esa-sceva/satcom-chunk-collection huggingface dataset:
# Invisible Threats in the Data: A Study on Data Poisoning Attacks in Deep Generative Models
## 2 Related Works
### Backdoor Attack
#### 2.2.2 Invisible Backdoor Attack  
As the development of backdoor attacks has progressed, some studies have identified that the invisible attacks are susceptible to detection by humans due to their obvious characteristics. Consequently, there has been a shift in focus towards the research of backdoor invisibility, with the objective of implementing more stealthy and effective backdoor attacks without being detected by the target system users or security tools.  
In 2020, [PERSON] et al. [26] proposed the implementation of an invisible backdoor attack in a DNN (Deep Neural Network) model by means of a pixel perturbation method. It is possible to poison the model in a manner that is almost imperceptible to the human eye. In 2021, [PERSON] et al. [27] proposed two innovative methods of embedding triggers: steganography and regularisation. These two approaches not only guarantee the success rate of the attack and maintain the original functionality of the model but also ensure a high degree of stealthiness. In the same year, [PERSON] et al. [28] introduced an untriggered backdoor attack method, which is able to attack DNN models without relying on visible  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Name** & **Proposal** & **Merits** & **Drawbacks** \\ \hline \multirow{4}{*}{**Attack**} & Input-Aware Attack & 2020 [25] & Efficiency & Visible Trigger \\ \cline{2-4}  & Invisible Perturbation & 2020 [26] & Invisible Trigger by Pixel Perturbation & Cannot Using in DGMs \\ \cline{2-4}  & Steganography\(\cdot\)Regularisation Method & 2021 [27] & High Degree of Stealthiness & Cannot Using in DGMs \\ \cline{2-4}  & Triggerless Backdoor Attack & 2021 [28] & Triggerless & Cannot Using in DGMs \\ \cline{2-4}  & BAAAN Method & 2020 [29] & Avoid Detection Mechanism & Visible Trigger \\ \cline{2-4}  & TrAIL\(\backslash\)ReD\(\backslash\)ReX Method & 2022 [1] & Realize Invisible Attack against DGMs & Change in Model Structure \\ \cline{2-4}  & Invisible Encoded Backdoor Method & 2023 [30] & High Stealthiness of Triggers & Cannot Using in DGMs \\ \hline \multirow{2}{*}{**Defence**} & DCT & & High Coding Efficiency & Weak Reliability and Durability \\ \cline{2-4}  & DFT-GI & — & Compatibility & Shift Smoothless \\ \cline{2-4}  & GI & & Simplicity and Speed & Limited Information \\ \hline \hline \end{tabular}
\end{table}
Table 1: Attack and defence summary.  
triggers. This provides a new direction for the development of defence strategies. Furthermore, [PERSON] et al. [30] innovatively proposed the use of a GAN structure to achieve the stealthiness of backdoor triggers and applied it to DNN models in 2023.
# Numerical Study of Geotextile-Reinforced Flexible Pavement Overlying Low-Strength Subgrade
## 2 Materials and Methods  
### Pavement Materials  
Mechanical properties of surfacing, the granular base, and subgrade materials are shown in Table 1. The main engineering properties considered are the elastic modulus, Poisson ratio, density, and the Mohr-Coulomb plasticity model parameters as shown in Table 2. To get as realistic parameters as possible for Finite Element Analysis, previous studies [27; 28] and codes of practice for low-volume seal roads [28] were considered.  
Three types of woven geotextiles were selected in terms of strength and elastic modulus. They have been classified as soft, medium, and stiff. Each geotextile's elastic moduli were sourced from published material [29], as determined based on wide-width tensile properties in line with American Society for Testing and Materials (ASTM) guidelines D4595 [30].
## 3 Results and Discussion  
The current study was carried out to investigate the possibility of extracellular biosynthesis of AgNPs using the green microalga _S. obliquus_ and to explain the effect of N-sources as well as algal growth stages on this process. Cell-free supernatants of _S. obliquus_ cultivated with different nitrogen sources during different growth stages were used as sources of bioactive reductive compounds. The release of bioactive compounds implicated in construction of AgNPs was reported by [PERSON] et al. [12]. Exudates-rich cultural supernatants of _S. obliquus_ grown in BBM medium with various nitrogen sources were collected after 6, 12, and 18 days of algal cultivation to be examined for green biosynthesis of AgNPs. The time intervals were selected to represent the different growth stages of _S. obliquus_ (i.e., log, early- and late-stationary phases).
## 5 Examples  
This section provides examples of altitude feature extraction, service environment clustering, and environment spectrum compilation to validate the effectiveness of the presented algorithms and explain the implementation processes further. All data in this section came from real flight data that had some sensitive information removed. These data also came from the same aircraft model with the same engine model and the same altitude limit, but the flight times differed due to the different flight missions.
