esa-sceva/satcom-chunk-collection huggingface dataset:
# Device Free Detection in Impulse Radio Ultrawide Bandwidth Systems
## 4 Device Presence Modelling
### Characteristic Function (CF)  
The CF for known \(j\)-th frame, \(l\)-th channel tap, conditioned on \(\alpha_{k}\) is given as  
\[\Phi_{i_{(j,l)}^{(k)}}\left(\omega\right) = \int_{\alpha_{k}}\Phi_{i_{(j)}|\alpha_{k}}(\omega)f_{\alpha_{k}}( \alpha_{k})d\alpha_{k} \tag{15}\] \[= \frac{1}{T_{f}}\int_{-\frac{T_{f}}{2}}^{\frac{T_{f}}{2}}\exp \left(i\omega h_{(l,k)}R(\alpha_{k})\right)d\alpha_{k},\]  
where \(\alpha_{k}\) is the time shift between the time hopping code and the \(k\)-th user, \(T_{f}\) is the frame duration and \(h_{(l,k)}\) is the UWB channel characteristics which the device experiences. The device moments can be calculated by the CF of \(i_{(j,l)}^{(k)}\), which can help in determining the detection and false alarm probability for the UWB system.
## 4 Experimental Results
### Sample Scenario #1  
An input video is divided into two parts based on the direction of the cars. The quality of the record is quite decent, cars are in the shadows, and their metallic can blend in with the background. Figure 10 depicts the first frames of the input videos to be analyzed.  
The results of the analysis concerning general information are shown in Table 3. Remarkably, the time for analysis of vehicles from the front view is almost the same as from the rear view. On the contrary, the count of bounding boxes is only 1.2 times higher than from the rear view. Such results are caused by the fact that cars that move from the camera get smaller; thus, more bounding boxes are detected than otherwise.  
Specific information about video analysis is shown in Table 4. The average confidence score reaches almost 100%, and the direction of the vehicles corresponds to the real state based on the camera view. The rear view reaches an accuracy of 93% in the estimation of the number of vehicles. Detection loss can be caused by the fact that the vehicle is considered a van, which is not a personal vehicle. The front view reaches 90%, and two cars are missed. It can be caused by the fact that the vehicle blends in with the background or the neural network cannot recognize the object category with a high confidence score.
# Carbazoles: Role and Functions in Fighting Diabetes
## 2 Carbazole Derivatives in the Pathogenesis of Diabetes
### 1,2,4-Triazine-Carbazoles  
As already mentioned, \(\alpha\)-glucosidase represents a valuable target for the treatment of type 2 DM. As a result, numerous efforts are being made by the scientific community to identify new compounds capable of inhibiting this enzyme in order to delay carbohydrate digestion and glucose absorption, thus leading to a reduction in postprandial glycemia values [63]. Accordingly, in 2016, [PERSON] et al. [6] developed a small series of triazines, used as different starting substitute carbazoles, in order to identify novel \(\alpha\)-glucosidase inhibitors.  
A number of the newly synthesized compounds showed noteworthy activity against the enzyme. In particular, compound **4** (1-((5,6-di(furan-2-yl) -1,2,4-triazin-3-yl)thio)-3-(3,6-dibromo-9H-carbazol-9-yl)propan-2-ol)) (Figure 5) was found to be the most promising,with IC\({}_{50}\) values of 4.27 \(\pm\) 0.07 uM, significantly higher than the control drug acarbose (IC\({}_{50}\) = \(995.55\pm 2.71\) uM).  
Molecular docking experiments confirmed its capability to interact with the active site of the enzyme, and furthermore, the kinetic analysis revealed that **4** acts as a non-competitive inhibitor. In particular, the carbazole ring of **4** forms arene-cation interactions with residues Arg-439 and Arg-312, respectively, and the furan ring interacts with residues Leu-176, Phe-157, Phe-177 and Pro-240. Overall, the findings of this study could represent a starting point for the future development of efficient \(\alpha\)-glucosidase inhibitors.
# Intelligent Resource Orchestration for 5G Edge Infrastructures
## 6 Edge Resource Optimization
### Problem Formulation
#### 6.2.1 Inputs  
Let \(\{v_{1},v_{2},\ldots,v_{n}\}\) be the set of virtual resources to be mapped to the edge infrastructure, and let \(\{s_{1},s_{2},\ldots,s_{m}\}\) be the set of physical servers available at this infrastructure, assuming one server per edge location. Virtual resources and servers are characterized by the following parameters (inputs of the model):  
* \(V_{i}^{c}\) is the number of cores assigned to virtual resource \(v_{i}\), \(\forall i=1,\ldots,n\).
