esa-sceva/satcom-chunk-collection huggingface dataset:
# KRT-FUAP: Key Regions Tuned via Flow Field for Facial Universal Adversarial Perturbation
## 4 Experimental Results
### Ablation Study
#### 4.5.3 Impact of Adversarial and Stealthiness Loss  
The loss function in this experiment consisted of two components: adversarial loss and stealthiness loss. The adversarial loss is achieved by directly selecting the optimization direction of the adversarial samples, set to be opposite for better attack effectiveness. Simultaneously, the VGG network was utilized to control stealthiness metrics, enhancing the imperceptibility of images. To demonstrate the effectiveness of our loss function settings, we compared them with commonly used loss functions in face recognition, such as cosine similarity loss and Euclidean distance loss. The other settings remain unchanged; the cosine similarity loss reduces the similarity between features, while the Euclidean distance loss increases the distance between features. We tested the fooling rates and stealthiness metrics on IResNet50 and MobileFaceNet. As shown in Table 5, the corresponding experimental results show that in simultaneously controlling adversarial effectiveness and stealthiness in the loss function, the two metrics of the generated universal adversarial perturbations exhibit superior performances.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Backbone** & **Method** & **FR \(\uparrow\)** & **SSIM \(\uparrow\)** & **PSNR \(\uparrow\)** \\ \hline \multirow{4}{*}{LFW} & IResNet50 & KRT-FUAP & 81.9\% & 0.9304 & 34.0157 \\  & Fixed key regions & 80.2\% & 0.9147 & 33.1845 \\ \cline{2-6}  & \multirow{2}{*}{MobileFaceNet} & KRT-FUAP & 80.1\% & 0.9044 & 32.9812 \\  & & Fixed key regions & 78.9\% & 0.8916 & 32.1108 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fooling rate and objective stealthiness metrics with and without learnable flow field.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Backbone** & **Method** & **FR \(\uparrow\)** & **SSIM \(\uparrow\)** & **PSNR \(\uparrow\)** \\ \hline \multirow{4}{*}{LFW} & IResNet50 & KRT-FUAP & 81.9\% & 0.9304 & 34.0157 \\  & Global regions & 77.8\% & 0.8926 & 31.6674 \\ \cline{2-6}  & \multirow{2}{*}{MobileFaceNet} & KRT-FUAP & 80.1\% & 0.9044 & 32.9812 \\  & & Global regions & 77.7\% & 0.8639 & 30.5034 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fooling rate and objective stealthiness metrics for key regions and global regions.
# Blind Turbo Equalization of Short CPM Bursts for UAV-Aided Internet of Things
## 4 EMLVA for Blind Channel Equalization  
### EM Algorithm  
The EM algorithm is divided into two steps, one of which is the expectation step (E-step) and the other is the maximization step (M step). For a blind equalization problem, the observations \(r\) and transmitted symbols \(\alpha\) can be written as a new data vector \(\mathbf{z}=(\mathbf{r},\mathbf{\alpha})\). The EM algorithm then consists of the following iteration.  
E-step: Compute the mean log-likelihood function:  
\[Q(\mathbf{\theta}|\mathbf{\theta}^{(m)})=E_{a}[Inf_{\mathbf{r}_{n}}(\mathbf{r}_{n}|\mathbf{\alpha},\theta)|r_{n},\theta^{(m)}]. \tag{16}\]  
where \(f(\cdot)\) represents a conditional probability density.  
M step: Compute new estimates of parameters:  
\[\mathbf{\theta}^{(m+1)}=argmaxQ(\mathbf{\theta}|\mathbf{\theta}^{(m)}). \tag{17}\]  
Figure 3: Typical UAV-aided IoT communications scenario with G2A cases.
# Accurate Landmark Localization for Medical Images Using Perturbations
## 3 Materials and Methods
### Network Architecture  
Our architecture is based on U-net [39], and U-net with attention module [40]. The contracting path and expanding path consist of the repeated application of two 3 \(\times\) 3 convolutions, each followed by LeakyReLU and a 2 \(\times\) 2 max pooling (for contracting path), or up-sampling (for expansive path). The number of feature channels was increased from 64, 128, 256, to 512 in the contracting path, and decreased to 512, 256, 128, to 64 in the expansive path. Attention gates filter the features propagated through the skip connections [40]. We used AC loss as a cost function and minimized cost function by using the Adam optimizer. AC loss is a function proposed by [11]. This function calculates the loss by considering the distance and angle between landmarks. The learning rate is initialized as 0.0001 and annealed by the Cosine annealing schedule.
