esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Generating and Searching the Index  
For searching, a hash table is generated from all n-grams of all target structures, in which n-grams generated from the query structure are searched. Generating the n-gram based index is a straightforward process (Figure 2). All target structures (chains) have to be translated subsequently to strings using a structure-to-string translator. The n-grams of each target structure are stored in the hash table. It has to be noted that the positions of the n-grams of query and target are stored as well, making it feasible to perform a 3D alignment for scoring and refinement. Searching a structure (query) in the index involves the transformation of the query chain into a string and the computation of each n-gram (Figure 4). The search results in a certain amount of target structures that have n-grams in common with the query. As these results may be statistical artifacts, a second hierarchical refinement step is applied. In this refinement step, the corresponding n-grams are subsequently aligned and thus anchors of query and target are determined. With that allocation, a superposition of query and target is performed [40]. The scoring is carried out by calculating the RMSD (Definition 3) and a qualitative score, TM-Score, as defined in [41] (Definition 4). The RMSD alone is not suitable as it does not allow conclusions to be drawn about the number of residues that have been aligned successfully.  
**Definition 3**: \[\mathrm{RMSD}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}d_{i}^{2}}\] (1)  
_where \(d_{i}\) is the Euclidian distance between N pairs of equivalent residues. The RMSD is calculated in Angstrom._  
**Definition 4**: \[\mathrm{TM-Score}=\frac{1}{L_{Target}}\sum_{i=1}^{L_{aligned}}\frac{1}{1+ \left(\frac{d_{i}}{1.24\sqrt[3]{L_{Target}-15-1.8}}\right)^{2}}\] (2)  
_where \(L_{Target}\) and \(L_{aligned}\) are the lengths of the target and aligned structure respectively. \(d_{i}\) is the Euclidian distance between the ith pair of residues._  
A conceptional advantage of the presented approach is that the strings (n-grams) that are being indexed and in turn searched using the hash table can be generated by an arbitrary approach. From the perspective of software engineering it is easily possible to exchange the discussed approach of protein \(\phi\)-\(\psi\) torsion with the Protein Blocks Method [42] mentioned in the introduction. For RNA structures it would be easily possible to replace the \(\eta\)-\(\theta\) torsion angles approach with the notion of suite codes proposed by [PERSON] _et al._[32], or any other representation.  
The principal parameters that have an impact on performance and accuracy are the size (n) of the indexed n-grams and complexity of the string a structure to string translator produces. In an extreme case a structure to string translator would produce always the same letter for each angle combination meaning each n-gram of the query will be compared to the each n-gram of the target. A clever translator reduces this by only comparing beta-sheets and helices or even combinations using a longer n-gram size reducing the search time dramatically.
## 4 Results and Discussion
### Performance Evaluation of F/R Automatic Shifting  
The results of the performance evaluation of the F/R automatic shift control under range shift: 3 rd, and driving shift: 2 nd are shown in Figure 14. According to the F/R automatic shifting, the signals of the shuttle shifting and the clutch actuators satisfied the shift range for all cases of forward, neutral, and reverse. For automatic shuttle shifting control from neutral to forward and from neutral to reverse, the response performance is shown in Table 6. The response performance was not affected by the gear stages. The performance of total duration time and RPM reaching time was higher in neutral \(\rightarrow\) reverse than neutral \(\rightarrow\) forward under the same gear stage. In all cases of neutral \(\rightarrow\) forward and neutral \(\rightarrow\) reverse, the total duration time was within 2.50 s, regardless of the gear stage, and the target RPM reaching time was within 3.00 s. In the case of forward \(\rightarrow\) neutral and reverse \(\rightarrow\) neutral, motor power was not transmitted to the transmission. Thus, this case was excluded from the measurement. The total duration time (i.e., shifting time) of the developed AMT system was confirmed to be applicable by comparing with the results of previous studies; for example, AMT in an automotive car achieved 2.62 s [35]; power shift, 3.00 s [21]; and power shuttle, 2.90 s [20].  
Figure 13: Position control of shuttle shifting actuator with determined PID coefficients.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Control Direction** & **Max. Overshoot (\%)** & **Settling Time (s)** & **Steady-State Error (\%)** \\ \hline Neutral \(\rightarrow\) Forward & 2.58 \(\pm\) 0.035 & 0.43 \(\pm\) 0.012 & 0.04 \(\pm\) 0.003 \\ \hline Forward \(\rightarrow\) Neutral & 3.11 \(\pm\) 0.085 & 0.37 \(\pm\) 0.023 & 0.03 \(\pm\) 0.005 \\ \hline Neutral \(\rightarrow\) Reverse & 3.36 \(\pm\) 0.068 & 0.40 \(\pm\) 0.020 & 0.02 \(\pm\) 0.010 \\ \hline Reverse \(\rightarrow\) Neutral & 2.78 \(\pm\) 0.040 & 0.31 \(\pm\) 0.012 & 0.03 \(\pm\) 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of response performance analysis for the shuttle shifting actuator.
