esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The study of the effect of the underlying mechanism on the formation of a single protein is a key topic in the study of the formation of a single protein. The study of the formation of a single protein is a key topic in the study of the formation of a single protein.  
## 1 Introduction  
Periodontitis is a serious and persistent inflammatory illness that affects approximately 20% to 50% of the world's population [1]. The consequences of individuals that suffered from periodontitis not only have a high risk of losing their teeth but also tend to develop certain systemic diseases like cardiovascular diseases and diabetes [1; 2]. According to several studies, _Porphyromonas gingivalis_ has also been linked to the progression of the illness, such as Alzheimer's disease [3], rheumatoid arthritis [4; 5], and cardiovascular diseases [6; 7].  
_P. gingivalis_ is an anaerobic, black-pigmented oral pathogen with outer lipopolysaccharide-charides and fimbiae that only colonize the subgingival area of humans [8; 9]. These bacteria are closely linked to the development and progression of periodontitis [2; 10; 11]. When _P. gingivalis_ invade and colonize subgingival areas of humans, they will tend to produce a complex bacterial biofilm with other oral bacteria and resulting in long-lasting inflammation, loss of gingival connective tissues, and resorption of alveolar bone [12; 13; 14].  
Three forms of cysteine proteases termed gingipains are produced by _P. gingivalis_, which are crucial for their pathogenesis, retrieval of amino acid and heme from the host, and maturation of their fimbiae [15; 16]. Gingipains are the key proteases that are involved in bacterial proteolytic activity [2; 17]. There are several mechanisms are proposed to be employed by gingipains to help this oral bacterium to invade humans [18; 19]. Gingipains are subdivided into two types, arginine (Rgp) and lysine gingipains (Kgp). RgpA (\(\sim\)95 kDa) is made up of one catalytic and several adhesion/hemagglutinin domains, which bonded non-covalently to form a complex protein [15; 16]. RgpB (\(\sim\)48 kDa) comprises only the catalytic domain [15; 16]. Kgp (\(\sim\)105 kDa) is similar to RgpA also consists of one catalytic and three to four adhesion/hemagglutinin domains, which differ between the strains of _P. gingivalis_[15; 16].  
A few chromatographic techniques are employed to separate the gingipains into their constituent parts in order to isolate and analyze them [20; 21; 22]. Besides that, cloning approach also be employed to produce gingipain of interest [17; 23; 24]. Nonetheless, these approaches are laborious, costly, and impracticable especially when a quick check of gingipains activity is required. In this study, relatively simpler, easier, and quick methods to isolate and characterize gingipains using passive diffusion-mediated gel elution and gelatin zymogram were demonstrated.
# Acoustic Localization for a Moving Source Based on Cross Array Azimuth
## 4 Experiments
### Experiment Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Length** & **Actual Coordinates** & **Test Coordinates** \\ \hline
30 & & (18.24, 52.31) \\
60 & & (6.79, 7.34) \\
65 & & (\(-\)24.36, 92.95) \\
70 & & (\(-\)24.35, 92.70) \\
100 & & (\(-\)24.20, 92.80) \\
300 & & (\(-\)24.07, 92.17) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Locating result of different lengths. Figure 12: Correlation performance of different lengths. Table 2 illustrates that localization has failed with a rather high error when the length of the signal involved in the correlation was \(n\leq 60\). When \(n\geq 65\), the test point was located nearly around the actual point. Therefore, the length of the signal extraction was assigned as \(n=65\), participating the calculation after interpolation. All the locating results are summarized in Table 3, and the moving paths are shown in Figure 13.  
