esa-sceva/satcom-chunk-collection huggingface dataset:
# PC5-Based Cellular-V2X Evolution and Deployment
## 3 C-V2X Standard and Application Scenarios
### PC5-Based C-V2X Application Scenarios  
To utilize C-V2X applications, RSU/OBU devices must be equipped with 3 GPP C-V2X [3] compliant chipsets. Commercial availability of such chipsets [10] is from Qualcomm, Intel, Huawei, Datang, Autotalks. Currently, Taiwan uses Qualcomm 9150 for PC5-based C-V2X field testing and commercial deployments. Many exciting applications [12; 18; 19; 20] have been realized. Some application scenarios are highlighted below:  
* **SPAT (Signal Phase and Timing Message)**: a V2I service that integrates traffic signal controller (light color and remaining time) with long-range wireless transmission device RSU, which broadcasts them to the OBU. Depending on this information, the driver or autonomous driving control unit can decide whether to change the route or speed up.
* **TSP (Traffic Signal Preemption)**: a V2I service that allows high-priority vehicles, such as ambulances, fire trucks, and police cars approaching the signal control intersection to send a priority signal so that the vehicles can pass through.
* **VRUCW (Vulnerable Road User Collision Warning)**: a V2P service that alerts the driver or autonomous driving control unit when a vehicle is sensing a potential pedestrian collision threat via IP Camera and RSU on the roadside.
* **ICW (Intersection Collision Warning)**: a V2V service that warns the host vehicle driving towards an intersection if there is a risk of collision.
* **EBW (Emergency Brake Warning)**: another V2V service, which warns the host vehicle if the front remote vehicle is making a sudden brake. The host vehicle receives an alert from the front vehicle and determines if a collision will happen.
* **DNPW (Do Not Pass Warning)**: a V2V service used when a host vehicle is planning to overtake the front vehicle via opposite direction lane. The host vehicle will alert the nearby vehicle traveling in the opposite direction. The host vehicle's OBU will receive a DNPW message to decide if it is safe to overtake.
* **HLW (Hazardous Location Warning)**: a V2I service that warns a host vehicle of potentially hazardous situations, such as deep waters after a storm, deep pits on the road surface, or slippery roads.  
All the above-described application scenarios can be deployed by using PC5-based C-V2X direct communication technology. However, the 4G LTE cellular network can't support advanced C-V2X applications due to limited performance. 5G NR has opened an opportunity for time-sensitive applications. The future is inspiring for C-V2X once full 5G coverage is available.
# Underestimates of Grassland Gross Primary Production in MODIS Standard Products
## 4 Discussion  
### Underestimation of MODIS GPP in grasslands and Comparison with Previous Studies  
This study showed that GPPMOD had a promising ability to explain GPPEC variance (\(R^{2}=0.66\) (0.63-0.69) and \(RMSE=2.46\) (2.33-2.58) g cm\({}^{-2}\) day\({}^{-1}\)) at grassland sites, but GPPMOD consistently underestimated GPPEC (Figure 4). We also found that different grassland biomes had different performances. Specifically, GPPMOD performed better in the temperate grassland sites than in the alpine and tropical grassland sites (Figure 5).  
This study indicated that GPPMOD substantially underestimated GPP in grasslands, which agreed with previous studies [29; 30; 31]. For instance, [PERSON] [29] used GPPEC measurements to verify GPPMOD over an alpine meadow on the Tibetan Plateau, which further pointed out that MOD17 GPP products underestimated the mean annual GPP by 30% to 50%. [PERSON] et al. [46] also reported that GPPMOD underestimated GPP at desert grassland sites. [PERSON] [47] evaluated GPPMOD by using GPP data from eddy covariance flux towers and their results showed that the GPPMOD products underestimated GPP by 45% at the Walnut Gulch Kendall grassland site. Despite the wide use of GPPMOD to analyze inter-annual variation at regional scales in previous studies [48; 49], the results from this study and the previously mentioned studies implied additional caution should be given when using GPPMOD for inter-annual and intra-annual variation analyses of GPP in grasslands. We found that GPPMOD underestimates observed GPP and that GPPMOD explained 66% (63%-69%) of the variance in GPPEC (\(R^{2}=0.66\) (0.63-0.69), \(RMSE=2.46\) (2.33-2.58) g cm\({}^{-2}\) day\({}^{-1}\)).  
