esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Effect of Sealing Sheets Number  
Figure 7 shows the effect of the number of sealing sheets on air leakage under the condition that the spacing, the total pressure difference, the gap size, and the bending angle are 300 mm, 5 kPa, 20 mm, and \(-25^{\circ}\), respectively. Obviously, the effect of the number of sealing sheets can significantly influence the air leakage. Compared with the single-seal and double-seal cases, the tri-seal has the best sealing performance under the same total pressure difference \(\Delta p_{\text{tot}}\). The leakage of tri-seal is less than that of the single and the double seals by 27.9% and 14.9%, respectively. Besides, the orifice coefficient would decrease with the increase in the number of sealing sheets. Therefore, the increase in the number of sealing sheets can reduce the leakage and enhance the sealing performance.  
If there is no throttling device in the flow path, the pressure drop is mainly caused by the viscosity and roughness of the inner wall surface of the flow path. In the boundary layer, there is a high velocity gradient perpendicular to the mainstream direction, and there is a high pressure gradient in the mainstream direction. The presence of the sealing sheets in the flow path can have a large disturbing effect on the incoming flow, vortexes will be generated in front and rear of the throttling device [26]. The theory of wall vortex generation indicates that the fluid particles on the wall surface cannot be linearly translated under viscous conditions, and they will rotate under the action of the shear force [27]. The mainstream energy would be continuously reduced by the vortexes which are generated by the viscous force [28].  
Figure 6: Effect of gap size: (**a**) numerical, experimental, and calculated results, and (**b**) contours of velocity.  
Figure 7: Effect of the number of sealing sheets on leakage.
# CLEANIR: Controllable Attribute-Preserving Natural Identity Remover
## 4 Experiments
### Evaluation on Preserving Facial Attributes
#### 4.3.2 Quantitative Analysis  
To quantitatively analyze the facial attribute preservation performance of our work, we adopt a facial expression recognition algorithm, Microsoft Azure face API [60], and compute confusion matrices with the ground truth labels for four types of image sets: original, \(\mathcal{I}^{0}\), \(\mathcal{I}^{90}\) and \(\mathcal{I}^{180}\). For this experiment, we choose Japanese Female Facial Expression (JAFFE) dataset [59], which contains 213 images of 7 facial expressions (i.e., angry, disgust, fear, happy, neutral, sad and surprise) by 10 Japanese female models. Among the facial expressions, in this experiment, we use only four of them (i.e., happy, neutral, sad and surprise) showing high accuracy from the adopted facial expression algorithm. Figure 9 provides the results. In the case of using original images shows the best accuracy, the transformed results processed by the proposed method also provide comparable accuracy except only from the case of'sad'. We analyze this as an effect of the degradation of details by our method. Since the proposed network architecture is based on VAE, it bounds to the generative power of  
Figure 8: Results from the Japanese Female Facial Expression (JAFFE) dataset. (**a,e**) show original input images, (**b,f**) show reconstructed images (i.e. rotated with 0 degrees), (**c,g**) show de-identified faces by rotating 90 degrees, and (**d,h**) show de-identified faces by rotating 180 degrees.  
Figure 7: Results from the Labeled Faces in the Wild (LFW) dataset. (**a,e**) show original input images, (**b,f**) show reconstructed images (i.e. rotated with 0 degrees), (**c,g**) show de-identified faces by rotating 90 degrees, and (**d,h**) show de-identified faces by rotating 180 degrees.  
stochastic sampling methods although it enables capturing meaningful features from the input domain, thus such disentanglement of latent feature space we benefit from in this work.  
We also provide F-measures which are harmonic means of the precision and recall calculated from the confusion matrices. Averages of F-measures for original images, \(\mathcal{I}^{0}\), \(\mathcal{I}^{90}\) and \(\mathcal{I}^{180}\) are 0.448, 0.338, 0.333 and 0.324, respectively. Since gaps between de-identification results are minimal, we can confirm that the proposed method can preserve facial expression while identity removes but the degree of preservation is bounded to the generative power of the VAE-based encoder we adopt in this work.
# State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System
## 4 Enabling Technologies for Transaction-Based Energy
### Distributed Energy Resources  
There has been a growing effort in the U.K. and the world at large to encourage the integration of DER into the existing power generation grid. With the right amount of control, this system has the potential to balance energy demand and supply and, thus, increase the reliability of the power grid. DERs mostly include photovoltaic arrays, wind turbines, fuel cells, etc. [44]. These sources are micro-power generators in the form of MGs that can provide energy for small communities or households. They are distributed with close proximity to where energy is produced and used and do not depend on the power grid because it can be operated in an isolated mode. This control ensures continuous energy availability with or without the main power grid. The closeness characteristics of DER to the community reduces energy wastage to the surroundings [45] due to the short range of energy transmission, thereby reducing the greenhouse effect. An example of MG with interconnection of DER, ESS and connected loads is illustrated in Figure 2.  
