esa-sceva/satcom-chunk-collection huggingface dataset:
# Defending against FakeBob Adversarial Attacks in Speaker Verification Systems with Noise-Adding +
## 5 Adaptive FakeBob Attacks  
As pointed out by [15], a defense system against adversarial attacks needs to consider the worst-case scenario that attackers can know about the detailed information of the defense system, and through which they can design a white-box attack to counteract the defense. Such a worst-case attack is called the _adaptive attack_. In this section, we study some possible strategies that can be applied by adaptive FakeBob attacks.  
In Section 6, we demonstrate that the noise-adding defense method performs much better than the denoising method against the FakeBob attack in both GMM and i-vector SV systems. Therefore, we focus on adaptive FakeBob attacks against the noise-adding defense in this section.  
It is noted that the noise-adding method introduces randomness through the probability distribution generator so that even for the same input audio, the resulting noise-added audios are different for different runs. Such randomness can cause the gradient decent in adversarial attacks (as shown in Equation (7)) hard to find the correct values. A common technique adopted by adaptive attacks is expectation over transformation (EOT) [15; 41; 42], which computes the average of gradient decent over multiple runs on the same input sample. However, we found that in the original FakeBob attack, it has applied the natural evolution strategy (NES) [38] that calculates the average of gradient decent over multiple samples of the input audio with Gaussian noise, which is similar to EOT. Therefore, in this section we do not consider EOT, but study two other intuitive strategies that can be used by adaptive FakeBob attacks.
## 4 Decomposition into Geometric Primitives
### Identification of Geometrical Primitives  
Given a point cloud \(C\), we find the largest planar sub-cloud using the PCL command pcfitplane, that results in a sub-cloud \(C^{\text{pl}}\) and the plane equation  
\[n\cdot x+d=0,\quad x=(x_{1},x_{2},x_{3})\in\mathbb{R}^{3},\]  
where \(n\in\mathbb{R}^{3}\) is the unit (\(\|n\|=1\)) normal direction and \(d\in\mathbb{R}\). In Figure 5, we depict the decomposition of an object into planar primitives.  
Next, we subtract the sub-cloud \(C=C\setminus C^{\text{pl}}\) and find the largest cylindrical subcloud using the PCL command, pcfitcylinder, that results in a sub-cloud \(C^{\text{yl}}\) and the cylinder equation  
\[\|x-P\|^{2}=r^{2}+\left[(x-P)\cdot(Q-P)\right]^{2},\]  
where \(P=(P_{1},P_{2},P_{3})\in\mathbb{R}^{3}\) and \(Q=(Q_{1},Q_{2},Q_{3})\in\mathbb{R}^{3}\) are two distinct points on the cylinder axis and \(r>0\) is the radius. In Figure 5, we depict the cylindrical primitives.  
We subtract the sub-cloud \(C=C\setminus C^{\text{yl}}\) and repeat the procedure to find the second largest planar and cylindrical primitives until the PCL commands fail or the number of points in the sub-cloud is below our tolerance. We arrive at \(N^{\text{pl}}\) planar sub-clouds:  
\[C_{1}^{\text{pl}},C_{2}^{\text{pl}},\dots,C_{N^{\text{pl}}}^{\text{pl}}, \tag{3}\]  
Figure 5: Decomposition of the model from Figure 2 into (**a**) planar primitives and (**b**) cylindrical primitives.  
each of which, \(C_{i}^{\text{pl}}\), is associated with the normal vector \(n_{i}^{\text{pl}}\in\mathbb{R}^{3}\) and the shift \(d_{i}^{\text{pl}}\in\mathbb{R}\). Furthermore, this results in \(N^{\text{cyl}}\) cylindrical sub-clouds:  
\[C_{1}^{\text{cyl}},C_{2}^{\text{cyl}},\ldots,C_{N^{\text{cyl}}}^{\text{cyl}}, \tag{4}\]  
each of which, \(C_{i}^{\text{cyl}}\), is associated with the points \(P_{i}^{\text{cyl}},Q_{i}^{\text{cyl}}\in\mathbb{R}^{3}\) determining the axis and the radius \(r_{i}^{\text{cyl}}>0\). Note that the order of the sub-clouds matters as larger sub-clouds were likely identified by the PCL commands earlier.
