esa-sceva/satcom-chunk-collection huggingface dataset:
# Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services
## 5 Conclusions  
In this paper, we proposed a QoE quantization method based on network traffic classification and nonlinear mathematical model. For network packet flow, we first identify the flow type provided by this session through a random forest model. Simultaneously, network QoS metrics are also measured, including time delay, packet loss rate, and throughput rate. According to the service type of the session flow and its corresponding QoS metrics, we use the QoE description mathematical model to quantify its QoE score. Experiment results show that compared with other works, our proposed method is more suitable for almost all the types of network traffic and further achieves better measurement accuracy. On the other hand, the QoE sensitivity to time delay, packet loss rate and throughput rate are also analyzed, respectively. Results suggest that the QoE sensitivity to QoS metrics varies with the types of network traffics according to the model parameters. Our future research will consider using some network traffic specific features to measure the QoE score in addition to the QoS metrics.  
[PERSON] conceived the idea and designed and organized the information in this paper. [PERSON] and [PERSON] wrote and reviewed the manuscript and contributed to changes in all sections. [PERSON] programmed the experiments. All authors read and approved the final manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.
## 4 Results and Discussion
### Roundabout Scenario  
The roundabout scenario is one of the most challenging for both the vehicular displacement and orientation rate estimation. The difficulty encountered by the INS and NN in accurately tracking the vehicle's motion is graphically shown in Figure 9 with a comparative illustration to other investigated scenarios presented in Figures 5-7. The results so obtained as presented on Table 10 show that the NN recorded a lower displacement (maximum CRSE and CAE of 17.96 m and 16.80 m) than the INS (maximum CRSE and CAE displacement of 171.92 m) as well as a lower orientation rate (maximum CRSE and CAE of 0.38 rad/s and 0.05 rad/s, respectively) compared to the INS (maximum CRSE and CAE of 5.71 rad/s and 2.14 rad/s, respectively). The relatively lower standard deviation across all analysed metrics is evidence that the NN is able to more consistently track the vehicles position and orientation on the roundabout scenario but less consistently on other investigated scenarios. The roundabout scenario study was carried out across 11 test sequences over a maximum travel distance of approximately 197 m. Figure 8a shows a sample trajectory of the vehicle on the roundabout scenario analysis.
# Proactive Service Caching in a MEC System by Using Spatio-Temporal Correlation among MEC Servers
## 5 Performance Evaluation  
In this section, we evaluate the performance of our method through simulation studies using real-world datasets. We compare our method with an LSTM method in terms of the service popularity prediction behavior and the cache hit rate. In the LSTM method, each MECS has an LSTM predictor that estimates the popularity of each service during the next time slot. After predicting the popularity of all services, the LSTM method determines \(S_{m}(t)\) by choosing the top \(\eta_{m}\) services with the highest predicted popularity. We use the publicly known default values of the ConvLSM model and LSTM model for configuring their hyperparameters. Specifically, we use the hyperparameters in [40] to configure the hyperparameters of the ConvLSTM model and employ the hyperparameters in [41] to configure the LSTM model. We summarize the parameters used for each model in Table 4. For our simulation study, we use a computer equipped with an Intel i9-10980 XE CPU and four Nvidia GeForce RTX 3080. The size of the random access memory is 128 GB, and its operating system is Window 10. When we run the ConvLSTM model, we use Pythonversion 3.8.3, TensorFlow 2.5.0, and TensorFlow-gpu 2.5.0. To run the LSTM model, we use Python 3.8.3, TensorFlow 2.10.0, and Tensorflow-gpu 2.10.0.
# Development of a Drone's Vibration, Shock, and Atmospheric Profiles
## 4 Conclusions  
The general shape and vibration intensity of the PSD profiles of the two drones showed no similarities to those that are currently used for representing package delivery via air  
Figure 11: SRS spectrum of shock event number 9 from Figure 11.  
