esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Shielding Effectiveness of a Cavity with Internal Dielectric Plate and Apertures
###### Abstract  
Focusing on the shielding effectiveness of a single-layer shielded cavity, especially the impact of aperture shape, aperture quantity, internal dielectric plate, and other factors on shielding effectiveness, an equivalent circuit model and BLT equation of shielding effectiveness for a single-layer shielded cavity under plane-wave irradiation were constructed by applying transmission line theory, [PERSON]'s algorithm, and electromagnetic topology theory. The shielding effectiveness of the cavity with an internal dielectric plate subjected to 0 to 1 GHz electromagnetic waves was calculated. The influence of circular and rectangular aperture arrays, the thickness of the dielectric plate, and the position change of the dielectric plate on the electric field shielding effectiveness of the cavity were analyzed. HFSS was used for simulation experiments. The research indicates that increasing the number of apertures can effectively enhance the shielding effectiveness while keeping the total area of the apertures constant. In addition, the insertion of a dielectric plate inside the cavity, especially near the resonant frequency, significantly enhanced the shielding effectiveness due to the absorption of incident electromagnetic waves by the dielectric plate. As the thickness of the dielectric plate was increased, the resonant frequency of the cavity gradually decreased; the shielding effectiveness was improved near the resonant frequency. Placing the dielectric plate at the lower end of the cavity is preferred, as it significantly improves the shielding effectiveness.  
cavity shielding; transmission line; BLT equation; shielding effectiveness; dielectric plate +
Footnote †: journal: Applied sciences  
Article
# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 5 Partial Fake Speech Against Machines
### PF Attack Against Resemblyzer
#### 5.2.1 Impact of Fake Segment Location  
We conducted an experiment using the same content and speakers but with a different location for the fake segment in each crated file. Our aim was to determine whether  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Speaker/Dialect**} & \multirow{2}{*}{**Gender**} & \multicolumn{3}{c}{**ASR—(VC: FreeVC)**} \\ \cline{3-5}  & & **2F, 4R** & **4R, 2F** & **2R, 2F, 2R** \\ \hline Welsh speakers & Male & 8.43\% & 93.80\% & 69.44\% \\ Welsh speakers & Female & 27.93\% & 88.94\% & 52.21\% \\ Scottish speakers & Male & 1.67\% & 90.63\% & 64.29\% \\ Scottish speakers & Female & 9.75\% & 84.16\% & 48.54\% \\ Northern speaker & Male & 19.93\% & 86.69\% & 42.51\% \\ Northern speaker & Female & 1.71\% & 89.97\% & 55.77\% \\ Midlands speakers & Male & 47.22\% & 99.18\% & 70.12\% \\ Midlands speakers & Female & 1.81\% & 64.30\% & 35.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Attack success rates against Resemblyzer based on different dialect speakers’ real speech and using FreeVC VC generation method.  
Figure 3: Attack success rate against Resemblyzer (TTS and VC).  
the location of the fake segment would affect the detection process. We conducted the experiment three times and found that when the speech started with four seconds of real speech followed by two seconds of fake speech, the attack was most successful in both TTS and VC generation methods. This points to the possibility that the detection process does not examine the entire audio file. If it did, we would not have observed such a significant difference between 4R, 2F, and 2F, 4R.
## 4 Case Study
### Data Decomposition  
As can be seen in Figure 4, prior to VMD decomposition, the decomposition modulus should first be determined so that the characteristics of the original data can be fully extracted, which is essential for the application of VMD. We propose a new method for determining the optimal K value by the variation in the SE values of the subsequences. The SE values of VMF subsequences at different K values and their curves are shown in Table 1 and Figure 8, respectively.  
Figure 5: Layout of wire alignment device observing horizontal displacements.  
