esa-sceva/satcom-chunk-collection huggingface dataset:
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 1 Introduction
This paper also evaluates the performance of these models on different datasets to highlight their strengths and limitations. A key focus of this paper is also to investigate the factors that have the greatest influence on detection accuracy, offering insights into how CNN-LSTM models can be optimised. Additionally, this paper compares CNN-LSTM models with alternative approaches that do not employ LSTM to assess their relative effectiveness in deepfake detection. This paper also explores the challenges associated with implementing these tools and proposes potential solutions to overcome these barriers. Lastly, open issues and future research directions related to the use of CNN-LSTM in video deepfake detection models will be presented. Through this systematic review, this paper contributes valuable guidance for future research and the development of more robust, efficient, and accurate video deepfake detection systems. Compared to similar reviews, this paper provides a distinct contribution by focusing specifically on the integration of CNN with LSTM for video deepfake detection. While other reviews may cover a broad range of detection techniques, this paper narrows its scope to examine the unique strengths, challenges, and optimisation strategies of CNN-LSTM models, providing a focused analysis of how this hybrid approach effectively captures both spatial and temporal features in videos. The contributions of this paper can be summarised as follows:  
* Conducting a comprehensive investigation of the current landscape of state-of-the-art video deepfake detection studies and tools that leverage CNN with LSTM. * Identifying the most common feature extraction techniques employed within video deepfake detection techniques utilising CNN with LSTM. * Examining the most commonly used datasets in the development and evaluation of video deepfake detection techniques that integrate CNN with LSTM and evaluating their performance. * Investigating the key factors that have the most significant influence on detection accuracy when employing CNN with LSTM in video deepfake detection. * Comparing the effectiveness of CNN-LSTM models against alternative models that do not incorporate LSTM in video deepfake detection. * Examining the challenges of implementing CNN-LSTM-based video deepfake detection systems and offering insights into possible solutions. * Investigating open issues and future research directions regarding the integration of CNN with LSTM in video deepfake detection. The rest of this paper is organised as follows: Section 2 provides an overview of video deepfake creation and detection as well as an overview of CNN and LSTM algorithms; Section 3 introduces the research methodology that was adopted to conduct this review; Section 4 presents the analysis of the results; Section 5 provides the results and discussion, with the answers to research questions presented in detail; Section 6 presents open issues and future research directions regarding utilising CNN and LSTM for video deepfake detection; and Section 7 provides the conclusions.
# Study on Improvement Characteristics of a Novel Geotextile with Stitched Transverse Ribs
## 3 Experimental Results
### Reinforcement Mechanism of the Novel Geotextile with Stitched Transverse Ribs  
The reinforcement effect between geosynthetics and soils contains passive resistance of transverse ribs, frictional resistance of longitudinal rib, and the embedded locking and occlusion of mesh.  
Geotextiles stitched with transverse ribs can not only restrict the lateral displacement of the fill but also contribute greatly to the whole shear strength due to the friction between sand particles on both sides of the ribs and the geotextile. The shear strength of the geotextile with stitched ribs is mainly composed of three parts, as shown in formula (1) and Figure 11.  
\[F=F_{1}+F_{2}+F_{3} \tag{1}\]  
where: \(F\) is the total strength; \(F_{1}\) is the friction force provided by the underlying geotextile; \(F_{2}\) is the lateral squeezing force of the soil on the geotextile transverse ribs; \(F_{3}\) is the component of the friction force provided by the geotextile transverse ribs in the lateral direction. Figure 11 shows a schematic diagram of the reinforcing effect of the stitched transverse ribs geotextile.  
According to Figures 10 and 11, the contribution percentage of the underlying layer and the transverse ribs to the overall shear stress can be obtained, as shown in Table 5. It can be seen that when the normal stress is low (25 kPa), the transverse rib \(F_{2}+F_{3}\) plays a greater role, the percentage of which, under the three working conditions, is 57.5%, 69.1%, and 72.6%, respectively. As the normal stress increases, the friction effect between the underlying geotextile and the sand \(F_{1}\) is enhanced, and the percentage of \(F_{1}\) in shear stress gradually increases. When the normal stress is 150 kPa, \(F_{1}\) plays a more significant role, the rate of which is 70%, 61.4%, and 50%, respectively. Taking the geotextile stitched with three transverse ribs, for example, as shown in Figure 12, it can be seen that the reinforcement effect of the geotextile is mainly from the transverse rib at 25 kPa. With the increase of normal stress, shear stress provided by both the underlying geotextile and the transverse rib gradually increases. At 150 kPa, the shear stress provided by both is essentially the  
Figure 11: Schematic diagram of reinforcement effect of stitched rib geotextile.  