# Multispectral Fusion Approach for Traffic Target Detection in Bad Weather
###### Abstract  
Visual traffic surveillance using computer vision techniques can be noninvasive, automated and cost effective. Traffic surveillance systems with the ability to detect, count and classify vehicles can be employed in gathering traffic statistics and achieving better traffic control in intelligent transportation systems. This works well in daylight when the road users are clearly visible to the camera, but it often struggles when the visibility of the scene is impaired by insufficient lighting or bad weather conditions such as rain, snow, haze and fog. Therefore, in this paper, we design a dual input faster region-based convolutional neural network (RCNN) to make full use of the complementary advantages of color and thermal images to detect traffic objects in bad weather. Different from the previous detector, we used halfway fusion to fuse color and thermal images for traffic object detection. Besides, we adopt the polling from multiple layers method to adapt the characteristics of large size differences between objects of traffic targets to accurately identify targets of different sizes. The experimental results show that the present method improves the target recognition accuracy by 7.15% under normal weather conditions and 14.2% under bad weather conditions. This exhibits promising potential for implementation with real-world applications.  
color and thermal images; traffic surveillance; vehicle detection; RCNN; bad weather +
Footnote †: journal:  
0000-0001-9991-4000-0000-0000-0000-0000-0000-0000-00  
thermal radiation between the objects and the background, in other words, it describes the difference in temperature between the objects and the background, which is effective in all weather conditions. In contrast, the color image represents the visible light that is reflected or emitted from the object and background, it has high spatial resolution and sharp texture details [8]. Therefore, the fusion of these two complementary types of information can effectively improve the robustness of recognition, which is very helpful for object detection and tracking in some limitations caused by weather conditions [1].  
This paper mainly introduces a dual input faster region-based convolutional neural network (RCNN) approach based on infrared and visible images for object detection inspired by thermal and visible properties. Two convolutional neural networks are used to extract the visible and infrared image features of two images, respectively, to obtain the information contained in the two images, and then we connect the feature maps obtained by the two convolution neural networks on the channel dimension. In order to solve the problem of the large change in size of the target when identifying the surveillance images, we adjusted the anchor size of the region proposal network (RPN) to identify targets of various sizes.  
The remainder of this paper consists of the following parts. Section 2 discusses the related works. Section 3 introduces the model of our work. The dataset and experiment are shown in Section 4. We give the conclusion in Section 5.
# Signal Source Localization of Multiple Robots Using an Event-Triggered Communication Scheme
## 4 Simulation Results  
In this section, we use two cases to show the effectiveness of the proposed decision-control approach for signal source localization.
## 5 Numerical Results
### Impact of Typical Fading Models  
We apply selection policy into four typical fading channel models, i.e., Rayleigh, Ricean, Weibull and Nakagami, to study the impacts. Each fading channel is characterized with a specific coefficient component. In particular, the coefficient component of Rayleigh, Rician, Weibull, and Nakagami is set to 2, 1, 2, and 0.5, respectively [30].  
In Figure 8, total power consumption increases with time going on regardless of distances. However, power consumption increases more sharply in short-distance cases (D = 500 m). That is because in short-distance cases, eavesdropping algorithms dominate eavesdropping performances, while in long-distance cases, fading channels dominate power consumptions rather than eavesdropping algorithms. This can also be interpreted by the eavesdropped packets in regards to the time slots, which is shown in Figure 9.  
Figure 8: Total power consumptions by \(UAV_{L}\) in different Ds under different fading channels.  
Figure 7: Eavesdropped packets by \(UAV_{L}\) in different Ds with different jamming methods.  
Figure 9 shows that eavesdropped packets under selection policy linearly grows with time in the four typical fading channels. Selection policy performs best in Weibull fading channel, but not obviously. Total eavesdropped packets are less in Nakagami fading channel than in other three channels with different time slots. This is because Weibull distribution is typically descriptive of channel fading with a dominant line-of-sight (LOS) propagation [41,42], which leads to a small amount of time the channel remains in a fade. For Nakagami channel with the coefficient component of 0.5, the received signal consists of a large number of noise waves with randomly distributed amplitudes, phase, and angles of arrival, which causes distortion and fading of the received signal.
## 4 Results and Discussion
### ASIC Implementation  
The proposed processor is manufactured on the HHGrace 110 nm CMOS process. The die photograph and specification are shown in Figure 15. The design occupied a total core area of 1.34 mm\({}^{2}\) and consumed a total statistical power of 4.08 \(\mu\)W. It integrates 8 KB of SRAM.
