esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 4 Comparison of Selected AI Models
### Results on the Talk2 Car Platform  
The Talk2 Car dataset intersects with various research fields, fostering the development of interdisciplinary solutions to enhance the knowledge on grounding natural language in visual space. Annotations were collected with a focus on high-quality, free-form natural language commands to stimulate practical solutions and a realistic task setting, specifically considering autonomous driving where a passenger could control the vehicle's actions through natural language commands. Built on the nuScenes dataset, Talk2 Car includes a comprehensive set of sensor modalities such as semantic maps, GPS, LiDAR, RADAR, and 360-degree RGB images annotated with 3D bounding boxes. This diversity of input modalities distinguishes the object referral task in Talk2 Car from related challenges that typically lack additional sensor modalities.  
Table 8 presents the current leaderboard for the Talk2 Car benchmark, showcasing the performances of various models. The evaluation metric used to rank these models is the Intersection over Union (IoU) of the predicted object bounding box and the ground truth bounding box, with a threshold of 0.5. This metric is also known as IoU0.5 or AP50. The table highlights the top-performing models, encouraging continuous improvement and innovation through the submission of new results and models.  
The table summarizes the performances, measured by AP50, of various Vision Language Models tailored for tasks related to autonomous driving and human-robot interaction. The Udder_HuBo-VLM model stands out with the highest AP50 score of 76.74, underscoring its efficacy in handling unified vision-language tasks and marking its relevance among recent advancements from 2023. Following closely, Deformable-MDETR scored 74.4 in 2021, indicating its strength in modulated detection for end-to-end multi-modal systems. The FA model, with an AP50 of 73.51, presented in 2022, exhibits robust capabilities in interpreting and executing commands autonomously. Stacked VLBert achieved 71.0 in 2020, showing a strong performance, though slightly lower than that of newer models. The Vilbert (Base) model, dating back to 2019, yields an AP50 of 68.9, providing foundational work for subsequent advancements, despite having the lowest AP50 among the listed models. Collectively, these models demonstrate a trajectory of improvement over recent years in the integration and performance of vision-language frameworks in autonomous and interactive tasks.
## 4 Experiments and Discussion  
### Dataset and Implementation Details  
We implement our network with Tensorflow. Model training and testing are performed on Ubuntu 18.04 with an Intel Xeon Platinum 8255C with eight cores and an NVIDIA Tesla T4 with 16 GB of memory. Moreover, the network was trained by an adaptive moment estimation (Adam) solver with mini-batch stochastic gradient descent.  
We evaluated our model's performance on two datasets. The first dataset we used for training and testing is provided by [42], including the categories, hash, and API call sequences of malware. This dataset was built from malware samples randomly from the Malicia project and VirusTotal and it was shared online. We chose this due to the rich variety of sample categories and the high quality of samples and labels, and it has been widely used and recognized by the academic community. To explore the robust ability of the model, we employed our lab's collected malware from online resources and ran the cuckoo sandbox to collect dynamic analysis results to form the second dataset. We pre-process the suffixes of the called APIs as paper [43] did. Details about two datasets are given in Tables 2 and 3.
