core in the LNR\(-\)g\(-\)MMA particles, as discussed earlier. A similar observation was found by [PERSON] et al. [2]. They reported that PLA toughness could be improved by direct melt blending with PP and LNR compatibilizer, which contributed to the enhanced elongation at break, impact strength, and flexural properties.
# Software Defined Networking Architecture for Energy Transaction in Smart Microgrid Systems
## 2 Related Work
Furthermore, their solution lacks network resilience, SDN scalability and security considerations for energy internet. The work of [PERSON] et al. (2020) developed an SDN-enabled communication network framework for energy internet. Their study confirms that energy internet (like transactive energy) experiences flexibility, reliability, and latency challenges. Their solution also proves that SDN is a suitable architecture for energy internet. However, their study only focuses on an SDN architecture in a specific, low-latency network environment. Therefore, it does not consider reliability, flexibility, or security requirements.
# The Integration of Azure Sphere and Azure Cloud Services for Internet of Things
## 4 System Implementation
### Software
#### 4.2.2 Software for MT3620 as Direct Connected Device  
The application that runs in MT3620 as direct connected device is an executable program which is responsible for collecting the data of the sensors via onboard interfaces and transmitting the data to the Azure IoT Hub. Benefiting from the hardware support of Seeed Studio, MT3620 Grove Shield, Grove Temperature & Humidity Sensor (SHT31), Grove Sound Sensor, Grove Relay, Grove Light Sensor are connected to the MT3620 board. The prototype of this scenario is shown in Figure 4b.  
The Wi-Fi network is connected and the necessary hardware interfaces are opened during the initialization period. In addition, Azure IoT related functions and callbacks including Azure IoT initialization, device twin update callback, connection status callback, message receive callback and direct method callback are executed in an ordered sequence. In the main loop, the data of the connected sensors are read via hardware interfaces periodically. The reading interval is set to be 30 s by default, which can be changed dynamically by direct method callback while the program is running. As soon as the data of sensors are obtained, the corresponding JSON values are set and serialized by JSON lib. Finally, at the end of the referred process, the message is transmitted to Azure IoT Hub.  
Relying on the capability of Azure IoT Hub to invoke direct methods on the devices from the cloud, the DirectMethod callback function is registered in the initialization process and can be called as soon as a direct method call is received from the Azure IoT Hub. In the callback function, the time interval of sensor data collecting, as well as the time interval of device-to-cloud message sending are updated according to the JSON values that are set in the payload of the message. The response is encapsulated immediately on the message payload and sent as a reply to the previous callback function.
# Smart Obstacle Avoidance Using a Danger Index for a Dynamic Environment
## 1 Introduction
These algorithms may help the robot to escape the local minimum region in a simple statistic environment, but require to detect whether the robot is trapped in the local minimum region. For the dynamic environment, the moving objects can affect the performance of the APF. [PERSON] et al. [20] define a virtual force which is the negative gradient of the potential with respect to both relative position and velocity between the robot and the target or obstacles. The motion of the mobile robot is then determined by the total virtual force through the Newtons Law or steering control depending on the driving type of the robot. [PERSON] et al. [21] propose a simultaneous target tracking and moving obstacle avoidance method using a threat coefficient based force function by taking into account the relative velocities of the target with respect to the robot and the relative velocities of the robot with respect to the obstacles. [PERSON] et al. [22] introduce a parallel evolutionary artificial potential field for the dynamic environment, which makes possible controllability in complex real-world sceneries with dynamic obstacles if a reachable configuration set exists. These methods make decisions according to the relative position and velocity direction between robot and obstacle, but the magnitude of the obstacle speed is not fully considered. In this paper, we propose a dynamic APF approach (DAPF). The contributions of our work are the following: first, to predict and avoid the local minimum region, we combine the APF with the dynamic window approach [23], which employs a cost function to evaluate simulated trajectories, such that the local minimum region can be found in the prior step, and the robot can avoid the obstacle in a short path without any oscillations. Second, we propose an evolutionary potential field function based on an improved minimum danger index (DI) [24; 25], which can plan an optimal path using not only the relative distance and velocity direction information, but also the magnitude information of the robot and obstacle speeds. The robot can make a smart decision to avoid moving obstacles, e.g., if the obstacle moves fast, the robot goes behind the obstacle. We demonstrate the proposed ideas using a real mobile robot system in static and dynamic environment, respectively, which shows the improved performance using our idea.