Figure 10: Relationship between maximum shear stress and normal stress.  
same. It can be summarized as follows: transverse ribs play a large role when normal stress is low, but the friction force of the underlying fabric requires a large normal force; therefore, as the number of transverse ribs increases when the normal stress is the same, the fraction of shear stress provided by the transverse ribs gradually increases.
###### Abstract  
Wireless Sensor Networks (WSN) have distributed a collection of tiny sensor nodes deployed randomly in the given symmetry environment to sense natural phenomena. The sensed data are disseminated symmetrically to the control station using multi-hop communication. In WSN, the energy conservation during node coverage plays a major role in detecting node failure and providing efficient and symmetrical data transmission to the nodes of WSN. Using the cluster method and efficient localization techniques, the nodes are grouped and the precise location of the nodes is identified to establish the connection with the nearby nodes in the case of node failure. The location accuracy is achieved using the localization estimation of the anchor nodes and the nearest hop node distance estimation using the received signal strength measurement. The node optimization can be performed **efficiently by the accurate estimation of the localization of the node.** To optimize the node coverage and provide energy efficient and symmetrical localization among the nodes, in this paper, a cluster-based routing protocol and a novel bio-inspired algorithm, namely, Modified Bat for Node Optimization (\(MB-NO\)), to localize and optimize the unknown nodes along with the reinforcement-based Q learning algorithm is proposed with the motive of increasing the accuracy estimation between anchor nodes and the other neighbor nodes, with the objective function to optimize and improve the nodes' coverage among the network's nodes in order to increase the nodes' localization accuracy. The distance metrics between the anchor nodes and other neighbor nodes have an estimated symmetry with three node positions, namely C-shape, S-shape and H-shape, using the Q learning algorithm. The proposed algorithm is implemented using the NS3 simulator. The simulation results show that the accuracy and precision of the proposed algorithm are achieved at 98% in the node coverage optimization with reduced Mean Localization Error (MLE) and computational process time compared with other bio-inspired algorithms, such as Artificial Bee Colony optimization and Genetic Algorithms.  
localization model; modified bat algorithm; routing protocol; Q learning; node optimization; wireless sensor networks +
Footnote †: 2022: 14, 2515. [[https://doi.org/10.3390/sym14122515](https://doi.org/10.3390/sym14122515)]([https://doi.org/10.3390/sym14122515](https://doi.org/10.3390/sym14122515))
# A Near-Far-Field Model for Bubbles Influenced by External Electrical Fields
###### Abstract  
In this paper, we present a model that is based on near-far-field charged bubble formation and transportation in an underlying dielectric liquid. The bubbles are controlled by the dielectric liquid, which is influenced by an external electrical field. This allows us to control the shape and volume of the bubbles in the dielectric liquid, such as water. These simulations are important to close the gap between the formation of charged bubbles, which is a fine-scale model and their transport in the underlying liquid, which is a coarse-scale model. In the fine-scale model, the formation of the bubbles and their influence of the electric-stress is approached by a near-field model, which is done by the Young-Laplace equation plus additional force-terms. In the coarse-scale model, the transport of the bubbles is approached by a far-field model, which is done with a convection-diffusion equation. The models are coupled with a bubble in cell scheme, which interpolates between the fine and coarse scales of the different models. Such a scale-dependent approach allows us to apply optimal numerical solvers for the different fine and coarse time and space scales and help to foresee the fluctuations of the charged bubbles in the E-field. We discuss the modeling approaches, numerical solver methods and we present the numerical results for the near-far-field bubble formation and transport model in a dielectric carrier fluid.  
bubble formation; dielectric fluids; electrical field; scale-dependent models; near-far-field approach; Young-Laplace equation; convection-diffusion equation; level-set method; coupling analysis 35K25; 35K20; 74S10; 70G65
# Real-Time Exercise Mode Identification with an Inertial Measurement Unit for Smart Dumbbells
## 2 The Mode Training and Identification of Data  
The dumbbell that is used is called a hex dumbbell, as shown in Figure 1. We attached the IMU sensor to one side. There are many IMU products that output 6-axis or 9-axis sensor data. The device is designed by incorporating an accelerometer and a gyroscope (6 DoF) in the first case; alternatively, another accelerometer, gyroscope, and magnetometer (9 DoF) are integrated on a printed circuit board.  