## 1 Introduction
In [12; 13], the authors described a set of preambles for FBMC-OQAM MIMO systems based on interference approximation methods (IAMs) and an interference cancellation method (ICM). Another method [14], applicable for pilot symbols and preambles integrated into data payload, is to generate additional helping symbols, whose values will cancel self-interference from surrounding symbols. However, these methods can be applied to cancel self-interference only for a preamble or pilot signal, because they significantly decrease the spectral efficiency of transmitted data. The above review shows that the FBMC-OQAM system shows good performance in distortion channels and is a candidate for high-speed, high-frequency ionospheric communication systems. However, this signal modulation technology introduces its own noise into the demodulated signal. When working with low-order modulations and low signal-to-noise ratios (SNRs), this interference does not have a significant effect on noise immunity, while for quadrature amplitude modulations (QAM) with higher orders, with SNRs at which the bit error probability begins to tend to zero, the level of intrinsic interference is such that it has a significant impact on the quality of signal reception. The purpose of this work was to determine the statistical properties of the FBMC-OQAM modem's self-interference in a MIMO channel with memory and to develop an equalization algorithm with self-interference compensation. The basic idea of this type of equalization depends on the phenomenon of the presence of a correlation between the real and imaginary parts of the intrinsic interference of the FBMC-OQAM communication system in memory channels (equalization with real interference prediction--ERIP), which was discovered for the first time by the authors in [15], where the gain in noise immunity compared to a single-tap equalizer was shown by compensating for the real part of the interference with its prediction. Additionally, in this paper, the authors proved that this correlation depends on the modulator and demodulator structures, parameters and channel impulse response. Thus, these coefficients can be calculated once and used until the channel impulse response is updated. For this reason, this algorithm is interesting for communication systems working with relatively slowly changing channels, allowing the achievement of better signal quality performance with practically the same computational complexity as the one-tap algorithm. Later, the authors in [16] extended this method for doubly selective channels by taking into account the emerging correlation between adjacent subcarriers and showed their solution's performance by comparing it with equalizers with similar computational complexity. However, this approach eliminates the low computational complexity advantage, because it requires computing new correlation coefficients for each narrowband symbol. For this reason, we focus on the application of an interference prediction approach for time-independent channels.
## 4 Reliability Analysis of Carbon-Fiber Structures Based on Surrogate Sampling and Kriging Model
### Fatigue Reliability Modeling and Assessment  
Given that the ALK-SS model demonstrates smaller errors and requires fewer sample quantities compared to the simulation analysis and ALK model, with a correlation coefficient as high as 0.99627, the fatigue life data obtained from this model are used for statistical analysis. The histogram of fatigue life obtained from the ALK-SS model is shown in Figure 14, and it is fitted using the Weibull, Normal, Gamma, and Lognormal distribution functions. The goodness-of-fit evaluation results are shown in Table 8.  
From Table 8, it can be seen that the Weibull distribution function fits the fatigue life of cracked carbon-fiber structures the best, providing a more accurate description of the fatigue life frequency distribution and its failure probability. The cumulative failure  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & \(R^{2}\) & **Sample Size** \\ \hline FEA & — & 2000+ \\ ALK model & 0.98232 & 97 \\ ALK-SS model & 0.99627 & 43 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of Different Life-Prediction Methods.  
Figure 13: Comparison of Life Prediction Results from Different Models.  
distribution function expression for the fatigue life of cracked carbon-fiber structures is given by:  
\[F(N)=1-e^{-(\frac{N}{80072})^{6.39255}} \tag{22}\]  
The reliability expression is:  
\[R(N)=e^{-(\frac{N}{80072})^{6.29258}} \tag{23}\]
## 2 Materials and Methods  
### Study Site  
The research was conducted on Lake Kortowskie, located in Olsztyn city (Olsztyn Lakeland, NE Poland). Basic morphometric characteristics are shown in Table 1.  
Lake Kortowskie is a dimitic waterbody located in the temperate climatic zone. Since 1956, the analyzed lake has been subject to restoration by the hypolimnetic withdrawal method. Cold and nutrient-rich, hypolimnetic water is removed into the Kortowka River outflow, with a maximum flow of 250 L s\({}^{-1}\).  