# Ribosomal Hiberation Factor Links Quorum-Sensing to Acid Resistance in EHEC
## 3 Results
### Ribosome Hibernation Underpins Quorum Sensing-Mediated Acid Survival in EHEC
A partial restoration of acid tolerance was achieved in \(\Delta mrf/prmf\), with key resistance metrics recovering to 80-89% of wild-type levels. These data establish ribosome hibernation--ocrehestrated through _rmf_/_niA_ induction and ribosomal protein suppression--as the principal effector mechanism of quorum sensing-dependent acid resistance. \begin{table}
\begin{tabular}{c c c c c} \hline
**Gene ID** & **Gene Symbol** & **Regulation** & **Fold Change** & **Function** \\ \hline EDL933\_RS05+ & _rmf_ & Up & 2.9381 & Ribosome Modulation Factor (RMF) \\ A3:E19985 & & & & \\  & & & & \\  & & & & \\ EDL933\_RS18465 & _raiA_ & Up & 2.0187 & Ribosome-associated inactivation factors \\  & & & & \\ EDL933\_RS21670 & _rpmA_ & Down & \(-\)2.3985 & L27, a core ribosomal protein of the \\  & & & & 50S subunit \\ EDL933\_RS22235 & _rplO_ & Up & 2.2216 & L15, a core ribosomal protein of the \\  & & & & 50S subunit \\ EDL933\_RS21870 & _rplM_ & Down & \(-\)2.6348 & L13, a core ribosomal protein of the \\  & & & & 50S subunit \\ EDL933\_RS16425 & _rplY_ & Down & \(-\)2.5399 & L25, a core ribosomal protein of the \\  & & & & 50S subunit \\ EDL933\_RS22375 & _rpsL_ & Down & \(-\)2.6338 & S12, a core ribosomal protein of the \\  & & & & 30S subunit \\ EDL933\_RS21050 & _rpsU_ & Down & \(-\)2.6047 & S21, a core ribosomal protein of the \\  & & & & 30S subunit \\ EDL933\_RS00125 & _rpsT_ & Down & \(-\)2.4991 & S20, a core ribosomal protein of the \\  & & & & 30S subunit \\ EDL933\_RS22335 & _rpsJ_ & Down & \(-\)1.54 & S10, a core ribosomal protein of the \\  & & & & 30S subunit \\ EDL933\_RS21675 & _rplU_ & Down & \(-\)2.1187 & L21, a core ribosomal protein of the \\  & & & & 50S subunit \\  & & & & Ribosome maturation enzymes catalyzing \\  & & & & structural remodeling of 30S ribosomal \\  & & & & subunits \\ EDL933\_RS21645 & _yhbY_ & Down & \(-\)1.7577 & Ribosome dimerization factors binding and stabilizing mature 50S ribosomal subunits \\  & & & & Ribosome-associated chaperones \\ EDL933\_RS02525 & _tig_ & Down & \(-\)3.0643 & coordinating nascent chain folding, protein translocation, and ribosome maturation \\ \hline \end{tabular}
\end{table} 
Table 1: Information on ribosome-related differentially expressed genes.
## 4 Discussion
### Limitations and Future Directions  
Long-term skill degradation is a concern. The application of virtual reality technology in sports training and rehabilitation has shown its potential. For example, in basketball decision-making training, virtual reality technology has been proven to improve training outcomes, and the virtual reality group outperformed the traditional training group in untrained scenarios [42]. This indicates that virtual reality technology has advantages in skill transfer and generalization.  
Additionally, virtual reality technology has shown positive results in rehabilitation training. In finger and hand function rehabilitation, serious virtual reality games developed iteratively significantly improve patients' motivation and daily exercise frequency [43]. This demonstrates that virtual reality technology not only improves training effectiveness but also enhances participants' motivation, contributing to the long-term maintenance of skills.  
Moreover, the application of virtual reality technology in high-intensity interval training has shown potential in improving training enjoyment and output power. Research indicates that gamified virtual reality training can increase the enjoyment of high-intensity interval training for untrained individuals and, in certain modes, improve training intensity [44]. This further supports the value of virtual reality technology in sports training.  
In conclusion, the application of virtual reality technology in sports training and rehabilitation demonstrates its potential in enhancing skill levels and maintaining long-term motivation. However, further research into virtual reality transition protocols, such as reducing prompts by 30% weekly, is needed to optimize human-AI interaction thresholds and ensure the long-term maintenance and enhancement of skills.