* \(V_{i,t}^{in}\) is the estimated (predicted) CPU usage (%) of \(v_{i}\) at time interval \(t\), \(\forall i=1,\ldots,n\), \(\forall t=0,\ldots,23\).
* \(S_{i}^{c}\) is the number of cores available on server \(s_{j}\), \(\forall j=1,\ldots,m\).
* \(Pref_{ij}\) is the preferred edge location (or preferred server) designated for each virtual resource, as defined by the following binary input parameter: \[Pref_{ij}=\begin{cases}1&\text{if $s_{j}$ is the preferred server designated for virtual resource $v_{i}$}\\ 0&\text{Otherwise}\end{cases}\]
## 5 Conclusions  
In this paper, we have proposed a method for DeepFake detection. A model consisted of CNN network with high-performance and lighter weight model compared to current models such as SOTA [39] and combined vision transformer and EfficientNet [4]. We found an 0.958 of AUC, 0.9243 of F1-score for the DFDC dataset and 0.978 of AUC, 0.9628 of F1-score for Celeb-DF v2 with 26M parameters. By combining a manual technique with an AI-based algorithm, we improved the DeepFake detection method. The most important information, regions, and features were carefully cleaned and processed before being put into deep neural networks. Important preprocessing steps were also recommended to improve the model's quality considerably. In the future work, we will look for ways to enhance the model so that we can continue to propose lighter models while also improving its accuracy.  
Conceptualization, V-N.T.; funding acquisition, K-R.K.; investigation, V-N.T.; methodology, V-N.T.; project administration, S-H.L., H-S.L. and K-R.K.; software, V-N.T., S-H.L., H-S.L. and K-R.K.; supervision, S-H.L., H-S.L. and K-R.K.; validation, S-H.L., H-S.L. and K-R.K.; writing--original draft, V-N.T.; writing--review and editing, V-N.T., H-S.L and K-R.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (2020R1 IIA306659411, 2020R1F1A1069124), and the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2021-2020-0-01797) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline
**Ours–Celeb-DF v2** & 0.9825 & 0.9438 & 0.9628 & 0.9749 \\ \hline
**Ours–DFDC** & 0.9254 & 0.9232 & 0.9243 & 0.9244 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Benchmark videos.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Celeb-DF v2** & **DFDC** & **Number of Parameters** \\ \hline [PERSON] et al. (2017) [5] & 53.8\% & 61.4\% & 24 M \\ \hline [PERSON] et al. (2018) [6] & 54.8\% & 75.3\% & 27.9 K \\ \hline [PERSON] et al. (2019) [47] & 48.2\% & 49.9\% & 22.8 M \\ \hline [PERSON] et al. (2019) [53] & 57.5\% & 53.3\% & 3.9 M \\ \hline [PERSON] et al. (2020) [54] & 64.6\% & 75.5\% & - \\ \hline [PERSON] et al. (2021) [28] & 90.56\% & - & 42.8 K \\ \hline [PERSON] et-al. (2021) [4] & - & 97.8\% & - \\ \hline
**Ours** & **97.8\%** & **95.8\%** & **26 M** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the detection performance of benchmarking methods with the AUC value. The AUC results of benchmarking methods are taken from [4, 28, 52].
## 4 Discussion
### Drought Duration  
[PERSON] et al. (2013) [66] stated that vegetation responds quickly to short-term water deficits below normal levels. Therefore, when drought occurs in the dry season, the primary productivity of the ecosystem will decrease significantly in the short term. With the development of drought events, vegetation quickly adapts to the ever-changing available water resources [66], controls water consumption, increases roots' absorption of soil water [67], and buffers drought pressure, and the loss of ecosystem productivity tends to gradually flatten. Drought occurs at the CD, and \(\Delta\)GPP begins to decrease after the drought lasts more than 5 months. In addition, when drought occurs in the HD, \(\Delta\)GPP also decreases after the drought lasts more than 3 months. This is because, at this time, the ecosystem enters the rainy season, and the precipitation increases significantly compared to previously. In the rainy season ecosystem, under a short-duration drought, vegetation can obtain deep soil water to relieve water stress [68], meaning that the primary productivity of the ecosystem will not change significantly. Long-term drought will cause vegetation leaves to fall away [69,70], and deep soil water or even bedrock water will become depleted [71], causing tree death. Furthermore, long-term precipitation reduction reaches a threshold, which may lead to the conversion of tropical forest ecosystems to other ecosystems [63], thereby significantly reducing ecosystem productivity. Vegetation in the dry season is more severely affected by water stress, and the soil moisture content is less than that in the rainy season. Therefore, the GPP in the dry season is more degraded under a shorter drought duration than in the rainy season.