In addition, we found that the GPPMOD could explain more GPP variance for the temperate and alpine grassland sites than for the tropical grassland sites (Figure 5). Thus, GPPMOD performed better in lower GPP situations (e.g., drought condition and temperate grassland biome), which further illustrated that the GPPMOD tended to underestimate GPP.
## 4 Experiments and Results
### Performance Evaluation
#### 4.2.3 Analysis of Cost Function  
For the four terms of the cost function, their contribution to the recognition accuracy is evaluated by incrementally taking each term into consideration. The result is displayed in Table 2. With two simple rules of \(L_{1}\) and \(L_{2}\) which measure the dissimilarity of fingers' positions and intervals, the template matching method achieves high accuracy of 96.87%. After adding the penalty \(L_{4}\), which involves some requirements on the boundary, the accuracy rises to 99.35%. The effect of \(L_{3}\) is not so predominant because its principle of punishing the overlap of adjacent fingers is theoretically similar to \(L_{2}\). On the whole, \(L_{1}\) and \(L_{2}\) are measurements of the dissimilarity between the candidate and the template, while \(L_{3}\) and \(L_{4}\) are the standards of the rationality of the predicted gesture. All four terms have their significance and have positively boosted the recognition.
## 3 Proposed Approach for the LG Far-Field Noise Prediction  
As demonstrated earlier, this work presents a new approach to predict the far-field noise based on multiple 2D near-field flow data obtained using multiple LG C-Ss. To obtain these 2D C-Ss, different locations are selected based on the LG complexity, such as the number of components, sizes, and orientations. Thus, the number of the 2D horizontal planar cuts along the LG would vary. This work is mainly focused on proving the concept of the proposed method. The far-field noise is predicted using the FW-H acoustic analogy. To predict the far-field noise from obtained 2D near-field flow data, the missing data along the span can be compensated by assigning an SCL. For a variety of bluff body shapes at moderate to high Re, the flow was found to be fully correlated just along a few body diameters [39; 41; 42; 43].  
The size of the computational domain for each C-S is specified individually based on the corresponding \(\mathrm{L_{a}}\). The 2D CFD analysis is then carried out first for each C-S to generate the near-field flow data. Ultimately, the total power spectral density (PSD) is calculated as the summation of all the noises propagated from each 2D C-S. The proposed methodology is applied to the LAnding Gear nOise database and CAA validatiON (LAGOON), a simplified nose landing gear (NLG), which was considered a benchmark problem during the second BANC workshop [6]. The LAGOON NLG experimental data [12] and the 3D numerical results [44] are used to validate the proposed method. One of the advantages of the proposed approach is that the CFD analysis of each 2D C-S is carried out individually. Thus, the domain size, mesh generation process, and computational time are considerably reduced compared to the corresponding 3D LG model.  
In our approach, the SCL is assumed in accordance with the calculated local Reynolds number, Re*, based on the characteristic length, \(\mathrm{L_{a}}\), of each C-S. In addition, any major change in the features of the C-S geometry altering the flow characteristics is taken into account to assume the SCL. In fact, in aerodynamic flow, besides the 3D nature of vortex shedding, there are two types of irregularities affecting the level of coherence [45]. For instance, disturbing the flow homogeneity could be triggered by either cylinder irregularity or flow irregularity or a combination of them. The first type is subdivided into surface irregularities, section irregularities, and spanwise irregularities. The latter can be classified into end effects, axis deviation, and variable local radius (For more details refer to [45]). The summary of steps for the proposed approach is illustrated for both flow and acoustic domains.
