esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Accumulation of CO\({}_{2}\) Concentration  
Since humans can lose consciousness within a few minutes when exposed to CO\({}_{2}\) gas at a concentration of over 10% [27; 28], a CO\({}_{2}\) concentration of 10% can be regarded as a safety threshold. The predicted CO\({}_{2}\) concentration distribution passing through the crack is provided in Figure 7. The results show that the maximum CO\({}_{2}\) concentration occurs at the corner region near the crack location. A much higher CO\({}_{2}\) concentration in comparison to other regions is also predicted around the corner. For efficient evacuation, it is important to analyze a critical time at which the CO\({}_{2}\) concentration reaches 10% at human nasal height. The first critical time is observed at the corner and estimated to be 195 s, 48 s, and 25 s for crack sizes of 10 mm, 20 mm, and 30 mm, respectively. With the increase in the crack size, the critical time is inversely proportional to the square of the crack size depending on the leakage mass flow rate.  
From an engineering viewpoint, as shown in Figure 8, it would be useful to define a dangerous volume of CO\({}_{2}\) concentration being over 10%, to characterize three-dimensional leakage behavior. Physically, the leaked CO\({}_{2}\) gas is rarely propagated toward the upper tween deck region because of the buoyancy effect. In the case of the 30 mm crack size, it was reported that the whole region below the tween deck could become a dangerous volume within 300 s. To investigate the propagation tendency, the dangerous volume fraction, defined as the ratio of the dangerous volume to the entire cargo volume, is presented in Figure 9. We see that a substantial difference in dangerous volume depending on crack size is observed at the initial stage of leakage. As noted previously, the initial leakage mass flow rates were estimated to be 2.175 kg/s for a crack size of 10 mm, which is about nine times lower than a crack size of 30 mm. Thus, the diffusion effect is dominant in the early stage of leakage owing to a lower mass flow rate, showing a lower increasing rate of the dangerous volume, compared to the other two cases. When the crack size increases, the gradient at the initial stage becomes higher than the ratio of the mass flow rate. From the results, it was noted that the gas sensor systems should be installed near the bottom corners of the container for the rapid detection of leaked gas.  
Figure 6: Volume-averaged static pressure evolution with time at different crack sizes.  
Figure 8: Dangerous volume evolution in the container.  
Figure 7: CO\({}_{2}\) concentration distribution at the cross-sectional plane passing through the crack.
## 4 Results
### Computational Cost  
In the process of computing, the matrix multiplication takes lots of computational cost. Considering a transducer array with P subarrays, the conventional covariance matrix requires P times of the matrix multiplication, and the cross-covariance matrix requires P\({}^{2}\) times. Thus, calculating the cross-covariance matrix as Equation (14) directly will greatly increase the amount of computation. However, in our GSC-CROSS method, we can obtain the cross-covariance matrix with only one-time multiplication. Table 4 shows the imaging speed of traditional GSC, GSC with conventional cross-covariance matrix using Equation (14) (GCS-cross) and GSC-CROSS to obtain the images in Figure 7. The computation time is measured on a platform with an Intel\({}^{\text{\textregistered}}\) Core IS-7400 CPU (Intel, USA) of 3.00 GHZ with a RAM of 32G. This experiment shows that GSC-CROSS can reduce the computational cost of calculating the cross-covariance matrix, and it provides a faster imaging speed than the GSC beamformer.
## 3 Results and Discussion
### FNR or FMV Deposited on the MF\({}_{\text{DSM}}\) Anode as Mediator
The data confirmed the results described above that the presence of FNR in the MF\({}_{\text{DSM}}\) anodic chamber increased the current density, while the presence of FMV in the \(\mathrm{MFC_{DSM}}\) anodic chamber elicited a less pronounced capacitive behavior. Figure S7 compares the EIS of the unmodified electrode (carbon cloth) and the modified electrode containing FNR and FMV. In the two latter cases, total impedance decreased significantly. Specifically, that containing FNR and FMV had an R\({}_{\mathrm{ct}}\) of 30.4 and 48.3 \(\Omega\), respectively, which represents a 33.7- and 21.4-fold reduction compared to the unmodified electrode (R\({}_{\mathrm{ct}}\) = 1025.6 \(\Omega\)). As for R\({}_{\mathrm{s}}\), values were similar (20.5, 18.3, and 21.7 \(\Omega\) for the unmodified electrode, that containing FNR, and that containing FMV, respectively).