# Spinel Magnetic Iron Oxide Nanoparticles: Properties, Synthesis and Washing Methods
## 5 Stabilization of Magnetic and Maghemite Nanoparticles in Solution  
### Iron oxide Nanoparticles Behaviour in Solution  
As we saw earlier, the magnetic iron oxide nanoparticles from the coprecipitation will tend to aggregate in aqueous solution at neutral pH. The breaking crystal continuity at the surface of the iron oxide nanoparticles reveals iron atoms with partial charges. These surface atoms behave as Lewis acid and will be able to react with electron donors. So, surface iron ions will bond in the aqueous solution with the atoms of water [(21)] which dissociate to form a hydroxide layer at the surface of nanoparticles. In function of the pH of the solution, the hydroxide groups -OH protonate in groups OH\({}_{2}\)\({}^{+}\) or deprotonate in groups -O\({}^{-1}\). So, the surface of iron oxide nanoparticles will be positively charged, negatively charged, or neutral in the function of the pH of the solution. The isoelectric point, i.e., the pH value which the surface of iron oxide nanoparticles is electrically neutral in aqueous solution is between 6.8 and 7.4 [(21; 83)]. Around these pH values, (5.0 < pH < 9.0), the surface charge of nanoparticles is no longer important enough for the electrostatic repulsions, preventing their aggregation under the effect of Van der Waals forces. The iron oxide nanoparticles lose their stability in water and flocculate. Since most biological applications require stablenanoparticles at pH \(\approx\) 7, the aggregation by functionalizing their surface by organic ligands or inorganic shells should be limited, thus increasing the inter-particle repulsions.
## 3 Results and Discussion
### Overall Evaluation of the Experimental Results  
Combined with the experimental results of three different real hyperspectral images, the algorithm of this paper has relatively good results in object detection, background suppression, and other aspects, compared to the seven algorithms for comparison in subjective evaluation. It shows fewer false alarms and no large area of false detection. In terms of objective evaluation, the summary of AUC values of different algorithms in three datasets is shown in Table 4. It can be seen from the table that the final detection parameters of RX, WSCF, and the proposed algorithm of this paper, show relatively excellent performance in different datasets. The other five algorithms can usually perform well on two datasets. As the proposed algorithm of this manuscript is based on CRD, the comparison between CRD and the proposed algorithm would be more significant. As can be seen from the table, obvious progress is obtained on the Saliens_Synthetic dataset and the AUC value increases from 0.9363 (CRD) to 0.9998 (ours). The above subjective and objective evaluation results verify the effectiveness of the algorithm of this paper.
## 2 Designing the \(4\times 1\) Microstrip Square Patch Array Antenna  
### The Single Microstrip Square Patch Antenna  
First, an inset-fed microstrip square patch antenna to be used as an element of the \(4\times 1\) microstrip patch array antenna was designed to cover the 24.0 to 24.25 GHz frequency range on an HF-350F substrate (\(\epsilon_{\mathrm{r}}=3.5\), \(h=0.254\) mm, \(\tan\delta=0.0029\)), as shown in Figure 1a. Initially, the length of square patch antenna was calculated as follows [27]:  
\[L_{1}=0.48\lambda_{\mathrm{g}} \tag{1}\]  
\[\lambda_{\mathrm{g}}=\frac{c}{f_{\mathrm{r}}\sqrt{\epsilon_{\mathrm{reff}}}} \tag{2}\]  
\[\epsilon_{\mathrm{reff}}=\frac{(\epsilon_{\mathrm{r}}+1)}{2}+\frac{(\epsilon_ {\mathrm{r}}-1)}{2\sqrt{1+\frac{(12h)}{W}}} \tag{3}\]  
\[W=\frac{c}{2f_{\mathrm{r}}\sqrt{\frac{\epsilon_{\mathrm{r}}+1}{2}}} \tag{4}\]  
in which \(L_{1}\) is the side length of the square patch antenna; \(\lambda_{\mathrm{g}}\) is the guided or effective wavelength considering the relative permittivity of the substrate;\(f_{\mathrm{r}}\) is the desired resonant frequency of the square patch antenna; \(c\) is the speed of light; \(\epsilon_{\mathrm{r}}\) is the relative permittivity of the substrate; \(\epsilon_{\mathrm{reff}}\) is the effective relative permittivity; \(h\) is the substrate thickness; and \(W\) is the width of the microstrip line.  
