esa-sceva/satcom-chunk-collection huggingface dataset:
# Content Management Based on Content Popularity Ranking in Information-Centric Networks
## 2 Related Work
### Content Caching and Removal Schemes
Several crucial criteria, including classifications of the content, interests of the users, the effect of the distance, feedback from caching system, and space within the storage, were considered to formulate content popularity model in [36] so that the efficiency of content distribution can be improved and the redundancy in terms of transmission for multimedia traffic can be reduced. Based on that content popularity model, new cache placement and replacement strategies were proposed using the CCN architecture.
# Autonomous Detection of _Spodoptera frugiperda_ by Feeding Symptoms Directly from UAV RGB Imagery
## 2 Materials and Methods
### UAV Image Collection  
The image acquisition device was a DJI Mavic air2 equipped with a half-inch CMOS sensor, which can achieve an effective pixel rate of 48 million. What is more, it is an ultra-small drone, weighing just 570 g, which is capable of capturing high-resolution images (standard red-green-blue or RGB photos) of corn at ultra-low altitudes.  
Figure 1: (**a**) The study area located in Xuzhou and the experimental field; (**b**) images of the experimental field obtained on 8, 9, and 24 September 2020 using a low-altitude UAV equipped with an RGB sensor. Each of the colors represents a different experiment date. Each drone image has its own coordinate.  
The data were collected three times in September 2020 during the critical growth period of maize across the jointing stage to the heading stage, and the specific time and resolution are shown in Table 2. In the jointing stage, corn was collected for the first time, and in the heading stage, it was collected for the second and third time. Between these two periods, maize grows rapidly, and its morphology changes greatly, especially the appearance of stamen changes the overall morphology of the maize plant to a great extent, which can be applied to the generalized type test of model. That is the reason we set the interval. The specific differences between the two stages are shown in Figure 2 Part A below, and maize leaf categories are shown in the Figure 2 Part B.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date** & **\#Images** & **Stage** & **Image Resolution** \\ \hline
8 September 2020 & 295 & Jointing & 4000 \(\times\) 3000 \\
19 September 2020 & 249 & Heading & 6000 \(\times\) 8000 \\
24 September 2020 & 84 & Heading & 6000 \(\times\) 8000 \\  & 272 & & 4000 \(\times\) 3000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Date and quantity of data collection.  
Figure 2: **(Part A)** The picture on the left shows corn in the jointing stage, the right picture shows corn in the heading stage; **(Part B)** The red box shows severely infected corn leaves, the blue box shows slightly infected corn leaves, and the purple boxes show corn leaves in healthy condition.  
The flight speed was controlled at 1.5 m/s and the flight altitude was from 2 to 5 m from the ground, which was very close to the corn canopy. Moreover, the shooting angle of the flight path along the ridges of the field was 90\({}^{\circ}\), with more efficient harvesting of corn canopy information. The specific information of the images is shown in Table 2, including the date, number, and image resolution.
# Technique Evolutions for Microorganism Detection in Complex Samples: A Review
## 3 Developments in Innovative Detection Methods
### Development of Physical and Computer Analysis Methods
#### 3.3.1 Raman Spectroscopy
As another promising example of SERS-based detection, [PERSON] et al. [100] used electrokinetic microfluidics to isolate and concentrate bacteria on a SERS substrate for detection directly from blood samples. They demonstrated the detection of _S. aureus, E. coli_, and _P. aeruginosa_ under 5 min, with a concentration of \(5\times 10^{3}\) CFU/mL. In conclusion, Raman spectroscopy allows for the detection and identification of pathogens by looking for their specific spectrum inside samples. Compared with infrared spectroscopy, Raman spectroscopy is less hindered by the signal of water and is thus more suited for biological complex media. Consequently SERS-based biosensors are a growing field. There are already many databases of pathogens spectra, but applications to detection in complex biological media are rare. Spectra are difficult to interpret and time consuming  
Figure 6: Averaged Raman spectra of different bacteria species. This figure is a partial reproduction from reference [100], licensed under CC BY-NC-ND 3.0.  
to analyze but this technique can be greatly improved by artificial intelligence as described in the next paragraph.