## 3 Results and Discussion
### Evaluation of Process Parameters
#### 3.2.2 Effect on Relative Penetration Depth  
The relative penetration depth S\({}_{\text{r}}\), defined as the ratio of the penetration depth for the W1 sample to that of the W10 sample, was calculated at the cross-sections (a) and (e), respectively, to evaluate the effect of the shielding gas flow rate and D\({}_{\text{nozzle}}\) (Figure 10).  
In contrast to the contamination index, S\({}_{\text{r}}\) increased with increasing shield gas flow rate. When a shielding gas flow rate of not less than 2.0 L/min was supplied, 95.5% or more S\({}_{\text{r}}\) could be obtained, as expected from the small contamination indices for those cases shown in Figure 9. When the shielding gas flow rate was 0.5 L/min, the calculated S\({}_{\text{r}}\) was similar to that of the reference case, and the difference for both was less than 5.81%, except for the cross-section (a), which is an outlier of the D8_F0.5 case. For that case, the contamination index was approximately 30%, and consistent welds were obtained in the welding trials.  
Figure 10: Relative penetration depth (\(S_{r}\)) according to \(D_{\text{nozzle}}\) and shielding gas flow rate: (a) cross-section (a) and (b) cross-section (e).  
When the shielding gas flow was 1.0 L/min, the mean S\({}_{\rm r}\) at the cross-section (e) was only 28.9% of that at the cross-section (a). The S\({}_{\rm r}\)'s for both cross-sections were very similar to the other shielding gas flow rates, except the D8_F0.5 case. Figure 11 shows the relative penetration depths for W10 at all five cross-sections from (a) to (e). The penetration depth tends to decrease from cross-section (a) to cross-section (d) as contamination of the coupling glass during the welding process accumulates. There is an abrupt reduction in penetration depth shown from cross-section (d) to cross-section (e). This originates with the shift in thermal focus with welding time, and it is assumed that a threshold exists between cross-sections (d) and (e) [13].
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 5 Experimental Results
### Detection Time  
Regarding the algorithms' detection time performance, our experiments revealed that the DDM algorithm achieved a notably lower detection time (y axis) compared to the others, overall. The drift detection methods based on the Hoeffding algorithms exhibited similar behavior, albeit with a higher detection time. In contrast, the EDDM algorithm displayed a marked distance from the other algorithms, with the results varying considerably throughout the analysis of each sample (identified on the x axis), as shown in Figure 6.  
We made the same evaluation process as when assessing the detection performance to evaluate the detection time performance using the non-parametric Friedman statistical test to identify any significant differences among EDDM, DDM, HDDMW, and HDDMA with 300 streams. Figure 7 presents the CD diagram for detection time. According to the statistical test, there are no statistically significant differences between only HDDMA and HDDMW. Thus, we can infer that these two detectors are the fastest in comparison to the others. When selecting a drift detector, both detection time and detection performance should be considered. HDDMW is a better option than HDDMA, DDM, and EDDM since it significantly reduces the detection time while maintaining adequate predictive performance.  
Figure 5: Critical distance diagram using the CD of 0.469 based on the Nemenyi post hoc test considering the F1 score of four drift detection methods (EDDM, DDM, HDDMW and HDDMA) with 100 paired streams.  
Figure 6: Time of detection methods when dealing with different drifts.
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 2 Background
### Transaction-Reverting Statements  
Developers use the statements revert, require, assert, and the deprecated throw to abort execution. Among the statements, require and assert evaluate a given condition and abort the execution depending on the result. require is intended for input checks, while assert guards invariants and, on failure, triggers the built-in Panic(0x01) error [8]. revert aborts unconditionally and can carry arbitrary error data, while throw (removed after Solidity 0.4.13) provides no message. Optionally, revert and require can specify an error message that returns as a binary-encoded payload. An explicitly defined custom error can be used instead of the error message. More specifically, each transaction-reverting statement has the following format:  
* require (_condition_, _error message_): This statement is commonly used to enforce input validation or preconditions. If the condition is false, the transaction reverts and returns the message.
* assert (_condition_): assert is designed to check invariants in the contract. If the condition is false, it reverts with Panic(0x01). assert does not support a custom message.
* revert (_error message_): If the transaction encounters revert, it unconditionally reverts and returns the message.
* throw: This statement has been deprecated since Solidity 0.4.13, but has been retained in legacy smart contracts. throw does not support a custom message and returns no message.