esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Object Detection Application Domains
### Military Applications  
One of the major applications of the military field covers the areas of remote sensing and flying-object detection. Remote sensing detection aims at detecting objects in remote areas. There are high resolution input images, but for practical use, small objects make the current detection procedure too slow, and due to complex backgrounds, it can cause serious misdetections.  
The data fusion technique was adopted by the researchers to solve these challenges. Captured images are located far away from a viewpoint, so strong pipeline CNN models are required, such as RetinaNet, SSD, YOLO, and SqueezeNet--these are difficult to adapt to new domains. So designing new remote sensing detectors and remote sensing datasets remains still a hot research topic.  
[PERSON] et al. [156] implemented a \"weakly supervised learning framework based on coupled CNN\" for aircraft detection. [PERSON] et al. [157] proposed a \"weakly supervised and high-level feature learning\" framework for detecting optical remote sensing images. [PERSON] et al. [158] implemented a novel cascaded DNN architecture by combining Hough transform and multi-stage RPN for dense detection of buildings in remote sensing images. [PERSON] et al. [159] proposed to a multitask learning network that performs two tasks concurrently: segmenting vehicles and detecting semantic boundaries. A hybrid DNN proposed in [160] exists only the same scale features for detecting small targets; i.e., vehicles in satellite images. A deep learning method starts by segmenting the input image into small homogenous regions which are used for detecting cars in UAV images [161].  
[PERSON] et al. [162] proposed a \"multi-model decision feature network which takes both contextual information and multi-region features\" for detecting objects in remote sensing images. A double multi-scale FPN which consists of a multi-scale RPN and multi-scale object detection network on very high resolution (VHR) remote sensing imagery, especially for small and dense objects [163]. Semantic attention DEEP neural networks separate objects of interest and cluttered background while detecting objects and improve detection accuracy in aerial images [164]. [PERSON] et al. [165] implemented \"rotation invariant CNN to deal with the problem of object rotation variations\" in optical remote sensing images.  
[PERSON] et al. [166] implemented \"R3-Net by combining rotatable RPN and rotatable detection network for multi-oriented vehicle detection\" in aerial images or videos. [PERSON] et al. [116] implemented \"improved Faster RCNN [which] aims at reducing false vehicle detection by negative example mining\". \"\(R^{2}\)-CNN real-time tiny object detection\" was implemented for large-scale remote sensing images [167]. A \"deep CNN model based on improved bounding box regression and multi-level feature fusion\" was implemented for detecting objects in remote sensing images [168].  
Typical datasets used for evaluating remote sensing object detection performance are NWPU VHR [169], DLR 3K Munich [170], VEDAI [171], DOTA [47], and HRRSD [172].
## Materials and Methods  
### Sampling  
Cavair samples were provided by an Italian caviar company (Agroittica Lombarda SpA, Calvisano, BS, Italy). Raw eggs were obtained from 12 sexually mature females of white sturgeon (_Acipenser transmontanus_). Roes were sieved and washed before the addition of the salt/preservative mixtures. Samples were treated following different formulas, added under aseptic conditions at the amount (% by weight) suggested by the good manufacturing practice rules which codify caviar production in the enterprise that supported the investigation--always complying, however, with the limit levels established and defined in the Commission Regulation (EU) No 1129/2011 [10] for fish roes and caviar (section 09.3)--as follows:  
1. NC series: sodium chloride (NaCl) was added at 3.8% by weight;
2. Experimental OAM (organic acids mixture) series: an experimental preservative, made of a mixture of sodium chloride, sorbic acid (E200), potassium sorbate (E202), and isoascorbic acid (E315), was added at 4% by weight;
3. BSM (borax and salt mixture) series: salt and sodium tetraborate (E285) mixture was added at 3.8% by weight.  
Each sample was placed in a special metal can called the \"original tin\" containing 500 g or 1800 g of caviar. This type of tin helps the air and any excess liquid escape. Then, cans were kept at a storage temperature of \(-2\)\({}^{\circ}\)C and handled according to the company's processing protocol. Samples were collected and analyzed starting from the day of extraction (t0), and then at three different time points during the ripening, corresponding to 4 months (t1), 8 months (t2), and 14 months (t3).  