Obviously, when K = 5, the SE values for VMFs show a monotonically increasing trend and the difference between the SE values of the two subsequence is relatively large. When the K value increases to 6-8, it can be seen from Figure 5 that the SE curves corresponding to the above values (K = 6-8) have similar results at both VMF\({}_{3}\) and VMF\({}_{5}\) points, which will lead to modal confusion, implying that the VMD is over-decomposed. Meanwhile, the decomposition will be insufficient if the value of K decreases. Therefore, in this case, K = 5 is the best value for VMD decomposition, and the results are shown in Figure 9. The built-in parameters of the VMD algorithm are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**K**} & \multicolumn{3}{c}{**SE Values**} & & & \\ \hline  & **VMF\({}_{1}\)** & **VMF\({}_{2}\)** & **VMF\({}_{3}\)** & **VMF\({}_{4}\)** & **VMF\({}_{5}\)** & **VMF\({}_{6}\)** & **VMF\({}_{7}\)** & **VMF\({}_{8}\)** \\ \hline
4 & 0.0590 & 0.2356 & 0.4641 & 0.5840 & / & / & / & / \\
5 & 0.0587 & 0.1958 & 0.4576 & 0.5857 & 0.6901 & / & / & / \\
6 & 0.0598 & 0.1442 & 0.5054 & 0.6155 & 0.5364 & 0.6868 & / & / \\
7 & 0.0600 & 0.1225 & 0.5359 & 0.6148 & 0.5419 & 0.6741 & 0.8714 & / \\
8 & 0.0586 & 0.1044 & 0.4989 & 0.5433 & 0.5648 & 0.5801 & 0.6875 & 0.8092 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SE values of VMF subsequences at different K values.  
Figure 6: Layout of tension wire alignment device observing horizontal displacements.  
Figure 7: Horizontal displacements at EX5 from 6 th June 2016 to 22 nd October 2018.  
Figure 8: Curves of SE values of VMF subsequences with different K values.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & \(\alpha\) & \(\tau\) & \(K\) & \(DC\) & \(init\) & \(tol\) \\ \hline
**Values** & 2000 & 0 & 5 & 0 & 1 & \(1\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of VMD algorithm used in our case.  
Figure 9: Decomposition results by VMD.
## 4 Conclusions  
We proposed the pattern distortion coefficient as a new FoM for the quantitative evaluation of both mutual coupling and pattern distortions in multi-antenna systems. The proposed coefficient is based on a cross correlation between unaffected and affected far-field patterns of an AUT, and the input patterns are weighted by a Gaussian function to consider the target operation angle. The feasibility of the proposed FoM was confirmed by fabricating the IFA and the patch antenna resonating at 1.41 GHz and 6.05 GHz, respectively, to describe the two-antenna system exhibiting a mutual coupling environment. The evaluation was further extended to the single-antenna system with a conducting wall that caused strong platform effects with serious pattern distortions. The results demonstrated that the pattern distortions are highly correlated with the proposed FoM of \(\rho_{PDC}\); thus, it provides quantitative insight into the amplitude and phase distortions of the far-field patterns. Furthermore, the proposed FoM has great potential to give critical design guidelines for multi-antenna systems that usually suffer from both mutual coupling and platform effects.  
Conceptualization and methodology, J.M.H. and J.Y.; validation and formal analysis, J.M.H.; resources and data curation, H.K. and Y.K.; writing--original draft preparation, J.M.H.; writing--review and editing, G.B.; visualization, J.M.H.; supervision and project administration, G.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This research was supported by the Defense Challengeable Future Technology Program of Agency for Defense Development, Republic of Korea.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Application of Artificial Intelligence in Wind Power Systems
## 3 Results
### Application of Artificial Intelligence in the Operation of Wind Turbines
#### 3.2.4 An Effective System for Avoiding Bird Collisions with Wind Turbine Blades  
Artificial intelligence plays a crucial role in the development of systems to avoid collisions between birds and wind turbine blades, especially in a real-time context. These systems use a variety of AI technologies to minimize the number of collisions between birds and wind turbines and reduce the potential damage to the wind turbines.  
AI systems use a variety of sensors, including cameras, radar and ultrasonic sensors, to collect data about the wind turbine environment [62]. Systems such as the MERLIN Detect radar and IdentiFlight camera system (DeTect Inc., Panama City, FL, USA) have been developed to monitor bird activity in real time, providing early warning and enabling automatic responses to prevent collisions [63; 64]. By recognizing birds up to 1.3 km away and classifying them as protected species, IdentiFlight provides wind turbine operators with the critical visual and quantitative data they need to reduce or avoid collisions.  
