esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Limitations and Research Gaps  
In the context of ET detection, most existing studies focus on designing binary detectors. However, their effectiveness in detecting new attacks is often limited and unpredictable. Anomaly detection offers an alternative solution, but anomaly detectors typically suffer from low detection accuracy and their performance is highly dependent on finding an optimal threshold. This is a challenging task, especially when malicious data are not known. Additionally, both binary and anomaly detectors are vulnerable to adversarial evasion attacks. While securing binary detectors against evasion attacks has received extensive attention from the research community, the robustness of anomaly detectors against such attacks has been less explored. This paper aims to bridge this research gap by addressing the following limitations:  
* Most existing defense mechanisms, such as adversarial training [19] and defensive distillation [20], are tailored to specific evasion attacks and their performance is unpredictable in the case of new attacks. Moreover, these defense mechanisms sacrifice model accuracy to improve robustness against evasion attacks and are primarily designed for binary detectors, rendering them unsuitable for anomaly detectors;
* Few defense mechanisms have been proposed for AE-based anomaly detection [22; 23; 24; 25]. These mechanisms are primarily utilized for applications other than smart grids, with only [24] specifically designed for smart grid use. All of these mechanisms primarily aim to improve the AE's decoder output to maximize the reconstruction error for adversarial samples. However, determining the optimal reconstruction error threshold requires some prior knowledge of the malicious data (i.e., the nature of attacks), which may not be possible practically.  
These limitations are addressed in this paper by proposing a hybrid anomaly detector that combines DAE and OCSVM to overcome the difficulty in determining the optimal threshold value. Additionally, we propose using SHAP explanations obtained from consumption readings to train a cluster-based anomaly detector, enhancing both its detection accuracy and robustness against evasion attacks.
# Coverage Path Planning with Semantic Segmentation for UAV in PV Plants
## 3 Results and Discussion
### Results with Deep Server, and OpenCV Functions  
OpenCV functions and a DL server were combined to accurately extract the mask and to trade off the DL server results of the PV plant area. Then, the CPP methods used the mask as a region of interest (RoI).  
Figure 5: UAV over A PV plant, simulated in Gazebo.  
The stages to obtain the results of the RoI in the three images are shown in Figures 6-8. In the first stage, a high-resolution image from Google Maps, of predefined height and width, was obtained, and used as an input image in the DL server, depicted in Figure 6a. In the second stage, the output image of the DL server was the mask, shown in Figure 6b. In the third stage, the opening function was used in the mask, Figure 6c. In the fourth stage, the RoI was obtained using the draw contour method, as seen in Figure 6d. Finally, the RoI was blended with the input image with the aim of comparing the results in Figure 6e. The results were satisfactory and can be adapted, depending on the environment.
## 2 Materials, Chemicals, Methods and Cell Line
### Method Development and Validation
#### 2.4.10. Cell Viability Assay  
HEI-OC1 cells were cultured under the conditions outlined in Section 2.4.9. Cells were trypsinised, collected and seeded in a 96-well plate at 1000 cell/well density for 24 h to attach prior to subsequent treatment. HEI-OC1 cells were exposed to 25 mM glucose (HG) to mimic diabetic conditions [55; 56; 57], to metformin only at concentrations 2.5 mM, 5 mM and 10 mM (M2.5, M5 and M10, respectively) and to combination treatments (HG + M2.5), (HG + M5) and (HG + M10). Control cells were only exposed to fresh culture medium without any other treatments (CTRL). A viability assay of HEI-OC1 cells was determined by the CellTitre-Blue kit (Promega, NSW, Australia) at 560-590 nm wavelength [58] using a PerkinElmer microplate reader (PerkinElmer, Waltham, MA, USA). The viability of HEI-OC1 cells was determined as mean % (n = 4, \(\pm\)SD) of treated cells with respect to CTRL as per Equation (5).  
\[\%\ \mathbf{Cell\ viability}=\frac{\text{Test\ absorbance}}{\text{CTRL\ mean\ absorbance}}\times 100 \tag{5}\]  
Viability assay of treated HEI-OC1 cells was conducted after 2 days, 3 days, 4 days and 5 days from treatment. And the whole experiment was repeated one more time for reproducibility.