## 2 Materials and Methods
### Research Methods
The view of the obtained mesh is shown in Figure 3; it has a minimum orthogonality of 0.353 (the minimum value of the cosine of the angle between the normal to the cell face and the line connecting the centers of neighboring cells), the maximum aspect ratio is 8.56, and the minimum asymmetry is 0.43. Figure 4 shows the created research area (cells of the reactor distribution grid): the dimensions of the lower part are 90 \(\times\) 90 \(\times\) 60 mm (axes dimensions, see Figure 3); and the dimensions of the upper part are 110 \(\times\) 110 \(\times\) 80 mm. In the proposed design of the reaction zone, the lower part is limited by vertical baffles, and the upper part with \"expansion\" has no physical boundaries. Therefore, the side faces of the model in this area were set as symmetry. The absence of solid baffles allows the gas flow in this area to be redistributed which leads to the destruction of bubbles and mixing of catalyst particles. This avoids the slugging regime while preserving the advantages of the arranged fluidized bed. Further, a study was carried out on the grid independence to confirm that the size of the finite elements of the grid is small enough. Two more models with a larger and smaller minimum mesh size were made. All three models were named according to the number of elements in the mesh. Since the value chosen is only the lower limit and the elements may have a larger size, albeit not much larger than the specified threshold, this will lead to a disproportionate change in the element number when the limit value changes. The first mesh has \(\sim\)5500 cells with a minimum mesh element size of 9 mm. ; the second mesh has \(\sim\)18,000 cells with a minimum mesh element size of 6 mm; and the third mesh has \(\sim\)34,000 cells with a minimum mesh element size of 4.5 mm. To compare the calculation results, the value of the area-weighted average pressure at the inlet of the cell (inlet from below) was used for one second of simulation. The simulation time, as well as the gas velocity of 1.5 m/s, were randomly chosen. The results are shown in Figure 5.  
Based on the results of comparing the area-weighted average pressure at the inlet of the distribution device cell, the mesh of 18,000 cells was selected for further calculations. It can be seen from Figure 5 that with an almost two-fold increase in the number of mesh cells as compared to the mesh we selected, the pressure pulsations have practically not changed in magnitude, but there is a shift of phase fluctuations, which is explained by a more accurate calculation of gas flows between particles due to a smaller mesh. However, such an increase in the number of elements has not led to a significant improvement in the results, and the calculation time has increased by several times. The reverse is the case for the model with the 5500 cell: a small number of cells have increased the calculation speed, but excessively large elements do not allow sufficient calculation of interparticle gas flows with significantly higher velocity than that at the inlet, and as a result, the layer remained fixed.
## 1 Introduction
Among these technologies, haptic devices bring more realism in an immersive virtual environment as a modality to touch and feel the sensation of force when exploring virtual artefacts. Haptic force-feedback is generated by software-controlled forces and by feeding it to the users to provide them the sensation of interaction. Beside seeing the virtual objects, the sense of touch is also important to increase the sense of presence. With haptic devices, users can perform procedures and maneuvers that trigger both force and visual responses from the VE, creating tangible cultural heritage experiences [3]. Visual and interactive content helps users comprehend and remember historical information [4; 5]. Following the recommendation from [6], we designed and developed a multi-modal virtual environment to present and preserve an antique carpentry tool via a visual and haptic interface. The idea of this paper appeared when the authors noticed that in the endowment of the teams of workers in the field of wood processing, the tools and hand tools had started to disappear. Now, the vast majority of woodworking operations are achieved with power tools, reducing the physical effort of operators, and increasing accuracy and productivity. It is noticed that newcomers in the field no longer recognize the old tools and their uses. Therefore, in addition to the normal activities of storing these tools, we also propose an approach using virtual reality technologies, especially those related to haptic return. This paper is structured as follows: in the Section 2, we present background information about the use of the hand saw in history, as well as the use of mechanisms for generating haptic feedback in virtual reality applications. In the following section, a study is detailed regarding the kinematics and dynamics of the operation of a hand saw, with the aim of finding the trajectory of the handle and the cutting forces, as well as the vibrations in the handle. Section 4 presents the operation of the custom-made mechanism, which is followed by the design, construction, programming, and realization of the virtual environment, and the connection between the physical mechanism and the components of the virtual environment. Section 5 presents a user study that sought to obtain feedback from users regarding the quality of the interaction with the created equipment; this is followed by a discussion and conclusions.
