esa-sceva/satcom-chunk-collection huggingface dataset:
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 11 Conclusions  
We evaluated the susceptibility of remote sensing imagery to backdoor attack threats. Satellite imagery-based neural network models are highly susceptible to such attacks. By poisoning only 10% of the images, we achieved a 100% ASR in satellite imagery. We compared traditional defense approaches, such as Neural Cleanse, Activation Clustering, TABOR, Deep Inspect, GangSweep, Pixel Backdoor, and Fine-pruning. These methods were unsuccessful in identifying backdoored models in the remote sensing domain. While STRIP successfully identified the backdoor, its effectiveness in consistently detecting backdoored models is limited, as it requires poisoned samples. Without access to poisoned samples, STRIP fails to identify backdoored models. In addition, distributing poisoned datasets to victims poses a significant challenge in practice, especially as most remote sensing models undergo close scrutiny. Our findings highlighted the need for increased attention in this emerging field.  
Conceptualization, K.A.I., H.W., C.X., R.N. and J.L.; methodology, K.A.I., H.W., R.N. and J.L.; software, K.A.I.; validation, K.A.I., H.W. and J.L.; formal analysis, K.A.I.; investigation, K.A.I.; resources, R.N. and L.Z.; data curation, K.A.I.; writing--original draft preparation, K.A.I. and J.L.; writing--review and editing, K.A.I., H.W., C.X., L.Z. and J.L.; visualization, K.A.I.; supervision, H.W., C.X. and J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Commonwealth Cyber Initiative (CCI)'s Cyber Acceleration, Translation, and Advanced Prototyping for University Linked Technology (CATAPULT) Fund; NSF's CNS-2153358, CNS-2120279 and OAC-232099 grants and Intergovernmental Personnel Act Independent Research and Development Program; NSA H98230-21-1-0165; ARFL FA8750-19-3-1000; DoD Center of Excellence in AI and Machine Learning (CoE-AIML) under Contract Number W911 NF-20-2-0277 grants.  
Data Availability Statement: The data will be made available by the authors on request.  
Conflicts of Interest: The authors declare no conflict of interest. AbbreviationsThe following abbreviations are used in this manuscript:  
AC Activation Clustering Acc Accuracy on clean data AI Artificial Intelligence ASR Attack Success Rate DCNN Deep Convolutional Neural Network DI DeepInspect DOD Department of Defense FP Fine-Pruning GAN Generative Adversarial Network GIS Geographic Information System GS Gangsweep MAD Median Absolute Deviation NASA National Aeronautics and Space Administration NC Neural Cleanse NIR Near-Infrared NOAA National Oceanic and Atmospheric Administration PB Pixel Backdoor PCA Principal Component Analysis SOTA State-Of-The-Art STRIP STRong Intentional Perturbation UAV Unmanned Aerial Vehicle
# Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management
## 4 Experiment and Simulation
### Multi-Robot Formation Control Simulation Experiment
#### 4.1.2 Simulation and Results Analysis for the Diamond Formation  
Based on the density function being a constant, by adjusting the density function to a Gaussian function, the diamond formation of the robot can be completed. The Gaussian function equation is shown in Equation (26). The density function distribution is shown in Figure 6a, and the formed diamond formation is shown in Figure 6b.  
Since the robot first needs to complete the Voronoi division under the constant density function distribution, the starting positions of the robots were the same as for the square formation. The cost function value at the initial moment was 6.2 \(\times\) 10\({}^{3}\). When the number of iterations of the CVT algorithm \(k\) = 30, the controller is affected by the integral saturation to produce overshoot, and the cost function increases to 3.3 \(\times\) 10\({}^{3}\). When the number of iterations is about 90, the generation points of the Voronoi cells reach their respective centroid positions, and the CVT algorithm converges under the constant density function distribution. After adjusting the density function to a Gaussian distribution, the CVT algorithm finally converged again when the number of iterations \(k\) = 200. The cost function is shown in Figure 6d. Figure 6c shows the position error between the robot and its corresponding centroid. When the number of iterations \(k\) = 140, the position of the center of mass changes due to the switching of the density function. It can be seen that the position error between the robot and the corresponding generating point has increased significantly. When the CVT algorithm reconverges, the position error tends to zero again.  
\[\sigma(x,y)=\exp^{-\delta(a(x-x_{c})^{2}+b(y-y_{c})^{2})} \tag{26}\]  
Figure 5: Multi-robot square formation simulation: (**a**) density function distribution; (**b**) square formation; (**c**) position error; (**d**) cost function.