## 3 Determining Optimal LUT
### Interference Modeling  
The standard specifies four interfering channels [15]. Channels adjacent to the desired channel transmit at same power level as the desired, \(-82\) dBm, whereas alternate channels should be considered transmitting \(-52\) dBm. Adjacent channels are \(5\) MHz apart from the desired channel on either side. Similarly, alternate channels are \(10\) MHz apart. For an IF of \(3\) MHz [16], input to the ADC can be given as  
\[x(t) = \tag{3}\] \[\text{Re}\left\{\begin{array}{l}x_{0}(t)e^{j(2\pi\,3\times 10^{6}t+ \theta_{0})}+x_{1}(t)e^{-j(2\pi\,2\times 10^{6}t+\theta_{1})}\\ +x_{2}(t)e^{j(2\pi\,8\times 10^{6}t+\theta_{2})}+x_{3}(t)e^{-j(2\pi\,7\times 1 0^{6}t+\theta_{3})}\\ +x_{4}(t)e^{j(2\pi\,13\times 10^{6}t+\theta_{4})}\end{array}\right\}\]  
\(x_{0}(t)\) is the desired baseband signal. \(x_{1}(t)\) and \(x_{2}(t)\) are adjacent baseband signals. \(x_{3}(t)\) and \(x_{4}(t)\) are alternate baseband signals.  
BER simulation to find all combination of \(Q_{dig}\) and \(f_{s}\) can be very time consuming [17]. Instead we have developed a technique to reduce the computation time. Initially we find the variance of correlations at the output of correlation demodulator. We use the same variance measure in our subsequent simulations with different receiver settings. We found that this technique reduces the simulation complexity lot in comparison with doing BER simulations with bandpass signals.  
Figure 7: Simulation Model, \(g\) is the variable gain of VGA, \(Q_{dig}\) and \(f_{s}\) are sampling frequency and bitwidth respectively.
# Black PEO Coatings on Titanium and Titanium Alloys Produced at Low Current Densities
###### Abstract  
Black coatings were successfully formed on Grade 2 (G2) and Grade 5 (G5) titanium alloy by means of a direct-current Plasma Electrolytic Oxidation (PEO) process at a very low current density of 0.05 A/cm\({}^{2}\). The impact of two different treatment times (30 min and 60 min) was examined. The electrolyte for the PEO process was a phosphate base solution Na\({}_{5}\)P\({}_{3}\)O\({}_{10}\) containing FeSO\({}_{4}\) and (NH\({}_{4}\))\({}_{6}\)Mo\({}_{7}\)O\({}_{24}\) as coloring additives. PEO-coated samples were subjected to optical, morphological, structural, chemical, and electrochemical characterization. XRD, EDS, and XPS data analyses revealed that anion MoO\({}_{2}^{2-}\) and metal cation Fe\({}^{3+}\) were successfully incorporated into the coatings. The results demonstrated that PEO-coated samples prepared after 60 min exhibit a stronger black color than those created after 30 min, with an absorptance maximum of 0.86. Furthermore, all prepared PEO coatings improve the corrosion resistance of bare titanium. Among them, the 60-minute PEO coatings on both alloys were the ones with the best corrosion properties.  
Plasma Electrolytic Oxidation (PEO); black coatings; titanium alloys +
Footnote †: journal: Applied Sciences
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 2 Background
### Communication Technologies for Resilience
#### 2.1.2 Self-Organizing Network  
A Self-Organizing Network (SON) aims to minimize the costs related to the life cycle of a wireless network by eliminating the need for manual configuration during deployment and operation. The responsibilities of a SON involve automatically planning, configuring, managing, optimizing, and healing the network, increasing the network's reliability, performance, and resilience. The main SON technologies are Mobile Ad hoc Networks (MANETs), Wireless Mesh Networks (WMNs), and 3 GPP-long Term Evolution (3 GPP-LTE).  
A MANET is a type of wireless network in which the nodes are interconnected without a centralized infrastructure and are also free to move randomly, causing corresponding changes in the network topology. Each node of the MANET network behaves like a router as it forwards traffic from other nodes to another specific node in the network. Although MANET routing protocols are primarily used for mobile networks, they can also be useful for stationary node networks that lack infrastructure [9]. Likewise, the concept of a MANET can be successfully applied to many existing wireless technologies, such as satellite and cellular networks, to increase their resilience [10].  