## 3 Background
### Fido
#### 3.3.1 Uaf  
Is part of the first FIDO specification. The protocol allows the user to choose a local authentication mechanism on their device, such as fingerprint, facial scan, or voice recognition, when they need to establish an authentication process while creating an account with a service. Through the local authentication mechanism, the user is authenticated, and they do not have to insert their passwords on said device in the future. UAF creates a key pair and stores the private key on the user's device [8].
# Passwordless Authentication Using a Combination of Cryptography, Steganography, and Biometrics
## 6 Comparative Analysis and Future Outlook  
While the proposed SA system demonstrates promising results in addressing the shortcomings of traditional password-based authentication, it is essential to contextualize its contributions by comparing it with other contemporary passwordless solutions.  
[PERSON] et al. [3] proposed a keystroke dynamics-based authentication scheme, which, although secure, lacks the multi-factor approach and account recovery mechanisms present in SA. [PERSON] et al. (2022) [4] introduced a certificate-based system that addresses some of the limitations of FIDO2, such as account recovery; however, their solution still relies on hardware tokens, potentially limiting scalability and usability. [PERSON] and [PERSON] (2022) [8] explored iris-based biometrics, which, despite high accuracy, faces practical challenges in large-scale deployment due to the specialised hardware requirements for enrollment.  
In contrast, SA leverages existing device capabilities and does not necessitate additional hardware, improving its deployability and reducing costs for both users and service providers. Furthermore, by combining multiple authentication factors, SA offers a more robust security posture compared to single-factor solutions. Additionally, the proposed system's account recovery mechanism, based on typing biometrics, provides a user-friendly alternative to traditional password reset processes.  
While the SA prototype demonstrates the feasibility of a passwordless authentication system that balances security and usability, further research is warranted to address the identified limitations, such as the reliance on a trusted third-party server and the potential vulnerability of the employed steganography technique. Nonetheless, the multifaceted approach adopted in SA establishes a foundation for the future exploration and refinement of passwordless authentication solutions.
## 2 Theoretical Background
### Multi-Task Learning  
A neural network model that handles multiple input and output variables is known as a multi-input, multi-output (MIMO) model. The methods for building MIMO models can be divided into single-task learning and multi-task learning. In single-task learning, all variables share a layer, which poses difficulties due to interdependencies. Multi-task learning, on the other hand, separates the variables into different layers within a single model, allowing tailored learning for each variable. This approach is more efficient and suitable for building MIMO models for multiple output predictions. Figure 4 shows the basic structure of multi-task learning.  
The goal of a multi-learning task can be briefly described as \" Multi-task learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared low dimensional representation; what is learned for each task can help other tasks be learned better\" [13; 14]. The fundamental assumption of multi-task learning is that the tasks being learned are either all related or at least some of them are, so jointly learning all tasks can lead to better learning outcomes compared to independently learning each task. The idea is that by having tasks learn together, shared information between different tasks can result in improved overall performance. In other words, in processes like injection molding, where the qualities of the molded product for process conditions are not entirely independently separated but influenced by the interplay known as \"Interaction\", with some aspects affecting each other and contributing to the overall output (qualities), multi-task learning is a suitable structure. This is because certain aspects jointly influence tasks, while others independently affect them, creating an intertwined relationship among tasks. Fundamentally, multi-task learning is commonly employed to enhance the pattern recognition accuracy in computer vision applications. This involves multiple tasks sharing the same input, and they are processed within a single neural network, a setup often referred to as multi-class learning [14]. However, in recent times, multi-task learning techniques have been applied to various deep learning architectures such as CNNs, LSTMs, and regression problems within artificial neural networks. [11; 14] Lately, the adoption of deep learning techniques, particularly the deep neural network structure, has gained prominence in multi-task learning due to its ability to learn latent representations of data without requiring explicit hand-crafted formulations [11]. In various applications, the approach often involves either hard parameter sharing (where the  
Figure 4: Multi-task learning architecture in deep learning [11; 12].  
hidden layers are shared among all tasks) or soft parameter sharing (where each task has its own model, represented by its own set of parameters, within the hidden layers) [11,12,14], as shown in Figure 4.