# Notes on Common Misconceptions in Microplastics Removal from Water
## 3 Microplastics Removal from Water
### Degradation  
Degradation is defined as a process that leads to the deterioration of some of the physical properties of polymers. It may affect thermal stability, optical and mechanical properties, or crystallinity [49; 50]. In the marine environment, mechanical degradation processes are facilitated by the action of waves, sand or other sediments [51]. Particles floating on the surface are exposed to sunlight and are subject to change due to UV radiation and oxidizing conditions of the atmosphere. In contrast, in deeper waters, where there is less radiation and oxygen, these abiotic processes are slower [52]. Biological activity is also rather negligible in deep oceans with reduced biodiversity. In less deep waters, many autotrophic, heterotrophic and symbiotic organisms have been found on the surface of microparticles, which play an important role in the degradation of these materials [53]. It could be presumed that biodegradability of microplastics depends on their material the same way as large plastic particles [54].  
Not only a model solution in fresh water is missing, but seawater is a completely different environment and it is very problematic to predict the course of the process. The irradiation intensity changes with depth and the amount of dissolved oxygen also differs with depth, therefore simulation of such phenomena in laboratory conditions is crucial and necessary to conduct.  
Additionally, there are studies that deal with various degradation ways of macroparticles of plastics. The main types include photo-oxidative degradation (photochemical and photocatalytic). There are also other techniques, such as thermal and mechanical-chemical degradation, ozone degradation, catalytic degradation and biodegradation. Partial change in their chemical structure, surface aging, weathering, surface properties change and loss of molecular weight have been proved, but complete removal via degradation is still a subject of research [45; 55; 56; 57; 58]. The first study on the complete mineralization of polystyrene microparticles appeared very recently, thus potential for their complete removal from water exists. The degradation was confirmed using in situ DRIFTS and mass spectrophotometry via formation of hydroxyl, carbonyl and carbon-hydrogen groups. Their presence was understood as evidence of polystyrene degradation [59].  
The paper focuses on the photochemical, photocatalytic and ozonation degradation methods. They do not require special treatment and can be applied directly in water streams. Furthermore, they can be sequentially placed right after common water treatment methods. Thermal degradation requires the collection and separation of microparticles from water and their consequent transfer to a degradation reactor. Biodegradation does not seem to be an appropriate option, as biodegradation of plastic microparticles is reported to last days and weeks [60; 61; 62; 63; 64; 65].
## 5 Triple-Frequency Medium RTK Performance Analysis  
### Satellite Number and PDOP  
Due to the baseline being a medium baseline, the number of available satellites and the PDOP can be analyzed using the HZDQ station as an example. Figure 6 plots the satellite number and PDOP of Scheme 2, i.e., only triple-frequency data are used and Scheme 3, i.e., both dual-frequency and triple-frequency data are used. It should be noted that the satellite number and PDOP for Scheme 1 are identical to those for Scheme 3; therefore, no analysis is conducted. It can be seen that, for Scheme 3, the number of satellites exceeding 90% of the epochs is greater than 17, while, for Scheme 2, it is 10. However, 10 satellites also fully meet the requirements for BDS/Galileo/GPS RTK positioning and provide faster computation speed. In addition, the average PDOP of both Schemes is less than 2, indicating a good geometric configuration [25].
# Privacy-Preserving and Explainable AI in Industrial Applications
## 3 Explainable Industrial AI Applications
### Review of Explainable Industrial AI Applications  
Manufacturers are now more interested in the use of AI to improve the overall quality of industrial applications, while at the same time unboxing the original black-box models. One of the domains in which AI is not widely used is air traffic management (ATM); however, introducing AI is a need which can help ATM in the future. As they are increasing in complexity, there is also a need for explainable AI techniques, to identify the most important features in model predictions [58]. [PERSON] et al. [59] introduced a tutorial in which they presented an overview of model explainability and interpretability, alongside techniques which are helpful in providing explainability for AI systems. Their examples were mainly applications from the industrial environment. [PERSON] et al. [60] and [PERSON] et al. [61] also reviewed XAI applied in industry. In the automotive field, explainable AI is used to create transparency and understand model decisions [62], but it turns out that XAI itself is not sufficient to increase the trust [63]. Other explanations need to be provided, not only for developers, but also for the end-user.  