Both Table 3 and Figure 13 depict that the discrete points along the moving path were obtained accurately in the localization experiment with only small associated error. According to Section 3.2, after compensation was considered, the relative locating error distribution is shown in Figure 14 before and after fixing. The lines in Figure 14 represent the original error of the localization, while dotted lines for error after fixing. To some extent, the locating result improved. In summary, the accurate localization  
Figure 14: Distribution of relative error. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Actual Coordinates**} & \multicolumn{2}{c}{**Test Coordinates/m**} \\ \cline{3-4}  & & **A\(\rightarrow\)B** & **B\(\rightarrow\)A** \\ \hline \(1\) & (\(-23.56\), \(97.92\)) & (\(-24.01\), \(92.82\)) & (\(-23.65\), \(98.29\)) \\ \(2\) & (\(-18.33\), \(97.92\)) & (\(-19.44\), \(94.33\)) & (\(-18.24\), \(96.86\)) \\ \(3\) & (\(-13.09\), \(97.92\)) & (\(-14.29\), \(94.62\)) & (\(-15.85\), \(99.70\)) \\ \(4\) & (\(-7.85\), \(97.92\)) & (\(-9.75\), \(98.12\)) & (\(-9.55\), \(98.38\)) \\ \(5\) & (\(-2.62\), \(97.92\)) & (\(-4.58\), \(101.67\)) & (\(-3.38\), \(98.15\)) \\ \(6\) & (\(2.62\), \(97.92\)) & (\(1.33\), \(98.53\)) & (\(2.81\), \(96.97\)) \\ \(7\) & (\(7.85\), \(97.92\)) & (\(6.39\), \(96.68\)) & (\(8.70\), \(97.28\)) \\ \(8\) & (\(13.09\), \(97.92\)) & (\(11.06\), \(93.80\)) & (\(13.68\), \(95.85\)) \\ \(9\) & (\(18.33\), \(97.92\)) & (\(16.61\), \(95.15\)) & (\(19.27\), \(96.00\)) \\ \(10\) & (\(23.56\), \(97.92\)) & (\(21.58\), \(94.49\)) & (\(25.04\), \(95.39\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Locating result of moving source. Figure 13: Actual and tested path of source. of a moving acoustic source was achieved and the error stayed below 5%. In further application, the complete moving path could be obtained by increasing the number of parts divided.
## 5 Probe Automatic Switching Scheme  
### Three Optically Pumped Probe Structure Design  
Regarding the working principle of the cesium optically pumped probe, its measurement range for the incident angle of the external magnetic field is 15\({}^{\circ}\) to 75\({}^{\circ}\) and 105\({}^{\circ}\) to 165\({}^{\circ}\), with dead zones (i.e., loss of lock ranges) in the polar direction from 10\({}^{\circ}\) to 15\({}^{\circ}\) and 165\({}^{\circ}\) to 180\({}^{\circ}\) and in the equatorial direction from 80\({}^{\circ}\) to 100\({}^{\circ}\). Therefore, a single cesium optically pumped probe, when in operation, risks entering the dead zone as the heading of the submersible or the geomagnetic azimuth of the area to be measured changes. A combination of three optically pumped probes is used to achieve the requirement for the magnetic measurement system to work continuously without loss of lock in any heading and any attitude. By adjusting the installation angle relationships among multiple optically pumped probes, their working angles complement each other. Automatic probe switching and correction algorithms are then used to ensure that the combination of cesium optically pumped probes can operate continuously and normally at any geomagnetic field angle. The three-dimensional dimensions of the optically pumped probe assembly are shown in Figure 14, and the three-dimensional diagram is shown in Figure 15.  
Figure 14: The dimensions of the optical probe unit (unit: mm).  
Figure 13: A schematic diagram of the detection circuit.  
Figure 15: A three-dimensional diagram of the probe assembly.
# Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities
## 3 State of the Art of Practice and Research
### Current Practice and Research
#### 3.1.2 Future Logistics--Inventory Management  
Transportation and logistics systems are highly interdependent. Good transportation infrastructures affect the success of global logistics activities. An efficient logistics system leads to a smooth day-to-day traffic environment and may promote other transportation development [73]. A primary area in the logistics sector where UAVs are emerging as the key element to improve the efficiency of the overall supply chain is inventory management.  
Warehouse inventory management is an essential part of the supply chain and logistics system. Inventory management in industrial warehouses is often complicated, where multiple identical tall racks full of goods need to be navigated around for material procurement. This task is often time-consuming and can even lead to workers' injuries due to the height of the racks and the weight of the goods procured. These factors also result in a high cost of operations with low turnouts. Automating inventory management systems with the help of UAVs can save a lot of time, cut costs significantly, and prevent worker injuries. UAVs can be incorporated with unmanned ground vehicles (UGVs) for a fully automated inventory management system, as shown in Figure 6.  
Figure 6: Autonomous warehouse inventory global architecture [63].  