The samples collection design is briefly reported in Table 1.  
Samples were sent to university laboratories under refrigeration conditions and kept at the storage temperature until the analysis. Unfortunately, an exception was represented by BSM samples at t0, which were stored in freezing conditions before the chromatographic analysis because of technical problems that unexpectedly occurred with the analytical equipment.
# Survey of Artificial Intelligence Model Marketplace
## 5 Model Tracking and Protection
### Marketplace Security  
Security is the last part of every application domain. Hence, the AI marketplace study will not be complete without exploring the privacy and security vulnerabilities that could emerge while the marketplace is operating. Figure 8 presents the threat model depicting the emerging privacy and security concerns, which necessitates the development of a secure and threat-free environment for tomorrow's AI marketplace.  
Evident from Figure 8, adversarial marketplace stakeholders, such as data owners, model buyers, etc., could launch man-in-the-middle (MITM) or distributed denial of service (DDoS) attacks or even infer sensitive information while uploading to or downloading from the marketplace. Similarly, brute attacks could be launched to bypass the system's authentication mechanism and engage in payment fraud by the model buyers. Skilled hackers could also leverage cryptanalysis to intercept and decrypt the system's communication messages without obtaining the actual keys.  
Several techniques were explored and utilized to enhance the AI marketplace's robustness against integrity and security threats. For example, blockchain technology, widely used in the financial industry for its transparency and immutability, was proposed to securely record and protect model development transactions [86; 99]. Blockchain can avoid security threats such as MITM, preserve marketplace stakeholders' privacy, and avoid data leakages. Other methods, such as token-based authorization, were proposed to ensure that only authorized users have access to the AI models [32]. Some recommendations include implementing multi-factor authentication (MFA) during the authentication process to minimize the risk of re-identification threats [112], even considering adding location  
Figure 8: A threat model for AI marketplace.  
information to ensure only consumers in specific regions have the right to order AI models [113]. Also, to mitigate the distribution of sensitive data across AI models within the AI marketplace, federated learning approaches [88; 114] have been adopted, which enables users to train models locally on their devices, eliminating the need to upload data to central servers for integration.
## 3 Combination Weighting Based on Improved AHP-Entropy Method
### Combinatorial Game Theory  
The basic aim of game theory is to find the best result that considers both subjective and objective weights by adjusting the conflicts between different weights, thus achieving Nash equilibrium. Therefore, the comprehensive weight calculation flowchart of each factor is shown in Figure 2. This method not only reduces the subjective arbitrariness but also keeps the result close to the fact; this makes the weight values of indicators more reasonable [15].  
