esa-sceva/satcom-chunk-collection huggingface dataset:
# The World of Cyclic Dinucleotides in Bacterial Behavior
## 4 Distribution of Proteins Containing GGDEF and DAC Domains in Bacteria  
Initial reviews of the distribution of DisA homologs across bacterial clades suggested that c-di-AMP would play a more important role in Gram-positive bacteria than in Gram-negative and that, in general, bacteria would avoid allowing these two signaling networks to co-exist, so as to avoid unintended crosstalk and to easily regulate the balance of these second messengers within the cell [21; 22]. Subsequent surveys on the distribution of DAC and GGDEF homologs don't support the idea that DAC homologs are rare among Gram-negative bacteria, as members of lineages such as Cyanobacteria, Spirochaetes, and Deltaproteobacteria often carry both DAC and GGDEF genes, a profile compatible with the complex lifestyles and genomes of these lineages. In addition, among Gram-positives, most members of Firmicutes and Actinobacteria, including model organisms such as _Bacillus_, _Clostridium_, _Streptomyces_, _Listeria_, and _Mycobacterium_, produce both signaling molecules and possess a wide array of GGDEF genes, following the general trend of having close to as many genomes with both DAC and GGDEF as possible (see Supplementary Table S1 and Figure 9). The only lineages were several of the genomes sampled that seem to have at least one DAC homolog, but no or very few and rare recognizable GGDEF homologs are Bacteroidetes and the Archaea. In both lineages, the number of genomes with both DAC and GGDEF falls below 50% of the maximum allowed, i.e., the smallest between the number of genomes carrying DAC or GGDEF. Genomic data strongly suggest that there is a tendency for bacterial cells to use both c-di-AMP and c-di-GMP signaling networks simultaneously, which would imply that both the control of their synthesis and turnover and the specificity of their sensors are carefully tuned.  
Figure 9: Lack of anti-correlation in the distribution of DAC and GGDEF genes per prokaryotic clades. Each dot represents a prokaryotic class, such as Gammaproteobacteria or Bacilli, as defined in the NCBI’s Taxonomy Database. For each class, the number of genomes harboring at least one DAC and one GGDEF gene and the number of genomes harboring both was calculated. If, for a given class, we consider the number of genomes with DACs and the number of genomes with GGDEF, the smallest of these numbers is the maximum number of genomes that could, in principle, carry both genes. That number is seen on the horizontal axis while the actual number of genomes carrying both genes is on the \(y\)-axis. These numbers are very close to the diagonal line, indicating that, in most cases, if members of a given lineage are carrying both DAC and GGDEF, they tend to keep both genes, instead of having to choose between them.
## 4 Evaluation
### Convergence Analysis  
In this part, we analyzed the impact of different hyperparameter settings on convergence performance to evaluate the convergence performance of our proposed algorithm.  
We first evaluated the impact of different learning rate settings on convergence performance. We evaluated the convergence performance under three settings, where the learning rate is set to \(2\times 10^{-5}\), \(2\times 10^{-4}\), and \(2\times 10^{-3}\), respectively. The result is shown in Figure 5. It can be seen that in the three learning rate settings, the rewards obtained by the agent increases with the training, and can eventually converge. When the learning rate is \(2\times 10^{-4}\), it has the best convergence performance. Too small or too large learning rate will lead to worse convergence performance.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Replay buffer capacity & \(1\times 10^{5}\) \\ Optimizer & Adam \\ Learning rate \(\alpha\) & \(2\times 10^{-4}\) \\ Discount factor \(\gamma\) & 0.9 \\ Minibatch size \(N\) & 16 \\ Soft update constant \(e\) & 0.01 \\ Number of training episodes & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter values.  
The convergence performance of the algorithm is also affected by the minibatch size. The minibatch size determines the number of samples used in gradient updates, which directly affects the learning speed and stability of the algorithm. We compared the convergence performance of our algorithm with minibatch sizes of 8, 16, and 32. The result is shown in Figure 6. The algorithm can converge under all three minibatch sizes. Compared with other settings, the algorithm can converge faster when the minibatch size is 16. When the minibatch size is set to 8, the convergence speed of the algorithm is slow. Because the number of samples is too small during a single update, the learning speed decreases.  