## 3 Neural Network Based DPD Architecture
### Features of the NN Model
#### 3.1.2 Optimizers
Adaptive Moment Estimation (ADAM)  
The Adam algorithm combines the features of both Momentum and RMSProp meaning that, along with accumulating the exponentially decaying average of the previous squared gradients, it also holds exponentially decaying average of previous gradients as well. Adam is an efficient optimizer due to the component of speed and the capability to adapt gradients acquired from momentum and RMSProp, respectively. This is also the reason it is the most used optimizer. Comparison of Optimizers  
There are two metrics to determine the efficacy of an optimizer, speed of convergence to the global minimum and generalization (the model's performance on new data). Performance of the optimizers and hence their choice is also dependent on the type of data provided, based on that, two types of functions exist- convex and non-convex (Figure 4). In case of the latter, optimizers must be carefully chosen as it has multiple hurdles where our algorithm might become stuck, some of which we will discuss ahead. The performance of mentioned optimizers on different obstacles faced on non-convex functions are discussed below (see Table 2). From the table, we see that ADAM has performed comparatively better than others, especially in terms of convergence speed which is why it is the most used and most efficient optimizer.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 3 Antenna Overview
### Log-Periodic Antenna
#### 3.7.1 Bowtie Antenna  
The demand for low-cost, effective, and less-weight antennas in the communication network and multi-band application has brought about the design of this antenna. Recently, communication network requires light, easy working, and moveable antennas. Due to its structure, it is known as a butterfly antenna. There are different types of bowtie antenna: the bowtie slot antenna, the double-sided triangular bowtie antenna, the bowtie antenna microstrip fed, the slotted bowtie patch antenna, and the CPW fed curved bow tie slot antenna. The bow tie antenna has two mirrors placed on a rectangular patch. The output of this design is limited by distance and the alignment of the mirrors. This type of antennas are widely used in mobile communication networks and wireless systems [68; 69]. Figure 8 demonstrates the bowtie antenna.
## 2 Properties of Fine Granular Area
### Characteristics of the FGA
#### 2.2.6 Characteristics Observed by Atom Probe Tomography  
To obtain additional conclusions about the microstructural changes within the FGA, [PERSON] [91] analyzed the FGA using atom probe tomography (APT). For this purpose, the basic material as well as the material within a fish-eye area and from the FGA area around non-metallic inclusions as well as artificial FIB defects were examined. By means of APT, phases, carbides and grain or phase boundaries of the martensitic and bainitic states can be distinguished because of the local carbon concentration. Both in the original state and in the area of the fish-eye fracture surface near an inclusion, all other alloying elements were observed to be homogeneously distributed. In contrast, the alloying elements within the FGA showed clear deviations from this homogeneous distribution. Elements such as Al, Cr, Si, Mn, or P showed local enrichment or local depletion, with an increased number of fine precipitates being observed within the FGA. These changes were observed around inclusions as well as artificial defects, but not in the initial structure or in the area of the fish-eye, [PERSON] therefore concludes that they must result from the VHCF process. By analyzing the mean values of the concentrations of all investigated areas, an enrichment of the FGA with elements from the surrounding volume could be negated. The local concentration differences are therefore attributed to a local redistribution within the FGA. [PERSON] refers to similar results acquired via APT by [PERSON] et al. [107] and [PERSON] et al. [108] on fine grained areas in samples after severe plastic deformation (SPD), and by [PERSON] et al. [109; 110] and [PERSON] and [PERSON] [111; 112] for investigations of fine-grained white-etching cracks (WEC) occurring under rolling stress in high-strength steels. [PERSON] attributes the enrichment of certain elements by redistribution inside the FGA to the fact that those elements preferentially diffuse into the distorted lattice areas of dislocations and thus accumulate during the formation of dislocation cells. The intense sustained dislocation movement during VHCF loading and the resulting highly cyclic local plastic deformation is believed to compensate for missing enabling factors such as higher degrees of deformation and elevated temperatures present in the case of enrichment during SPD and WEC-formation.
## 2 Related Work  
Currently, Dedicated Short-Range Communication (DSRC) is a VANET communication protocol. The coverage range of DSRC is about 1 km, with achievable data rates in the order of 2-6 Mbps. The massive data rate that is expected to be required by the next generation of automotive applications cannot be achieved using these technologies [5]. A multi-band orthogonal frequency division multiplexing (MB-OFDM)-based ultra-wideband technique is used to transmit large data blocks in VANET [6]. Various studies have been carried out regarding bit error analysis. BER was studied on mobile station MS velocity variations using 802.11p. Results showed that there is very little impact from velocity variations on bit error performance. However, when the data frame length or relative speed increases, performance is affected. Millimeter-wave is a good solution for high-speed Internet access. The power delay profile (PDP) of the wireless intra-vehicular channel was derived from 1000 data sets. A tapped-delay-line (TDL) was derived based on the proposed PDP profile. The TDL model was used for a bit error study of millimeter-wave by varying data rates and link length. Since millimeter waves cannot penetrate walls, a relay device is required to redirect signals [7]. In [8] bit error rate probability was evaluated for interference in a multi-hop millimeter-wave system. To achieve reliability and continuity in vehicular communication, an adaptive transmission mechanism was proposed. The mechanism should adjust the data rate according to the number of connected vehicles [9].  