The steps undertaken in combinatorial game theory with weighted assignments are as follows [16]:  
1. The weights obtained by the exponential extension AHP and the weights determined by the improved entropy weight method are taken as the basic weights for calculation; then, the weight set obtained using the \(k\)th method is \(W_{k}=\{w_{1}\), \(w_{2}\), \(\ldots\), \(w_{n}\}\)(\(k=\) 1,2, \(\ldots\), \(n\)), where \(n\) is the number of evaluation indicators and \(L\) is the number of weight methods. Assuming that \(\alpha=\{\alpha_{1}\), \(\alpha_{2}\}\) is a linear combination coefficient, then any linear combination of these vectors is as follows: \[W=\alpha_{1}W_{1}^{T}+\alpha_{2}W_{2}^{T}\] (8)
2. According to the Nash equilibrium theory in game theory, the two linear combination coefficients in Equation (8) are optimized to find the value that minimizes their shortcomings. In order to obtain the optimal value of weight \(W\), the objective function can be determined as follows: \[\min\left\|\sum_{k=1}^{L}\alpha_{k}W_{k}^{T}-W_{k}\right\|_{2}\] (9)
3. According to the differential properties of the matrix, the optimal linear equations equivalent to Equation (9) can be obtained as follows: \[\begin{bmatrix}W_{1}W_{1}^{T}&W_{1}W_{2}^{T}\\ W_{2}W_{1}^{T}&W_{2}W_{2}^{T}\end{bmatrix}\begin{bmatrix}\alpha_{1}\\ \alpha_{2}\end{bmatrix}=\begin{bmatrix}W_{1}W_{1}^{T}\\ W_{2}W_{2}^{T}\end{bmatrix}\] (10)
4. The optimized combination coefficients \(\alpha_{1}\) and \(\alpha_{2}\) are calculated and normalized from Equation (11): \[\begin{cases}\alpha_{1}^{*}=\alpha_{1}/(\alpha_{1}+\alpha_{2})\\ \alpha_{2}^{*}=\alpha_{2}/(\alpha_{1}+\alpha_{2})\end{cases}\] (11)
5. Finally, the comprehensive weight \(W\) based on the combination of game theory is determined as follows: \[W=\alpha_{1}^{*}W^{T}+\alpha_{2}^{*}W^{T}\] (12)  
Figure 2: Comprehensive weight calculation flowchart.
# Anomaly Monitoring of Process Based on Recurrent Timeliness Rules (AMP-RTR)
### Characteristics of Manufacturing Data  
Manufacturing big data is the core of intelligent manufacturing. Based on \"big data and industrial Internet of Things\", cloud computing, big data, Internet of Things, artificial intelligence and other technologies are used to lead the transformation of industrial production methods and drive the innovative development of industrial economy. In addition to the characteristics of general big data (volume, variety, velocity, value), manufacturing big data also has the characteristics of sequence, strong relevance, accuracy and closed-loop. The essential characteristic of manufacturing data is the complex dynamic system. Certainty is the basis for the effective operation of industrial systems. The premise of dealing with uncertainty is anomaly monitoring and eliminating uncertainty. As factories become more and more intelligent, smart interconnected products embedded with sensors have become an important symbol of the industrial Internet era. These sensors will periodically collect various data in the production process. The generation of these data is often periodic and has a clear time stamp. Therefore, a regular time series will be formed.
## 5 Verifying Accuracy via Physical Structure Test
### Test Results  
1. Figure 15 illustrates the process of fine-tuning a neural network at a loading angle of \(10^{\circ}\). The horizontal axis represents the experimental time steps, and the vertical axis represents the measured stress values. The neural network generates the stress data for three randomly selected validation elements; after 249 rounds of fine-tuning, the neural network can accurately predict the stress value.
2. Experimental data within the load range of 800 to 1200 kN were collected at two different shearing force angles: \(10^{\circ}\) and \(13.3^{\circ}\). For the \(10^{\circ}\) angle, some of the data underwent fine-tuning with an ANN previously. The stress values generated by the ANN at the validation element (Index No. 26299) were compared to the actual measured values. In contrast, for the \(13.3^{\circ}\) angle, the data were not trained or fine-tuned with the ANN. The stress values predicted by the ANN at the validation point were again compared to the actual measured values (see Figure 16). Both comparisons provide insights into the accuracy and effectiveness of the ANN in predicting stress values under different shear force angles.
3. The percentage deviation between the ANN-generated values and the actual measured values for the validation element (Index  
A real-time global stress viewer for the connector was developed and integrated into the SHM system using the fine-tuned neural network. Figure 18 illustrates the global stress obtained by the system under a vertical shear force of 1936 kN. Regarding the physical properties, it is impossible to achieve an exact match between the values generated by AIGC and the actual measurement for all elements. Therefore, the focus should be on the accuracy of the ANN in identifying high-stress positions and magnitudes.  
1. The software effectively identifies the load direction as vertically upward, with the maximum stress located beneath the pinhole of the connector. Additionally, high-stress areas are recognized on both sides and the upper portion of the pinhole. Moreover, as one moves away from the pinhole, the stress amplitude of the structure decreases, which is consistent with the contour map obtained through Finite Ele  
Figure 16: Comparison between the ANN-generated and actual measured stress at the validation element.  