[PERSON] et al. [64] proposed a new XAI framework for predicting maintenance applications for automotive applications and others. [PERSON] et al. [65] used XAI for diagnosis and fault detection in rotating machinery. Anomaly detection was used for performing, while, for interpreting fault diagnosis models, they employed Shapley additive explana  
Figure 4: Heatmaps yielded by different techniques for two animals identified in a picture. Adapted with permission from [55], 2020.  
Figure 3: Explaining Google’s Inception neural network predictions: electric guitar (**b**), acoustic guitar (**c**) and Labrador (**d**). Highlighted parts from the (**a**) original image are those that contributed the most to each prediction. Adapted with permission from [52], 2016.  
tions (SHAPs). To reduce energy consumption in mineral processing, [PERSON] et al. [66] highlighted the importance of using high-pressure grinding rolls (HPGRs). The main problem is HPGR modeling; they proposed to expand the existing conscious laboratory (CL) and used XAI systems to innovate powder technology industries. SHAP and extreme gradient boosting (XGBoost) were the selected methods for achieving model explainability.
## 3 Description of the Dataset and Data Processing
After several experiments and comparisons, the maximum frame difference parameter \(time\_thresh\) is set to 20 frames (2 s), and the maximum distance threshold \(dis\_thresh\) is set to 1000.
## 2 Methods
### Step 2
#### 2.4.2 Assessment of Time to Fix Ambiguities for the Positioning Solution  
The time to fix ambiguities (integer ambiguities) for the positioning solution was assessed at measurement start. The time, from the start of the GNSS measurement to the instant when integer ambiguities were fixed, was calculated for methods \(A\) to \(D\) (method \(E\) was a non-differential code solution). The mean and SD of the times to fix integer ambiguities was calculated for the 12 trials.
## 5 Simulation Experiments and Analysis  
### Design of LEO-SMC-ICN-IoT Simulation Platform  
For the design of the LEO satellite mega-constellation network simulation platform, our team has completed Section 4.1 of [41]. For ease of understanding, we will briefly reiterate it.  
The NS-3, a discrete event-driven network simulator, consists of interdependent modules; the ndnSIM module is the latest ICN simulation module based on NS-3, which implements the basic components of ICN modularly [12]. Yet the current ndnSIM can only provide the lower layer support of the ICN protocol stack, such as CS, PIT, and FIB, forwarding policy, and cache replacement strategy [59]. Therefore, this paper expands the NS-3 structure by adding LEO modules and develops a hierarchical simulation platform, as shown in Figure 11[41]; the simulation platform consists of four layers: infrastructure, a network model that considers the number of orbital planes in the LEO-SMC-ICN-IoT network, the number of satellite nodes in each orbital plane, the orbital height of the satellite constellation, and ISLs to construct a mobile model with mobility prediction [41]; a network configuration configures the broadcast channel for the LEO-SMC-ICN-IoT; the control layer is responsible for maintaining and updating the ISLs and satellite-to-ground links, and is also responsible for data packet caching and routing between satellite nodes in the network.
# A 7.5-9 GHz GaAs Two-Channel Multi-Function Chip
## 1 Introduction  
Phased array technologies have been widely applied to satellite communications and defense systems to implement high-performance radar systems [1]. However, phased array radar systems consist of thousands of T/R modules which increase the systems' complexity and cost. By integrating several functions such as phase and gain control, driver amplifiers, and switches on a single multi-function chip [2], the sharp problem can be solved. Packing the multi-function chip (MFC) for compact and low cost T/R modules has already been realized in a phased array system [3]. With the development of advanced 3D integration technology, MFC chips are introduced to further improve the integration density of 3D microsystems in space applications [4].  
In recent years, several GaAs multi-function chips have been presented, but higher integration implies a large number of control lines and pads to the chips [5; 6; 7]. By combination of serial-to-parallel converter on-chip, the number of control lines and pads can be substantially reduced [8; 9]. Meanwhile, integrating multi-channels on one multi-function chip is beneficial as it decreases the system's size and cost. With high performance and high integration, the MFC chips must be promising.  