In WMN, wireless nodes are arranged in a mesh topology. However, not all of the nodes behave as routers, and some of them are stationary. Only the intermediary nodes between the origin and the destination are routers that forward data and work cooperatively to make decisions on route prediction based on the actual network topology [11]. As proposed in [12], a WMN enables broadband wireless communications in the absence of existing infrastructure, which is ideal for first-response situations during emergencies as it can provide the interoperability, scalability, and performance required in cases where the areas involved are considerable in size and a mesh network topology is the most suitable. 3 GPP-LTE, also known as 4G, is an evolution of the Universal Mobile Telecommunications System (UMTS). It implements the SON paradigm, as this is one of the most promising areas for an operator to save Capital Expenditure (CAPEX), Implementation Expenditure (IMPEX), and Operational Expenditure (OPEX), and can simplify network management through self-directed functions (self-planning, self-deployment, self-configuration, self-optimization, and self-healing) [13]. A clear example of SON applications related to resilient mobile networks is autonomous Cell Outage Detection (COD), which is a prerequisite for triggering fully automated self-healing recovery actions after cell outages or network failures [14].
# Efficient Parallel Algorithms for 3D Laplacian Smoothing on the GPU
## 2 Background  
### Laplacian Mesh Smoothing  
Mesh smoothing is one of the most important methods for improving the quality of a mesh by adjusting the location of nodes in the mesh while keeping the topology of the mesh unchanged. Laplacian smoothing is one of the most basic and common method. The core idea of Laplacian smoothing is to reposition each node to the centroid of its first-order neighboring nodes; see Figure 1. On the basis of Laplacian smoothing, many improved versions such as Taubin [11], weighted Laplacian smoothing [9; 23], smart Laplacian smoothing [10; 24] and HCLaplacian [25], have been developed.
## 2 Materials and Methods  
### Process Overview  
The proposed method for identifying the positions of idler frames in the image from 3D LIDAR consists of several stages, illustrated in Figure 2.  
The process encompasses the following:  
Data Acquisition (DA): In this stage, distance measurements from the LIDAR are filtered and transformed into a point cloud.  
Cropping and Alignment (CA): The points are cropped to reduce their number and aligned to match the local coordinate system with the global one.  
Density Segmentation (DS): This stage determines the regions where the objects (belts and idler supports) are most likely located based on the density of points in the regions.  
Support Clustering (SCt): The points from regions of interest found in the segmentation stage are grouped into spatial clusters for further analysis.  
Support Classification (SCs): A classification of the clusters of points is performed in this stage.  
Position Estimation (PE): The final stage of the process calculates the location of the detected objects.  
The data processing software was built as nodes for ROS 2 Humble [38] on Ubuntu 22.04 [39]. The core software was implemented in C++ using the Point Cloud Library version 1.12 with the visualization written in Python. The source code of the ROS package is available at [[https://github.com/wust-dcr/fast_idler_supports_detection](https://github.com/wust-dcr/fast_idler_supports_detection)]([https://github.com/wust-dcr/fast_idler_supports_detection](https://github.com/wust-dcr/fast_idler_supports_detection)) (accessed on 8 November 2024).  
In this section, we describe the processing stages and methods selected, along with the rationale for their choice, highlighting parameters influencing process performance. Section 3 will present the test parameters' values and justifications.
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 5 Results
### Trajectory Tracking  
To compare the operation, PID control is simulated, considering predefined trajectories, including \"evade from the side\" and \"evade from above\".  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Controller** & \multicolumn{3}{c}{**Computed Torque PD**} & \multicolumn{3}{c}{**PD-PI Fuzzy**} & \multicolumn{3}{c}{**MPC**} \\ \hline