# Security Vulnerabilities in LPWANs--An Attack Vector Analysis for the IoT Ecosystem
## 3 State-of-the-Art Review
### Attacks and Defense Strategies
#### 3.4.2 Bit-Flipping Attack
Considering the integrity protection, it is better if the protocol can provide end-to-end encryption. Therefore the security between the application server and the network server can be independent of the transmission method. Apart from that, if the transmission method is not secure, the LoRaWAN network is not secure any longer. One strategy to secure the integrity between the network server and the application server is to check the Message Integrity Code (MIC) again when the message arrives at the application server. In the LoRaWAN specification, the MIC is checked in the network server ensuring that the messages received are not modified. After verification, the message is transmitted to the application server, but it does not verify the MIC again. The author [72] suggests that it is necessary for the application server to also check the MIC with NwkSKey to ensure that the message is not modified during the communication between the two servers. NwkSKey is owned by the network server, which in practice, is operated by the network administrator. It would be better if the application server could also have NwkSKey, to be able to calculate the MIC to check the signature.
###### Abstract  
Multiparty Private Set Intersection (MPSI) is dedicated to finding the intersection of datasets of multiple participants without disclosing any other information. Although many MPSI protocols have been presented, there are still some important practical scenarios that require in-depth consideration such as an unbalanced scenario, where the server's dataset is much larger than the clients' datasets, and in cases where the number of participants is large. This paper proposes a practical MPSI protocol for unbalanced scenarios. The protocol uses the Bloom filter, an efficient data structure, and the ElGamal encryption algorithm to reduce the computation of clients and the server; adopts randomization technology to solve the encryption problem of the 0s in the Bloom filter; and introduces the idea of the Shamir threshold secret-sharing scheme to adapt to multiple environments. A formal security proof and three detailed experiments are given. The results of the experiments showed that the new protocol is very suitable for unbalanced scenarios with a large number of participants, and it has a significant improvement in efficiency compared with the typical related protocol (TIFS 2022).  
Multiparty Private Set Intersection; unbalanced scenario; ElGamal cryptography; Shamir threshold key-sharing scheme; Bloom filter
## 2 Materials and Methods
### Data Analysis  
A drop or EFFDH study usually aims to create a laboratory test procedure that simulates the real-world parcel shipping environment of a one-way shipment. Therefore, treating and analyzing the collected data from multiple shipments as one large population may result in distortions and is not recommended by the ISTA data analysis guideline [30]. In this study, all the data were analyzed as individual one-way shipments to ensure accurate results that could guide the creation of an effective laboratory test.  
Data points below the 152.4 mm (6 in.) drop height were excluded from the analysis since they tend to be caused by small shock events during vibration [19; 20]. Among the filtered data points, the Nth highest EFFDH (highest, second, third, and fourth) and the number of impacts during each individual one-way trip were determined. Datasets of the number of impacts during each individual one-way shipment and the Nth highest EFFDH from each individual one-way shipment were then fitted to a Weibull distribution using MATLAB R2022b (MathWorks Inc., Natick, MA, USA) to provide the Nth highest EFFDH and the number of impacts to be expected with 90 th, 95 th, and 99 th percentile levels from the parcel delivery system in Korea.  
Then, the two-sample Kolmogorov-Smirnov (KS) test was applied to analyze the significance of the discrepancy between the cumulative distribution functions (CDFs) of the number of drops and the EFFDHs for small and medium boxes. The two-sample KS test is a nonparametric statistical test method that assesses the similarity between two CDFs. The null hypothesis assumes that the two CDFs are derived from the same underlying distribution. The two-sample KS statistic quantifies the maximum absolute distance between the empirical cumulative distribution functions of two samples using the following statistic (Equation (3)):  
\[D=max\big{(}\big{|}\hat{F}_{1}(x)-\hat{F}_{2}(x)\big{|}\big{)} \tag{3}\]  
where:  
\(D\) = the distance between the empirical distribution  
\(\hat{F}_{1}(x)\) = the proportion of first-sample values less than equal to x  
\(\hat{F}_{2}(x)\) = the proportion of second-sample values less than equal to x  
The percentage of each impact orientation, including faces, edges, corners, on and around the bottom (O/A bottom), on and around the top (O/A top), vertical faces, and vertical edges, was also calculated. This analyzed data could then be used to establish new laboratory test procedures for Korea.  
In addition, the time stamp of each data point was compared with tracking information. This analysis allowed us to determine which delivery stage produces the most impacts. It provided useful information for packaging engineers about what to target to optimize their packages and for courier engineers about which stages need to be improved to reduce the parcel damage rate.