# A Systematic Revision of the NFIP Claims Hazard Data in Florida for Flood Risk Assessment
## 2 Materials and Methods
### Additional Data to Claims
#### 2.2.2 PRISM Rainfall  
The PRISM (Parameter-Elevation Regression on Independent Slopes Model) analysis, from the PRISM climate group at Oregon State University, is particularly interesting because of its high spatial resolution (800 m to 4 km). It provides daily rainfall from 1 January 1981 to recent [12]. As a demonstration of the quality of the PRISM, rainfall total and frequency statistics for the period of 1981-2014 are shown in Figure 2. The area averaged rainfall total amount was approximately 46,000 mm for these 34 years. The right panel shows a frequency count map of rainfalls of more than 100 mm/day (i.e., \(\sim\)4 inches; extremely high chance of flooding events). The Western Panhandle region of Florida appeared to be most susceptible to heavy rainfall events, along with coastal areas. Indeed, one notable recent event was heavy rain around Pensacola in 2014. In addition, Hurricane Ivan (2004) made landfall around this area, producing a strong inundation event by surge and inland flooding. Figure 3 shows the total rainfall for the Hurricane Ivan event.  
Precipitation maximum data were derived from the 4 km PRISM daily rainfall database. The largest precipitation amount that occurred with \(\pm 1\) day and within 10 km of the claim property location was recorded. This information helped determine whether the cause of loss could have been accumulation of rainfall.
## 2 Results and Discussion
### Volatile Compounds

Table 2: VOCs from _Krvavica_, _Masnjaca_ and _Leccino_ VOOs detected by HS-SPME/GC-MS. The composition of volatile compounds and sensory properties of monovarietal olive oils have been extensively studied [11,20]. [PERSON] _et al._[5] observed the differences in the composition of volatile compounds of olive oil obtained from 39 varieties. According to [PERSON] _et al._[21] accumulation of volatiles depends mostly on the variety since climatic and environmental conditions have an indirect effect e.g., by changing the degree of maturity of fruit trees. Since the investigated varieties were grown in the same orchard, and the fruits harvested in the same maturity index and processed in the same way, differences in the VOCs composition can be attributed to varietal characteristics. [PERSON] _et al._[4] found that the concentration of (_E_)-hex-2-enal can be useful for distinguishing different oil varieties. Still, there are only a few studies investigating the volatile compounds of varietal VOOs from Croatia. [PERSON] _et al._[22] compared the volatiles of olive oils from two Istrian olive varieties (_Buza_ and _Istarska bjelica_) and one introduced cultivar (_Leccino_). Varietal specificity of these oils has been established by the presence of high-value of the C6 aldehydes in cultivar _Leccino_ and the C6 esters in cultivar _Buza_. In addition, _Leccino_ oil from Istria contained the highest content of (_E_)-hex-2-enal [23], similar to our results. [PERSON] _et al._[23] investigated VOCs and sensory characteristics of the oils from three Istrian olive varieties (_Buza_, _Crna_ and _Rosinjola_) and found that the aroma compounds accumulate differently, depending on the varieties which indicate a close relation with the activity of an enzyme that is genetically determined. It can be helpful to understand the potential of indigenous varieties to produce high-quality monovarietal VOOs with specific organoletic characteristics. The obtained results provide new data on the volatile profiles of oils obtained from two other Croatian cultivars _Masnjaca_ and _Krvavica_ which may be useful to distinguish between other Croatian as well as introduced cultivars.
# Uncertainty Analysis of 3D Line Reconstruction in a New Minimal Spatial Line Representation
## 2 The Proposed Method
### Uncertainty Update by Multiple Triangulations  
Each time a new observation of the spatial line is obtained, a new constraint equation is imported and contributes to decreasing the uncertainty of reconstruction. Each pair of observations yield a sub-uncertainty. Uncertainty of multi-view reconstruction is a fusion of these sub-uncertainties.  
Figure 6: Uncertainty of line reconstruction with fixed direction by triangulation. The cylinder region illustrates the uncertainty region, which depends on the biases of the planes and the angle between the planes.  