## 2 Materials and Methods
### Anti-Planktonic Activity  
The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values of the isolated _N. officinal_ and _P. guajava_ hydroethanolic extracts for each bacterial strain were determined using the broth microdilution method, according to the Clinical and Laboratory Standards Institute (CLSI) guideline M7-A9. In this study, a standard strain (ATCC 29212) and two clinical strains (nominated 2 and 4) were used.  
The reference strains sourced from the American Type Culture Collection (ATCC) (Rio de Janeiro, RJ, Brazil), and the clinical strains were obtained from the National Institute of Quality Control in Health (INCQS) of the Oswaldo Cruz Foundation (FIOCRUZ). The antibiotic sensitivity profile for clinical isolates of _E. faecalis_ and _E. ficecium_ was published in a previous study by our research group [14], where _E. faecalis_ 3 from that study corresponds to _E. faecalis_ 4 in the present research.  
The bacterial strains were cultured on Brain Heart Infusion (BHI) agar (HiMedia@ Mumbai, India) at 37 \({}^{\circ}\)C for 24 h. Subsequently, bacterial inoculums were prepared in which colonies of the respective strains were diluted in sterile saline solution (0.9% NaCl) and homogenized in a vortex for 1 min to standardize the microbial solution according to the requirements of each protocol. The inoculums were standardized using a spectrophotometer (Micronal B-582, Sao Paulo, Brazil) at a wavelength of 625 nm and an optical density of 0.080, resulting in a standard suspension of 1 \(\times\) 10\({}^{6}\) cells/mL.  
Then, in separate 96-well plates (Kasvi K12-096, Sao Jose dos Pinhais, Brazil), 100 \(\upmu\)L/well of Mueller Hinton broth (HiMedia@ Mumbai, India) was added. A 100 \(\upmu\)L aliquot of one of the prepared _N. officinal_ and _P. guajava_ hydroethanolic extracts was added only in the first well, followed by 10 serial dilutions of the extract (1:2) in Mueller Hinton broth. Then, 100 \(\upmu\)L of each microbial inoculum was added to all wells in different 96-well plates for each bacterial strain. The plates were incubated at 37 \({}^{\circ}\)C for 24 h, in which the MIC values were determined as the last well in the microplate without turbidity, indicating no microbial growth (the test was performed in triplicate).  
To determine the MBC values of the _N. officinal_ and _P. guajava_ hydroethanolic extracts, aliquots from all wells were plated on BHI agar and incubated at 37 \({}^{\circ}\)C for 48 h. The MBC of each extract for each bacterial strain was determined in plates without colony growth. A control group treated with the vehicle (EtOH:H\({}_{2}\)O/50:50) was included to assess any possible interference with the anti-planktonic activity of the extract [14].