**Index** & **RMS** & **RSD** & **IA** & **RMS** & **RSD** & **IA** & **RMS** & **RSD** & **IA** \\ \hline X Coordinate & 0.0720 & 0.1595 & 0.9940 & 0.0641 & 0.0133 & 0.9989 & 0.0675 & 0.0161 & 0.9981 \\ \hline Y Coordinate & 0.0904 & 0.1731 & 0.9885 & 0.0742 & 0.0451 & 0.9991 & 0.0721 & 0.0572 & 0.9984 \\ \hline Z Coordinate & 0.1447 & 0.1443 & 0.970
## 2 Materials and Methods
### Model Description
#### 2.2.3 Mass Balances  
A model that was proposed to describe the suspended and attached microbial formation [8; 9] in an aqueous environment was modified and adopted in this study. In this model, bacteria could divide, suspend and decay in the reactor medium, and could also attach to a surface with a dynamic equilibrium between attachment and detachment. Since bacterial growth and electricity generation consumed substrate when it was available, the three components of the system, the substrate concentration (S), the suspended biomass concentration (u), and the attached biomass concentration (w), which were dictated by mass balances, were expressed in three ordinary differential equations as follows:  
\[\frac{\mathrm{d}\mathrm{S}}{\mathrm{d}t}=\mathrm{D}(\mathrm{S}_{\mathrm{in}}- \mathrm{S})-\gamma^{-1}(\mathrm{u}\mathrm{\mu}_{\mathrm{u}}+\delta\mathrm{w} \mathrm{\mu}_{\mathrm{w}})-\mathrm{r}_{\mathrm{e}} \tag{10}\]  
\[\frac{\mathrm{d}\mathrm{u}}{\mathrm{d}t}=\mathrm{u}(\mathrm{\mu}_{\mathrm{u}}- \mathrm{D}-\mathrm{k}_{\mathrm{u}})+\beta\delta\mathrm{w}+\delta\mathrm{w} \mathrm{\mu}_{\mathrm{w}}(1-\mathrm{G})-\alpha\mathrm{u}(1-\mathrm{W}) \tag{11}\]  
\[\frac{\mathrm{d}\mathrm{w}}{\mathrm{d}t}=\mathrm{w}(\mathrm{\mu}_{\mathrm{w}} \mathrm{G}-\beta-\mathrm{k}_{\mathrm{w}})+\delta^{-1}\alpha\mathrm{u}(1- \mathrm{W}) \tag{12}\]  
where \(\mathrm{D}\) was the dilution rate, \(\mathrm{S}_{\mathrm{in}}\) was the influent substrate concentration, \(\gamma\) was the biomass yield of substrate, \(\mathrm{\mu}_{\mathrm{u}}\) was the suspended bacterial growth rate, \(\mathrm{\mu}_{\mathrm{w}}\) was the attached bacterial growth rate, \(\delta\) was the ratio of projected anode surface area to reactor volume, \(\mathrm{r}_{\mathrm{e}}\) was the substrate utilization rate by electricity and heat generation, \(\rm{k_{u}}\) was the suspended bacterial death rate, \(\rm{k_{w}}\) was the attached bacterial death rate, \(\rm{\beta}\) was the detachment rate of bacteria from wall, \(\rm{\alpha}\) was the attachment rate of bacteria to anode surface, \(\rm{G}\) was the fraction of daughter cells attached to anode, and \(\rm{W}\) was the wall occupation fraction. Some of those parameters in the equations could be defined through further fundamental design and operating parameters:  
\[\rm{D}=\rm{Q/V} \tag{13}\]  
\[\rm{\delta}=\rm{A/V} \tag{14}\]  
\[\rm{\mu_{u}}=\frac{\rm{m_{u}S}}{\rm{a_{u}+S}} \tag{15}\]  
\[\rm{\mu_{w}}=\frac{\rm{m_{w}S}}{\rm{a_{w}+S}} \tag{16}\]  
\[\rm{G}=\frac{(1-\rm{W})}{(1.1-\rm{W})} \tag{17}\]  
where \(\rm{Q}\) was the dilution rate, \(\rm{V}\) was the MFC reactor volume, \(\rm{A}\) was the projected anode surface area, \(\rm{m_{u}}\) was the maximum specific growth rate of suspended bacteria, \(\rm{a_{u}}\) was the half saturation coefficient of suspended bacteria of the Monod kinetics, \(\rm{m_{w}}\) was the maximum specific growth rate of attached bacteria, \(\rm{a_{w}}\) was the half saturation coefficient of attached bacteria.
# Wavelength Conversion Enhancement Achieved by Using Resonance in an Array of Nanocylinders
## 1 Introduction  
Upconversion (UC) materials absorb light with low energy and emit converted light with higher energy [1, 2]. For this, UC materials are have the potential to enlarge the finite spectral working range of the absorption band in practical devices, which is typically limited to visible light, so that it encompasses near infrared light. A number of devices based on UC processes have been studied for applications in solar cells [3, 4, 5, 6], bio-imaging [7, 8], and lasers [9, 10]. However, the light absorbance in UC materials and the conversion efficiency of UC processes are usually too low for application in practical devices [11]. Therefore, several studies have combined metallic nanostructures with UC materials to improve the UC process in various ways [12, 13]. For example, light absorption can be enhanced by applying localized surface plasmon (LSP) resonance to metal nanoparticles and nanowires [13, 14].  
On the other hand, the dipole resonances due to Mie scattering can be introduced to increase light-matter interactions among subwavelength scale particles [15]. Electric dipole (ED) resonance can be induced not only in metal structures but also in dielectric structures. On the other hand, magnetic dipole (MD) resonance can be induced only in dielectric structures. Owing to the dipole resonances in dielectric structures, we can influence the direction of scattered light, enhance the interactions between light and particles (e.g., absorption), or enhance the emission of light depending on the resonant wavelengths. Indeed, various devices such as thin absorbers [16, 17, 18, 19], lenses [20, 21], and reflectors [22, 23, 24] have been reported that implement Mie resonances and appear in dielectric nanostructures.  