The total topographical catchment of Lake Kortowskie occupies 3799.7 ha, but the actual total drainage basin area is smaller [3546.2 ha] because of the development of a storm sewerage system [[PERSON] et al. 2005]. The main inflow, the Kortowka River, begins as the outflow from Ukiel Lake (area 412 ha, max depth. 43 m), and after running 1.6 km, the river flows into Lake Kortowskie at the NE shore. The partial catchment (Lesny Stream drainage basin) occupies 969.9 ha and aliments Lake Kortowskie at the SW shore. The other partial catchments are small, and their percentage in the total drainage basin area is between 1.0-1.7% of the total area [Table 2].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Area & 89.7 ha \\ Maximum depth & 17.2 m \\ Mean depth & 5.9 m \\ Halbflass’ relative depth & 0.02 \\ Depth index & 0.34 \\ Volume & 5323 thousand m\({}^{3}\) \\ Maximum length & 1660 m \\ Maximum width & 715 m \\ Length index & 2.3 \\ Mean width & 541 m \\ Shoreline length & 4800 m \\ Shoreline development index & 1.43 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Morphometric characteristics of Lake Kortowskie (data from the Department of Water Protection Engineering and Environmental Microbiology).  
The direct catchment of Lake Kortowskie occupies 91.9 ha. The largest area is used for agricultural purposes (35.6% arable land and orchards); 30% of the direct drainage basin is barren, forests cover 19.3%, and the remaining 15.1% of the land is urban area.  
In the past, Lake Kortowskie was polluted by sewage from a pig husbandry farm located on the NE shore of the lake and domestic sewage from the Kortowo and Dajtki districts. Currently, most sewage pollution has been eliminated (point sources were cut over thirty years ago) [19]. Hence profundal deposits of the analyzed lake were formed under the influence of anthropogenic sources in the past. According to [19], assessed annual phosphorus loading (723.1 kg P/year) comes from different sources, including 28.6 kg P/year from direct anthropogenic sources (arable lands, orchards, urban areas, and recreation). Most of the P loads have been entering the lake with hydrological inflows (672.0 kg P/year) [19].
# Characterization of the Evolution of Energy Loss Rate in Cyclic Load Testing of Marine Soft Soil
## 2 Test Materials and Programs
### Test Plan  
The geotechnical dynamic triaxial test simulates the response of soil under dynamic loads, such as traffic, earthquakes, and wind, making it a critical method for studying the dynamic characteristics, seismic performance, and stability of soil. By applying dynamic loads with varying frequencies and amplitudes, the test measures parameters such as deformation, strength, and strain, while evaluating key indicators like shear strength, stiffness, and fatigue performance. The test can replicate different stress and strain conditions by adjusting perimeter pressures, drainage conditions, and load amplitudes. Additionally, through extended loading and cyclic loading, the test reveals consolidation behavior, plastic deformation, energy dissipation, and other phenomena, providing insights into the long-term stability of soil.  
The tests were conducted in accordance with China's Geotechnical Laboratory Test Regulations (GB/T 50123-1999) [12] and the United States' Standard Test Method for Consolidated Drained Triaxial Compression Test for Soils (ASTM D7181-11) [13]. The testing equipment used in this study consisted of a fully digital, pneumatic, closed-loop repetitive loading triaxial apparatus, as shown in Figure 1. This computer-controlled system ensured precise control over stress or strain rate loading tests, with the test frequency and loading magnitude carefully regulated to maintain accuracy. The triaxial chamber was designed to accommodate specimens with diameters of 1.4 inches and 2.8 inches, with maximum pressures of 100 kPa in the inner chamber and 200 kPa in the outer chamber.  
The specimen preparation process consisted of the following steps:  
1. Soil sample collection and air drying: Soil samples were collected and placed in a well-ventilated area to air dry. This step aimed to remove excess moisture to avoid any influence on the soil's properties.
2. Crushing and sieving: After air drying, the soil was spread on a rubber sheet and crushed using a wooden mallet. It was then sieved through a 2 mm aperture sieve to ensure uniformity of the soil particles, which is essential for maintaining consistency across the tests.
3. Water content adjustment: The water content of the air-dried soil was measured, and the required amount of water was calculated based on the target design water contents (e.g., 40%, 45%, 50%, 55%, 60%). Water was evenly sprayed onto the soil to ensure uniform moisture distribution, and the soil was then placed in a sealed plastic bag to equilibrate for 24 h.  