# NOx Formation Mechanism and Emission Prediction in Turbulent Combustion: A Review
## 4 The Empirical Formula Approach
[44] developed correlations to predict the NOx emissions of a stationary CI engine fueled with biodiesel at the standard and retarded injection timing. \[ \begin{array}{ll}NOx\ in\ ppm=&20,956.69\rho\\ &\times\left[1+C\left[-5.740\times 10^{-3}L^{2}+0.0995L-0.603\right]\right] \\ &\times\exp(0.948\times ID-18.83)\times\exp\left(4626.44/T_{f}\right)\end{array}  \tag{20}\]where \(L\) represents the load, \(ID\) represents the ignition delay, and \(T_{f}\) represents the flame temperature. The error between the prediction and the experiment is quite marginal at standard and retarded injection timing. The advantages of this empirical-formula method are that it can simplify computational complexity and obtain data quickly. For the engineers who designed the combustion chamber, these advantages satisfied their requirement, which is that, by changing design parameters, they could obtain the response data quickly. However, this empirical-formula approach only includes temperature and pressure information; it does not contain the concentration of species and temperature distribution. For researchers who aim to reduce the production of NOx, this oversimplified method has many drawbacks, so it cannot give an accurate description of the temperature field, along with reactants and radical species' concentrations [45]. With the development of artificial intelligence, neural networks were introduced to improve models. [PERSON] et al. [46] applied a hybrid neural network architecture combining the convolutional neural network (CNN) and long short-item memory (LSTM) neural network to predict NOx emissions. The results demonstrated that the fitting coefficient of the CNN-LSTM network model is 0.977. Similarly, [PERSON] et al. [47] developed a machine-learning model for predicting NOx, using the Random Forest method to reduce the negligible input features. The final model showed an accuracy of R\({}^{2}\) = 0.965. [PERSON] et al. [48] proposed a hybrid deep neural network model for NOx emission prediction. In this model, an adversarial denoising autoencoder (ADAE) and the least support vector regression (LSSVR) were in the model to extract flame-deep features and to analyze the extracted features, respectively. The satisfactory prediction accuracy of this model is 0.97, meaning that it outperforms other state-of-art models. The application of artificial intelligence and neural networks could effectively improve the prediction accuracy of the model, but the large number of training data required and the small adaptation range of the models are still the disadvantages of this method. According to empirical formula, the formation of NOx is mainly related with combustion temperature, oxidant concentration or its partial pressure, and retention time in the high-temperature region.
# Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links +
## 3 Simulation Results
### Sensor Scheme  
Initially, the sensor architecture was analyzed, with the sensors' positions given by a \"circles inside a circle\" packing algorithm, as a means to increase the tolerance to the position and angular misalignment problem. The analysis of the number of sensors of this scheme was performed for clear waters and a propagation distance of \(1\,\)m.  
The results shown in Figure 10 reveal a distinct improvement in the tolerance to misalignment for a higher number of sensors, mainly due to the higher total receiver area.  
The seven sensors in a packing disposition, as depicted in Figure 8e, increased the MALO by approximately a factor of three compared with the one-lens-only case. In addition, the seven-sensor case was trivially optimal and conveniently required seven equally sized lenses, and thus the case corresponding to eight lenses was not simulated.  
Figure 10: MALO calculation as a function of the divergence of the emitter for a different number of sensors in the sensor scheme, considering a clear water type and a Tx-Rx distance of \(1\,\)m.  
Figure 9: Moving scheme of sensors for MALO calculation. The sensor scheme was offset through the 2D plane radially from \(0\,\)m to \(0.4\,\)m and at angles from \(0^{\circ}\) to \(360^{\circ}\) in steps of \(45^{\circ}\).
# Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite
## 3 On-Board Processing Unit Design
### Software Design
#### 3.3.1 Booting Procedure and Operating System
The main application software is responsible for command handling, HSI control, file transfer and telemetry, as well as on-board processing pipeline configuration and control. The application software comes as a single application file, which is run as the last step in the boot sequence. Before the main application is run, time is synchronized between the OPU and the satellite platform, as the OPU does not feature an on-board Real Time Clock (RTC). The OPU system time is set by requesting the time of the RTC on the FC, which is in turn set using the on-board GNSS module of the satellite bus.
## 7 Future Prospects of Multi-Source Data Fusion and Analysis Algorithms in Smart Cities  
### Data Quality and Real-Time Challenges That Need to Be Overcome  
With the accelerated advancement of smart city construction, multi-source data fusion and analysis algorithms have increasingly stringent requirements for data quality and real-time performance. At present, uneven data quality and insufficient real-time processing capabilities have become key factors restricting the effectiveness of algorithms. In terms of data quality, multi-source data often have problems such as noise interference, data missing, and data inconsistency, which seriously affect the accuracy of fusion and analysis results. For example, sensor data may produce outliers due to equipment failure or environmental factors; social media data may contain a large amount of false information or irrelevant content; citizen feedback data are greatly affected by subjective factors; GIS data may have geographic information deviations caused by untimely updates. In terms of real-time performance, despite the continuous advancement of technology, it is still difficult for algorithms to process and respond immediately when faced with massive and high-speed data, and they cannot meet the urgent needs of smart cities for real-time decision-making, such as real-time diversion of traffic congestion and emergency response to emergencies.  