Figure 17: The percentage deviation with the angles of 10\({}^{\circ}\) and 13.3\({}^{\circ}\).  
Figure 18: The comparison between the measured and the AIGC stress.  
ment Analysis (FEA). This consistency demonstrates adherence to the principles of mechanics.
* The comparison curve between the measured stress values and the stress values generated by AIGC indicates overall solution accuracy as high as 93.6%.
# A Cognitive Environment Modeling Approach for Autonomous Vehicles: A Chinese Experience
## 5 Trajectory Planning Compatibility
### Scenario 1  
Figure 16 shows the trajectory planning results of a dynamic obstacle avoidance scenario with one dynamic obstacle and multiple static obstacles. This case is deployed to reflect the capability of the proposal to evade the dynamic obstacle. The black frame represents a moving vehicle approaching in the opposite direction, and the blue dots represent static obstacles. The ego vehicle needs to drive forward. To that end, the trajectory planner needs to balance efficiency and safety, obtaining a feasible path and speed of the ego vehicle to avoid collisions. The initial speed of the vehicle is 4 m/s; there is a static obstacle at an offset of 0.6 m in front of the vehicle and a dynamic obstacle traveling 4.3 m/s in the opposite direction in front and to the left. The planning result is represented by the brown line (projection in the X-Y domain of the planned trajectory). Figure 16b shows what the spatial-time environment model looks like in this scenario. The yellow boxes represent obstacles, the trajectory search process is represented by the green line, and the final trajectory is represented by the brown line. The speed profile of the planned trajectory  
Figure 15: Traffic light scenario. (**a**) Scenario diagram; (**b**) Spatial–time model of the scenario.  
is shown in Figure 16c. Obviously, the brown line bypasses all obstacles, providing a trajectory for the ego vehicle. The trajectory planned by the spatial-time approach guides the vehicle from 4 m/s to 2.8 m/s at 2.5 s to avoid the dynamic obstacle coming from the opposite direction. Then, the vehicle accelerates and veers to the left to overtake the static obstacle, after the dynamic obstacle has been identified as no threat. The HFSM approach also presents a trajectory of slowing down to avoid the dynamic obstacle before avoiding the static obstacle ahead. However, the passage is less efficient than the spatial-time approach, as the speed varies more.
# A THz Passive Image Generation Method Based on Generative Adversarial Networks
## 4 Results Analyses
On the contrary, if the SSIM value is relatively small, it means that the generated images are obviously different. In this paper, 400 images for each category are generated, 100 pairs of generated images were randomly extracted from the current category to calculate the SSIM values, and its mean value is defined as the interclass SSIM. If the interclass value is small, it means that the diversity of the generated images is better. Then, the average SSIM value of the real image in the same verification method is calculated as a reference; the real image is more variable than the generated image, that is, it usually corresponds to a smaller interclass SSIM value [23]. The test results are shown in Figure 9. The average intraclass SSIM of the three types of real data is 0.287, indicating that real data has good diversity. The average intraclass SSIM of the three types of samples generated by Res-WGAN-GP was 0.323, which means have a good diversity. In addition, the trend of the intra-class SSIM values of the three types of samples is similar to that of the real samples, indicating that the data generated by the proposed model has the same category information as the real samples. Although the test results of the images generated by WGAN-GP have very low SSIM values, even lower than the real samples, this is due to the lack of network learning ability, resulting in the overall low quality of the generated \begin{table}
\begin{tabular}{c c c} \hline
**Model** & **Cross Test** & **Hybrid Test** \\ \hline WGAN-GP & 73.7\% & 90.3\% \\ Res-WGAN-GP & 92.7\% & 96.7\% \\ \hline \end{tabular}
\end{table} 
Table 2: The performance of the generated image on the classification model. images. It is manifested that the gray distribution is uneven, and the difference between the generated images is large, which can also be seen from the visual comparison of the images in Figure 8. In summary, the image generated by the proposed model satisfies the third requirement. For the fourth requirement, generally speaking, the input of the GAN model is random noise sampled from a normal distribution. Although the model has a strong learning ability, the probability of completely fitting the real data distribution is very small. Therefore, the phenomenon of overfitting is not common in the GAN field, but in order to avoid this unexpected situation and generate useless data, the SSIM indicator is also used for evaluation. Specifically, 100 pictures are generated for each of the 3 categories, and then the SSIM values are calculated for all the generated pictures and all the real pictures to obtain the maximum SSIM value. If it is less than 1, it means that the generated pictures and the real dataset do not overlap. After calculation, the maximum SSIM values of each category are 0.77, 0.81 and 0.73, respectively. Therefore, it can be judged that there is no rare over-fitting phenomenon in the model, and the generated data can be used for data enhancement of target detection after simple screening and eliminating abnormal conditions.