[PERSON] et al. presented an autonomous warehouse inventory management scheme in which a UGV and a UAV worked together controlled by a ground station [63]. In the proposed methodology, the UGV navigated the UAV to the inventory racks, and the UAV scanned the inventory marker for information collection. [PERSON] et al. described the design and testing of UAVs using Radio-Frequency Identification (RFID) for scanning warehouse inventory [64]. The inventory data obtained by the UAV was proposed to be received using blockchain technology. [PERSON] et al. investigated the inventory in an outdoor storage yard using UAV and RFIDs and implemented a prototype system to demonstrate the feasibility of the approach [65].  
An example of a real-life case scenario where warehouse inventory management is done with the help of UAVs is a German company, Doks Innovation, which uses UAVs for automated warehouse management and inventory maintenance. The UAVs can find items in warehouses and outside areas and monitor barcodes and RFID tags, allowing them to be incorporated into warehouse administration systems, saving time and money, and minimizing errors.  
An efficient warehouse inventory system leads to timely dispatch of goods and products to customers, which might help reduce traffic congestion due to delayed times and minimize traffic accidents. The next step in logistics from warehouse locations to customers is product delivery, which is another arena where UAVs are slowly establishing themselves as essential players in this field. The following sub-section will provide an overview of the current use of UAVs for delivery services in various industries.
# Opportunistic Multi-Technology Cooperative Scheme and UAV Relaying for Network Disaster Recovery
## 7 Open Challenges and Future Directions
### Survivor and Rescuer MOBIILITY  
We have assumed that survivors have a low mobility since they could be wounded and buried under rubble. Our scheme periodically re-computes cliques and time to serve as a representative at each communication layer and thus is assumed to be reliable to faster mobility schemes but this should be better investigated. In addition, as in our approach, all nodes are not all active at the same time, rescuers or drones can be in range of a survivor at a given time but not of its representative during this period and thus messages can be missed.
# Toward an SDN-Based Web Application Firewall: Defending against SQL Injection Attacks
## 6 Implementation
### SQL Injection Detection
#### 6.2.1 Module 1: Signature-Based  
This module aims to demonstrate the usability of signature-based detection systems within the scope of the SDN, without the need for intermediate software or hardware provided by vendors. Several common signatures were selected for detecting SQL injection attacks, as shown in Table 1. This module relies on storing the signatures in a list and implementing the first two stages, namely reception and processing. Subsequently, these signatures are passed into a for loop to match them with the traffic directed to the web server. Algorithm 1 summarizes the mechanism of this module. Note that, in practice, web servers can run on non-standard ports. In our work, we considered the standard port 80 for the HTTP protocol. This assumption was made for simplicity and to align with common practices.
## 2 Results
### Binding of ppGpp and cAMP to H-NS  
E. coli H-NS also showed an intrinsic fluorescence when excited at 280 nm, and the fluorescence intensity decreased in the presence of ppGpp (Figure 3a). The sole tryptophan residue at the position 109 might be responsible for the fluorescence of H-NS. The fluorescence changes were again utilized in determining the dissociation constants (Figure 3b). As shown in Table 2, the dissociation constants of ppGpp and cAMP to H-NS were \(80\pm 11\) uM and \(270\pm 40\) uM, respectively. ATP binding did not show a saturation curve, indicating a high \(K_{D}\) value (Table 3 and Supplementary Figure S2). We compared in vitro transcription in the presence and absence of ppGpp and found no changes with ppGpp (data not shown). The effect of ppGpp binding with H-NS is not clear at this point.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Binding Pocket Residue** & **1g6n (cAMP Form)** & **1n9i (cGMP Form)** \\ \hline Q80 & \(-9.90\) & \(-10.53\) \\ E81 & \(-7.07\) & \(-10.43\) \\ R82 & \(-9.42\) & \(-13.48\) \\ S83 & \(-10.05\) & \(-14.01\) \\ A84 & \(-10.33\) & \(-12.60\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The docking parameters.  
Figure 2: Interaction of ppGpp with CRP. (**a**) The ppGpp-binding site proposed by a molecular modeling study. The interactions between ppGpp and CRP are shown as dotted lines. (**b**) Interacting residues at the binding site. G71, R82, and S83 are commonly interacting amino acid residues in cAMP, cGMP and ppGpp-bound CRP.