To participate in energy trading and sharing, a prosumer should be able to either generate, consume or be willing to trade or share energy. Thus, DERs are an attractive technology for P2P energy trading and sharing.
## 3 Results  
In the present study a total of 21 patients, 17 males and 4 females were included in the study. The plaque and gingival index scores are elaborated below (Refer Table 1). The change from Baseline to 6 months AC-BD (Bone Fill) mean values were 0.71 \(\pm\) 0.460 mm in control group, 2.06 \(\pm\) 0.744 mm in Test group A and 1.77 \(\pm\) 1.250 mm in Test group B with 'P' value (0.026), which is statistically significant. Comparison between the groups showed a mean difference of \(-1.34\) between control group and Test group A, comparison between control group and Test group B showed a mean difference of \(-1.06\) \begin{table}
\begin{tabular}{l l c c c} \hline \hline  & \multicolumn{1}{c}{**Control**} & \multicolumn{1}{c}{**Group A**} & \multicolumn{1}{c}{**Group B**} \\ \hline AGE & 39.7 & 37.6 & 33.3 \\ GENDER & M = 6, F = 1 & M = 5, F = 2 & M = 6, F = 1 \\ PLAQUE & & & \\ INDEX & BASELINE & 1.6 & 1.63 & 1.51 \\ (PI) & 6 Months & 0.94 & 0.67 & 0.80 \\ GINGIVAL & BASELINE & 1.57 & 2.06 & 1.57 \\ INDEX (GI) & 6 Months & 1.11 & 0.94 & 1.03 \\ GINGIVAL & & & & \\ BLEEDING & BASELINE & 0.93 & 1.0 & 1.0 \\ INDEX (GBI) & 6 Months & 0.43 & 0.31 & 0.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Showing mean values of demographic characteristics and PI, GI and GBI at baseline and 6 months in control, group A and group B.  
comparison between Test group A and B showed a mean difference of 0.29. None of these values were statistically significant. (Table 2, Figure 3)  
The mean difference of bone density change from baseline to 6 months in control group was 92.3 \(\pm\) 22.12 HU, 219.9 \(\pm\) 51.50 HU in Test Group A and 199.7 \(\pm\) 44.10 HU in Test Group B which was statistically significant (\(p\) < 0.001). The comparison between the groups showed a mean difference of \(-\)163.57 at baseline and \(-\)291.14 at 6 months between control group and Test group A, comparison between control group and Test group B showed a mean difference of \(-\)105.00 at baseline and \(-\)212.43 at 6 months, comparison between Test group A and B showed a mean difference of 58.57 at baseline and 78.71 at 6 months. (Table 2, Figure 4)  
The test group A and B showed a greater reduction in PPD as compared to the control sites at all recall visits, with a mean difference between the visits being 1.57 \(\pm\) 0.5 mm, 2.71 \(\pm\) 0.4 mm, 3.86 \(\pm\) 1.4 mm in control group 1.86 \(\pm\) 0.6 mm, 3.57 \(\pm\) 0.9 mm, 5.0 \(\pm\) 0.8 mm in group A and 2.14 \(\pm\) 0.6 mm, 4.0 \(\pm\) 1.2 mm, 5.4 \(\pm\) 1.5 mm in group B from baseline to 1 month, baseline to 3 months, baseline to 6 months, respectively, which were not statistically significant. RAL also showed similar results compared to control group. (Table 2)
## 2 Materials and Methods
### Cell Culture and Exposure of NPs  
HUVEC cells were procured from the National Cell Bank of Iran (Tehran, Iran) and selected as a widely used in vitro model of normal cells. Michigan Cancer Foundation-7 (MCF-7) human breast cancer cells, obtained from the same cell repository, were chosen as an appropriate in vitro model of solid tumors. Cells were cultivated in DMEM culture medium supplemented with 10% heat-inactivated FBS, 100 U/mL penicillin-streptomycin, and preserved under standard conditions (5% CO2, 37 degC). At 80% confluence, cells were detached from culture flasks using Trypsin 0.25% and sub-cultured into 75 cm2 flasks, 96-well microplates for further assessments. CoNiFe, and CoNiZn NPs were suspended in culture medium and diluted to appropriate concentrations (0, 12.5, 25, 50, 100, 200, and 400 \(\upmu\)g/mL).