## Materials and Methods
### Matrix Effects  
Matrix effects (ME) were assessed using the post-extraction spiking method adapted from [PERSON] et al. [39] with matrix effects calculated as follows: ME (%) = (B - A/A \(\times\) 100), where (A) is the response of analyte in neat solvent, and (B) is the response of analyte in matrix extract, spiked post-extraction. Negative control samples (\(n=30\)), from different groundwater and surface water sources in Ireland (spring, boreholes, streams and lakes), were extracted and spiked post-extraction corresponding to a concentration of 100 ng L\({}^{-1}\) for all analytes, except BITH, CLOR, MOR and OXY at 200 ng L\({}^{-1}\). These post-extraction spiked samples were compared to solvent standards to quantify the ion enhancement or suppression due to matrix. Using this approach, negative (-) ME values indicated suppression (decrease in analyte response due to endogenous and/or exogenous matrix components), while positive (+) values indicated enhancement (increase in analyte response due to matrix components).
## 3 Results and Discussions
### Compressive Properties
#### 3.3.2 Compressive Test Results for Re-Entrant Structures  
The highest compressive stresses in re-entrant structures were observed in RG (0.83003 Mpa) and uniform (0.40694 Mpa) specimens. As seen in Table 4, the RG specimen carried higher stresses than the other configurations in the re-entrant lattice structure. On the other hand, the elastic modulus of the RG specimen was 76% lower than that of the HG configuration and was very close to the other combinations because it reached its maximum stress at a higher strain (\(\varepsilon\) = 0.2852). The HG specimen reached a maximum stress value of 0.39785 Mpa at a very low strain value (\(\varepsilon\) = 0.077) compared to the RG specimen. The HG specimen fractured without densification at very low strain values (Figure 10). This may be due to the weak connection characteristic of the re-entrant structure. The lowest maximum stress and elastic modulus in the re-entrant structures were found in the VG specimen, with 0.07614 Mpa and 2.4837 Mpa, respectively (Table 4). The maximum compressive stress value of the uniform specimen was 51.8% lower than that of the RG specimen, while the elastic modulus was 11.5% lower. This was due to the relatively high tensile value of the RG specimen. The RG specimen had higher stresses in the plateau and densification regions, resulting in a higher toughness value. This is also reflected in the energy absorption property discussed in the next section. Observations in the tests confirmed that in each configuration, the maximum stresses were seen in the collapse of layers in the lattice structure, with the layer with the thinnest supports collapsing first.  
Figure 9: Stress–strain curves for chiral specimens.
## 2 Data Collection and Analysis
### Data Collection  
In order to accurately determine the context of the dog sounds, behavioral analysis of the recorded data was performed. The two breeds of dog were situated at different houses, and sound recorders were placed on the house walls at a height of 2 m above the ground. The dogs were subjected to different scenarios, such as being subjected to friendly behavior, being alone, sensing strangers, etc., and their reactive barking sounds were recorded. These recorded data formed datasets to be used by the machine learning algorithm.