Given a prior estimation \(\mathbf{D}_{p}\sim N\left(\mathbf{x}_{p},\mathbf{\Sigma}_{p}\right)\) and a new noisy estimation \(\mathbf{D}_{n}\sim N\left(\mathbf{x}_{n},\mathbf{\Sigma}_{n}\right)\), the posterior estimation \(\mathbf{D}_{m}\sim N\left(\mathbf{x}_{m},\mathbf{\Sigma}_{m}\right)\) can be obtained by multiplying \(\mathbf{D}_{p}\) and \(\mathbf{D}_{n}\):  
\[\mathbf{x}_{m} =\mathbf{\Sigma}_{p}(\mathbf{\Sigma}_{p}+\mathbf{\Sigma}_{n})^{-1 }\mathbf{x}_{n}+\mathbf{\Sigma}_{n}(\mathbf{\Sigma}_{p}+\mathbf{\Sigma}_{n})^{ -1}\mathbf{x}_{p},\] \[\mathbf{\Sigma}_{m} =\mathbf{\Sigma}_{p}(\mathbf{\Sigma}_{p}+\mathbf{\Sigma}_{n})^{- 1}\mathbf{\Sigma}_{n}. \tag{19}\]  
Each time we get a new observation of the line, we can update the uncertainty with (19) incrementally.
# An Integrated Safety Monitoring and Pre-Warning System for Fishing Vessels
## 4 Experiment Results and Discussion  
This section presents the experiment results and discussion. It first introduces the case study, including the installation of the system on a research vessel and the vessel's navigation routes. Then, it discusses the results, focusing on vessel attitude motion prediction using the fuzzy logic method and the warning notification outcomes. These findings show the system works well in predicting hazards and giving timely alerts.
# Speech Enhancement Algorithms: A Systematic Literature Review
## 4 Results and Discussion
The majority of studies focused on single-channel speech enhancement processing due to its simplicity and widespread use [1; 51; 53; 54; 56; 57; 59; 60; 61; 62; 63; 64; 67; 69; 70; 71; 72; 80; 84; 86; 87; 88; 89; 92; 94; 95]. Multi-frame filtering techniques are often used for single-microphone scenarios in combination with other methods, such as Wiener filtering or machine learning [86]. Few of the retained studies addressed multi-channel approaches in SEA. These mostly addressed multi-channel scenarios based on different methods, including Wiener filtering, subspace projection, and multi-channel Wiener filters for spatial noise reduction [66; 78; 79; 85]. **RQ4: What are the major models used to identify speech and noise signals? **  
Modeling of speech and noise signals has always been an issue due to environmental randomness. The evaluated publications used range of models for noise and speech signals. These models describe the variety of approaches used by researchers to address issues related to speech enhancement, such as Wiener filter and MMSE. As presented in the existing SEAs based on statistical approaches, the Probability Density Functions (PDF) introduced to model speech and noise transform coefficients are assumed to be either Gaussian or super-Gaussian functions. Because of these assumptions, the aforementioned PDF types provide high allowable mapping patterns for the speech and noise component distribution; however, this is not always the case in real situations. The most widely used model to represent noise signal is Gaussian, although it cannot represent the true situation due to the high number of different noise characteristics in various environments [42]. Both speech and noise are assumed to obey random processes and are treated as random variables; thus, the basic models that represent speech or noise signals are Gaussian [79; 87; 91; 93; 95],  
Figure 8: The main types of transforms used in SEAs. Laplacian [1; 97], super-Gaussian [3; 92], and Gaussian Mixture Model (GMM) [78]. Other reviewed works were based on hybrid models: GMM+HMM [86], Gaussian (noise) and Laplacian (clean speech) [89], Gaussian+Laplacian (clean speech) and Gamma (noise) [72], GMM+HMM [54], and Gaussian+Gamma [60]. **RQ5: What are the well-known datasets used in existing papers? **  
Datasets are essential to advancing the development of SEA, as they provide results with scale, robustness, and confidence. They are a fundamental tool in speech analytics that provide the data from which analysts extract significant information, and can be used to conduct and identify trends in speech enhancement research. Speech signals under clean and noisy conditions are usually taken from different well-known databases to build a strong foundation of the proposed work. Based on the reviewed papers, some studies were adopted from the same datasets. The datasets adopted in the 47 included studies are listed in Table 9 along with some brief details.
## 2 Materials and Methods  
### Test Materials and Non-Thermal Atmospheric Pressure Plasma Treatment  
The epoxy resin used in this study was prepared by mixing diglycidyl ether bisphenol A (DGEBA) monomer (Allied High Tech Products, Inc., Compton, CA, USA) with hardener (Allied High Tech Products, Inc., Compton, CA, USA) in the weight ratio of 10 to 1. The mixture was poured into the mold (30 mm in diameter and 15 mm in height) and left for 24 h. This epoxy resin surface was polished sequentially by #300, #600, and #1200 SiC polish paper and washed by immersion in distilled water for 10 min before being tested for contact angle, surface energy, X-ray photoelectron spectroscopy (XPS), and shear bond strength. Commercially available epoxy resin-based fiber post (DT Light post, Bisco Inc., Schaumburg, IL, USA) and core resin (Luxacore, DMG, Hamburg, Germany) were used for push-out shear bond strength experiment.  