Figure 1: Test preparation and test loading.  
* Specimen preparation: The soil was compacted to 95% of its maximum dry density. Five specimens were prepared for each water content level, resulting in a total of 25 specimens. The mass error for each specimen was carefully controlled within \(\pm\)2 g.
* Preservation: The prepared specimens were immediately wrapped in plastic to prevent moisture loss, ensuring stable water content throughout the testing process.
## 3 Routing Issues and Challenges in WBSNs
### Energy Efficiency  
Energy efficiency covers both the local energy consumption of nodes and the overall network lifetime. For implanted bio-medical sensors, it is not possible to replace the power source, while for wearable bio-medical sensors replacing the batteries might lead to discomfort of patients. Therefore, both energy consumption and network lifetime are major challenges in wireless body sensor networks. Communication among the sensor nodes consumes more energy as compared to sensing and processing [24]. Any proposed algorithm should be able to use different paths and/or nodes to send the data instead of depending on a single path and/or node preventing the consumption of total energy of that specific node(s). In [22], the authors define the network life as the time from which the network starts till the time when the first node of the network expires. The network life is very much important in WBSNs because of energy constraints and the impossibility of replacing the energy source for implanted sensors.
## 5 Experiments
### Experiments to Evaluate Marker Detection Accuracy  
The objective of this evaluation was to ascertain whether the proposed method was capable of detecting the RCM and whether the accuracy of the RCM direction calculated from the RCM coordinates met the requisite performance standards. In the experiment, the ground station detection system was installed in such a way that the RCM was centered within the image. Furthermore, the ground station detection system was rotated using a high-precision rotary table that was capable of measuring the rotation angle. The discrepancy between the initial detection angle of the experimental system and the angle obtained after high-precision rotary table operation, denoted as \(\theta_{s}\), was compared with the drive angle of the high-precision rotary table, \(\theta_{l}\). The experimental system is illustrated in Figure 13.  
The relationship between the angle \(\theta_{s}\) obtained from the ground station detection system and the drive angle \(\theta_{l}\) measured from the high-precision rotary table is shown by the red marker in Figure 14. The experimentally obtained angle \(\theta_{s}\) is considered to have  
Figure 12: RCM detection rate when errors are applied to ArUco markers.  
Figure 13: Experimental system to evaluate the minimum marker drawing size at which the RCM can be detected.  
a scale factor added to the actual drive angle \(\theta_{a}\). Therefore, to obtain higher detection accuracy, it was corrected using Equation (12).  
\[\theta_{a}=\theta_{s}/A \tag{12}\]  
The deviation \(\Delta\theta\) between the corrected detection angle \(\theta_{a}\) and the drive angle \(\theta_{t}\) is shown in Figure 15. The measurement error of the ground station detection system in the experimental system was 0.62 [deg.]. However, the field-of-view angle of 26.5 [deg.] of the camera used in the experiment is different from that of 5.1 [deg.] of the camera used to calculate the target performance. Therefore, to compare the target performance with the experimental results, the scales were adjusted using Equation (13).  
\[\Lambda\theta=0.61\times(5.1/26.5)=0.12\ [\text{deg.}] \tag{13}\]  
As shown in Equation (13), the proposed method had a marker direction detection accuracy of 0.12 [deg.]. The proposed method was required to have an accuracy better than 0.14 [deg.]. Therefore, the proposed method met the required performance standard.