## 4 Discussion
### Practical and Managerial Implications  
From a practical perspective, those educational institutions aiming to adopt ChatGPT must address several key areas to facilitate its successful integration.  
First, building trust in the technology by ensuring its reliability, accuracy, and security is crucial. Trust was confirmed as a significant adoption factor in six studies, including [22], significantly impacting user engagement. However, two studies found trust to be insignificant, including [4], suggesting that trust might be more relevant in some contexts than others. To address this, institutions should prioritize transparent communication regarding data handling practices and implement robust data protection measures to alleviate user concerns as security and privacy issues were frequently mentioned as barriers to adoption in studies like [43].  
Institutions should also leverage social influence to encourage broader adoption of ChatGPT. Seven studies found social influence to be a significant driver of adoption, such as in [32; 53]. However, four studies found social influence to be insignificant, including [36; 37]. This suggests that social influence may vary depending on the academic setting or cultural context. Encouraging early adopters to share their positive experiences and create communities of practice around ChatGPT could foster greater adoption across diverse user groups.  
Ensuring the necessary technical infrastructure and support is another critical aspect. The facilitating conditions were confirmed as significant in six studies (e.g., [27; 53]) but found to be insignificant in three studies (e.g., [32; 39]). This suggests that, while technical resources such as internet access and reliable hardware are critical in some educational settings, their impact on adoption might be less pronounced in environments where these resources are already well established. Institutions should provide continuous technical support through help desks, online services, and dedicated personnel to assist users with any issues. Integrating ChatGPT into existing educational platforms and learning management systems can also enhance its usability and acceptance, making it a more seamless part of the educational experience.  
Finally, offering incentives for faculty and students to engage with ChatGPT can also increase adoption. Such incentives could include recognition awards, professional development credits, or other forms of acknowledgment for the innovative use of technology in education. This would encourage more users to experiment with ChatGPT and integrate it into their academic work, further driving adoption.  
By addressing these practical and managerial implications, educational institutions can create a conducive environment for the successful adoption of ChatGPT.
# Technique for Searching Data in a Cryptographically Protected SQL Database
## 5 Performance Evaluation  
We tested our prototype on a single machine that simulates both a proxy and a database server. Machine hardware resources: CPU--Intel(R) Core(TM) i5-8300H CPU 2.30 GHz 2.30 GHz; Cores--4; Logical processors--8; RAM--8 GB; SSD--128 GB; HDD--1000 GB. The database is implemented on the Oracle 12.2 c DBMS platform installed on the Windows 10 (x64) operating system.  
Figures 2-6 present (in various forms) the results of a comparative analysis of the average times (in seconds--T) of the search for the required unencrypted (plaintext) and  
encrypted data with their full output for display depending on the number of obtained values (lines--N) when executing the corresponding SELECT query. Namely, queries that satisfy the main types of search conditions: comparison (equality, inequality), range, set membership (IN), pattern matching (Like). Two almost identical tables were used to obtain estimates of the corresponding time values. One table stored unencrypted data, and the other stored the same data in encrypted form. At the same time, the first table, unlike the second one (for encrypted data), was not associated with security policies (RLS and FGA). Figure 7 shows separately the average data search and decryption times (with their full output for display) for the case of encrypted data stored in the corresponding database table, depending on the number of obtained relevant data rows. The data search was performed in tables with more than 200,000 rows of records.  
The presented results of the analysis indicate a reasonable overhead in the proposed solution. The increase in the corresponding search and decryption times, at first glance, may seem significant relative to the search time for data in unencrypted databases, as well as the search and decryption time in some existing searchable encryption schemes. However, in our opinion, this is an adequate price to pay for the security of the data stored by the service provider (in the context of not allowing anything but the access pattern to be leaked), query expressiveness (in the context of the variety of supported search queries), and the usability of standard SQL (same as in the unencrypted database). Since It is known [6] that security is never free, there is always a trade-off between security on the one hand and efficiency and expressiveness of queries on the other. Therefore, we once again focus on what was said at the beginning of the paper, namely, data owners and users must understand how suitable one or another option for protecting database systems is for their various applications.  
Figure 6: Average values of the search and data output times under the condition of pattern matching.  
Figure 7: Average search times (for various conditions) and data decryption (with their full output for display) for encrypted data.  
Are they willing to accept longer time delays in response to their database queries as a corresponding price to pay for security, and what compromises are acceptable to them?