Figure 12: Exemplary atmospheric data collected by the bottom data logger on the DJI Matrice 600 Pro.  
transport. However, the PSD profiles of the drones selected for this study were similar in their overall shape. They share the same range of exciting vibration frequencies caused by the motor and 2-blade propellers due to the similar propeller speeds, which ranged from the minimal frequency 40-70 Hz for keeping drone steady, to high Grms values in the 165-182 Hz range in which the drones were at cruising speed. Both PSD profiles also share similar patterns in lower frequency zones, for which the interaction between the drone and airflow is responsible. The interaction of the drone with airflow, in the form of bump and lurch, is characterized by low frequencies between about 1.56 to 10 Hz.  
The overall recorded Grms levels of the drones were significantly higher than those currently published for package testing for air transport. The recorded Grms values range from 0.49 to 1.69 in the lateral direction and from 0.54 to 0.99 in the vertical direction. The larger size drone generally tended to have higher Grms values than the smaller drone, especially in the horizontal plane. Most of the high vibration Grms values took place at the turning point of the drones.  
The motor and 2-propellers are also sources of the shocks at the horizontal level. These shock values can reach numbers as high up as 14 G. The throttle jumps of the drone were likely created by the significant horizontal impacts. In contrast, the vertical shock levels, originally thought to have the greatest severity, were significantly lower than horizontal impacts, even during landing.  
Fluctuation of atmospheric data during the flight is unique to the drone and attention should be paid when considering what kind of products can be shipped out as all the atmospheric conditions were affected by the change in altitude.  
The data collected by the data logger on the platform of the payload and the data logger on the quadcopter frame are the most relevant information that can be applied to simulate in-flight vibration and shock of the drone chassis in a laboratory environment. Data in Tables 2 and 3 can be used to drive vibration test systems and results shown in Table 5 can be channeled into a shock tester for simulation. Future studies should involve additional trip destinations, utilization of different models of drones, as well as placing the data logger in different locations to develop test methods that could be used to evaluate packaged products traveling through this emerging delivery channel.
# Assessment of Modal Characteristics of Steel-Concrete Composite Grider Bridge with V-Shaped Piers
## 6 The Effects of Shear Connector on Self-Vibration Characteristics
### Effect of Local Damage of Shear Joints
#### 6.2.2 Effect of Damage to Shear Connectors on Self-Vibration Frequencies in Different Regions  
It is known that different degrees of damage to shear joints in vulnerable areas will have some effect on the vertical stiffness of the overall structure. This section analyzes and compares the effect of damage to shear joints in different areas on the overall structural self-vibration frequencies [25; 26].  
Three areas of damage to shear connectors are set separately, assuming that the damage longitudinal bridge length is 0.1 m. The damage areas are as follows:  
Area 1: P3# V-shaped piers and main beam connection near the A0# abutment side.  
Area 2: 5 th span across the middle region.  
Area 3: V-shaped piers of Pier P4#, connected with the main beam near the side of the A0# abutment.  
The damage area of the shear-resistant connector is shown in Figure 18.  
The damage degree of the shear joints is set to complete damage, i.e., the case in which the shear stiffness is reduced to 0. The self-vibration frequencies of shear joints in different regions after damage are shown in Table 12.  
From the results in Table 12, it can be seen that because the selected areas are small, the effect on the overall structural self-vibration frequency is not significant, but still reflects a certain law. Area 1 and Area 3 are located in the connection area between the V-shaped piers and main beam, and the vertical stiffness of the structure is reduced when the shear joint is damaged, which shows that the shear joint has a greater influence on the overall vertical stiffness of the structure. When the damage occurred in Area 2, only the third-order self-vibration frequency, that is, the second-order vertical self-vibration frequency, was  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Modal** & & & & \\
**Orders** & **Area 1** & **Area 2** & **Area 3** & **No Damage** \\ \hline
1 & 1.403 & 1.403 & 1.403 & 1.403 \\
2 & 1.910 & 1.911 & 1.910 & 1.911 \\
3 & 3.728 & 3.725 & 3.727 & 3.729 \\
4 & 3.944 & 3.945 & 3.944 & 3.945 \\
5 & 4.365 & 4.366 & 4.365 & 4.366 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Effect of damage in different areas of shear joints on the self-vibration frequency of the structure.  