# Visual Analysis of Spatiotemporal Data Predictions with Deep Learning Models
## 5 System Evaluation with Air Pollutant Prediction Models
### Analysis Based on Correlation and Time Lag Settings at Initial State  
First of all, the correlations between variables can be identified in the scatter plot matrix in (a). The scatter plot shows the features that correlate strongly with the PM\({}_{2.5}\) that we attempt to predict. The Pearson correlation coefficient between PM\({}_{2.5}\) and PM\({}_{10}\) is close to 1, and the scatter plot shows a strong linear correlation, which confirms that PM\({}_{10}\) has the highest correlation with PM\({}_{2.5}\). Therefore, we can attempt to predict PM\({}_{2.5}\) by inserting PM\({}_{2.5}\) and PM\({}_{10}\) features together in the GRU network and the LSTM network. Our system supports three time lags as an input time range, including 6, 24, and 72 h. The results are summarized in Table 1. Overall, it is difficult to tell that all six network models have good predictive performance. Note that we observe the high correlation between PM\({}_{2.5}\) and PM\({}_{10}\) within our data, and this is also reported in the study by [PERSON] et al. [54].  
Now, we compare the model performance with different time lags. In both GRU and LSTM networks, when only the parameters of PM\({}_{2.5}\) and PM\({}_{10}\) are selected, setting the time lag to 6 h produces lower MAPE than 24 or 72 h. Since the visualization shown in Figure 2 is proposed to set an appropriate time lag, we check that the autocorrelation of each variable changes according to the time lag. We observe the temporal autocorrelation graphs of PM\({}_{2.5}\) and PM\({}_{10}\) in Figure 2 to infer the cause for these results. Since the temporal autocorrelation of PM\({}_{2.5}\) and PM\({}_{10}\) has a major decreasing trend, we can interpret it as the accuracy for a long time lag tends to decrease. In other words, when only two variables are used, including much data from a past time, it may degrade the prediction performance. We can try two approaches to improve the performance of the GRU and LSTM. First,  
Figure 1: Our visualization system for analyzing deep learning models. (**a**) is the scatterplot of the correlation and probability distribution between input variables. (**b**) shows spatial autocorrelation (Moran’s I) of the selected variable. (**c**) presents line density map with temporal autocorrelation. The Sankey diagram supports the modeling of the spatiotemporal prediction by combining features, deep learning models, and interpolation models in (**d**). (**e**) presents our prediction modeling parameter settings. (**f**) presents interpolated predictions with the nearest neighbor algorithm. (**g**) shows the observed data. (**h**) presents the errors between the observed data and predictions. (**i**) shows the standard deviation of prediction over time. (**j**) presents the LISA visualization. (**k**) The box plots show temporal predictions with the actual observed values.  
the models are fixed with GRU and LSTM and features are reselected for the training. Second, we fix the selected features and apply another model, such as the ConvLSTM.
## 5 Case Study: Detecting Fault-Prone Software Requirements Specification for Edge/Cloud Application  
### The Evolution of Edge/Cloud Computing  
Cloud computing has evolved significantly since the concept emerged in the 1990s. In the 2000s, the use of cloud computing continued to grow with the introduction of new services, such as IaaS and PaaS. In the 2010s, edge computing gained traction with the rise of IoT devices and the need for faster data processing at the edge [26]. Fog computing and open-source platforms for edge computing also emerged during this time. Hybrid cloud solutions and containerization with Kubernetes became key technologies for managing cloud applications in the late 2010s. In 2020, the COVID-19 pandemic led to a surge in demand for cloud services, and serverless computing became more widely adopted [27]. In 2021, AI and ML technologies in cloud services continued to grow, and edge computing saw increased adoption. In 2022, cloud providers focused on making their services more accessible to smaller businesses and individuals while also increasing cloud-based security solutions. Finally, in 2023, quantum computing technologies and blockchain technology are expected to have an impact on cloud computing, with cloud providers offering quantum computing services and blockchain-as-a-service solutions. Figure 9 illustrates the evolution of edge/cloud computing.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **SRS** & **Precision** & **Recall** & **F-Measure** \\ \hline AdaBoost (without proposed features) & Fault-prone & 0.9200 & 0.9700 & 0.9400 \\  & Clean & 0.9200 & 0.8000 & 0.8600 \\ \hline GBM (without proposed features) & Fault-prone & 0.8000 & 0.8600 & 0.8300 \\  & Clean & 0.9400 & 0.9200 & 0.9300 \\ \hline XGBoost (without proposed features) & Fault-prone & 0.9500 & 0.9700 & **0.9600** \\  & Clean & 0.9200 & 0.8500 & 0.8800 \\ \hline AdaBoost (with proposed features) & Fault-prone & 0.9100 & 0.8300 & 0.8700 \\  & Clean & 0.9500 & 0.9700 & 0.9600 \\ \hline GBM (with proposed features) & Fault-prone & **1.0000** & 0.7500 & 0.8600 \\  & Clean & 0.8900 & **1.0000** & 0.9400 \\ \hline XGBoost (with proposed features) & Fault-prone & 0.9800 & **1.0000** & 0.9500 \\  & Clean & **1.000** & 0.9700 & **0.9900** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance for detecting fault-prone SRS.