# Maintenance Prediction through Sensing Using Hidden Markov Models--A Case Study
## 4 Case Study
### Features Generation  
Six-hour time windows were created to represent four shifts of the equipment's workday, where each window has 360 data. Thus, 4558 time windows were additionally created with 360 data each. The objective is to create statistical characteristics of each window to better characterize the signal and to make a dimensional reduction of the data. Ten different characteristics were obtained from each data window to better define the signal and understand its behavior over time. The characteristics generated were 1. Average; 2. Standard deviation; 3. Variance; 4. Kurtosis; 5. Skewness; 6. Coefficient of variation; 7. Maximum; 8. Minimum; 9. Mode; 10. Median. These 10 characteristics were chosen in order to have a limit of resources for the study to verify if they are sufficient to obtain a diagnosis. The features are all performed in the time domain, as none of the variables need frequency analysis. Furthermore, as stated in [52], \"statistical resources based on the time domain provide a high performance to characterize trends and changes  
Figure 2: Data cleaning of the signals with through Confidence Indexes and shutdowns of the equipment.  
in signals\". The corresponding equations of the considered set of characteristics can be found in the references [52; 57]. In Figure 3, we can see the features generated for each variable.  
Based on these 10 characteristics of each sensor, 60 variables were generated from a total of 4558 data. That is, we are left with 4558 mean values for each sensor, 4558 variance values for each sensor, etc. Therefore, we get an array of a dimension of \(4558*60\).
# Temporal Variations of Heavy Metal Sources in Agricultural Soils in Malta
## 2 Materials and Methods
### Analysis of Heavy Metals Using Spectroscopy  
Upon digestion to extract heavy metals from the soil samples collected, analysis using Atomic Absorption Spectroscopy (AAS) was used to give the concentrations of such metallic elements, based on the quantity of light being absorbed at a specific wavelength corresponding to the known characteristics of the element being tested. AAS gives readings with a high degree of accuracy, results generally fall in the range of 0.5-5% accuracy, and is a highly sensitive method of analysis [21]. The amount of light absorbed by the sample is proportional to the concentration of metal ion in solution. Since AAS is used to measure trace elements, contamination from equipment or storage of the sample can lead to severe sources of error. The sample must be conserved in terms of pH, and constituent elements. Since the material of the storage containers may absorb some of the analyte, perfluoroalkoxy (PFA) polymers, or glass were used as storage materials to affect the analyte as least possible. To prevent analyte ions from adhering to the walls of the containers used, solution acidification with nitric acid was carried out. A continuous flame-type Atomic Absorption Spectrophotometer produced by Shimadzu Europa GmbH model AA-7000 was used. The lamp mode was set to BGC-D2 type for all elements tested except for Sr for which a Non-BGC lamp mode was used. Burner height was set at 7 mm for all elements tested except for Cr and Fe for which this parameter was set to 9 mm. For all elemental readings, burner lateral and burner angle were set at 0 pulse and 0 degrees. The Air-Acetylene flame was used, which supply flow rate was in the range of 1.6 L/min to 2.8 L/min, and the support gas was supplied with a flow rate of 15.0 L/min for all elements. Wavelength settings used were: 240.7 nm Co, 324.8 nm Cu, 357.9 nm Cr, 248.3 nm Fe, 279.5 nm Mn, 232.0 nm Ni, 283.3 nm Pb, 460.7 nm Sr, 213.9 nm Zn respectively. Slit widths were set at 0.2 nm for Co, Fe, Mn, and Ni respectively, and at 0.7 nm for Cu, Cr, Pb, Sr, and Zn respectively.  
Upon sample analysis using AAS, absorbance values were obtained. To obtain concentration of analytes in the solution, standard calibration curves of absorbance against concentration were created with water. Percentage recovery values computed showed a substantial amount of matrix interference; thus, calibration curves were then set up using sample analyte solution to reduce the matrix effect to a minimum using standard addition calibration method. All analysis was carried out in triplicates, against a method blank.  
A calibration check was carried out prior to each experiment and included every 6-10 samples. Apart from that precision was checked by preparing 6 standards of same concentration to check that the Percentage Relative Standard Deviation (% RSD) was always less than 2%.