Using deep learning algorithms, such as convolutional neural networks (CNN) and computer vision, the AI can identify birds in the vicinity of wind turbines. These algorithms analyze images and videos to distinguish birds from other objects and can even identify protected bird species. AI can also use predictive models to assess the behavior of birds based on historical data about their migration patterns and weather conditions to predict potential collisions and take appropriate system responses. When a potential collision is detected, the system can activate collision avoidance mechanisms, such as changing the rotation speed of the rotor blades or stopping the rotation of the rotor blades.
# IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial
## 2 Several Promising Techniques: IRS, LIS, and Radio Stripes
### Categorizing Recent Studies on Intelligent Reflecting Surfaces
#### 2.1.3 Channel Estimation for IRS-Aided Communications  
Despite the benefits of an IRS, it has gained a lot of attention in the cellular communication industry. Channel estimation (CE) is not easy, despite the IRS's passive nature and extensive sources for reflecting arrays. Even though it is hard to obtain, CSI is one of the most important things in data transmission [25]. As IRS is passive, traditional CE methods cannot be used to estimate the reflected channels of wireless communication systems that use IRS. To progressively resolve the CSI at the RIS, a new channel estimation approach  
Figure 1: Block diagram of an IRS-enhanced wireless system.  
is provided, and the passive beamforming at the RIS is adjusted to improve the channel gains [26].  
The representative findings of recent studies on an IRS are shown in Table 1.
## 3 Link Model  
In this section, we elaborate on the link model adopted in order to calculate the physical layer performance and the required optical transmitter power to maintain connectivity. Figure 5 shows the basic block diagram of an OIL. The data source modulates the amplitude produced by a laser diode, and after the beam is shaped by the transmitter optics, it propagates in free space until it reaches the receiving end where a _pin_ receiver converts the incoming light back to an electrical signal. The performance is impaired by the additive white Gaussian noise (AWGN) \(n(t)\) added to the signal. In what follows, we assume an intensity modulation/direct detection (IM/DD) with on-off keying (OOK) and note that the model could be extended to also account for multi-level schemes [30], polarization [31] or wavelength division multiplexing [32], and coherent detection [33].
# Performance Comparison of Deep Learning Models for Damage Identification of Aging Bridges
### Scope and Methods of Research  
In this study, we apply three representative deep learning models--Mask R-CNN, BlendMask, and SWIN--that can automatically identify six typical types of damage found in aging bridges: cracks, water leaks, efflorescence, concrete scaling, concrete spalling, and corrosion of reinforcing steel. We analyze the performance of these models by comparing the loss values when training each model individually. The loss value measures the difference between the predicted results and the actual ground truth during the training process. It evaluates how accurately the model segments objects and identifies instances. Commonly used loss-functions include pixel-wise cross-entropy loss and dice loss, which are determined by calculating the pixel-level agreement between the predicted and actual masks. This value typically ranges between 0 and 1, with a lower value indicating better performance. To minimize the loss value, the model is updated to decrease the difference between the predicted mask based on the training data and the actual mask. Through this process, the model learns to accurately segment object boundaries and internal regions, training on features that allow the distinction of individual object instances. The loss value mainly distinguishes between the background and foreground (objects), taking into account classification accuracy and pixel-level agreement for both regions. Moreover, weight adjustments for overlapping areas can be incorporated to address overlaps between adjacent objects. In conclusion, the loss value in an instance-segmentation model is important for evaluating and optimizing the model's performance. By minimizing this value, we can increase the model's ability to segment and recognize objects with high accuracy and detail.  
The paper is organized as follows: Section 2 introduces studies relevant to this paper; Section 3 describes the proposed technology; Section 4 explains the experimental results and evaluates the performance of the proposed technology; and Section 5 presents the conclusions.
## 3 Results and Discussion
### Implementation and EEG Measurements
Utilizing smaller, higher-count electrodes would allow for more precise mapping of brain activity, leading to a better understanding of the neural dynamics associated with cognitive stress. Previous research has highlighted the prefrontal lobe's critical role in emotion recognition, further emphasizing the need for targeted electrode placement in this area [33]. By optimizing electrode placement and count, we can improve the resolution of our data and gain more detailed insights into the specific neural mechanisms at play.