According to actual application demand, a 7.5-9 GHz two-channel amplitude-phase control multi-function chip (MFC) was developed successfully via the 0.5 \(\upmu\)m GaAs E/D pHEMT process. The variation of amplitude-phase in channel will influence the performance of T/R modules. Therefore, a single pole single throw (SPST) switch was designed to control array calibration, and an absorptivetopology was selected to optimize the return loss. In order to achieve extended bandwidth, good attenuation flatness, and low additive phase shift, a switched-path-type topology was employed in 8 dB and 16 dB attenuation bit. It is complex and difficult to assemble the T/R modules due to the existence of numerous pads. A 27-bit serial-to-parallel converter was integrated to decrease the control pads, which also reduces the size and cost of T/R modules. To the best of our knowledge, the insertion loss, phase error, attenuation error, and return loss of the presented MFC represent state-of-art performances.
# Generation of Pure State Photon Triplets in the C-Band
## 3 Numerical Simulation Results  
In this section, we discuss the generation of photon pairs from different materials, and finally obtain the pure-state photon triplets. Among the numerous nonlinear crystals, lithium niobite has a relatively higher nonlinear coefficient [34; 35], which leads to a greater conversion efficiency. There is a wide range of transparency, from 420 nm to 5200 nm. In addition, lithium niobite doped with MgO has higher damage threshold, thus the periodically poled lithium niobate doped with 5% MgO (PPMgLN) will also be used as a reference for comparison. We get a set of crystal lengths which are optimum for each SPDC process through theoretical arithmetic.  
In the first SPDC, the crystal and pump parameters are taken as the variables and the calculation of the Schmidt number is done. We select the appropriate pumping duration and crystal length \(L_{1}\) by analyzing the obtained data. After calculation, the frequency distribution of photonic \(\omega_{0}\) is obtained. That is to say, the envelope information of the pump in the second SPDC is determined, which is \(\exp[-(\omega_{0}-\overline{\omega}_{0})/\sigma_{0}^{2}]\), where \(\sigma_{0}\) is the bandwidth of the new source \(\omega_{0}\). Then, the amplitude function of photon \(\omega_{2}\) and \(\omega_{3}\) is described as  
\[F(\Omega_{2},\Omega_{3})=\exp[-\frac{(\Omega_{2}+\Omega_{3})^{2}}{\sigma_{0}^ {2}}]\exp(-i\frac{\Delta k_{2}L_{2}}{2})\sin\varsigma\left[\frac{(k_{0}(\omega _{0})-k_{2}(\omega_{2})-k_{3}(\omega_{3})-k_{g2})L_{2}}{2}\right] \tag{12}\]  
In the second SPDC, the Schmidt numbers of photon state between \(\omega_{2}\) and \(\omega_{3}\) are calculated by using the bandwidth information of the generated photons \(\omega_{0}\) and taking the crystal length \(L_{2}\) as the variable. Then we select the appropriate crystal length \(L_{2}\). Each time the most appropriate parameters are determined, the two-photon joint spectrum and the final three-photon joint spectrum are given to verify the theoretical calculation.
# Capacity of Heterogeneous Mobile Wireless Networks with \(D\)-Delay Transmission Strategy
###### Abstract  
This paper investigates the capacity problem of heterogeneous wireless networks in mobility scenarios. A heterogeneous network model which consists of \(n\) normal nodes and \(m\) helping nodes is proposed. Moreover, we propose a \(D\)-delay transmission strategy to ensure that every packet can be delivered to its destination nodes with limited delay. Different from most existing network schemes, our network model has a novel two-tier architecture. The existence of helping nodes greatly improves the network capacity. Four types of mobile networks are studied in this paper: i.i.d. fast mobility model and slow mobility model in two-dimensional space, i.i.d. fast mobility model and slow mobility model in three-dimensional space. Using the virtual channel model, we present an intuitive analysis of the capacity of two-dimensional mobile networks and three-dimensional mobile networks, respectively. Given a delay constraint \(D\), we derive the asymptotic expressions for the capacity of the four types of mobile networks. Furthermore, the impact of \(D\) and \(m\) to the capacity of the whole network is analyzed. Our findings provide great guidance for the future design of the next generation of networks.  