The NTAPP treatment was carried out in this experiment using a system shown schematically in Figure 1, which have been used in previous studies [21; 22; 23; 24] and allowed the treatment in atmospheric pressure at low temperature. Briefly, the nitrogen gas was flown into the jet type of NTAPP at rate of 5 L/min while the voltage and current values of 15 kV and 13 mA, respectively, were applied. The distance of the test specimen from the exit of the plasma jet was maintained to 5 mm throughout the experiment.
## 2 Model Establishment  
### Finite Element Model  
The anti-climb device mainly consists of several components, including anti-climb teeth, an anti-climb tooth mounting plate, an outer tube, a honeycomb structure, a connecting diaphragm, a mounting plate, guide beams, and an inner tube. The finite element model of the device is depicted in Figure 1. The active vehicle impacts the passive vehicle at a certain impact velocity, and the subway car is organized in 6 groups. The anti-climbing energy absorption devices are respectively installed on the front end of the subway vehicles. The guide beams provide bending stiffness to the structure of the anti-climb energy-absorbing device and serve a guiding function. They are welded to the front anti-climb teeth and, during the collision process, are designed to retract through the mounting plate's aperture without undergoing plastic deformation. The aluminum honeycomb energy-absorbing blocks and the inner and outer thin-walled tubes constitute the primary energy-absorbing structures. These energy-absorbing blocks, connecting diaphragms, and guide beams are housed within a metallic thin-walled outer tube, which is connected at its front and rear ends to the anti-climb tooth mounting plate and the mounting plate, respectively. The anti-climb device employs a multi-stage honeycomb series structure, in which different honeycombs are separated by connecting diaphragms, allowing for a more orderly and controllable deformation while the honeycomb is crushed. An induction groove is set at the front end of the outer tube to reduce the initial peak force. During a collision, the thin-walled shell structure will first undergo folding and crushing, followed by an orderly folding deformation. The material parameters of each component of the anti-climb device are presented in Table 1.  
Our finite element model of the anti-climb energy-absorbing device is primarily composed of shell elements and solid elements. The connections are predominantly simulated through rigid connections that mimic the welding between components. The model consists
## 3 Results
Figure 5c shows the change in PRD value between 120 ECG signals in the test set and the ECG signals reconstructed by the model. By comparing Figure 5b,c, it is found that the RMSE and PRD values between the reconstructed ECG signal and the reference ECG signal of the proposed CNN and BiLSTM hybrid model are lower than those of the CNN model. It is found in Figure 5 that the performance of the proposed CNN and BiLSTM hybrid model is better than that of the CNN model. Table 1 presents the experimental numerical results that are completely subject-independent models. When the proposed CNN and BiLSTM hybrid model reconstructed  
Figure 5: ECG reconstruction performance of the proposed CNN and BiLSTM hybrid model and the CNN model. The statistics of (**a**) Pearson’s correlation coefficient \(r\), (**b**) the root mean square error (RMSE), and (**c**) the percentage root mean square difference (PRD). the ECG signal and the reference ECG signal without using cross-correlation, the \(r\), RMSE, and PRD of the reference ECG and the reconstructed ECG were 0.963, 0.119, and 7.855, respectively. After using cross-correlation between the reference ECG signal and the reconstructed ECG signal, the \(r\) value between them increases to 0.965, the PRD value decreases to 7.867, and the RMSE value remains unchanged. When the CNN model reconstructed the ECG signal and the reference ECG signal without using cross-correlation, the \(r\), RMSE, and PRD of the reference ECG and the reconstructed ECG were 0.953, 0.237, and 12.884, respectively. After using cross-correlation between the reference ECG signal and the reconstructed ECG signal, the \(r\) value between them increases to 0.954, and the RMSE and the PRD value remain unchanged. It can be seen from Table 1 that the performance of the CNN and BiLSTM hybrid model proposed in this study is better than that of the CNN model. After the ECG signal reconstructed by the model and the reference ECG signal are aligned using cross-correlation, the model performance does not change much. Therefore, in this study, cross-correlation alignment has almost no effect on the model performance.
## 4 Discussion
### Lesions Resembling or Ascribable to Peri-Implant Mucositis and Peri-Implantitis  
Lesions likely resembling peri-implant mucositis and peri-implantitis, with or without implant loss [36], were recorded in 58.61% (n = 16) of the overall cases, corroborated by the radiographic finding of peri-implant bone loss in 6.89% (n = 2) of the cases [36]. In detail, dental implants were lost within one week to 2 years [34].  