Figure 1: Prototype of hex dumbbell.  
In this study, we use a 9 DoF Razor IMU on the dumbbell, which includes the sensors and control process unit onboard. Different parts of the muscles are used while exercising; however, the person training with the dumbbell focuses on biceps, deltoids, and triceps. Six types of actions with dumbbell exercises were selected and classified into two categories, namely simple and complex actions. In terms of simple actions, the dumbbell bicep curl was selected to develop the bicep muscle in the biceps' mode. It is an action in which humans turn their palm up while simultaneously bringing the dumbbell to align with their shoulders. The second mode is the deltoids mode, in which the elbow is slightly bent, and this position is maintained in order to raise the arm until it is parallel to the floor [20; 21]. The third method is the triceps mode. On the other hand, complex mode actions include the dumbbell shoulder modes that enable the improvement of the supraspinatus, teres major and infraspinatus muscles. The fifth one is squat and press mode, which will develop quadriceps, biceps and deltoids muscle. The final one is side lunge mode. Figure 2 presents the simple versions of the three modes and positions of the active muscles for these modes. Figure 3 shows the three complex modes of exercise that are used in our research.  
The data of the IMU from the person who was performing the exercise with the dumbbell was collected. Figure 4 shows the range of mode motions with dumbbells performed by the angle (Roll, Pitch, Yaw of IMU). It is easy to identify modes through the different range of angle distributions. In the research, the threshold range of motion  
Figure 3: Three complex modes with groups of muscles in dumbbell train.  
Figure 2: Three simple modes and groups of muscles in dumbbell training.  
(ROM) and SVM in Machine Learning is focused to identify actions. The range of motions which are used determines the range of parameters the IMU uses to define the action mode. Furthermore, the data could be used for the training model in which the label is the action mode. The vector variables is the signal from the IMU.
## 1 Introduction
The combination of liquid-like properties (e.g., high density, high solvent power) and gas-like properties (e.g., high diffusivity, low viscosity, near-zero surface tension) of scCO\({}_{2}\) allows the production of particles/crystals of smaller dimensions and with narrower PDSs than those obtained with conventional techniques. Nanomaterials with both amorphous and crystalline structures can be obtained by modulating the operating conditions during supercritical processing, particularly temperature and pressure. Operating in supercritical conditions, a proper comprehension of the mechanisms involved in the particles/crystals formation is possible only considering the high-pressure vapor-liquid phase equilibria (VLEs). According to [PERSON], binary mixtures can be classified into six basic types according to their VLEs [33; 34]. Many binary systems consisting of an organic solvent and CO\({}_{2}\) are  
Figure 1: Sketch of the phase diagram of a pure substance. classified as type I systems (Figure 2); they are characterized by a critical curve, namely, the vapor-liquid critical curve starting from the critical point of a component up to the critical point of the other component. Although the [PERSON] classification describes the behavior of binary systems, it is often used to describe modifications of VLE behavior caused by the presence of the solute/solutes, i.e., in the case of ternary/quaternary mixtures [35]. Until now, the microscopic size of numerous active compounds, both naturally occurring and chemically synthesized, have been successfully reduced by scCO\({}_{2}\)-assisted processes [36; 37; 38; 39; 40]. The APIs have been processed alone or using polymers/additives used as stabilizers, protecting agents, and/or as carriers to control the release of the active compound contained in the nanoparticles/nanocrystals [41; 42; 43; 44; 45]. Polymers are also used to improve the processability of pharmaceuticals in the presence of scCO\({}_{2}\), in terms of recovery, morphology, and/or particle size. Given the growing interest of the scientific community in nanotechnologies, this updated review is focused on the application of scCO\({}_{2}\)-assisted processes to produce pharmaceutical nanoparticles and nanocrystals, indicating the techniques used for their production and the differences so far. In this review, the focus is on literature studies that managed to prepare particles smaller than or at least equal to 250 nm. The advantages and drawbacks of using scCO\({}_{2}\) in manufacturing nanosized drug delivery systems are highlighted. This review also provides guidelines for future perspectives on the use of supercritical technologies to prepare drug-loaded nanoparticles and nanocrystals for high therapeutic efficacy.