The current communication technologies cannot meet the high data rate requirements in vehicular communication. Therefore, a radio on fiber architecture was proposed [10]. To achieve a high data rate, a higher modulation order is used in radio on fiber architecture. An adaptive modulation mechanism was proposed [11]. An architecture of VANET using multi-gigabit communication was proposed in [12]. In the proposed architecture, i.e., the Giga-V2V (GiV2V) network, millimeter-wave is used to deliver high-quality video and sensor data of smart and self-driving cars. A few years back, only wireless access in the vehicular environment (WAVE)/IEEE 802.11p was available for V2V communication. New release 14 of long term evolution (LTE) and the arrival of 5G opened up new challenging possibilities. Due to the widespread deployment of the cellular systems and low latency, LTE V2V supplanted WAVE/IEEE 802.11p. However, LTE V2V is in the preliminary phase. There are certain drawbacks in current communication technologies. In WAVE/IEEE 802.11p, performance degrades with an increase in channel load. In high density areas, collisions occur, due to which network performance degrades in terms of throughput and latency [13].
## 3 Microgrid Sizing Approaches
### Mathematical Approaches  
Within the realm of mathematical approaches, MILP has a significant role [67]. This technique is selected for its capacity to secure a globally optimal solution to the optimization problem. In [68], a convex optimization approach was used to determine an island microgrid's optimal sizing and energy management while considering the battery degradation. In [69], the study focuses on a battery's optimal sizing, placement, and daily charge/discharge scheduling within the distribution network. In [70], the paper focuses on utilizing a two-stage stochastic programming. The model addresses uncertainties in DER power and load demand by ensuring a reliable PV power supply for essential services. In [71], an investigation is conducted on a microgrid system in an island territory, which incorporated multiple technologies such as PV, WT, biomass, and geothermal sources, among others, with the objective function of minimizing the overall costs of the system. The authors in [72] conducted a temporal decomposition using Benders' algorithm to determine the optimal sizing and operation of a hybrid railway power substation annually. In [73], the study explores multi-year economic energy planning optimization in microgrids using a MILP method. The authors in [74] utilized linear programming to minimize costs or emissions. They explored the effects of policy on generation investment choices.  
As the literature shows, non-linear approaches significantly optimize microgrid systems, where various complex problem formulations are addressed using advanced mathematical models. A mixed integer non-linear program (MINLP) is notably applied to tackle multi-objective optimization problems, such as the one from [75], which focuses on minimizing the system cost related to equipment sizing and emissions. This application highlights the capability of MINLP to simultaneously address multiple aspects of microgrid optimization. It provides solutions that balance economic and environmental concerns. A single objective function is presented in [76] to minimize the system's costs through a non-linear problem formulation. This study extends the application of non-linear optimization to managing multiple microgrids by incorporating probabilistic modeling of energy resources and load demand. Doing so accounts for the inherent uncertainties in energy production and consumption. This approach enhances the decision-making process for microgrid operation and planning. These non-linear approaches underline the complexity and multi-dimensional nature of optimizing microgrid systems. They reflect the ongoing advancements in mathematical modeling techniques to achieve more sustainable and cost-effective energy solutions.
## 4 Trusted Network Secure Routing Algorithm
### Reputation and Ant-Colony Optimization
#### 4.4.3 Integrated Pheromone Updating  
The ants will leave more information elements along shorter paths. To simulate this phenomenon, when each ant reaches the end of its path, the pheromone concentration on that path must be updated. The amount of pheromone on a path will increase as the number of ants that have traveled along it increases, and the pheromone will also evaporate as time elapses. Therefore, we consider both factors in the process of pheromone updating, which is shown in Equation (2). Where \(\delta\) is the pheromone evaporation rate. Both ant-colony algorithms and genetic algorithms, as well as a variety of other simulated evolutionary algorithms, exhibit slow convergence rates and can easily fall into local optima. The pheromone evaporation rate is directly related to the global search capability of the ant-colony algorithm and its convergence rate: if the value is small, it may cause residual pheromones to dissipate too quickly, which is not conducive to pheromone accumulation, whereas if the value \(\delta\) is too large, then the algorithm may have a high risk of falling into a locally optimal solution. Previous studies in the literature indicate that an evaporation rate between 0.5 and 0.9 is best, therefore, the evaporation rate is set to 0.6 in the proposed algorithm. \(\Delta p_{ij}(t)\) is the pheromone increment associated with ants traveling on the path from \(i\) to \(j\), and its value is fixed, which can be achieved by Equations (3) and (4). Among them, \(\Delta p_{ij}^{k}(t)\) is the pheromone increment associated with ant \(k\) traveling on the path from \(i\) to \(j\). \(Q\) is the pheromone strength, which can affect the convergence speed of algorithm to a certain extent. And previous studies in the literature indicate that \(Q=100\) is suitable.  