Figure 18: Different damage area settings for shear-resistant connectors.  
reduced. The third-order vertical self-vibration mode of the bridge shows that the third-order self-vibration has the maximum amplitude in the fifth span. This demonstrates that only the third-order self-vibration frequency decreases when the shear joint is damaged in Area 2.
## 5 Discussions
### Explainability of the AI  
A crucial aspect of the CAE network highlighted in this study is the explainability of the AI system. Since the classification hinges on the mean squared error (MSE) between the input and its reconstruction, retracing the classification process becomes straightforward. This can be achieved by identifying segments with high error and comparing the input against the reconstructed output of the network, as demonstrated in earlier figures within this study. The poor reconstruction of anomalous data readily reveals where the AI encounters challenges in recognising such anomalies.  
This operation of the AI in this study differs from that of other anomaly detection options. In these other techniques, the AI outputs only a probability of the input being a specific class rather than a more tangible output, such as the reconstruction and associated errors. This does not help human experts determine what contributed to the classification or verify the correct classification process. Systems such as the CAE highlighted in this paper, or fully convolutional neural networks (FCNNs), allow for tracing back to the portion of the input signal that contributed significantly to the classification.  
The network effectively reconstructs the normal data surrounding the anomaly for less pronounced signatures, like the DragonBoard port scans or Hydra_T1, where the attack signature's amplitude is low. This clarity enables precisely identifying the segment within the input signal associated with the anomaly. Conversely, with substantial amplitude signatures, the network may struggle to reconstruct even normal information. However, the existence of data with such amplitude clarifies why the AI made its decision despite the challenges in reconstruction.  
By examining the input-output pair associated with the high error, a human expert can readily discern the reasons behind the classification of a segment as anomalous. When dealing with low-amplitude attacks, it becomes apparent that the primary factor influencing classification is the shape of the anomaly signature, which the network struggles to recognise. Conversely, in cases of high-amplitude anomalies, it is evident that the network cannot reconstruct them at all, as they surpass the maximum amplitude expected from the learned normal class.  
Considering these factors, the proposed CAE system can be confidently labelled as highly explainable, and its decisions should be easily comprehensible to security experts managing a similar system.  
The transparency of the AI in this study not only ensures the correctness of its operation but also eliminates the possibility of fabricated results. In AI-related studies, understanding the exact operation of the AI can be challenging for readers, with details such as the training set, test set, actual input to the AI, output, decision-making process, etc., often being concealed. The clarity in the network operation in this study confirms its authentic functioning.
# Phosphorus Distribution in Soils from Australian Dairy and Beef Rearing Pastoral Systems
## 2 Materials and Methods
### Sample Analyses
#### 2.4.2 Total Soil Phosphorus Determination  
Soil samples (0.3 g) were accurately weighed into a 100 mL Erlenmeyer flask and 10 mL of H\({}_{2}\)SO\({}_{4}\) (98%) was added to the soil in a fume hood, with a glass funnel placed into the neck of the flask. The mixture was heated to approximately 160 \({}^{\circ}\)C until sulphute fumes occurred. The mixture was reduced by evaporation to approximately 2 mL and then allowed to cool for 3 min. Four further 1 mL aliquots of H\({}_{2}\)SO\({}_{4}\) were added sequentially, with each followed by reduction of volume to 2 mL by evaporation and cooling. Following the final addition, the mixture was heated (160 \({}^{\circ}\)C) continually until there was no further sulphute mist produced. The funnel was rinsed with a small volume of deionised water into the flask. The contents of the Erlenmeyer flask were cooled to room temperature, transferred with rinsing to a 25 mL volumetric flask and made up to 25 mL with deionised water. Samples were allowed to settle overnight, before the supernatant was isolated using a pipette.  
