esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Short-Term Traffic Flow Prediction Model Construction
### Association Rule-SVR Model  
To enhance the precision of short-term traffic flow forecasting accuracy and to improve the prediction accuracy for branch roads with small traffic flow, we constructed a targeted association rule-SVR model. The model demonstrated high prediction accuracy in traffic flow prediction. The overall structural framework is shown in Figure 11. Utilizing the developed model, we are able to more effectively forecast the immediate traffic patterns on minor arteries with less traffic, thereby enhancing the comprehensive accuracy of predictions.  
Figure 11: The structure of the association rule–SVR model.  
Figure 10: Error variation curves with 95% confidence intervals.  
The detailed procedure for the integrated association rule and SVR model in forecasting short-term traffic flow is outlined below:  
1. Preprocessing of collected data such as road information, traffic information, and weather information;
2. Converting road data into attributes based on traffic flow information, congested, more congested, severely congested, smooth, and smoother, and perform road correlation analyses to identify strongly correlated roads;
3. The road traffic flow data associated with the target branch road and the road characteristics are used as input vectors of the SVR model, and then, the input parameters of the SVR model, such as the kernel function \(\gamma\), the penalty factor C, etc., are set to establish the SVR model, and finally, the predicted value of the traffic flow is used as the output.
# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 5 Discussion  
### Analysis Based on the Method Used  
#### 5.1.1 CNN  
Some findings can be drawn from reviewing the CNN models:  
1. According to the datasets used, 6 papers used a combination of technical analysis, and sentiment and news analysis to predict the stock. The rest of them used the method of technical analysis only.
2. For the variables, the closing price was the choice of all CNN models, and five papers used closing price only.
3. It could be found that 12 of the papers changed the traditional CNN model to pursue higher performances in prediction. The combination of CNN and LSTM was the most common model.
4. The metrics used in each paper were different; there were 11 metrics used for measuring the performance of the CNN model.
5. Multiple articles selected RMSE, return rate, F-measure, Sharpe ratio, and accuracy. We found that paper [22] had the highest accuracy compared to any other papers, paper [16] had the highest return rate followed by papers [14; 18; 21]. Paper [24; 31] had the lowest RMSE. Paper [19] had a higher F-measure than that of papers [14; 18], but paper [18] achieved a higher Sharpe ratio than that of paper [14].
# Local Government Cybersecurity Landscape: A Systematic Review and Conceptual Framework
## 4 Analysis and Results
### Data Landscape within Local Governments  
Regardless of their size and geographical setting, local governments store and manage a wide array of data, influencing policy execution, decision-making, and community engagement. This review identified 20 different types of data that local governments typically store and manage. Figure 6 presents the data that local governments typically store and manage.  
Figure 6: Classification of data that local governments store and manage.  
Depending on their functionality, we can classify these data into four broad categories: (a) individual-centric data focus predominantly on data that belong to residents, including personal information [7; 11; 53], financial information [13; 65], medical records [35; 66], education records [19], and utility usage data [11; 67]; (b) public safety and governance data refer to the data that are crucial for governmental operations and public welfare, such as surveillance and monitoring data, public safety data, election and voter information, vendor and procurement data, and employee and contractor data; (c) infrastructure and utility data are those that provide insights for traffic and public transportation patterns, infrastructure management, utility consumption, property records, waste management, and IoT device data; and (d) community and environment data refer to data such as recreation and public events, chats with residents and between residents and local governments officials, public feedback and suggestions sent through an online medium, environmental data such as pollution data, air quality data, weather data, water quality data, and other environmental monitoring and historical data.
## 2 Materials and Methods
### Dimensionality Reduction  
Upon creating the training datasets for both parameters, which include the variables listed in Table 3, these datasets were inputted into a dimensionality reduction algorithm to identify the most relevant parameters for the investigation.  