## 5 Classification Experiments
### Evaluation Metrics  
Although accuracy is one of the most frequently used metrics to assess the performance of a classification system, it is not suitable if the test database is unbalanced. In those cases, the use of another metric such as recall is recommended. We have used the macro-average recall [53] as main assessment metric. For calculating the macro-average recall, first, the recall is computed for each class (1), and then we average all the recall values of the n (number of classes) to get the macro-average recall (2).  
\[Recall(R)=\frac{True\;Positives}{True\;Positives+False\;Negatives} \tag{1}\]  
\[macro-average\;recall=\frac{\sum_{i=1}^{i=1}Ri}{n} \tag{2}\]  
For each of the evaluated architectures, we presented the baseline results in two different ways. The first one calculated the macro average recall at the fragment level, while  
Figure 4: A CBIB block of the ResNet showing the Convolutional block (CB), and the Identity Block (IB).  
the second one calculated the metric for the entire sound clip, which was composed of several fragments. For calculating the metrics for the entire audio clip, we used a modified majority voting method.  
Normally, in the majority voting method, the predicted output class for the whole audio clip would be the class that has a greater number of predictions for the fragments of that audio. Taking into account the criteria that we used during the labelling phase, especially for \"Dog\" and \"Impact\" categories, we made some modifications on the majority voting method. This modification consisted on assigning \"Dog\" or \"Impact\" category to the whole audio clip if just one frame is classified as \"Dog\" or \"Impact\".
# Contrast-Enhanced Ultrasound Imaging Based on Bubble Region Detection
## 3 Experiment and Results
### In Vivo Results  
To demonstrate the effectiveness of the proposed algorithm, we also performed in vivo rabbit experiments. The ear vein and kidney of rabbit were studied, respectively. Figure 12 shows a scene from the rabbit experiment targeting the kidney.  
The rabbit (2 kg, 4 months old) was first anesthetized and then placed on an autopsy table where the four limbs were fixed by ropes. Before imaging, the region of interest was epilated to remove the influence of conv hair. Medical ultrasonic coupling agent was applied to the region of interest. A total of 500 \(\upmu\)L Sonovue microbubbles (no dilution) were injected through the right ear vein, which was followed by 500 \(\upmu\)L of physiological saline.  
The image results after microbubble injection are shown below (3 MHz, 10 V), and we added the detected UCA region to the DAS image. Figure 13 is the image targeting the ear vein (\(c=1\)) and Figure 14 is the kidney (\(c=1\)).  
Bubble region detection can remove the noise around the kidney edge effectively, and makes the image clearer. However, the upper part of the muscle and part of the speckle noise in the image cannot be eradicated. This indicates that the tissue signal and the UCA signal still have partial overlap in terms of eigenvalues and ESBCF.  
Figure 11: The image result of bubble region detection under different thresholds: (**a**) Higher threshold and (**b**) lower threshold.  
Figure 12: The in vivo rabbit experiment targeting the kidney.  
Figure 14: The image result of kidney: (**a**) DAS; (**b**) MV; (**c**) ESBMV; (**d**) BWT; (**e**) bubble region detection; and (**f**) UCA -enhanced image.  
Figure 13: The image result of ear vein: (**a**) DAS; (**b**) MV; (**c**) ESBMV; (**d**) BWT; (**e**) bubble region detection; and (**f**) UCA-enhanced image.  
Taking the intensity in the red rectangle as the UCA and the white rectangles as the tissue region (CTR1 is calculated for the tissue and UCA regions with the same depth, while CTR2 is calculated with the same width, see Figure 14a), the CTR of different beamformers is shown in Table 3. CTR1 and CTR2 are improved by 18.0 dB and 18.0 dB compared with PWI based on DAS, while this improvement is 3.4 dB and 7.3 dB compared to imaging based on BWT.
## 4 Results  
### Bearing Capacity Based on SPT  
Figure 6 shows the soil profile and corresponding blow counts (N) of SPT (SPT-N) at different testing points. The blow counts (N) were adopted to calculate the UBC of a single pile in this study based on the JGJ94-2008 method [50], Meyerhof method [51] and Schmertmann method [52], respectively. The calculated values were compared with that given in the survey report, as shown in Figure 7. For 21 m long PHC pipe piles embedded in medium sand ((c) & (d)), the recommended values of UBC  
Figure 4: **(A)**: PHC pipe pile types (C-type, D-type, and K-type) and schematic figure of pile driving method by hammer; **(B)**: Pile cap making for C-type and D-type PHC pipe piles: **(C)**: Pile cap making for K-type PHC pipe piles.  