Soil extracts were analysed using a Westco SmartChem 140 automated discrete analyser (Westco Scientific Instruments, Inc., Brookfield, CT, USA). This photometric based analysis utilised SmartChem 140 Method 420-3651 and was adapted for P determination in soil extracts by replacing deionised water with 0.01 M HNO\({}_{3}\) as the sample and reaction dilutant. However, the reaction chemistry remained unaltered with reaction of ammonium molybate, antimony potassium tartate and dilute phosphorus solutions in an acid medium, forming an antimony-phospho-molybate complex. This complex was reduced via the addition of ascorbic acid to form a blue complex. Measurement was made at a wavelength of 880 nm [43].  
Blank digestions were prepared following the same procedure outlined above without sample soil additions. Final total P concentrations were offset by the P contributions of the blank solutions. Corrections were also made for soil moisture content, with five replicates of each soil sample heated at 105 \({}^{\circ}\)C and cooled in desiccators until a constant mass was achieved.  
All glassware used for total P determination was soaked in nitric acid (2 M HNO\({}_{3}\)) for a minimum of 7 d and rinsed three times with deionised water, soaked in deionised water and finally soaked in hydrochloric acid (0.1 M HCl) until used. Each soil sample was digested in duplicate and the concentration was averaged.
## 2 Materials and Methods
### Research Flight Plan  
Five NOAA Gulfstream IV SP (G-IV) research flights were conducted as part of IFEX/APHEX over the tropical Atlantic and took place on 20 September 2018 (targeting a Saharan Air Layer outbreak), 18 September 2019 (Hurricane Jerry), 27-28 September 2019 (Hurricane Lorenzo), and 7 September 2021 (Hurricane Larry). Dropsondes were released from the G-IV aircraft, which had a typical altitude of 41-45 k feet (12.5-13.7 km), which roughly corresponds to 175-200 hPa. Each flight released on average 30 dropsondes, one roughly every 20 min during a typical 5 h flight after excluding the transit time to the research target. For optimal comparability, the flights were planned to be coincident with the satellite overpass in mid-flight. We typically have some dropsondes in the troposphere within 6 min of an overpass. Coordinating the flight in this fashion also ensured that most dropsondes were within 3 h and 150 km of a NOAA-20 and/or Suomi NPP overpass over the entire flight to optimize validation utility [4; 5]. The 2019 flights were especially cost-effective because both Suomi NPP and NOAA-20 were operational, and their overpasses were only separated by an hour. In 2018 and 2021, only Suomi NPP and NOAA-20 were respectively operational.  
The aircraft released Vaisala-RD41 dropsondes, which contain similar instrumentation to balloon-deployed rawinsondes but are more rugged to account for fast aircraft speeds [24; 25]. Dropsondes complete the profile measurement more rapidly than rawinsondes (<15 min versus up to 2 h). While both are useful for validation, satellite soundings are instantaneous column measurements, so rapid profile collection is more desirable for comparisons.  
Rather than define specific targets at the onset of the experiment, we closely collaborated with APHEX scientists to identify tropical cyclones of interest to the hurricane community as they developed. This approach helped us understand what applications are important to operations and better define quality requirements. We could also test if new data delivery methods and visualizations were useful for situational awareness [11].  
All dropsonde data collected from the flights are publicly available on the NOAA/Atlantic Oceanographic and Meteorological Laboratory (AOML)/Hurricane Research Division's Hurricane Data Webpage, [[https://www.aoml.noaa.gov/data-products/#hurricanedata](https://www.aoml.noaa.gov/data-products/#hurricanedata)]([https://www.aoml.noaa.gov/data-products/#hurricanedata](https://www.aoml.noaa.gov/data-products/#hurricanedata)) (accessed on 7 September 2021).