This was necessary as integrating irrelevant parameters may lead to complex models that present considerable challenges in both interpretation and execution, in contrast to models constructed with the most essential parameters. To identify the most significant dimensions or parameters in the input space, Principal Component Analysis (PCA) was employed. PCA is a multivariate statistical technique aimed at consolidating information from multiple variables observed on the same subjects into fewer variables [33]. Following the computation of the PCA, it was established that the principal dimensions essential for predicting SSS and SST encompassed the blue, green, red, and infrared spectral bands. Additionally, for SSS predictions, SST data was significant. Subsequently, the remaining dimensions were excluded from the datasets as they were deemed irrelevant to the present study.  
Figure 5: (**a**) SST satellite data in raster form. (**b**) SST satellite data in vector form superimposed on a satellite-derived image.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Variables in the Training Dataset** \\ \hline SST & 13 spectral bands, in situ SST \\ SSS & 13 spectral bands, in situ SSS, SST data \\ \hline \hline \end{tabular}
\end{table}
Table 3: Variables in each training dataset before dimensionality reduction.
# Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images
## 3 Results and Discussion
### Usability Verification of the Sharpened Image
Through the classification results using the sharpened image, when the SWIR bands are used with the VNIR bands, the superiority was confirmed in terms of image utilization, such as classification. In particular, this approach is considered to be appropriate for the classification of artificial structures, etc. for urban areas.
## 1 Introduction
### Related Work  
Spatio-temporal web data visualization often involves the use of the web GIS and various graphical representations of data with combined views of space and time [2]. Oceanographic data management relies on visualization for insight of both state and trends of the environment [3]. Visualization in a web environment also includes an interface (human-computer interaction), transfer of data to the client side, and user interactions [4]. Of additional importance for a successful web application is the time needed to collect the data and draw the scene (it should be short enough for smooth use) [5]. Relational databases are standard today as a source of data for general types of websites (content management systems). They are used for spatio-temporal visualization for the same reason: easy and fast query and extraction of subsets of data based on user queries [6].  
Traditionally, spatio-temporal (environmental) data are collected, validated, and stored in a file system using specialized software. Oceanographic data validation additionally involves setting the \"validation factor\" that describes the quality of the data. This flag should be set as a result of data visualisation by a local expert, and this information states that this value corresponds to the expected value range for a given parameter, location and time [7]. The increasing prevalence of relational databases has led to their use for managing environmental data as well [8]. Special client programs are used to access the database and visualize and validate the data. The user creates a data product from a subset of data and produces some images with maps and graphs. A schematic representation of this process can be found in Figure 1. The data product is a validated and visualized dataset tailored to the specific purpose. The result is a \"one-dimensional\" view of the dataset used. In the end, the result can be published as a web page or text document. Errors identified and corrected in this process are in most cases not returned to the database or data source, and for another data product, the analyses and validation of the data must be performed again.  
In the next section we will present the currently used tools for environmental spatio-temporal data visualization and outline their shortcomings.
## 5 Results and Analysis
### Root Mean Square Delay Spread  
The time delay between multiple reflected waves is another important parameter to consider while characterizing the multipath channel in underground mines. It is used to determine the frequency-based degradation in the erratic environment of mines, where the signal is facing a lot of deteriorating factors to disperse it into multiple paths. It restricts the data rate of the wireless link during the transmission of signals in mines. It is computed from the PDP expression derived in Equation (26) and the generic model is expressed in Equations (36)-(38) [29; 36]:  
\[\tau_{rms}=\sqrt{\overline{\tau^{2}}-\overline{\tau}^{2}} \tag{36}\] \[\overline{\tau}=\frac{\sum_{k}a_{k}^{2}\tau_{k}}{\sum_{k}a_{k}^{ 2}}=\frac{\sum_{k}p(\tau_{k})\tau_{k}}{\sum_{k}p(\tau_{k})}\] (37) \[\overline{\tau}^{2}=\frac{\sum_{k}a_{k}^{2}\tau_{k}^{2}}{\sum_{k} a_{k}^{2}}=\frac{\sum_{k}p(\tau_{k})\tau_{k}^{2}}{\sum_{k}p(\tau_{k})} \tag{38}\]  
where \(a_{k}\) is the gain coefficient, \(p(\tau_{k})\) is the PDP of the \(k^{th}\) multipath component and \(T_{k}\) is the delay coefficient.  