The discount factor also affects the convergence performance of the algorithm. Figure 7 shows the convergence performance comparison of the algorithm under different discount factor settings. It can be seen that the algorithm has the best convergence performance when the discount factor is set to 0.9. A larger discount factor indicates that the agent places more emphasis on long-term benefits. When the discount factor is set to 0.7 and 0.8, the  
Figure 5: Comparison of different learning rates.  
Figure 6: Comparison of different minibatch sizes.  
agent's emphasis on long-term benefits decreases and becomes more shortsighted, thus affecting convergence performance.
## 3 Results and Discussion
### Effect of Process Conditions on TPC
The model \(F\)-value was superior to the one obtained during the first calculation. An increase in the recovery of phenolic compounds was obtained by maximizing the addition of ethanol and the density of CO\({}_{2}\). A graphical representation of the results is shown in Figure 2.  
The values of the yield obtained by model computations were very close to the empirical findings. The adjustment improved the fit in comparison with the earlier calculations, the \(F\)-values of both models (15.229 versus 84.660) confirm the results. The computed yield at 35 \({}^{\circ}\)C, 10 MPa, and 80% m/m ethanol addition (density of ca. 766 kg\(\cdot\)m\({}^{3}\)) was the same as the experimental result (1.52 g per 100 g). Figure 1: Effect of the temperature and pressure of extraction on the yield of phenolic compounds during the Supercritical Fluid Extraction (SFE) of chokeberry pomace (50% m/m addition of ethyl alcohol). Abbr. : GA = gallic acid, TPC = total phenolic content, \(T\) = temperature, \(p\) = pressure. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Constant** & **Value** & **Significance (\(p\)-Value)** \\ \hline a\({}_{0}\) & 882.60 & - \\ a\({}_{3}\) & 232.08 & \textless{}0.0001 \\ a\({}_{4}\) & 470.51 & \textless{}0.0001 \\ a\({}_{33}\) & \(-\)70.79 & 0.1645 \\ a\({}_{44}\) & 55.64 & 0.2467 \\ a\({}_{34}\) & 43.47 & 0.2897 \\ Model \(F\)-value & 84.660 & \textless{}0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of two-parameter optimization of the extraction process.
# Secure Voting Website Using Ethereum and Smart Contracts
## 4 Proposed System
### Methodology
#### 4.1.1 Frontend Module  
In this phase, the frontend module will be covered, which involves building the interactive user interface for the admin and the user. Research on implementing Blockchain in a decentralized application will be done in parallel. The frontend module has two main components: the admin and user modules.  
Admin Module: The admin module is divided into five components: dashboard, add candidate, create election, election details, and candidate details. Figure 2 shows the functions of the admin module.  
* Dashboard: The dashboard component will contain various charts to display information such as the number of parties, voters, etc. This component will give the admin an overview of the election process and help them make informed decisions.
* Add Candidate: In this admin feature, they can add candidates standing in the election. After the candidate is added, it will be displayed on the user side. This component will enable the admin to manage the list of candidates and ensure that only authorized candidates are on the ballot.
* Create Election: This admin feature will allow them to create an election. A user can cast their vote only after the admin makes the election. A user can cast a vote between the start date and the end date. This component will enable the admin to set the parameters for the election, such as the start and end date, the number of voters, and the type of election.
* Election Details: In this section, the admin can update election details such as the start date, end date, etc. This component will enable the admin to manage the election process and make changes if necessary.
* Candidate Details: All the candidates added by the admin will be displayed in the candidate details component. The admin can update the candidate details if a wrong entry is made. This component will enable the admin to manage the list of candidates and ensure that the correct information is displayed [22; 23].  
User Module: The user module has four components: dashboard, voter register, voting area, and results. Figure 3 shows the functions of the user module.  
* Dashboard: The user dashboard contains information about parties and their candidates. A user can see all the information about a candidate. This component will give the user an overview of the election process and help them make informed decisions.
* Voter Register: In this section, the first user will have to register themselves; only then will they be able to cast their vote. This component will enable the user to register and ensure that only authorized voters can cast their votes.