## 2 Methods  
In this present study, the authors compared five ML techniques to identify the best-performing method. Firstly, two decision tree methods (CART and CHAID) that can be used for predicting a target variable with continuous values were selected. Then, the authors developed another tree-based model, random forest (RF). RF was developed since several studies in the literature acknowledged the better performance of RF compared to other tree-based methods. RF uses an ensemble technique that creates a final tree using more than 100 single trees. Thus, the results should be more accurate. Finally, the authors added two more models to this study to have a more comprehensive comparison of these methods. Thus, SVM and ANN were used since these two models showed desirable performance in previous studies, and since they can be used to predict a target variable with continuous values. Before developing these models, a feature selection (FS) technique was used to reduce the dimensionality of data and to identify/select the most important and relevant input variables.
# Counter-Terrorism Video Analysis Using Hash-Based Algorithms
## 6 Implementation and Performance Study
### Structure  
Our implementation consists of two programs and two databases as we believe it to be easily implemented in real-world situations.  
_Program 1_ implements the pre-processing algorithm and writes the hashes in _database 1_.  
_Program 2_ takes as input the hashes stored in _database 1_ and a new video from which hashes are extracted using the pre-processing algorithm. Afterwards, it applies the matching algorithm to the corresponding frames and writes the matches in _database 2_.  
Therefore, _database 1_ stores our hashes, their respective video title and frame position. _Database 2_ stores hash matches. It contains the title of the (original) video which had a match, and the positions of the frames which matched.  
Figure 6 represents _program 1_ and the initial part of _program 2_.
## 3 Numerical Model
### Material Parameters  
According to the investigation report of tunnel surrounding rock in this section, the physical and mechanical parameters of rock, soil and lining in the numerical simulation were determined. In the model, the elastic modulus of the rock and soil in the fault fracture zone was reduced to a certain extent on the basis of the surrounding rock. The rock and soil mass was set as an elastic-plastic constitutive model, and the mechanical behavior conformed to the Mohr-Coulomb failure criterion. The parameters of rock and soil mass and support structure adopted by the double-arch tunnel are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Density** & **Elastic** & **Poisson’s** & **Frictional** & **Cohesive** & **Coefficient of** & **Pertemability** & **Thickness (m)** \\  & **(kg/m\({}^{3}\))** & **Modulus** & **Ratio** & **Angle (\({}^{\circ}\))** & **Force (kPa)** & **(10\({}^{-6}\) m\({}^{-3}\))** & **Thickness (m)** \\ \hline Miscellaneous fill & 1510 & 10.76 & 0.32 & 19.4 & 26.8 & 52.52 & 2–13.46 \\ Completely & 1630 & 13.00 & 0.35 & 23.4 & 49.4 & 63.00 & 10 \\ Weather & 1680 & 200.00 & 0.3 & 30.5 & 56.5 & 53.00 & 32 \\ Database & 1430 & 18.00 & 0.42 & 22.0 & 26.95 & 16.0 & 25 \\ Fault fracture zone & 1400 & 2000 & 0.34 & — & — & — & 0.01 \\ Pipeline & 1400 & 2000 & 0.34 & — & — & — & 0.24 \\ Contact surfaces & — & K\({}_{\rm a}=964.7\) & K\({}_{\rm s}=964.7\) & 30.4 & 242 & — & — \\ The pilot tunnel & 2500 & 25,000 & 0.23 & — & — & — & 0.24 \\ Inverted arch initial & 2600 & 34,000.00 & 0.15 & — & — & — & 0.3 \\ support & 2500 & 30,000.00 & 0.20 & — & — & — & — \\ Partition & 2600 & 34,000.00 & 0.15 & — & — & — & 0.3 \\ Primary lining & 2500 & 32,000.00 & 0.15 & — & — & — & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model material parameters.  
Figure 4: The contact surface: (**a**) The contact surface between the pilot tunnel and the partition wall; (**b**) The contact surface between the lining and surrounding rock; and (**c**) The contact surface between the inverted arch and surrounding rock.
# HH-NIDS: Heterogeneous Hardware-Based Network Intrusion Detection Framework for IoT Security
## 1 Introduction
### Contribution  
This research is extended from our existing FPGA Hardware Acceleration Framework [11]. This paper introduces HH-NIDS--a heterogeneous hardware-based network intrusion detection framework for IoT security. HH-NIDS applies anomaly-based IDS approaches for IoT devices using hardware accelerators. The first prototype system utilises the supervised-learning method on the IoT-23 [12] and UNSW-NB15 [13] datasets to generate lightweight ANN models. We implement the proposed framework on a heterogeneous platform, including a microcontroller, an SoC FPGA and GPU platforms under the handling of host processors. The training phase is conducted on GPU, while the inference phase is accelerated on two hardware platforms:  
* The MAX78000 AI microcontroller with ultra-low power consumption [14].