network capacity; heterogeneous; mobile networks; delay constraints +
Footnote †: journal: Sensors
## 3 Substrate Recognition by L/F-Transferase
### tRNA Availability  
As opposed to the previously mentioned example with cell wall biosynthesis in _S. aureus_, the most optimal tRNA substrate (isoacceptor 5'-CAG-3) for L/F-transferase is also the most abundant leucyl-tRNA isoacceptor in _E. coli_ which decodes the most frequently used codon (5'-CUG-3') in a variety of growth rates and media conditions [53; 54; 55]. Thus, this optimal tRNA substrate for L/F-transferase is not specific for tRNA-dependent ribosome-independent peptide bond formation but is also significantly used by the translational machinery. This results in what appears to be a direct competition for this tRNA between EF-Tu and L/F-transferase. With the much higher abundance of EF-Tu (\(\sim\)100 uM) and its significantly higher affinity for aminoacyl-tRNAs (K\({}_{\text{D}}\) of \(\sim\)5 nM) compared to the low _in vivo_ concentration and lower affinity for L/F-transferase (\(\sim\)0.5 uM and a K\({}_{\text{D}}\) of \(\sim\)200 nM) [26; 27; 56], an apparent conundrum exists with regard to how L/F-transferase can compete for an aminoacyl-tRNA substrate. It has been predicted that EF-Tu is in excess of all cellular pools of aminoacyl-tRNAs, suggesting that no excess aminoacyl-tRNAs are available for L/F-transferase and a direct competition of tRNA substrates exists between the two systems. To overcome the competition with the translational machinery, EF-Tu binding to aminoacyl-tRNAs may have to be inactivated under specific cellular conditions, such that aminoacyl-tRNAs may become available for L/F-transferases, such as conditions of nutrient limitation during the stringent response. Alternatively, other mechanisms must exist for which L/F-transferase obtains aminoacyl-tRNA substrates such as specific interaction with the aminoacyl tRNA synthetase, which could potentially enable a direct exchange to the tRNA to L/F-transferase or the role of yet unknown cellular factors that could assist in aminoacyl-tRNA binding by L/F-transferase.
## 4 Numerical Results
### Relationship of Bending Moment and Curvature  
In this analysis, central pier (P4) of continuous span, which supports more weight and shall afford more inertial force during earthquakes, is likely subjected to large bounding forces between roller bearing and stopper during earthquakes. Therefore, the hysteretic curve of bending moment to curvature at pier bases for P4 in X and Y direction in cases of D0, D100, D500, and D2000 are shown Figure 17. M and My represent bending moment and yield bending moment, respectively. The hysteretic curve of bending moment to curvature at other pier bases present similar results. Figure 17 shows that the absolute value of M/My overpasses 1 in X and Y directions, which indicates plastic deformation at pier base in the two directions. Maximum bending moment and maximum curvature in X direction are larger than those in Y direction. All the bearings are restrained in Y direction, which leads to relatively less bearing forces. Thus, maximum bending moment and maximum curvature at pier bases in X direction play a more important effect on the damage of pier bases. As stopper value increases, maximum bending moment and maximum curvature at pier bases in X direction obviously increase. However, maximum bending moment and maximum curvature at pier bases do not exhibit a significant difference in case of different viscous dampers applied on the viaduct. In other words, the type of viscous dampers does not affect the maximum bending moment and maximum curvature at pier bases. Viaduct equipped with a viscous damper also presents less maximum bending moment and less maximum curvature at pier bases than those without viscous dampers. In conclusion, stopper value plays a more important role in maximum bending moment and maximum curvature at pier bases than viscous dampers.  
Figure 17: Hysteretic curve of bending moment to curvature at pier bases (P4). (**a**) Stopper = 4 cm. (**b**) Stopper = 8 cm.
## 3 Results and Discussion
### Part II: Case Study
#### 3.2.3 Take-Off and Early Flight Phase
The horizontal length from the in-run edge is shown on the x-axis and velocity is shown on the y-axis. A negative sign on the x-axis indicates the area before take-off. considerably for the complete phase, which will be addressed in Section 3.3. The angular velocities presented in Figure 12b are in the range of earlier lab and field studies [2; 54]. [PERSON] et al. [2] found a correlation between the angular velocity of the hip at the release instant and the jump distance. In this case, the angular velocities of the hip showed similar trends between the two jumps, but the jumps differed in peak velocity of the knee. To summarise, a difference in the CoM trajectory emerged from \(\sim\)10 m after the in-run edge with Jump 1 being at a higher altitude. The main explanation for this divergence in vertical position is the higher horizontal speed in Jump 1, mainly caused by a higher start gate. The peak force normal to the take-off platform is explained by a higher angular velocity of the knee angle, together with a higher \(\psi\) in the take-off, resulting in a more upright position, which might cause a higher take-off force in the vertical direction. Interestingly, the \(F_{L}/F_{D}\) ratio varied during the first 20 m of the flight phase, even though the athlete seemed to reach a constant position around 5 to 10 m after the take-off.