## 3 Results
### Quantitation of Important Odorants and Calculation of OAV  
It is well known that AEDA is a worthy method for the screening of odor-active compounds in a given food. However, the application of AEDA does not provide immediate information about the contribution of a single odorant to the overall aroma. To get a deeper insight into the contribution of the quantitated aroma compounds to the overall aroma of the braised pork, OAV was calculated for each aroma component. Moreover, OAV was usually used to provide a rough evaluation of the real contribution of each compound to the overall aroma.  
For quantification purposes, calibration curves for each odor-active compound in the extracts of two types of braised pork by SDE-AEDA-GC/O were drawn (Table 4). From Table 4, the coefficient of determination was determined, which indicated strong linearity for each of the standards.  
Table 3 lists the concentrations (calculated by the standard curves), odor threshold values, and calculated OAV of the odor potent compounds. In terms of OAV, 3-methylbutanal, pentanal, nonanal, (E,E)-2,4-decadienal, phenylacetaldehyde, dodecanal, andlinalod had OAV values \(>\)200, indicating that these compounds, especially aldehydes, might significantly contribute to the overall aroma of both types of braised pork. These results are consistent with the fact that aldehydes have an important potential effect on the global flavor of meat species [32]. By comparison, sample A had a higher content of (E,E)-2,4-decadienal (OAV = 1304). Furthermore, many compounds such as (\(Z\))-cinnamaldehyde, limonene, \(\gamma\)-undecalactone, anethole, and 1,8-cineole were deemed to be the key-aroma compounds in braised pork, due to their concentrations clearly exceeding their odor thresholds. It is worth mentioning that two unknown compounds cannot calculate the OAV, so they can only be preliminarily considered as potential aroma-active substances through the odor description and FD factor. In addition, for some compounds, the concentrations did not reach their odor thresholds and they showed an OAV of \(<\)1. However, the combination of these compounds might also be linked with the aroma of braised pork due to the synergistic effect of similar compounds. Lack of agreement between FD factor and OAV results also existed in braised pork; 3-Methylbutanal had a high OAV (OAV = 335, 211) but a low FD factor (FD = 3), while \(\alpha\)-Pinene had a high FD factor but a low OAV, which showed the influence of the food matrix.
# False Alarm Reduction Method for Weakness Static Analysis Using BERT Model
## 3 False Alarm Reduction Method for Reliability Analysis of Static Analysis Results
### Select Multiple Static Analysis Tools and Experiment
#### 3.2.2 Tools Experiment  
Tools configuration in Figure 1 shows the structure of configuration for integrating analysis results of multiple static analysis tools. In this study, we experimented on Ubuntu, the Juliet test suite C/C++ 1.3 version [38]; the dataset expected in Section 3 was used, and all tools were tested using the command line. There are differences in the required configuration files or options for each tool. The Infer tool was used to compile the entire test case through the prepared \"Makefile\" and then analyze weaknesses. The Clang Static Analyzer was executed by adding the option \"analyzer-checker = alpha,core\". The Cppcheck and Frama-C tools only passed the path of the test case and the resulting output file, whereas the PVS-Studio tool generated the object file for each test case through \"Makefile\" compilation and passed the config file \"PVS-Studio.cfg\". Because the output results of each tool are different, CWE mapping and groups were conducted for each tool's analyzed results, and all results were written in one file based on CWE-ID. Table 3 shows the results analyzed for each test case file in one CWE class. In one test case file, multiple tools may analyze the same results but may have different analysis results. One tool can be analyzed as a weakness with multiple lines in one test case. Perhaps the results of the analysis contain a number of several false alarms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**File Name** & **Tool Name** & **Line** & **CWE-ID** & **Alarm Message** \\ \hline /NULL\_Pointer \(\ldots\) 01.c & CPC & 7 & 476 & nullPointer \(\ldots\) \\ \hline /NULL\_Pointer \(\ldots\) 01.c & CLG & 7 & 476 & [core.NullDereference] \\ \hline /NULL\_Pointer \(\ldots\) 01.c & IFR & 7 & 476 & pointer \\ \hline /NULL\_Pointer \(\ldots\) 01.c & FRC & 7 & 476 & warning \\ \hline /NULL\_Pointer \(\ldots\) 01.c & PVS & 7 & 476 & the num pointer \\ \hline /NULL\_Pointer \(\ldots\) 01.c & FLF & 18 & 476 & security-related \\ \hline /NULL\_Pointer \(\ldots\) 01.c & PVS & 18 & 476 & the num pointer \\ \hline /NULL\_Pointer \(\ldots\) 02.c & IFR & 10 & 476 & pointer \\ \hline /NULL\_Pointer \(\ldots\) 02.c & CLG & 10 & 476 & [core.NullDereference] \\ \hline /NULL\_Pointer \(\ldots\) 03.c & CPC & 15 & 476 & nullPointer \(\ldots\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Example analysis result of weakness multiple static analysis tools with CWE-476.  