* The SoC FPGA is implemented in high-level synthesis (HLS) and Verilog approaches. The Verilog implementation is customised in a pipeline fashion, which has achieved high-throughput and low latency in processing network packets.  
Finally, the first prototype system is deployed on the MAX78000 evaluation kit [15] (MAX78000 EVKIT) and PYNQ-ZZ SoC FPGA board [16]. The implemented systems are evaluated using a number of testing scenarios on the two public datasets: IoT-23 and UNSW-NB15. Hardware resource usage and power consumption are also discussed and compared to relevant works.
## 5 Finite Difference Approximation of the Inverse Problems  
In this section, we present the _basic algorithm_ that solves the coefficient problems IP1 and IP3.  
First, we approximate the direct problem (1)-(4). The _semi-discretization in time_ is based on the [PERSON]'s method. We define a uniform time grid  
\[\overline{\omega}_{\tau}=\omega_{\tau}\cup\{T\}=\{t^{j}=j\tau,\,j=0,\ldots,M, \,\tau M=T\}\]  
and approximate the system (1)-(4) on the new time level by a variant of the Crank-Nicolson method:  
\[\frac{u^{j+1}-u^{j}}{\tau}-\frac{\
u}{2}\bigg{(}\frac{\partial^{ 2}u^{j+1}}{\partial x^{2}}+\frac{\partial^{2}u^{j}}{\partial x^{2}}\bigg{)} = \beta^{j+1/2}\frac{\partial B^{j+1/2}}{\partial x}-\frac{1}{2} \bigg{(}f^{j+1}+f^{j}\bigg{)} \tag{28}\] \[\frac{B^{j+1}-B^{j}}{\tau}-\frac{\
u_{m}}{2}\bigg{(}\frac{ \partial^{2}B^{j+1}}{\partial x^{2}}+\frac{\partial^{2}B^{j}}{\partial x^{2}} \bigg{)} = \beta^{j+1/2}\frac{\partial u^{j+1/2}}{\partial x}. \tag{29}\]  
When \(\beta(t)\) is an unknown function (see IP1 and IP3), then the linearization based on the approximation to the expression \(\varphi(t)\psi(t)\) at the time point \(t=t^{j+1/2}\) is used [27]:  
\[\varphi(t^{j+1/2})\psi(t^{j+1/2})=(\varphi(t^{j+1})\psi(t^{j})+\varphi(t^{j}) \psi(t^{j+1}))/2+O(\tau^{2}).\]  
The next stage is the _discretization in space_. We introduce the uniform mesh in space  
\[\overline{\omega}_{h}=\{x_{i}=ih,\,i=0,1,\ldots,N,h=X/N\}\]  
and the approximations of the second order of the derivatives at point \((x_{i})\):  
\[\left.\frac{d^{2}\varphi}{dx^{2}}\right|_{x_{i}}\approx\varphi_{\overline{x },i}=\left.\frac{\varphi_{i+1}-2\varphi_{i}+\varphi_{i-1}}{h^{2}},\quad\frac{ d\varphi}{dx}\right|_{x_{i}}\approx\varphi_{\dot{x},i}=\frac{\varphi_{i+1}- \varphi_{i-1}}{2h}. \tag{30}\]  
Then, the full discretization of the system (1)-(2) is  
\[\frac{u_{i}^{j+1}-u_{i}^{j}}{\tau}-\frac{\
u}{2}u_{\overline{x},i}^{j+1}-\frac{\beta^{j}}{2}B_{\dot{x},i}^{j+1} = \frac{\
u}{2}u_{\overline{x},i}^{j}+\frac{\beta^{j+1}}{2}B_{ \dot{x},i}^{j}-\frac{1}{2}\left(f^{j+1}+f^{j}\right) \tag{31}\] \[\frac{B_{i}^{j+1}-B_{i}^{j}}{\tau}-\frac{\
u_{m}}{2}B_{\overline {x},i}^{j+1}-\frac{\beta^{j}}{2}u_{\dot{x},i}^{j+1} = \frac{\
u_{m}}{2}B_{\overline{x},i}^{j}+\frac{\beta^{j+1}}{2}u_{ \dot{x},i}^{j}. \tag{32}\]  
This system can be solved by the matrix Thomas algorithm; see, e.g., [19]. However, each of the problems IP1, IP2, and IP3 requires specific algorithms for solving the concrete algebraic system of equations.