* Voting Area: After a user is registered, they will only be directed to this page and can vote. This component will enable the user to vote and ensure that only authorized voters can participate in the election.
* Result: The Results page will provide users with access to view the outcome of the voting process, including the final results and other relevant details. This section ensures transparency and informs users about the election's outcome.
# A Self-Testing Platform with a Foreground Digital Calibration Technique for SAR ADCs
## 3 Measurement Results
### Proposed Self-Testing Platform  
The 12 bit SAR ADC and RDFT processor is implemented in the 0.18 \(\upmu\)m standard CMOS process to demonstrate the proposed method. The chip microphotograph is shown in Figure 16, in which the core area is 1.118 mm \(\times\) 2.13 mm.  
Figure 16: Chip micrograph of SAR ADC and RDFT.  
Figure 14: Chip micrograph of the multi-mode SAR ADC.  
Figure 15: Measurements (**a**) without and (**b**) with calibration.  
Figure 17a,b show the output power spectrum with and without calibration under a 1 kHz and 1 V\({}_{\text{P-P}}\) input signal with an 18.75 kHz sampling rate. According to the measurement results, the third harmonic distortions without and with calibration are \(-57.23\) dB and \(-74.15\) dB, respectively. The SNDR can be improved from 55.07 dB to 61.35 dB. Figure 18 shows the differential nonlinearity (DNL) and integral nonlinearity (INL) measurement results for this work. The INL is improved from \(+8.3/-7.2\) least significant bit (LSB) to \(+4/-2.9\) LSB. This calibration method enhances INL but not DNL because this method is only adopted using the RDFT processor to evaluate the real radives, and thus the DNL has no significant change. The calibration procedure is also tested with different testing chips (Figure 19). It is easy to observe that the performance of the ENOB  
Table 1 compares the proposed method and other state-of-the-art ADCs. Although the proposed method does not have a better performance than that in Reference [12], it still exhibits better power consumption and area improvement. In addition, the performance of the split-capacitive digital-to-analog converter (CDAC) method with 0.9 bit improvement is not as good as that of our proposed method.
# High-Resolution Radio Observations of Five Optically Selected Type 2 [PERSON]
## 3 Observational Data
### VLA Sky Survey Data  
We obtained the raw 8.4-GHz VLA data analyzed by [PERSON] [11] from the NRAO Archive ([[http://archive.nrao.edu/](http://archive.nrao.edu/)]([http://archive.nrao.edu/](http://archive.nrao.edu/))) (project code AV288) and calibrated them in a standard way using the NRAO AIPS software ([[http://www.aips.nrao.edu/index.shtml](http://www.aips.nrao.edu/index.shtml)]([http://www.aips.nrao.edu/index.shtml](http://www.aips.nrao.edu/index.shtml))), but failed to reproduce the images of our five target sources. To obtain independent information on the overall radio spectrum of the sources, we then turned to the publicly available preliminary radio images from the ongoing VLASS [15] at 2.7 GHz and compared the obtained flux densities to the 1.4-GHz values in the FIRST survey catalogue [31]. The VLASS supersedes previous VLA radio sky surveys at GHz frequencies such as the NVSS [32] in terms of angular resolution and sensitivity. In 2019, the first phase of the survey was completed with covering the entire sky north of \(-40^{\circ}\) declination at the 2-4 GHz frequency range in total intensity (Stokes I). The angular resolution is 2.5\({}^{\prime\prime}\) and the imaging sensitivity is \(\sigma\sim\) 120 \(\upmu\)Jy beam\({}^{-1}\) at the first survey epoch. Later, with the planned three epochs combined, the sensitivity should reach \(\sim\)70 \(\upmu\)Jy beam\({}^{-1}\). All our target sources fall into the VLASS coverage area. Quick Look images with \(1^{\circ}\times 1^{\circ}\) field size are available in the VLASS archive ([[https://archive-new.nrao.edu/vlass/](https://archive-new.nrao.edu/vlass/)]([https://archive-new.nrao.edu/vlass/](https://archive-new.nrao.edu/vlass/))). Five tiles (T18t11, T25t08, T11t16, T10t16, and T11t23) were selected based on the celestial coordinates, corresponding to our targets in the order of their appearance in Table 1. The Quick Look images are capable of revealing arcsec-scale morphological features of radio galaxies like radio cores and jets [33]; however, they may suffer from deconvolution issues due to the extremely short integrations and resultant \((u,v)\) coverage.