# A Long-Distance WiFi Network as a Tool to Promote Social Inclusion in Southern Vercarruz, Mexico
## 3 Methodology
### Diagnosis of the Municipality of Mecayapan
#### 3.1.5 Non-Formal Education  
Statistics show that more than a quarter of the population above 15 years old is illiterate, specifically 26.92% (this number is worse in the case of women: 34.78% of women and 18.58% of men are illiterate). The percentage of the population of schooling is lower at 5.09% (in this case with fewer differences between genders: 4.63% for women and 5.57% for men). The maximum level of education is high school.  
The university or technical schools do not have schools or facilities to develop training about data networks. However, in the municipality, there is a person who works as a teacher in this field who teaches small groups of people. For example, in 2015, 72 students were trained (26 men and 4 women).  
In this sense, sustainable antennas offer education the possibility of increasing the accessibility and dissemination of information, not only enabling the generation of knowledge among their community, but also, bridging the digital divide and generating a network across the region.
# DOSS Protocol for Data Caching and Sharing Security in a 5G Network
## 4 The Proposed Data Caching and Sharing Security (DCSS) Protocol
### Architecture Overview  
The proposed DCSS protocol leverages FldM model [7] in Figure 2 aligned with 5G system [46], security entities [1], and federated entities. It can be applied in both 5G core network (5 GC) or third-party SP environment. It uses IdPs, third-party SP Authentication, Authorization, and Accounting (AAA) servers, and Service Servers (SS) to provide federated security. To enable FldM, it can also be coupled with 5G systems and security entities. It enables the redefinition of the UE ID settings as well as the sharing of the security context such as keys, tokens, nonces, and IDs, both inside and outside the 5 GC. The following entities are defined for DCSS protocol and may play many roles:  
* UE: end-user is the entity trying to access the service.
* SMF: 5G HN Session Management Function (SMF) manages the session between the UE and HN and acts as a pass-through authenticator.
* SPAAA: The AAA servers of the SP. It verifies the ID of the user, checks the authenticity of the request, and generates authorization tokens used by the UE to request services.
* SS: service server, which hosts the service and grants access to the protected services.  
The UE registers to the MNO prior to initial authentication to 5 GC via the SEAF controlled AUSF, whereby the user's subscription data and security context are stored in Unified Data Management (UDM)/Authentication Credential Repository and Processing Function (ARPF) [1,3]. Simultaneously, the MNO registers the UE with SP based on the service agreement and access policies. The MNO and third-party SP also must agree on the security process and parameters such as PKI mechanism, access control policies, identities, authentication, and authorization levels protocols to be used [4]. In addition, the SP registers its services, access control policies, shared secret and keys, SP identity (SPID), and credentials to the IdP. To complete the trust and federated security interoperability the HN registers the UE's GPSI, user attributes, credentials, and Home Network ID (HNID). This can be managed by an internal or external entity such as the IdP to implement federated security in 5G [7].  
Then the UE obtains authenticated to access the 5G network using 3 GPP primary authentication protocols such as 5G authentication and key agreement (AKA) protocol [3]. After being granted access to the network, the UE requests access to a service which in this case is provided by the third-party SP, the authentication and authorization are provided by protocols such as [4, 5], respectively hence the UE gains access to the service, access/fresh tokens and SSO for further requests such as caching and sharing. The fresh token can also be used to refresh its permissions if the original token is expired or to request permissions to other services.