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 2 Background
### Routing in VANETs
#### 2.3.2 Challenges  
Efficient and robust routing is still a key challenge in VANETs. This is because the developers of the routing protocols have to consider various technical challenges encountered in VANETs [5; 6; 42; 43; 44].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **Signal** & **Data Rate** \\ \hline \multirow{8}{*}{Physiological [35; 36]} & ECG & 24–288 kbps \\  & EEG & 43.2 kbps \\  & EMG & 320 kbps \\ \cline{1-1}  & EOG & 1.2 kbps \\ \cline{1-1}  & EDA & 1.2 kbps \\ \cline{1-1}  & Heart Rate & 2–5 kbps \\ \cline{1-1}  & Blood Pressure & 2–5 kbps \\ \cline{1-1}  & Respiration & 800 bps \\ \hline \multirow{2}{*}{Audio [35]} & Voice & 4–25 kbps \\ \cline{1-1}  & Sound Diagnostic & 32–256 kbps \\ \hline \multirow{2}{*}{Camera} & Images [37] & 200 kbps \\ \cline{1-1}  & Video Telephony [38] & 32 kbps \\ \cline{1-1}  & Video Streaming [39; 40; 41] & 100–500 kbps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data rate requirements to enable remote monitoring applications for different sensors.  
Scalability is one of the characteristics of VANETs and poses a significant hurdle to efficient routing, as the protocol needs to adapt to a varying number of vehicles in the network. To cope with the scalability issue, a routing protocol should make routing decisions solely based on information available in its vicinity. Performing localized operations will eliminate the need for a routing protocol to know the topology of the entire network hence resulting in considerably less overhead. Apart from this, due to the dynamic nature of VANETs, most of the topology-based routing protocols result in degraded network performance [42]. Geographic protocols are more suitable for vehicular networks because of their ability to make routing decisions on the fly. Also, these protocols leverage the constrained movement pattern of vehicles on road by using the location information to further optimize the routing decisions. However, sender-based forwarding requires periodic exchange of information among neighboring vehicles in order to enable the sending vehicle to select the next best vehicle to forward the data packet towards destination. This may result in significant overhead. A better alternative is to use receiver-based forwarding schemes where the receiving vehicle decides whether or not to forward the data packet based on the information available in the header of data packet. The approach offers low overhead since there is no need for periodic information sharing among vehicles [45]. However, minimizing duplicate packet transmissions and reducing transmission delay are the key challenges in receiver-based forwarding schemes.
# The Application of an Impedance-Passivity Controller in Haptic Stability Analysis
## 3 Planning and Design of Impedance-Passivity Controller (IPC)
### Experimental System and Integration Test of Haptic Stability  
#### Framework Message Design  
A typical haptic-based virtual training system was successfully developed and tested for passivity stability, according to the scheme presented in Figure 2. The framework was designed to present a computational process of deformation and tactile interaction to provide a realistic and stable virtual contact experience for the user. The system architecture can be roughly divided into two parts, namely the hardware and software parts. The hardware side includes the tactile device and visual display, whereas the software side includes the haptic rendering module (HRM), deformable process module (DPM), and graphical rendering module (GRM).  
The system must clearly define the framework messages, especially the communication messages among various system components, to finalize a solution that can support a particular application. A physically-based image is loaded into the system as a deformable object for display. When collisions occur between virtual objects, nodes of the virtual objects are assigned to DPMs by the GRM. Therefore, the GRM also performs graphical refreshing to update the position of the virtual pen on the visual display and the deformation status of the visual object.  
We developed the HRM through point-based haptics, with the device's stylus tip being modeled as a haptic contact and interaction with the virtual object. The degree of virtual object deformation or depth in the virtual environment is obtained using the DPM's propagation model. In the present framework, the force between the visual object and the virtual probe is calculated using the displacement observers of the HRM. Subsequently, the HRM converts the position and surface information into a torque command vector, which is transmitted to the drive motor of the PHANToM unit through the stability controller to provide stable force feedback.  
Finally, to provide realistic force feedback, a multirate simulation technique is used to ensure that one runs at the visual update rate (at 30 Hz) and the other runs at the tactile update rate (at least 1 kHz). Through frame information processing, human hand motions could be efficiently mapped to the correct virtual probe positions in the experimental system.  
Figure 4: Region of passive stability.