# Coordinated Control of Virtual Power Plants to Improve Power System Short-Term Dynamics
## 2 Control of VPPs
### Frequency Control of Energy Storage Systems  
ESSs can be used to improve the transient behavior of low-inertia systems. In this paper, we use the ESS control structure shown in Figure 2 and proposed in [28]. The controller includes a Storage Input Limiter (SIL) to smooth the transients that derive from the energy saturation/exhaustion of the ESS. The input signal is the deviation of the measured frequency \(\omega_{i}\) with respect to a reference frequency \(\omega_{\text{ref}}\). The frequency error is processed by the devices through a Dead-band (DB), a Low pass filter (LPF), a frequency controller composed of a PI controller and droop controller, and a SIL as well as an anti-windup first-order lag filter. The coordinated signal \(u_{i}\) is added between the DB and the LPF. The last block of Figure 2 represents the actual energy storage device and its output \(p_{\text{ESS}}\) is the active power injected by the ESS into the grid.
# Advanced Medical Image Segmentation Enhancement:
## 4 Results and Discussion
### Experimental Setup
#### 4.1.3 Evaluation Metrics  
This section assesses the segmentation performance of our particle swarm optimization (PSO) method. We utilize fundamental evaluation metrics to gauge the effectiveness of the PSO-based approach. These metrics provide a detailed perspective on the method's segmentation precision, recall, and overall accuracy [103]. Accuracy measures the overall precision in classifying regions of interest, while precision evaluates the method's ability to pinpoint relevant areas with minimal false positives. Recall, also known as sensitivity, examines the effectiveness at capturing all relevant regions. The F1-score, defined as the Dice similarity coefficient (DSC), is a balanced metric that considers both precision and recall to provide an overall performance measure.  
Specificity evaluates the method's capability to exclude non-relevant regions. Overlap-based metrics--the DSC and the Jaccard index (JI)--assess the degree of overlap between the segmented regions and the ground-truth data. These evaluation metrics can be expressed mathematically as follows:  
\[Accuracy=\frac{(TP+TN)}{(TP+FP+TN+FN)} \tag{6}\]  
\[Precision=\frac{(TP)}{(TP+FP)} \tag{7}\]  
\[Recall=\frac{(TP)}{(TP+FN)} \tag{8}\]  
\[F1-Score\;or\;Dice=\frac{2(PrecisionxRecall)}{(Precision+Recall)}= \frac{(2 xTP)}{2x(TP+FP+FN)} \tag{9}\]  
\[Specificity=\frac{(TN)}{(TN+FP)} \tag{10}\]  
\[JI=\frac{(TP)}{(TP+FP+FN)} \tag{11}\]  
This comprehensive array of evaluation metrics offers a holistic perspective of the PSO method's segmentation quality and alignment with ground-truth data. It allows us to scrutinize its precision, recall, and overall accuracy: thus facilitating a robust evaluation of performance.  
Figure 7: Initial process of parameter setting for PSO based on optimal iterations vs. population size (_nPop_) vs. computation time (s). The evaluation was conducted using MATLAB R2023 via Windows 10 Pro 64-bit powered by an Intel Core i7 processor running at 2.9 GHz with eight CPU cores.
## 6 Comparison with Existing Works  
A comparison of the proposed highly efficient AEBS is performed with the existing works in this section. It was seen earlier that the proposed AEBS has increased the reliability and efficiency of the system due to the usage of three kinds of sensors in sensor fusion and non-linear speed controllers.  
The proposed AEB system sensor fusion model's results can be seen. It integrates radars, lidars, and vision to improve the detection accuracy and reliability of the system and that model proposes the model that has a combination of the two different kinds of sensors for fusion.  
In contrast to earlier linear systems, our advanced system features a non-linear super twisting speed controller. Figure 21 shows how the ego vehicle's velocity graph behaves when earlier models employ PI as the speed controller. This controller works slowly as the ego vehicle reaches its desired velocity in 1.7 s and takes almost 3.8 s when applying the brake to reach a velocity of zero. As seen from the graph of the velocity in Figure 17, the ST-SMC speed controller takes only 1 s to reach the desired velocity, which means that the settling time is half of that of the previous traditional controller and that the braking time is also less than the previous systems. The proposed AEBS improves the stability and reliability and provides a more accurate and stable output of the fusion model than the earlier approaches. An unstable vehicle detection graph based on noise was produced using prior [34] paper sensor fusion methods. Figure 22 shows the results of the earlier sensor fusion technique when using two sensors. Figures 15 and 22 depict the interactions between the proposed model's sensor fusion reading and earlier models. A comparison of two previous and advanced models' fusion of sensors is given in Table 2.  
Figure 20: The behavior of time-to-collision and the stopping times for FCW, first stage partial, second stage partial, and full brake.  
Figure 21: Speed graph of previous PI-based controller.  
Figure 22: Results of earlier sensor fusion technique when using two sensors.