# Multi-Device to Multi-Device (MD2 MD) Content-Centric Networking Based on Multi-RAT Device
## 3 Proposed Method
### Scenario for the Proposed Method  
In this section, scenarios that can be utilized in an MD2 MD CCN environment are described. Scenarios consist of MD2 MD content delivery and network configuration between mobile devices.
# A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices
## 3 Vulnerability Analysis, Discovery, Detection, and Mitigation
### Research on Vulnerability Detection
#### 3.3.2 Similarity Detection Method
However, the firmware of IoT devices is highly heterogeneous, including multiple architectures such as MIPS, ARM, PPC, x86, and so on. Their opcodes, register names, and memory addressing methods are different. Thus, the above methods are difficult to be applied to cross-architecture code vulnerability detection on a large scale. Until the last two or three years, researchers began to study the issues of cross-architecture similarity detection based on binary code [88; 89; 90]. _Multi-MH_[88] is the first proposed binary-based method of similarity detection for cross-architecture. Above all, the binary code is converted into intermediate code. Then, this method uses specific input to test the program and captures the semantics of the base block based on the behavior of I/O. Finally, it adopts captured CFG to detect vulnerabilities. However, its performance overhead is too expensive in the face of a large set of functions. _DiscovRE_[89] checks whether the CFG of a set of function pairs is similar through the graph matching algorithm and accelerates CFG matching by pre-filtering. However, its pre-filtering process is not reliable and leads to too many under-reporting of vulnerabilities. _BinGo_[90] captures complete functional semantics by introducing selective inline related library functions and user-defined functions for cross-platform code search. However, it is not designed specifically for IoT devices. The _Genius_[91] uses a traditional method of machine learning to learn high-level feature representations from CFG. In addition, it encodes graph embedding [92] as a high-dimensional numerical feature vector. Then, the graph matching algorithm is used to measure the similarity between the objective function and a set of function binaries, which can effectively improve the performance and scalability. [PERSON] et al. [93] first adopt the deep learning method for cross-platform similarity detection on binary code, which is graph embedding technology based on the neural network model. In cross-version code similarity detection, _aDiff_[94] has taken an important step. It extracts three semantic features, including function, inter-function, and inter-module features, to detect based on the Deep Neural Networks (DNN) model. [PERSON] et al. also proposed _VulSeeker_[95] and _VulSeeker-Pro_[96], those vulnerability search methods combined with a deep learning model to improve the accuracy of vulnerability detection. These two methods were verified to be more accurate than existing methods such as _Gemini_[93].
# Data Privacy in the Internet of Things: A Perspective of Personal Data Store-Based Approaches
## 4 PDS-Based Solution for IoT Privacy
### System Model
#### 4.1.1 Personal Data Store Definition  
Users' perceptions of the value of their data has been changing over the years, driving a movement toward more control. Despite the 'privacy paradox', users are increasingly concerned about exchanging personal data for services. It has become more apparent to them that _\"if the service you receive is free, then you must be the product\"_[51]. Furthermore, the services provided may not be sufficient to tolerate the risks associated with indiscriminate collection, storage, and personal data processing. This awareness has been pushing users' desire to regain control over their data for protection and explore opportunities to monetize them through trading and selling [56].  
These desires led to the emergence of a user-centric paradigm for exploring the value of personal data. The idea encompasses the principles of _\"transparency regarding what data is captured, control over how it is shared, trust in how others use it and value attributable because of it\"_[50]. This approach acknowledges the crucial role of users in generating and exchanging value. They act as owners and need to have control over their data.  
In this context, the Personal Data Store (PDS) is a user-centric approach serving as a private data repository, addressing user data control and privacy concerns through decentralized data processing. The PDS denotes a paradigm change in the relationship between individuals and service providers, placing them at the forefront. It refers to a secure and private repository dedicated to managing user's data, enabling individuals to store, manage, and share personal data and digital assets while controlling who can access and utilize them. In essence, third parties cannot have permission to access, extract, or perform any computation on the data without the user's consent. Furthermore, the user'sPDS can process data in line with their agreement and, for example, over specific pieces of data. In this way, service providers receive results that have the potential to be less personal, less sensitive, less invasive, and potentially more valuable [54; 57].  