The RMS value is computed for mine sub-gallery where the link is classified into LOS and NLOS cases. The RMS delay spread is computed at multiple locations in the sub-gallery using the threshold level 10 dB. Figure 8 shows the graphical result and it can be observed that the value of RMS delay spread rises to a certain limit in the case of distant reception nodes and then it gradually starts decreasing. The maximum-to-minimum variation captured in RMS delay spread is 2.40 ns to 10 ns for the LOS link and 4.20 ns to 15.95 ns for the NLOS, respectively. The values of RMS delay spread are found to be higher for occupied mine galleries due to the existence of mining equipment and pillar structure. It indicates that the signal propagation over the NLOS link must face more spatial variation than the LOS link due to the presence of more scattering objects. The propagated waves take an elongated path due to the severe number of reflections caused by the mine environment, which will further enhance the delay at reception. The irregularity in the simulated graph depicts the unevenness of mine walls, which generates non-uniform reflected propagation.  
Figure 8: Root mean square delay spread response.  
Figure 7: Received power comparison between theoretical and experimental results (**a**) 2.4 GHz, (**b**) 5 GHz.
## 3 Results and Discussion
glutamicum_ was applied (CC/CS/Fc with Cg), the onset potential was shifted to a more negative potential (\(-0.36\) V) and a double peak potential originating from indirect electron transfer from the bacteria was observed at \(+0.3\) V and \(+0.5\) V, respectively. The co-presence of ferrocene and _C. glutamicum_ might increase oxidative peak current with double peak potentials originating from bacterial redox systems [23]. As compared with the maximum current density of the bare carbon cloth electrode with _C. glutamicum_ (\(261\) nA cm\({}^{-2}\) at \(+0.5\) V, Figure 2A), that of the ferrocene-branched chitosan redox-hydrogel anode with _C. glutamicum_ (CC/CS/Fc with Cg) was drastically increased to \(120\)\(\upmu\)A cm\({}^{-2}\) at \(+0.5\) V. Figure 6B plots the power density and polarization curves obtained in the _C. glutamicum_ MFC with the ferrocene-branched chitosan redox-hydrogel anode (CC/CS/Fc) using glucose as an electron donor. The MFC produced a maximum power density of \(3.55\pm 0.27\) W m\({}^{-2}\) (\(n=3\)) at a current density of \(35.2\) mA m\({}^{-2}\) after \(72\) h cultivation, which was higher than that for a _Corynebacterium_ strain MFC03 (\(41.8\) mW m\({}^{-2}\)) [6]. Biofilm formation by bacterial attachment to the electrode surface is essential for bioelectricity generation in MFCs [2]. In this regard, abundant functionalities such as polycationic and nucleophilic properties as well as the stereoscopic scaffold structure of chitosan hydrogel might allow for extensive colonization by electricigens (Figure 5D,E). Indeed, _Shewanella oneidensis_ MR-1, a well-known bacterium for bioelectricity generation, has shown the ability to directly colonize chitosan-carbon nanotube scaffolds [24; 25]. The ferrocene bound on the modified anode, meanwhile, could serve as a redox mediator for enhanced electron transfer from bacteria to electrode. Recently, the concept of anodic electro-fermentation has been proposed for anaerobic production of l-lysine by the _C. glutamicum_ strain, because it can rely on an anode, instead of oxygen, as a terminal electron acceptor. In the anodic reaction, electrochemical interaction of _C. glutamicum_ with the anode was enhanced by providing K\({}_{3}\)[Fe(CN)\({}_{6}\)] in electrolyte (cultivation medium) as a redox mediator with the anode at an applied potential of \(+0.697\) V (vs. SHE) [26]. This suggests that, in such an electro-fermentation process, ferrocene-branched chitosan hydrogel grafted onto the anode surface would also facilitate both electron transfer from _C. glutamicum_ to the anode and bioelectricity generation. Furthermore, [PERSON] and co-workers reported that metallic modification of electrode, for example, deposition of gold nanoparticles (50\(\sim\)500 nm-thickness) on to the carbon paper electrode, increased the power output of MFCs [27; 28]. Based on the results, an extension of our approach, electrode modification with redox-hydrogel, combined with high-capacitant and stereostructural materials would be another effective way to enhance electricity generation in bio-electrochemical systems.