## 3 Results and Discussions
### Secondary Particle Yields Difference between Layer Structure with and without SiGe  
Figure 8 shows the difference in secondary yields between the structure with and without SiGe layer. At Z between 30 and 32, which mostly come from Ge (Z = 32), the cases with SiGe give much higher yields than the cases without SiGe. The reason is that in cases without Ge, these secondary particles with Z between 30 and 32 can only be generated in the W layer (Z = 74) with a very low probability, thus the yield is much lower compared to the number of secondary particles of another Z. In Z between 35 and 45, the cases without SiGe give higher yields because the Ge can stop the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **LANSCE** & **63 MeV** & **105 MeV** & **150 MeV** & **200 MeV** & **230 MeV** \\  & **Neutron** & **Proton** & **Proton** & **Proton** & **Proton** \\ \hline EI & 0 & 0.560 & 0.544 & 0.543 & 0.405 \({}^{\star}\) & 0.443 \\ \hline \hline \end{tabular}  
* Indicates the best choice.  
\end{table}
Table 2: EI for secondary particle yields in layer structure without SiGe.  
Figure 7: LET spectra of the structure with and without SiGe irradiated by 63 and 230 MeV protons and LANSCE neutron. The plot is in log-log scale.  
movement of the heavy secondary particles due to the high Z and density of Ge. In comparison to the particle yields with Z \(<\) 30, however, the difference in secondary yield cannot be recognized, since these particles only make up less than a thousandth of all secondary particles.  
Tables 3 and 4 giving the EI evaluation on LETs and secondary particle yields of the structure with SiGe layer irradiated by the LANSCE neutron and selected monoenergetic protons. Similar to the results shown in Tables 1 and 2, the 200 MeV proton gives the lowest EI which means the best neutron equivalence.
## 3 Energy Consumption of Distributed Solutions under Different Scenarios
### Scenario 2  
At the end of scenario 1, the DKF with the the highest power consumption from both simulations and experiments is selected. The selected DKF is then implemented on the sensor nodes alongside hierarchical sensing and duty cycling, and the energy consumption is monitored. The worst case was selected in order to evaluate the extent to which the hybrid technique will affect the energy consumption of the sensor nodes. The objective was to develop a WWPM system that can achieve both low-power consumption and real-time monitoring. Figure 4 depicts the flow of operations in our proposed fully distributed, real-time and low-power leak detection solution for WWPM using low-cost MEMS accelerometers that implements the hybrid technique.  
The operation of the sensor nodes implementing this hybrid technique is described as follows:  
* On starting, the main core of the ESP32 is in deep sleep mode and the nRF24L01+ and LSM9 DS1 are in the power down mode, while the ESP32 ULP and ADXL344 are in the active mode.
* The ADXL344 continuously monitors the pipeline to detect an activity. An activity (a leak event) is detected once the measured acceleration is above the predefined threshold value (1.01 g) that is stored in the activity register of the ADXL344 accelerometer. Once an activity is detected, an external interrupt is sent to wake up the other components of the sensor node.
* When no activity is detected by the ADXL344 accelerometer, the ESP32 stays in the deep sleep mode with the ULP coprocessor active, while the nRF24L01+ transceiver and the LSM9 DS1 accelerometer both remain in the power-down mode.
* Once an activity is detected, the ADXL344 triggers a wake-up interrupt to the ESP32, LSM9 DS1, and nRF24L01+. The LSM9 DS1 wakes up and collects more accurate measurements. The measurements are then processed by the ESP32 main core by running the DKF algorithm. The nRF24L01+ is used to communicate the local estimates of the sensor node to its direct neighbours to achieve distributed data fusion.
* Once the fusion of local estimates from neighbouring nodes has been performed, the computed estimate is then compared with the baseline value for leak detection. If the final estimate exceeds the baseline value by some threshold value, then a leak alarm is triggered and the node goes back to sleep. Otherwise, no leak alarm is triggered and the node goes back to sleep.  
In scenario 2, only physical experiments were conducted using the laboratory testbed described in Section 2.1. The power consumption of the sensor nodes implementing the hybrid energy reduction technique were monitored using the power meter over a period of 1 day. We introduced leak events every hour over a twelve-hour period during the 1-day experimentation phase. The results and discussion of the power consumption reduction are found in Section 4.2.