Coherently, it has been proposed that the immune-inflammatory reaction to Titanium could contribute to implant failure since bone resorption may be directly affected by the Titanium particles released. In detail, Titanium particles within 20 um could induce the release of Tumor Necrosis Factor-alfa (TNF-a) and Interleukin (IL)-6 from fibroblasts in vitro [40], whereas those between 0.25 and 7 um could increase PGE2 and IL-6 production by osteoblasts in vitro [41; 42] and induce the expression of IL-6, TNF-a, and IL-1b in macrophages [43]. Consequently, IL-1b released by macrophages may induce the expression of RANKL [44], activating, in turn, osteoclasts and thus determining bone resorption [45].  
Figure 4: Frequency of Titanium allergy in relation to the described lesions, categorized as those likely resembling peri-implant mucositis and peri-implantitis, reactive lesions of the peri-implant mucosa, and orofacial, periodontal, and peri-implant hypersensitivity reactions.  
Notably, evidence of Titanium allergy was reported in most cases (77.27%) with lesions resembling or ascribable to peri-implantitis [34; 36]. Therefore, it may also be hypothesized that an altered bone turnover, combined with a possible hypersensitivity reaction to Titanium, further triggering the local inflammatory process, may cause implant failure.  
Furthermore, attention should be paid to lesions potentially mimicking peri-implantitis in early stages, underlying, instead, primary or secondary malignancies [46].
# Elemental Mercury Removal over CeO\({}_{2}\)/TiO\({}_{2}\) Catalyst Prepared by Sol-Gel Method
## 3 Results and Discussion
### The Effects of flue Gas Components on Catalysts Performance
#### 3.2.5 The Effect of SO\({}_{2}\) on Catalytic Performance  
SO\({}_{2}\) is an important pollutant in the coal-fired boiler flue gas. The Hg\({}^{0}\) removal efficiencies of the catalysts under various SO\({}_{2}\) concentration were shown in Figure 4e. The value of \(\eta\)shrank as more SO\({}_{2}\) was introduced into the simulated flue gas, whether for CeO\({}_{2}\)/TiO\({}_{2}\) catalysts or pure TiO\({}_{2}\) catalyst. The Hg\({}^{0}\) removal efficiency of Ce0.2 Ti dropped from 78.7% to 58.3% when the concentration of SO\({}_{2}\) rose from 0 to 750 \(\upmu\)L\(\cdot\)L\({}^{-1}\). The influence of SO\({}_{2}\) on the elemental mercury removal from flue gas by a catalyst is controversial in previous literature, which is closely related to the type of catalyst [27]. Some researchers believe that the competitive adsorption between SO\({}_{2}\) and Hg\({}^{0}\) would inhibit the oxidation and removal of elemental mercury when cerium oxides are added into the catalyst [12]. Besides, another reason for the adverse effect is that the functional group changed due to the introduction of SO\({}_{2}\), which could result in an irreversible impact on the Hg\({}^{0}\) removal efficiency of the catalyst [28]. The value of \(\eta\)stayed at a low level, even the concentration of SO\({}_{2}\) fell back. This process can be described by the following reaction:  
\[2\text{Ce}^{4+}+\text{SO}_{2}+2[\text{O}]\to 2\text{Ce}^{3+}+\text{SO}_{4}^{2-} \tag{3}\]  
where [O] represents the reactive oxygen. On the other hand, some researchers pointed out the introduction of SO\({}_{2}\) could promote the oxidation of elemental mercury to a certain extent [27]. The reaction between SO\({}_{2}\) and the reactive oxygen would produce SO\({}_{3}\), which has strong oxidation. Elemental mercury could be oxidized by SO\({}_{3}\) and produce HgSO\({}_{4}\). The reaction process can be presented as follows:  
\[\text{SO}_{2}+[\text{O}]\to\text{SO}_{3} \tag{4}\]  
\[\text{Hg}^{0}+\text{SO}_{3}+[\text{O}]\to\text{HgSO}_{4} \tag{5}\]  
According to the results in Figure 4e, the negative influence caused by Equation (3) overweighed the positive influence brought by Equations (4) and (5). Therefore, the Hg\({}^{0}\) removal efficiency of the CeO\({}_{2}\)/TiO\({}_{2}\) catalyst prepared by sol-gel method would decrease as the SO\({}_{2}\) concentration increased.