# Object-Based Approach for Adaptive Source Coding of Surveillance Video
## 3 Proposed Adaptive Coding Method Using Object Dynamics
### Modeling the Statistical Relationship
This is expressed as follows:  
\[\sum_{i=0}^{n}{(y_{i}-\overline{y})}^{2}=\sum_{i=0}^{n}{(\hat{y_{i}}-\overline {y})}^{2}+\sum_{i=0}^{n}{(y_{i}-\hat{y_{i}})}^{2} \tag{6}\]  
Please note that R-square is caused by the \(SSR\) in the \(SST\) proportion:  
\[R^{2}=\frac{SSR}{SST}=\frac{\sum_{i=0}^{n}{(\hat{y_{i}}-\overline{y})}^{2}}{ \sum_{i=0}^{n}{(y_{i}-\overline{y})}^{2}}=1-\frac{SSE}{SST}=1-\frac{\sum_{i=0 }^{n}{(y_{i}-\hat{y_{i}})}^{2}}{\sum_{i=0}^{n}{(y_{i}-\overline{y})}^{2}} \tag{7}\]  
Here, an adjusted R-square measure method is used to solve the R-square method for a small sample or independent variable increase, which can cause regression degrees of freedom decrease will get higher R-square value. This can be expressed generally as follows:  
\[R_{adj}^{2}=1-\left(1-R^{2}\right)\frac{n-1}{n-p-1}=1-\frac{SSE(n-1)}{SST(n- p-1)} \tag{8}\]  
Here, variable \(n\) is the observed value number (i.e., the regression equation input data points) and \(p\) is the number of independent variables. Please note that the adjusted R-square value may be less than zero, and values that are closer to 1 indicate a better fit. Figure 2a shows the moving object's area, speed, and PSNR results obtained at a 64 Kbps bitrate after robust linear regression. Here, the SSE is 0.7183, and the adjusted R-square is 0.6202. Under different coding bitrates, we use the same reference moving object in 64 Kbps video to build robust linear regression equations for each bitrate using the above mentioned method. Using different bitrates, we compared the PSNRs to that of the 64 Kbps video transferred by a wired network. By observing those equations synchronously, we created the results shown in Figure 2b. Here, we can see that the planes created by those equations are nearly parallel. Figure 2b also shows that the area and the speed of the moving object are critical points that affect the PSNR in a linear relation. The \(z=0\) plane (black) shows the same PNSR as the 64 Kbps video transferred by the wired network.
## 1 Introduction  
The development of information technologies brings many benefits to people and businesses. At the same time, information technology serves as a gateway for criminal activities. While hacking or malware usage is a specific skill, requiring activities and not executed by an ordinal person, the field of social engineering is not so technically demanding. Therefore, the popularity of social engineering attacks is on the rise. In the third quarter of 2019, social engineering attacks were the number one threat for individual users and number two for organizations [1]. Social engineering, in most cases, is the initial phase for the next cybercrime steps. \"In 81 percent of cases, malware infections of corporate infrastructure started with a phishing message\" [1].  
A phishing attack is a social engineering attack aimed at fraudulently acquiring private and confidential information from intended targets [2]. A phishing attack might use different communication channels, and the most common are e-mail messages, phone calls, messages in social networks and others. It is very important to identify phishing messages to fight phishing and mitigate the leading cybercrime. While technical anti-phishing solutions are not accurate enough, the education and understanding of the phishing attack landscape is very important to ensure personal and organizational security. In this paper, we focus on e-mail-based phishing attacks. In an e-mail-based phishing attack, e-mail messages used as a contact environment to the victim to obtain targeted information.  
E-mail-based phishing attacks were selected as this is the most popular environment for phishing messages, and e-mail messaging is vulnerable to spoofing and e-mail address camouflage methods. E-mail messages are not in real-time; therefore, victims are motivated to act without questioning.  
This paper aims to highlight the variety of e-mail-based phishing attacks by proposing an e-mail-based phishing attack taxonomy. The proposed taxonomy concentrates on e-mail-based phishing attack specifically and includes e-mail address gathering techniques and e-mail sender address classification. There are specific e-mail-based phishing attack classifications, while the rest of the taxonomy can be applied to a wider range of phishing attacks. We also do not analyze person-to-person phishing attack peculiarities, which are more common when instant messaging or live communication is used.  
In the second section, we overview existing methods for presentation of the phishing attack taxonomies. The content of the existing scientific papers on e-mail-based phishing attack taxonomies is summarized in the second section as well. The proposed e-mail-based phishing attack taxonomy is presented with each phase, criterion and class of the taxonomy in Section 3. To validate the proposed taxonomy, it is compared to existing phishing attack taxonomies. On top of that, the taxonomy is adapted for phishing attack notation used in an incident management system. The flow and the results of this validation are presented in Section 4.
# Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya
## 3 Methodology  
Figure 2 summarizes our methodological approach for mapping the two cropping systems (mono- and mixed cropping). Basically, we employed a 2-step hierarchical classification approach to map the maize cropping systems using bi-temporal RapidEye data and the RF classification algorithm. In the first step, we produced a general land use/land cover (LULC) classification map to separate cropland from non-cropland in order to reduce the data complexity for subsequent classification [10]. In the second step, we classified the extracted crop mask into two cropping systems (_viz._ mono- and mixed cropping).
## 1 Introduction
### Global System for Mobile Communications (GSM) Architecture  
The GSM network is a digital cellular communication system that operates by the cellular making of a geographic area and reusing frequencies for non-contiguous cells. This network consists of some components, such as the network and switching subsystem (NSS), base station subsystem (BSS), and operation support system (OSS). Figure 1 shows the GSM network architecture [6].  
As seen above, the base station subsystem includes all the related radio capabilities of the GSM network and is responsible for establishing communication between the network subsystem and network users. Therefore, the BSS is composed of several base station controllers that manage the operations at base transceiver stations (BTSs) through the Abs interface. Each transmitter and receiver of the base station is responsible for serving the users of its coverage area through the A (air) interface [7; 8].  
There are three types of data sources to analyze and check the quality of these networks and identify and troubleshoot the faults that occur in the networks. The most critical data used in the fault management of these networks is the statistical data reported from the BTS side, which indicates the status of various statistical indicators from different traffic and signaling perspectives. The second category of used data results from an experiment in the areas covered by the networks through human power. This test is a method to measure and check the coverage status, capability, and quality of the networks. This test is conducted by making a call with a mobile phone that can record the reported measurements and parameters from the BTS side. The name of this test in the industry is the driving test. In the third section, some detailed information about this category is given. Finally, the last category of used data in improving the network performance is the use of collected information from subscriber complaints. Section four reviews the registered reports of the Mobile Telecommunication Company of Iran (Hamrahe Avval).  
In general, our main goals in this research are as follows:  
* To use accurate data to identify the cause of a fault that occurred in the network (the required data was obtained in cooperation with the mobile telecommunication company, Hamrahe Avval.)
* To appropriately use available data sources to improve the accuracy of fault diagnosis systems
* To analyze different indicators' values and how they affect the occurrence of faults
* To model events and unpleasant events (faults) in the network
* To have a comprehensive view of the network and the fault types that occur in the network
* To provide a model that has a minor dependency on expert opinion.
## 3 Proposed Model
### Blade Angular Velocity Estimation Method  
In the last section, our blade detection approach was presented. The object detection step leads to blade score \(S_{B}\) that can be used for decision making between presence or absence of a blade in the \(K\)th frame. The final decision can be simply made by comparing \(S_{B}\) to \(S_{t}hr\). If it is greater than or equal to \(S_{t}hr\), it will mean that a blade has been detected in the input image. According to Equation (12), non-zero \(S_{B}(k)\) indicates absolutely that a blade is detected for frame index \(k\).  
\[S_{B}(k)=\left\{\begin{array}{ll}S_{t}hr&S_{t}hr\leq S_{B}\\ 0&otherwise\end{array}\right. \tag{12}\]  
The procedure must be recalled for all consecutive image frames in the observation time, \(T_{w}\). As a result, all needed information for the estimation angular velocity is gathered according to Equation (13):  
\[S_{B}^{T}w=\{S_{B}(i)|i=1,2,..,N\},N=T_{w}f_{s} \tag{13}\]  
where in the last equation \(f_{S}\) would be the frame rate of the input video in frames per second (fps). Finally, the velocity of blade, \(V_{B}\), can be calculated by our proposed formula:  
\[V_{B}=2\pi(W^{i}f_{s})^{-1}60(RPM) \tag{14}\]  
where \(W^{i}\) is the dominant frequency of \(S_{B}^{T}w\) sequence.