The updating formula for the synthetic pheromone is only used during the path optimization process; when a new path query begins, the initial amounts of pheromone on all edges must be recalculated according to the node attribute information at that time.
# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 7 Partial Fake Speech and Human Speech
### Methodology and Key Findings  
Our research aimed to investigate the effect of partial fake speech on human listeners in two different scenarios. Firstly, we aimed to understand how easily human listeners can be deceived by partial fake speech when it is presented as an audio-only stimulus. Secondly, we were interested in how they respond to partial fake speech when it is presented within a completely real video. To achieve these objectives, we developed experimental protocols and procedures for the study, which were carefully evaluated and approved by our local ethics committee.  
**User Study A (Online Survey: PF as Audio-Only).** We conducted an experiment to determine whether humans can differentiate between real, fake, and, in particular, partially fake speech. We categorised the voices into two types: familiar voices (i.e., famous voices) and unfamiliar voices. Participants were asked to listen to 10 audio recordings and determine whether each recording contained real, fake, or partially fake speech. Four of the audio clips belonged to [PERSON], whose voice was assumed to be familiar to the participants.  
**Findings.** In this study, participants had difficulty identifying partially fake speech, regardless of familiarity with the speaker. As shown in Table 9, the PF clip accuracy of identifying familiar voice ranged from 11% and 24% while for unfamiliar voices, it ranged from 14% to 18%.  
**User Study B (Online Survey: PF within Real Video).** For this survey, we asked the participants to watch six videos. All of the videos were real, but we manipulated the audio. As with User Study A, we gave the participants three options for each video: real, fake, and partially fake. Our goal in conducting this survey was to determine the participants' ability to identify partially fake audio in two different scenarios: when the speakers were in front of the camera and their lips were clearly visible, and when the speakers were far from the camera.  
**Findings.** We found that in both cases, when the speaker was in front of the camera and when the speaker was far from the camera, participants were unable to identify partial fake audio. As presented in Table 10, the accuracy when the speaker's face was close to the camera ranged from 17% to 31%, while when the speaker's face was far from the camera, the accuracy ranged from 11% to 23%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Audio Type**} & \multirow{2}{*}{**Familiar Voice?**} & \multicolumn{3}{c}{**Accuracy of Participant Responses**} \\ \cline{3-5}  & & **Real** & **Fake** & **Partial Fake** \\ \hline PF & No & 34\% & 48\% & 18\% \\ PF & No & 12\% & 74\% & 14\% \\ PF & Yes & 73\% & 16\% & 11\% \\ PF & Yes & 63\% & 13\% & 24\% \\ Fake & No & 77\% & 17\% & 6\% \\ Fake & yes & 11\% & 85\% & 4\% \\ Fake & No & 36\% & 42\% & 22\% \\ Fake & Yes & 19\% & 54\% & 27\% \\ Real & Yes & 76\% & 12\% & 12\% \\ Real & No & 53\% & 23\% & 24\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Average accuracy of participants’ responses for 10 audio clips.
###### Abstract  
COVID-19, a novel coronavirus infectious disease, has spread around the world, resulting in a large number of deaths. Due to a lack of physicians, emergency facilities, and equipment, medical systems have been unable to treat all patients in many countries. Deep learning is a promising approach for providing solutions to COVID-19 based on patients' medical images. As COVID-19 is a new disease, its related dataset is still being collected and published. Small COVID-19 datasets may not be sufficient to build powerful deep learning detection models. Such models are often over-fitted, and their prediction results cannot be generalized. To fill this gap, we propose a deep learning approach for accurately detecting COVID-19 cases based on chest X-ray (CXR) images. For the proposed approach, named COVID-CGAN, we first generated a larger dataset using generative adversarial networks (GANs). Specifically, a customized conditional GAN (CGAN) was designed to generate the target COVID-19 CXR images. The expanded dataset, which contains 84.8% generated images and 15.2% original images, was then used for training five deep detection models: InceptionResNetV2, Xception, SqueezeNet, VGG16, and AlexNet. The results show that the use of the synthetic CXR images, which were generated by the customized CGAN, helped all deep learning models to achieve high detection accuracies. In particular, the highest accuracy was achieved by the InceptionResNetV2 model, which was 99.72% accurate with only ten epochs. All five models achieved kappa coefficients between 0.81 and 1, which is interpreted as an almost perfect agreement between the actual labels and the detected labels. Furthermore, the experiment showed that some models were faster yet smaller compared to the others but could still achieve high accuracy. For instance, SqueezeNet, which is a small network, required only three minutes and achieved comparable accuracy to larger networks such as InceptionResNetV2, which needed about 143 min. Our proposed approach can be applied to other fields with scarce datasets.  
COVID-19 detection; generative adversarial network (GAN); conditional GAN (CGAN); deep learning; chest x-ray (CXR)