The calculated length of the square patch using Equations (1) to (4) is \(L_{\text{1,calculated}}\) = 3.34 mm. By using this information, the length of the inset-fed square patch was then adjusted through simulation by varying the width and length of the inset in order to make the patch resonate at 24.125 GHz, as shown in Figure 1b. The final length of the square patch was \(L_{\text{1}}\) = 3.22 mm. For the simulation, the length of the square ground plane was set at \(L_{\text{g1}}\) = \(W_{\text{g1}}\) = 7 mm, and a 100 \(\Omega\) microstrip feed line with a width of \(w_{\text{H}}\) = 0.125 mm was used. The final width and length of the inset were \(w_{\text{s1}}\) = 0.25 mm and \(l_{\text{s1}}\) = 1.0 mm, respectively. The simulated frequency bandwidth for a voltage standing wave ratio (VSWR) less than 2 in the input reflection coefficient was 23.853 to 24.404 GHz (2.28%), which covers the 24.0 to 24.25 GHz frequency band.  
Simulated realized gain in the +\(z\)-axis main lobe direction as a function of frequency and radiation pattern at 24.125 GHz are plotted in Figure 1c,d, respectively. Gain in the 24.0 to 24.25 GHz band ranged from 6.18 dBi to 6.26 dBi with maximum gain at 24.125 GHz. Half power beam width (HPBW) for the y-z plane was 88.7 degrees, whereas for the z-x plane, it was 85.3 degrees.  
Designing 4 \(\times\) 1 Shunt-Connected Series Feed Networks Using Uniform and Tapered Power Distributions  
In this subsection, 4 \(\times\) 1 shunt-connected series feed networks using uniform and tapered power distributions are designed, and theoretical and simulated scattering parameters of the networks are compared.
## 2 Materials and Methods
### Study Area and Sites Selected  
The Surrey Hills was one of the first landscapes in the UK to be 'designated' as an 'Area of Outstanding Natural Beauty' (AONB) in 1958. It should be noted here that AONB is not a subjective term of the authors but a formal one created by the UK Government. In June 2000, it was confirmed that AONBs have the same level of landscape quality and share the same level of protection as National Parks, and the primary purpose of an AONB is stated as '_to conserve and enhance the natural beauty of the area_'. There are 34 AONBs in England, and together they cover 15% of the total land area. Notably, AONBs and National Parks are found in Category V--landscapes managed mainly for conservation and recreation--of the International Union for the Conservation of Nature and Natural Resources' global network of protected landscapes [86].  
The Surrey Hills AONB covers 422 km\({}^{2}\) with myriad landowners and community nature spaces. These include a wide diversity of broad priority habitats in the UK Biodiversity Action Plan (BAP) such as ancient woodland, broadleaved mixed and yaw woodland, coniferous woodland, arable and horticulture, improved grassland, acid grassland, calcareous grassland, heather, freshwater, built-up areas, and traditional orchards. These provide habitat for BAP priority species, 85 specially protected species, and at least 300 species recognised as being a priority for conservation [87].  
Figure 1: Workflow, analysis, and outcomes in this study.  
We selected for intensive study three farm sites within the SH-AONB area that comprise different habitats and biodiversity opportunities (Figure 2):  
* Hampton Estate, located in the Greensand Plateau: Shackleford (~739 ha), 51.2116\({}^{\circ}\) N, 0.7034\({}^{\circ}\) W (Site 1) Sondes Place Farm, near Dorking, between Greensand Valley (Pippbrook and Tillingbourne) and The North Downs (~78.6 ha), 51.2304\({}^{\circ}\) N, 0.3425\({}^{\circ}\) W, (Site 2)
* Landbarn Farm also located near Dorking, between Greensand Valley (Pippbrook and Tillingbourne) and The North Downs (~100 ha), 51.2309\({}^{\circ}\) N, 0.3740\({}^{\circ}\) W, (Site 3).