_applied sciences_  
Correction: [PERSON] et al. Application of Semi-Empirical Models Based on Satellite Images for Estimating Solar Irradiance in Korea. _Appl. Sci._**2021**, _11_, 3445  
**[PERSON], [PERSON] and [PERSON] \({}^{\star}\)**  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Clearness Level**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**RMSE (W/m\({}^{2}\))**} & \multirow{2}{*}{**rRMSE (\%)**} & \multirow{2}{*}{**MBE (W/m\({}^{2}\))**} & \multirow{2}{*}{**rMBE (\%)**} \\  & & & & & & \\ \hline \multirow{6}{*}{Beyer} & \(k_{T}\leq 0.25\) & 0.60 & 87.23 & 90.29 & 10.25 & 10.61 \\  & \(0.25<k_{T}\leq 0.5\) & 0.88 & 116.16 & 39.85 & \(-26.86\) & \(-9.21\) \\ \cline{1-1}  & \(0.5<k_{T}\leq 0.75\) & 0.97 & 121.59 & 19.65 & \(-67.87\) & \(-10.97\) \\ \cline{1-1}  & \(k_{T}>0.75\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\ \cline{1-1}  & All \(k_{T}\) & 0.94 & 113.74 & 29.18 & \(-36.84\) & \(-9.45\) \\ \hline \multirow{6}{*}{Rigollier} & \(k_{T}\leq 0.25\) & 0.68 & 90.02 & 79.40 & \(-30.26\) & \(-26.69\) \\ \cline{1-1}  & \(0.25<k_{T}\leq 0.5\) & 0.88 & 145.46 & 41.45 & \(-83.68\) & \(-23.85\) \\ \cline{1-1}  & \(0.5<k_{T}\leq 0.75\) & 0.95 & 133.29 & 20.98 & \(-12.83\) & \(-2.02\) \\ \cline{1-1}  & \(k_{T}>0.75\) & 0.99 & 145.97 & 16.85 & 121.16 & \(13.98\) \\ \cline{1-1}  & All \(k_{T}\) & 0.91 & 137.38 & 35.09 & \(-30.12\) & \(-7.69\) \\ \hline \multirow{6}{*}{Hammer} & \(k_{T}\leq 0.25\) & 0.73 & 84.34 & 78.64 & \(-21.65\) & \(-20.18\) \\ \cline{1-1}  & \(0.25<k_{T}\leq 0.5\) & 0.87 & 105.56 & 40.14 & \(-11.01\) & \(-4.18\) \\ \cline{1-1}  & \(0.5<k_{T}\leq 0.75\) & 0.96 & 106.23 & 19.89 & 0.39 & 0.07 \\ \cline{1-1}  & \(k_{T}>0.75\) & 0.98 & 126.50 & 15.88 & 90.87 & 11.41 \\ \cline{1-1}  & All \(k_{T}\) & 0.94 & 103.92 & 26.54 & 0.09 & 0.02 \\ \hline \multirow{6}{*}{Perez} & \(k_{T}\leq 0.25\) & 0.52 & 84.73 & 89.46 & \(-1.10\) & \(-1.16\) \\ \cline{1-1}  & \(0.25<k_{T}\leq 0.5\) & 0.82 & 122.56 & 40.55 & \(-16.96\) & \(-5.61\) \\ \cline{1-1}  & \(0.5<k_{T}\leq 0.75\) & 0.96 & 111.29 & 20.46 & \(-17.50\) & \(-3.21\) \\ \cline{1-1}  & \(k_{T}>0.75\) & 0.98 & 103.03 & 16.35 & 57.88 & 9.19 \\ \cline{1-1}  & All \(k_{T}\) & 0.93 & 112.57 & 28.64 & 14.53 & 3.70 \\ \hline \multirow{6}{*}{Hybrid} & \(k_{T}\leq 0.25\) & 0.64 & 82.83 & 85.03 & 2.32 & 2.38 \\ \cline{1-1}  & \(0.25<k_{T}\leq 0.5\) & 0.85 & 113.39 & 40.90 & \(-7.36\) & \(-2.65\) \\ \cline{1-1}  & \(0.5<k_{T}\leq 0.75\) & 0.97 & 89.60 & 15.67 & \(-19.17\) & \(-3.35\) \\ \cline{1-1}  & \(k_{T}>0.75\) & 0.99 & 48.24 & 5.47 & 10.06 & 1.14 \\ \cline{1-1}  & All \(k_{T}\) & 0.95 & 97.08 & 24.12 & \(-7.24\) & \(-1.80\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy of the GHI models by level of clearness index.