Figure 2: Example of CWE group with CWE-664.
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 4 Experimental Implementation
### Monitoring Server Integration  
The system is integrated with a central monitoring server using Node-RED for data visualization and alert management. The server processes the data from the sensors, identifies fire incidents, and generates alerts for immediate action. The server configuration for remote monitoring in the multi-hop IoT network is designed to facilitate seamless data transmission from remote areas to a centralized server for real-time visualization and analysis. The system relies on the Mosquito MQTT broker and the Node-RED server, both hosted on the Raspberry Pi and offering a lightweight yet powerful platform for managing the data flow between the network's gateway node and the remote monitoring interface. The Node-RED used for remote monitoring is configured to handle the reception, processing, and display of fire detection data.  
The flow starts with MQTT input nodes that subscribe to specific topics to receive fire detection data transmitted by the gateway node, ensuring real-time data are captured from the IoT devices. The incoming data are processed through function nodes of Node-RED flow, which restructure and organize them for visualization. Once processed, the data are displayed using UI elements such as text fields and tables for real-time monitoring of fire detection data. These elements are part of a user-friendly dashboard, managed by group and tab nodes to ensure smooth navigation and organized data presentation. Figure 7 illustrates the revised Node-RED flow configuration for real-time fire monitoring and alert management. The figure highlights critical components, including MQTT input nodes that process real-time data from IoT devices, function nodes that restructure data and generate fire alerts, and dashboard UI elements for real-time visualization of the fire status. This streamlined configuration ensures clear and effective monitoring while unrelated workflows have been excluded for clarity. This system enables real-time data monitoring, providing users with an intuitive interface to view environmental conditions, such as fire detection status.
## 2 Proposed Framework for Probabilistic Stability and Performance Assessment
### The Stopping Criterion for Monte Carlo (MC) Simulations  
Although increasing the amount of sampled data and MC simulations (\(N_{C}\)) resulted in a higher accuracy of the obtained results, it also increased the computational demands. Therefore, the selection of \(N_{C}\) was a compromise between the accuracy and computational burden. Accordingly, a stopping criterion was needed. Figure 3 shows the error of the estimated damping factor mean value under different iterations of MC simulation, which were associated with the ideal case (damping factor mean value under an approximately infinite number of iterations). Moreover, the time needed to conduct MC simulations for some cases is also depicted.  
It could be seen that 2000 iterations were sufficient to provide a satisfactory result since the stability criteria surely had errors in a mean of less than 2% and the required time to carry out MC simulations was acceptable. The same conclusions could also be obtained for other stability and performance criteria and grid conditions, which were not presented here due to limited space.  
Figure 2: Region of eigenvalue location based on the different stability and performance objectives. (**a**) Region in the left-side of the s-plane where \(\sigma<\sigma_{\max}\); (**b**) a wedge-shaped sector in the s-plane where \(\xi>\xi_{\min}\); (**c**) a D-shaped sector in the s-plane where \(\sigma<\sigma_{\max}\) & \(\xi>\xi_{\min}\).
###### Abstract  
Tropospheric systems are widely used by military forces as they provide long-distance, real-time communication. Slow-fading propagation loss reduces link availability and limits its data-carrying capacity. Beam pointing dynamics provides knowledge of favorable heights at different times of the day in different seasons and a useful steering range. Beam steering, based on the beam pointing dynamics of the link, can overcome slow fading. The main contributions of this paper are the derivation of a realistic and accurate tropospheric channel model obtained by making important modifications to [PERSON]'s ray-based model. This paper also presents a method for determining beam pointing dynamics using the modified model. Beam pointing dynamics for two different links located in India have been determined in this paper using real-world data obtained from the Indian Meteorological Department. Another significant contribution of the paper is presenting the prospect of dual data streaming on tropospheric links using a fixed beam and a dynamically steered beam, based on the examination of beam pointing dynamics obtained for the two links. The main result presented in this paper is the comparison of powers received from the most favorable heights in a steerable beam system with the powers received in conventional fixed-beam systems for different days of the year. It has been shown that a higher received power can be achieved with beam steering. Another important result shown is the comparison of the achievable data rates for a single fixed-beam system and a dual-beam (one fixed beam and one dynamically steered beam) system. It has been shown that almost double the data rate is achievable in a dual-beam system. The method for the determination of beam pointing dynamics and the possibility of dual data streaming presented in this paper can significantly enhance the availability and capacity of tropospheric links.  
t roposcatter communication link; ray-based tropospheric channel model; beam pointing dynamics; beam steering; dual data streaming on tropospheric links +
Footnote †: journal: _etlecom_  
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