In the future, in order to improve data quality, a more complete data cleaning, verification, and updating mechanism needs to be established. On the one hand, intelligent data-cleaning algorithms can be developed to automatically identify and correct errors and outliers in the data. Moreover, machine-learning technology can be used to perform semantic understanding and consistency verification of data to ensure the compatibility and accuracy of data from different sources. On the other hand, establishing real-time data collection channels and dynamic update mechanisms can strengthen the timeliness of data updates and ensure that data can always reflect the latest status of the city. In terms of improving real-time performance, efficient data-processing architectures and algorithm optimization strategies, such as using distributed computing, stream computing, and other technologies, to achieve parallel processing and real-time analysis of data ensure that the algorithm can quickly respond to dynamic changes in the city and provide timely and accurate data support for urban management decisions [68].
# The Future of Healthcare with Industry 5.0: Preliminary Interview-Based Qualitative Analysis
## 4 Results and Discussion
### Technological Trends and Their Applicability in Health
* Gradual and patient-centered implementation: The implementation of technologies should be gradual and tailored to the specific needs of patients (E2, E4, E11). This allows for a better assessment of needs and technology adaptation, ensuring that the patient remains at the center of care. \"For example, while an AI-based system can provide valuable insights, the final decision should always be made together with the patient, considering their preferences and values.\" (E11). * Needs-driven clinical development: From the outset, technologies should be designed to meet the real needs of clinical practice and be led by qualified healthcare professionals (E10). Additionally, it is mentioned that \"the implementation process should involve multidisciplinary collaboration between healthcare professionals and IT professionals is essential to optimize the use of these technologies in healthcare delivery\" (E8). * Training and integration are keys to success: Healthcare professionals need proper training to effectively use emerging technologies (E6, E8). Integrating these technologies into existing systems and interdisciplinary collaboration is essential to optimize their use in patient care. Before implementing new technologies, it is necessary to assess needs, train all healthcare professionals in their use, and establish rigorous data-management protocols (E9). * Legal and security challenges with technologies: The integration of artificial intelligence into healthcare systems faces significant obstacles related to legal and security issues, especially regarding access to sensitive data (E7). * Robust infrastructure and data privacy policies: Effective implementation requires robust technological infrastructure and well-established data privacy and security policies (E8). * Ongoing evaluation and implementation science: Ongoing evaluation of technology implementation is crucial, and the use of implementation science methodologies can help measure the real impact of technologies on patient health (E8). * Continuous feedback and improvement: The importance of continuous feedback and continuous improvement is emphasized, suggesting an iterative and responsive development model for technology in healthcare (E9).
## 2 Related Work
### Metaheuristic Algorithms for Feature Selection  
This section summarizes recent advancements in using MHs for FS, focusing on the four algorithms utilized in this study: IDA, bGWO, BHHO, and GA.  
IDA builds on the original Dragonfly Algorithm by introducing enhancements to address premature convergence and improve global search capabilities. IDA performs especially well in FS for high-dimensional datasets, as shown in the studies where it yielded better results compared to conventional algorithms such as Particle Swarm Optimization (PSO) and the standard Dragonfly Algorithm (DA) in terms of feature reduction and accuracy [25; 26].  
The bGWO adapts the social hierarchy and hunting behavior of gray wolves to binary FS problems. This algorithm efficiently balances exploration and exploitation, making it well-suited for high-dimensional medical datasets such as classifying diabetes and cancer. Studies have shown that bGWO outperforms other algorithms, including GA and PSO, by reducing feature redundancy while maintaining classification accuracy [27; 28; 29].  
The BHHO, inspired by the cooperative hunting strategies of Harris hawks, has been effectively applied to FS tasks in biomedical datasets. BHHO uses adaptive strategies to explore the search space, demonstrating strong performance in selecting relevant features and achieving high classification accuracy. Its binary adaptation makes it highly effective in discrete optimization tasks like FS [30; 31].  
The GA remains a foundational optimization method, drawing inspiration from natural selection. GA employs crossover, mutation, and selection operators to explore feature subsets. Despite its simplicity, GA remains a benchmark in FS research and has been widely used in various domains, including microarray data and medical applications, where it has shown reliable performance in reducing dimensionality. [PERSON] et al. demonstrated GA's consistent capability to provide reliable feature subset selections, though occasionally falling short of the advancements made with BHHO and IDA in terms of convergence speed and accuracy [26].  
These four algorithms have shown that MH methods can be generally applied and efficiently solve FS problems. This study utilizes IDA, bGWO, BHHO, and GA to tackle high-dimensionality challenges in glucose monitoring datasets for non-invasive estimation of glucose level.