In this paper, we introduced a nanocylinder composed of UC materials and a gold substrate to enhance the efficiency of the UC process. The absorption of 980 nm wavelength light or spontaneousemission in the UC nanocylinder with a wavelength of 660 nm can be enhanced through dipole resonances, surface plasmons, or electric hotspots due to coupling with the MD of each nanocylinder. In addition, we expect that the upward emission of the proposed structure is \(\times\)155 larger than that of the reference structure when only the upward emission of visible light is collectable and meaningful.
# Rhythm-Based Attention Analysis: A Comprehensive Model for Music Hierarchy
## 3 Proposed Methods
### Recursive Detection Module
#### 3.3.3 Loss Calculation and Evaluation Function  
In this part, we will introduce the loss calculation and evaluation function for our proposed task. **Loss Function Integration:** We design the loss function as two parts, including accuracy loss for the position of predicted boundaries and distributional loss for the internal distribution of structure trees. 1. **Accuracy Loss:** This component evaluates how accurately the model predicts the boundaries of phrases. Cross-entropy is used. Cross-Entropy for Segment and Phrase Boundary Prediction: For the extracted layer for phrases from the structure tree, let * \(y_{s}\) be the ground truth one-hot encoded vector indicating the segment boundaries; * \(\hat{y}_{s}\) be the predicted probability distribution over segment boundaries by the model. Then, the cross-entropy loss for phrases can be computed as follows: \[L_{acc}=-\sum_{i}y_{s}(i)\log\hat{y}_{s}(i).\] (12)
2. **Distributional Loss:** This component aims to capture the similarity between the node content distribution of the structure tree and the ground truth distribution. We leverage the Kullback-Leibler (KL) divergence for this purpose. KL Divergence for Node Distributions: In this component, we use the distribution of rhythmic attention quantization values for all elements among each node as what is measured by the KL dispersion. Let\(P_{r}\) be the ground truth distribution of rhythmic attention quantization values in the music piece;
* \(Q_{r}\) be the predicted distribution of rhythmic attention quantization values by the model. Then, the KL divergence can be computed as follows: \[L_{dist}=D_{KL}(P_{r}||Q_{r})=\sum_{i}P_{r}(i)\log\frac{P_{r}(i)}{Q_{r}(i)}.\] (13)
3. **Overall Loss Function:** Finally, we combine the distributional and accuracy losses with another weight: \[L_{total}=\alpha_{L}L_{acc}+(1-\alpha_{L})L_{dist},\] (14) where \(\alpha_{L}\) is a weight controlling the relative importance of the distributional and accuracy components.
## 7 System Performance Optimization
### Packet Loss Rate Calculation
#### 7.3.2 Packet Loss Rate (CSI)  
In order to obtain a high success rate for the CSI-based attack, the attacker needs to obtain fine-grained CSI measurements. In this part, we will discuss the influence of packet loss rate on success rate. What the attacker received at the receiver end is CSI time series, and the Quality\({}_{CSI}\) is measured by the packet loss rate. For input sensitive information recovery, feature length (the number of received packets after feature extraction) can be a factor to differentiate different finger motions. For example, the feature lengths of simple unlock patterns and complex unlock patterns on smart devices are usually different. If the influenced signal traces that the attacker received are not complete, the attacker will recover the complex unlock patterns as simple unlock patterns.  
However, if the packet loss rate is small and one to several packets are lost, it will not influence the final recovery results. However, if Quality\({}_{CSI}\) is larger than a threshold, it will influence the CSI measurements greatly, and the success rate will decrease dramatically.  
The attacker can use the DTW method to quantify the similarity of two influenced signal traces; the shorter the distance that DTW calculated is, the more similar the two influenced signal traces will be. In this part, the influence of the packet loss rate on calculated distance is considered instead of calculating the recovery accuracy in order to give a bottom view of the success rate calculation.  
The DTW distances of CSI waveforms between 15 unlock patterns on smart phones is calculated, and the results are as shown in Figure 6. We can see from Figure 6 that with the increase of the packet loss rate, the distances between 15 unlock patterns become shorter, and the tested unlock pattern will be more similar to the 15 unlock patterns. Then, the tested unlock patterns will be recovered for one of those 15 unlock patterns according to the calculated distances; thus, the recovery accuracy will decrease.