# Leader-Follower Role Allocation for Physical Collaboration in Human Dyads
## 2 Methods
### Experimental Protocol  
The leader-follower dynamics were studied on a reaching task, as it is one of the most common tasks performed by humans on a daily basis. The reaching task was repetitive so as to simulate repetitive motions commonly found in our daily lives such as the task of pick-and-placing objects. Furthermore, the repetitive motion of the reaching task helps with the implementation of Fitts' law in the data analysis of the study.  
Subjects stood in front of a screen, holding the handle of the robot with their right hand, as shown in Figure 1. The experimental session began when the subjects moved the controlled point on the screen to its starting position by moving the robotic arm. When the controlled point was in its starting position, a random target appeared on the screen. The subjects were instructed to reach this target as fast as possible and stay inside the  
Figure 1: Experimental setup of the study. The graphic user interface (GUI) can be seen on the screen in front of the left subject. The GUI displays the starting point (black), target point (red), and the controlled point (white) in real time.  
target for at least 0.5 s, at which point the target disappeared. By keeping the movable point inside the target for 0.5 s, we prevented the subjects from simply running over the target without aiming for it. When the target disappeared, the subjects had to return to the starting position.  
Throughout the experiment, the subjects were not told whether they were performing a collaborative or an individual task. However, although the partners were not explicitly made aware of their connection, both subjects were able to sense the other partner's physical interaction through the haptic interface only when in collaboration. This meant that, when the two subjects were coupled together, they could feel an external force that was produced by their partner. This established an open channel for non-verbal communication between the partners.  
The reaching task was repeated 180 times, with subjects performing half of the tasks individually and half in collaboration by being coupled together through the virtual model. In the 180 trials, 9 different targets with varying distances (5 cm, 15 cm, and 25 cm) and sizes (small, medium, and large) were used. In total, the experiment equalled to 20 cycles in which the 9 different targets were used in random order. To exclude the influence of human learning through repetitions on the performance in individual and collaborative tasks, the subjects were separated into two groups, each consisting of four dyads--three male and one female dyad. The first group conducted the first 10 cycles individually and the last 10 cycles in collaboration, while the second group performed the first 10 cycles of the experiment in collaboration and the last 10 cycles individually. With this we were able to lower the influence of the sequence of performing individual and collaborative tasks on the overall task performance.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### ICT for in-Home and Building Energy Management
#### 4.3.7 VERYSchool  
Another example of energy management in buildings through ICT solutions has been the \"VERYSchool\" project involving participants from nine countries. It has been about providing with ICT hardware and software solutions so as to optimize energy consumption in school buildings. Firstly, energy consumption has been examined through smart metering options and further on, data has been saved on a database--the so-called Navigator--regarding energy efficiency. The data is utilized through energy management programs by the system coordinator. The database has been fed with all necessary data by Building Energy Management System (BEMS) devices [87].