## 4 Experimental Results and Discussion
### Datasets
#### 4.1.1 Colonoscopy Images (CVC-ClinicDB)  
We have used a colonoscopy image CVC-ClinicDB [29] dataset for our experiments. This dataset contains 612 polyp images and the corresponding ground-truth images are taken from 29 colonoscopy videos. The images are resized to \(256\times 192\) pixels from the original \(384\times 288\) pixels, which is convenient in terms of computational resources.
## 1 Introduction
On the basis of previous work, this paper used the electrothermal coupling module in COMSOL to design simulation models to explore the arc heat and conduction current effects on the damage of CFRP with fasteners, as well as the damage difference under different lightning current conditions. Experimental investigations are often used to study the damage in CFRP laminates with fasteners subjected to lightning strikes. Previous studies have found a relationship between the damaged area and the mounting depth of the fasteners during a lightning strike. The shallower the mounting depth, the larger the surface damage area [18]. CFRP laminates with fasteners show penetrating damage under lightning strikes, with damage occurring on both sides of the specimen [19; 20]. The lightning current component D is influential in developing out-gassing, whereas no out-gassing is observed when component C is used [21]. To make the simulated lightning strikes in the laboratory more closely approximate natural lightning strikes, it is important to ensure that the lightning current waveforms and current amplitudes used in the tests meet the standard requirements [19]. However, in the study of lightning damage in CFRP laminates with fasteners, many of the waveforms and amplitudes used in previous studies did not meet the standard requirements [22; 23], and the roles of the arc and conduction current in the process of damage were not clearly distinguished. In this work, simulated lightning strike tests were performed on CFRP laminates with fasteners using lightning current components C and D, which comply with the standards. Ultrasonic C-scans and stereomicroscopy were used to evaluate the differences in specimen damage under the two components, and an electrothermal coupling model was adopted to verify the test results to study the different effects of the conduction current and arc after the action of lightning current components C and D. The results were compared with the lightning damage of pure CFRP under components C and D [17].
## 1 Introduction
Launched from China's Taiyuan Satellite Launch Center in August 2022, the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS) represents China's inaugural quantitative remote sensing satellite dedicated to terrestrial ecosystem monitoring. Equipped with the next-generation Carbon Sinks and Aerosol LiDAR (CASAL) as its primary payload--the nation's first multi-beam full-waveform LiDAR system--TECIS specializes in the high-precision measurement of carbon stocks, forest resources, and productivity [27, 28, 29, 30]. The satellite features an advanced payload configuration, including (1) the CASAL with dual-mode vegetation measurement and aerosol detection capabilities (see Figure 1 for operational schematic); (2) a directional multi-spectral camera (DMC) for spectral characterization; (3) a fluorescence spectral imager (FSI) for assessing vegetation physiological status; and (4) a directional polarization camera (DPC) for atmospheric correction. These payloads work synergistically to form a comprehensive Earth observation system. [PERSON] et al. [31] validated TECIS's forest monitoring capability through waveform analysis, demonstrating strong consistency between the satellite's canopy parameters and the 75 th percentile heights from airborne laser scanning (ALS) (random forest model: \(R^{2}=0.85\), \(RMSE=1.38\) m). Table 1 systematically compares the mission-critical parameters of four generations of spaceborne LiDARs-ICESat, ICESat-2, GEDI, and TECIS. While forest canopy height inversion studies utilizing ICESat/GLAS and GEDI datasets have been extensively documented, research employing laser full-waveform data from domestic terrestrial carbon monitoring satellites remains comparatively limited. Given significant variations in payload hardware specifications, the performance of terrestrial carbon monitoring satellites for forest canopy height retrieval warrants comprehensive investigation. This study employs China's Northeast Tiger and Leopard National Park as a test site to (1) evaluate the accuracy of CASAL-derived ground and canopy height estimates and (2) establish a methodological foundation for subsequent TECIS applications in forest canopy height and aboveground biomass quantification.