The primary goal of the PDS is to enable users to maintain sovereignty over their personal information, promoting data privacy, security, portability [3; 58], and even granting individuals the right to create economic and social value [59]. This decentralization underscores the importance of user choice, empowering individuals to determine the destiny of their data [54].  
A PDS platform can be a cloud-based storage solution, with APIs as an intermediate layer and an access point to web-based technologies. Alternatively, it can be a local-based storage solution requiring users to have a physical device to maintain their data and enable access through APIs [55].
## 4 Model Formulation
### Cost in a Renewal Cycle  
The proposed system describes the joint optimization problem of production and maintenance, taking into account possible costs, including setting cost \(C_{Set}\), inventory cost \(C_{I}\), repair cost \(C_{R}\), shortage cost \(C_{S}\), punishment cost \(C_{P}\), preventive maintenance cost \(C_{PM}\), and corrective maintenance cost \(C_{CM}\). The setting cost \(c_{Set}\) is a constant. For any maintenance event \(A_{k}\), let \(U_{PM}(A_{k})\) be the set of preventive maintenance units and \(U_{CM}(A_{k})\) be the set of corrective maintenance units. Figure 5 shows the state of each unit when the system does not need maintenance. The punishment cost \(c_{P}\) exists when unit 2 or unit 3 exceeds the failure threshold, and the system does not need maintenance. In this case, although the system has no fault, unit 2 or unit 3 fails, and another unit without faults in parallel may need to undertake additional work, which may cause losses. The existing costs are calculated below.  
* inventory cost \(C_{I}\) According to the change of inventory level in the cycle described in Figure 4, the expected inventory level in a cycle can be represented by the area of the triangle in the inventory graph. The inventory cost \(C_{I}\) can be described as \[C_{I}=c_{I}\times\frac{Q^{2}(p_{r}-d_{r})}{2p_{r}d_{r}}\] (16)
* repair cost \(C_{R}\) The number of repairable unqualified products is obtained by Equation (6), and the repair cost can be described as \[C_{R}=c_{R}\times\theta_{2}\times Q\times\int_{0}^{t_{n}}p(x)dt\] (17)
* shortage cost \(C_{S}\) The shortage cost is generated only when the system is maintained, and it is related to the maintenance time. The calculation is divided into two parts.  
The first is that the shortage occurs when the system needs preventive maintenance, and the other is when the system needs corrective maintenance. The shortage time is shown as Equations (13) and (14), and the shortage cost \(C_{S}\) of event \(A_{k}\) can be described as  
\[C_{S}(A_{k})=c_{S}\times d_{r}\times(\sum_{i\in U_{PM}(A_{k})}t_{spm}^{(i)}+ \sum_{i\in U_{CM}(A_{k})}t_{scm}^{(i)}) \tag{18}\]  
* Only when the one of unit 2 or unit 3 exceeds failure threshold does the punishment cost exists. The punishment cost \(C_{P}\) of event \(A_{k}\): \[C_{P}(A_{k})=\left\{\begin{array}{l}c_{P},A_{k}=A_{11}\\ 0,else\end{array}\right.\] (19)
* Preventive maintenance cost \(C_{PM}\) of event \(A_{k}\): \[C_{PM}(A_{k})=\sum_{i\in U_{PM}(A_{k})}c_{PM}^{(i)}\] (20)
* Corrective maintenance cost \(C_{CM}\) of event \(A_{k}\): \[C_{CM}(A_{k})=\sum_{i\in U_{CM}(A_{k})}c_{CM}^{(i)}\] (21)  
The expected cost of event \(A_{k}\) can be described as:  
\[E(C|A_{k})=c_{Set}+C_{I}+C_{R}+C_{S}(A_{k})+C_{P}(A_{k})+C_{PM}(A_{k})+C_{CM}( A_{k}) \tag{22}\]