# _applied sciences_
## 3 Results and Discussion
### Biological Evaluation
#### 3.2.1 Enzymatic Screening
At the same time, compound **17** with piperazine fragment showed only a weak activity. When the heterocycles, such as \(N\)-methylpiperazine and morpholine, were conjugated directly with an oleanane core via amide function at C28 (compounds **12** and **13**), the inhibitory activity against \(\upalpha\)-glucosidase decreased. Meanwhile, piperazine amide **14** exhibited higher activity (IC\({}_{50}\) 38.3 \(\pm\) 6.9 \(\upmu\)M) than the propargyl containing analogue **17**. The derivatives of erythroidoid with indole **18** and seco-amine **30** fragments showed potent activity with IC\({}_{50}\) values of 12.37 \(\pm\) 3.34 \(\upmu\)M and 20.5 \(\pm\) 1 \(\upmu\)M, respectively, whereas compound **29** with A-azepano-cycle was inactive. Among the series of C2-derivatives **21**-**28** obtained using Claisen-Schmidt condensation, 4-pyridinyllideno-OA **21** demonstrated an activity with an IC\({}_{50}\) value of 5.56 \(\pm\) 1.88 \(\upmu\)M, being 78-fold more active than acarbose. The enzyme inhibition activity decreased when the carboxyl group of the compound **21** was amidated using pyridine-2-amine up to **23** (IC\({}_{50}\) 6.6 \(\pm\) 1.12 \(\upmu\)M), \(N\)-methyl-piperazine up to **25** (IC\({}_{50}\) 79.35 \(\pm\) 14.04 \(\upmu\)M), or in the case of carboxamide derivative **26** (IC\({}_{50}\) 38.84 \(\pm\) 6.63 \(\upmu\)M). The introduction of the furfurylidene fragment (compound **22**), as well as the modification of the carboxyl group to \(N\)-ethyl- **27** or \(N\)-methyl-piperazine amide **28**, led to a negative effect on inhibitory activity. Pyridine **19** or \(N\)-ethyl-piperazine **20** amides exhibited high activity with an IC\({}_{50}\) of 14.2 \(\pm\) 6.71 \(\upmu\)M and 18.88 \(\pm\) 7.35 \(\upmu\)M. Modification of **20** by introducing 2-pyridinylidene- **24** or furfurylidene **27** fragments led to the loss of activity, while the presence of isomeric 4-pyridinyllideno fragments increased the activity of **23** (IC\({}_{50}\) 6.6 \(\pm\) 1.12 \(\upmu\)M) compared to **19** almost twice. Several studies identified triterpenoid inhibitors of PTP1B, a valuable antidiabetic target [34; 35; 36]. We have also screened compounds against this enzyme, but no activity was observed up to 100 \(\upmu\)M (). This may be attributed to the lack of a 3-hydroxy group, which appears to be a requisite for PTP1B inhibition [37].
## 1 Introduction  
Understanding a scene's environment is an effective task for humans. We recognise a scene with the knowledge we gain through our short-term or long-term observations and experiences in similar situations. Making a machine recognise a scene as a human does has tremendous applications in the field of robotics. When it comes to machines, recognising a scene by understanding the semantic-level details is a challenging task. Recognising the different scene categories, which include outdoor scenes, indoor scenes, human activity scenes, aerial scenes, etc., and identifying the different objects in the scene while preserving their semantic relations is still a great challenge in many state-of-the-art approaches. The advances in artificial intelligence, with the great success of deep learning, have elevated its performance in many computer-vision applications such as object detection, scene classification [1], face recognition, human-action recognition [2], etc. Scene classification or recognition is one of the most significant and challenging tasks in the area of robotics [3] and computer vision. Unlike the object recognition task, which consists of images having single or multiple occurrences of the same object, scene recognition is difficult since a scene involves the occurrence of multiple dissimilar objects. Indoor scene recognition has attracted great attention in recent years due to its widespread application in numerous areas, including smart navigation systems, elderly monitoring systems, domestic robotics, intelligent surveillance, etc.  
A scene could be either dynamic or static. Dynamic scenes include the video scene [4], and static scenes include the still image scenes. Recognising static scenes is more complicated than recognising dynamic scenes. This is because, in order to recognise an object in a video, we need a number of frames, compared to the partial information obtained from still images. Static scene recognition can be classified into two categories: outdoor scene recognition and indoor scene recognition. Outdoor scene recognition involves recognising scenes like a street, forest, beach, etc. that consist of more or less similar objects in a scene. But indoor scene recognition involves scenes like the living room, classroom, bedroom, etc., which consist of different objects with varying dimensions, positions, and occlusions. Indoor environments provide rich decorations, complex occlusions, different textures and scales, and a cluttered background, making it a challenging task in the area of scene recognition.  
In our society, we have a large population of people suffering from visual ailments, ranging from mild to severe visual impairments. Indoor scene recognition and understanding can effectively assist the visually impaired, sighted, and elderly in navigating through their indoor environment by analysing their surrounding environment. In our paper, we have proposed a hybrid model using the concept of transfer learning-based deep approaches for understanding indoor scenes that can provide assistance for indoor navigation.