## 6 Discussion and Recommendations  
The integration of IoT technology in education has the potential to transform the way we learn and teach. However, it requires significant investment in infrastructure, training, and collaboration between academia and industry. By addressing these challenges and implementing a comprehensive framework for the next generation of smart campuses and universities, we can ensure that education remains relevant in the face of technological change and keep sensitive data safe and secure. Additionally, as these systems collect and analyze large amounts of data about students, it is crucial to ensure that privacy and security measures are in place to protect their personal information. To fully realize the benefits of this integration [62], it is recommended that:  
1. Teachers and students should be given plentiful chances to experience U-learning, allowing them to learn anytime and anywhere.
2. Educational institutions should collaborate with industry partners and government agencies to ensure that ubiquitous learning initiatives align with industry needs and labor market demands. This can help bridge the skills gap and prepare learners for the jobs of the future.
3. Educational institutions should provide students and faculty with access to cutting-edge hardware and software, as well as training and support to effectively utilize them. This will enable them to take full advantage of the benefits of IoT and ubiquitous learning.
4. Provide training and education to all stakeholders, including teachers, students, and administrators, on the use of IoT technology and its potential impact on education. This will ensure that everyone is familiar with the technology and can use it effectively.  
5. Develop policies and guidelines for the use of IoT technology in education to ensure that it is used ethically and responsibly. This includes guidelines on data privacy, security, and ethical considerations related to the use of personal data.
6. Foster collaboration between academia and industry to promote innovation and the development of new IoT technologies that are specifically tailored to the needs of education.
7. Implement regular security audits and updates to ensure that the network and devices are protected against the latest threats. This includes using firewalls, encryption, and access controls to prevent unauthorized access to sensitive data.
8. Educate students and staff on best practices for cybersecurity, such as strong password management, avoiding phishing scams, and reporting any suspicious activity.
## 4 Structural Design Based on VPL
### Free-Geometry Structures--Described by Trigonometric Functions  
The parametric description of geometry that has been used in the modeling of structural systems is based on the description of component elements using basic geometric shapes such as lines, arcs, circles, ellipses, etc. Their shape and placement are controlled by changing the parameters describing their function, and although this had the characteristics of mathematical parameterization, in practice, it was implemented implicitly. The user manually or smoothly changed the characteristic points using number slider nodes, effectively causing a change in the functions that describe the above elements.  
The parametric design gives designers possibilities limited only by the mathematical apparatus. Examples of interesting objects are those where the shape or geometry of the component elements is described by more specific functions than, for example, a parabola, ellipse, or exponential function.  
In the Dynamo environment, as in other text-based programming languages, the user has the ability to define his own equation describing the geometry of the structure. This example shows how this can be used in practice. Below is a script that allows the generation of an organically shaped roof, the geometry of which is described by trigonometric functions. By skillfully adjusting their course, it is possible to generate a structure reminiscent of the South Pond Pavilion in Chicago (Figure 15).  
The skeleton of the structure is the outline of its section generated by an arc. It was defined on the basis of three characteristic points using the Arc.ByThreePoints node. The designed structure is stretched between two curves created by copying the first curve to a given length. The roof is closed by a SurfaceByLoft type surface defined using NURBS cross-section curves (Figure 16).  
Figure 15: South Pond Pavilion in Chicago [28].  
The second part of the script is the layout of the definition of the elements that fill the walls of the roof. The following formula was used to describe their geometry:  
\[u=0..1..\#x,\] \[i=0..x-1,\] \[arc1=(1/a)+(1/a)\times\text{Math.Cos}(i\times\pi\times 360/(x-1)),\] \[arc2=(3/a)-(1/a)\times\text{Math.Cos}(i\times\pi\times 360/(x-1)).\]  
As you can see, trigonometric functions can be used to shape real shapes and profiles of structural elements. Points were extracted from these defined curves for specific parameters \(u\) and \(v\) using the Surface.PointAtParameter node (Figure 17).  
Figure 16: Definition of the roof structure.  
Figure 17: Definition of shapes of the arcs.  
In the final stage, B-spline curves were generated between the aforementioned characteristic points using the NURBSCurve.ByControlPoints node. Their cross-sections were given using the Solid.BySweep and Rectangle.ByWidthLength functions. As a result, a roof with a shape similar to that of the South Pond Pavilion in Chicago was generated (Figure 18).  
Of course, other mathematical functions can also be used to describe the waves of individual beams, yielding other interesting shapes.