## 4 Results and Discussion  
### Case Study  
The proposed HRES was utilized in a residential area located in a subject district in Shinchi-machi of Fukushima prefecture, Japan. The hourly load curves for a typical household located in the selected residential area is shown in Figure 6[41]. As can be observed from this figure, the first ramp takes place between 5:00 a.m. and 8:00 a.m. when people are starting to work in the morning, followed with the second ramp between 4:00 p.m. and 8:00 p.m. when people are getting back to their homes in the evening. Furthermore, the amount of electricity consumption is much higher during winter-time, since the demand for space heating increases. The average annual electricity demand in the selected household was estimated at 4.37 MWh.  
The ambient temperature and average global irradiation in Fukushima were collected from the Japan Meteorological Agency (JMA) throughout the entire period in 2019, which is shown in Figures 7 and 8[42]. From April to June, the global radiation had the highest value. In August, the global radiation decreased slightly. Because this region is affected by seasonal wind, the weather condition in this area, especially in summer, is variable.
## 3 Experimental Results and Discussion
### Particle Image Velocimetry for Characterizing the Flow Field
This consideration of the disturbance enhancement in the boundary layer with DBD-PA actuation is supported by the comparison results between the positive displacement for \(Re=1.9\)\(\times\)\(10^{5}\)-\(5.7\)\(\times\)\(10^{5}\) and the negligible displacement for \(Re=9.4\)\(\times\)\(10^{5}\) in \[K_{CL}=\frac{C_{L}}{C_{L_{NoDA}}}-1, \tag{3}\]3 The boundary layer at the leading edge of the wing is considered laminar in the former \(Re\) number conditions, whereas it is considered turbulent in the latter \(Re\) number conditions [39; 64]. Considering the relationship between these boundary layer conditions and the directions of displacement, and the well-known principle that the turbulent boundary layer is more resistant to flow separation than the laminar boundary layer, a laminar-turbulent transition of the boundary layer by plasma actuation occurs in the major region of the positive displacement in \[K_{CL}=\frac{C_{L}}{C_{L_{NoDA}}}-1, \tag{3}\]4  
Given these considerations, the second and third phenomena do not increase with plasma actuation at \(Re=9.4\)\(\times\)\(10^{5}\) if the boundary layer has already transitioned to a turbulent flow and grown sufficiently thick without plasma actuation.
## Part B: Network Design for Energy Optimization
### Our Contribution  
Our approach indeed tries to avoid the expensive, massive placement of all the time functioning sensors all over the monitoring area, _by exploiting possible overlaps of routes_ at certain places in the network area, using sensors that can simultaneously monitor pieces of several (\"nearby\") routes during their operation, or even exploit sensors on top of certain moving objects of our own that wander in the maze of routes. Thus, we somehow \"multiplex\" (both over space and time) the use of deployed sensors, since our approach identifies overlaps over space and time and thus deploys fewer sensors compared to the trivial approach.  