# An Effective Off-Grid DOA Estimation Algorithm Using Difference Coarrays with Limited Snapshots
## 5 Simulation Results  
Through rigorous simulation studies, this section provides an in-depth evaluation of the proposed algorithm's performance and effectiveness. Specifically, the performance of the proposed method for ULA and NLA configurations is thoroughly analyzed in Sections 5.1-5.4, particularly focusing on multi-source scenarios and varying snapshot conditions. The number of snapshots is set to 10 for the low-snapshot case and 100 for the high-snapshot case. Further, the resolution and the convergence performance of the proposed method are provided in Sections 5.5 and 5.6, respectively. The simulation platform consists of an Intel i7-6700 processor with 16 GB RAM running a 64-bit operating system. The RMSE metrics are computed through 200 independent Monte Carlo trials, with the corresponding CRBs provided as theoretical performance benchmarks.
## 3 Risk Assessment System Development
### Technical Framework  
The system adopts a layered design, which is divided into four layers: the data access layer, logic layer, control layer, and the data presentation layer. The overall technical framework is shown in Figure 5.  
The presentation layer is intended for the experts and on-site workers in the steel plant and is responsible for processing the user interface, collecting user data, and displaying data to the user. The user interface is the frontend of a Web application, and generally uses HTML and CSS for page layout, as well as JavaScript to achieve page data processing. In order to improve development efficiency, we used a jQuery-based Easy UI plug-in for development and Ajax technology to achieve partial refreshing of the page.  
In both the business and presentation layers, the control layer acts as a bridge and plays the function of forwarding requests in the system. The control layer is responsible for intercepting the user requests, encapsulating the user data, and passing it to the business logic layer. The business logic layer then performs corresponding processing, according to the user's request, and returns the result to the client. The entire request processing uses the MVC model for control.  
The business logic layer is the core of the Web application, enabling business logic to be realized. The requested data is sent through the control layer, received by the business logic layer, and then corresponding logic processing is performed to return the result to the control layer. It can also provide manipulated data for the data layer, as well as receive the result returned by the data layer.  
Realizing interaction with the database is the core function of the data access layer. We selected Tomcat for database management. The data layer provides basic creation, query, and deletion operations for data objects, which correspond to associated operations in the database, such as queries, modifications, deletions, and additions. These operations enable users to continue accessing the database and ensure access to related services through the business logic layer.  
As introduced above, in the calculation of the fuzzy comprehensive evaluation, the implementation process in RAS is summarized in Algorithm 1:  
Figure 5: Technical framework of the risk assessment system.  
```
0: statistical vector \(\mathsf{X}\)= (\(X_{1}\), \(X_{2}\),..., \(X_{n}\)) of all the indices
0: evaluation vector \(\mathsf{F}\)  Build the fuzzy membership matrix \(\mathsf{R}\) for each \(i\in[1,n]\)do for each \(j\in[1,m]\)do \(r_{ij}=p_{ij}/\sum_{j=1}^{m}p_{ij}\) endfor endfor  calculate the weight vector \(\mathsf{A}\) of EWM  Build the ratio matrix \(\mathsf{X}\) for each \(i\in[1,n]\)do \(x_{i}=u_{i}/u_{i-1}\) endfor  calculate the weight vector \(\mathsf{B}\) of ORM  calculate the comprehensive weight vector \(\mathsf{W}\) of CWM  calculate the comprehensive evaluation vector \(\mathsf{F}\)
```  
**Algorithm 1** General process of fuzzy comprehensive evaluation in the risk assessment system