Figure 5: Schematic of static load test. (**a**) Schematic of static load test; (**b**) photograph of static load test in the test; (**c**) hydraulic jack and dial gauge in the test.  
in survey report are in the range of 2191-3055 kN, while the UBC values calculated with the JGJ94-2008 method, Meyerhof method, and Schmertmann method were in range of 3570-4560 kN, 3437-5268 kN, 3316-5283 kN, respectively. The corresponding ratios of calculated values to the recommended value in the survey report are in the range of 1.30-2.10, 1.22-2.35, and 1.17-2.29, respectively. Based on SPT-1\(\#\), SPT-2\(\#\), and SPT-3\(\#\), for short PHC pipe piles with a length of 12.5 m embedded in coarse sand ((\(\lx@sectionsign\)2)), the recommended values of UBC in the report are in the range of 1547-1568 kN, while the UBC values calculated with the JGJ94-2008 method, Meyerhof method, and Schmertmann method, were in the range of 2828-3087 kN, 1799-3776 kN, 3304-5009 kN, respectively. The corresponding ratios of the calculated values to the report's recommended value are in the range of 1.80-2.00, 1.15-2.41, and 2.11-3.24, respectively. It is evident that all calculated values are bigger than the recommended values with no regard to pile length, and the value recommended in the survey report is rather conservative, which cannot truly reflect the formation characteristics on bearing capacity.
## 3 Experimental Verification and Discussion
### Lift Profile Optimization  
The cam lobe profile determines the valve's motion and influences acceleration and contact forces, affecting stress levels in components. Slender components like bars and beams in the valve train exhibit flexibility, impacting the system's dynamics, especially at high speeds [22]. As a result of these dynamic effects, tilt motion on the bridge part, the valve bounce, and no-contact phenomenon conditions occur. In addition, there is a difference between the kinematically targeted lift profile and the dynamically achieved lift. This changes the duration of the targeted valve overlap. Valve overlap is an important  
Figure 6: Test and simulation results on flywheel housing point.  
Figure 7: Test and simulation results on crankcase point.  
engine performance parameter because it provides effective scavenging and is included in the targeted valve lift profile. Valve overlap, defined as the time that both intake and exhaust valves are open simultaneously, is influenced by the dynamic behavior of the valve train. However, in real-world operating conditions, factors such as mechanical limitations and dynamic forces at high engine speeds can prevent the targeted valve overlap from being achieved. During optimization studies, solutions to the valve bounce, which causes deviations from the intended valve lift, help to achieve the desired valve overlap. By optimizing the dynamic opening curves of the valves to be closer to the kinematic profile that represents the theoretically ideal valve lift, valve bounce is reduced, and the desired overlap is maintained. The targeted valve lift profiles shown in Figure 8 were used in the cam lobe design.  
According to the results of the dynamic analysis of the valve train with the new spring parameters, the thermodynamic targets are met. As a result of the dynamic analysis, the separation between the follower and the lobe, the difference between the valve opening heights and the valve reopening after closing remained within the limits. Simulations were performed to evaluate the elephant foot position at three different points. Design positions are shown in Figure 9. The first position is determined to ensure that the contact zone occurs at the midpoint in the static position when the valve is closed. The second position was determined to ensure that the contact zone occurs at the midpoint of the bridge part, when the valve is at maximum opening. The third position was determined to ensure that the contact zone occurs at the midpoint of the bridge part at the instant of maximum force on the elephant foot.  
The characteristics of the valve spring, including stiffness and damping, play a vital role in the dynamics of the valvetrain [23]. The spring characteristics are optimized to improve rebound and no-contact points.  
Figure 8: Targeted valve lift profiles and dynamic valve lift profiles achieved in optimized design.