In fact, the analysis of our method shows that _it is very efficient_, especially in very large domains, both with respect to computational time and the deployment cost, since it finds in polynomial expected time a deployment solution which approximates the optimal solution within a logarithmic factor. Thus, we avoid expensive dense deployment of sensors, where the information about the target is simultaneously generated by multiple sensors; we instead achieve a low cost solution (by removing unnecessary redundancy) that still keeps tracking accuracy at high levels.  
To be able to handle overlaps, _we propose a novel combinatorial model for possible routes_ in the network domain. This model, although abstract, captures several of the technical specifications of real sensor devices, such as the energy spent as a function of the transmission range, the ability to vary this range to save energy or increase connectivity, the ability of sensors to employ power saving (sleep-awake) schemes to save energy _etc._ This combinatorial model allows us to reduce the tracking problem to a variation of a combinatorial problem of set covering (in particular to \"at least 3 cover\", i.e. having each point of the domain covered at any time by at least 3 sensors, and thus being localized). We feel that this combinatorial model is of independent interest and can (itself or its variations) be used in modeling other problems as well.
## 4 Materials and Methods
### Site-Directed Mutagenesis of the lapD Gene and In Vivo Complementation Analysis  
To identify amino acid residues and specific domains in LapD, PCR and Gibson cloning was used. Firstly, two sets of deletions of the _lapD_ gene were constructed using oligonucleotides for PCR amplification to clone only the region encoding either the soluble domain or removal of the coding region for the last 32 C-terminal amino acid residues. PCR products were cloned in the pCA24N vector where the expression is tightly regulated from P\({}_{\text{TS}}\)-_lac_ promoter and for the protein induction in T7 promoter-based pIVEX 4.2 vector (Roche diagnostics, Basel, Switzerland). Three specific sets of mutations (replacement of D69, Y70 and R71 with Ala substitutions in the coding region of the _lapD_ gene were introduced by using gene synthesis and Gibson cloning. Similarly, L84, L85 and P86 were substituted by three Ala, and in the third set, N93, P94 and F95 were also replaced with Ala residues. In the three resulting plasmid sets, the expression of mutated _lapD_ gene is regulated from P\({}_{\text{TS}}\)-_lac_ promoter. For in vivo complementation, \(\Delta\)_lapD_ and _lapC190_ mutant bacteria were transformed by empty vector and plasmid expressing the wild-type gene and where the _lapD_ gene is mutated. Transformants were plated on LA agar at 30 \({}^{\circ}\)C. Cultures of such transformed derivatives subsequently were grown to an exponential growth phase and growth properties measured by spot dilution assays on LA medium at 30 \({}^{\circ}\)C and 43.5 \({}^{\circ}\)C.
## 2 Research Framework  
This research is structured into four main sections: image capture, dry beach detection, length calculation, and verification and analysis. Figure 2 displays the study framework diagram.  
1. Image acquisition involved checking public information on tailing ponds from emergency management department websites in different provinces and cities in China, pinpointing the exact locations using the Gaofen-7 satellite, and downloading satellite images of the tailing ponds. On the websites of emergency management departments in various provinces in China, information about tailings ponds in each province is publicly available, including their geographical location, level, and scale. After obtaining the geographic information of tailings ponds, we can search for their locations on a map.
2. Dry beach detection included creating a dataset of dry beach images of tailing ponds, labeling images with Labelme software (Version 5.4.1), and training the dataset using the YOLOv5-seg model. Labelme is an image annotation tool developed by the Computer Science and Artificial Intelligence Laboratory (CSAIL) of MIT. The source code of the software is open source and has been widely used in object detection semantic segmentation and instance segmentation [28].
3. To calculate the length, the edge detection algorithm was used to extract points on the mask boundary of the dry beach area. Then, an equation was fitted to the beach crest line. Finally, a calculation model was created to determine the distance from the dry beach boundary to the beach crest line.
4. A tailings pond in China was used to verify the method. The dry beach length was calculated and compared with the measured length of monitoring points on each site to verify the accuracy and practicality of the proposed method.