esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cooperative Communication Protocol for QoS Provisioning in IEEE 802.11p/Wave Vehicular Networks
## 5 Performance Evaluation
In Figure 17, the WAVE standard archives the lowest packet delivery ratio. This is because the WAVE standard is affected by the shadow zone between neighboring RSUs and the congestion. The OCBDF scheme is less affected by the shadow zone between neighboring RSUs, but it cannot avoid the congestion due to an increase in the number of vehicles. However, the proposed scheme is less affected by the shadow zone between neighboring RSUs and the congestion since OBUs can transmit data frames using the two-hop connection and can transmit data frame without contention. Figure 16: Throughput as a function of the number of vehicles. Figure 17: Packet delivery ratio as a function of number of vehicles. Therefore, the proposed scheme shows better packet delivery ratio than the legacy two protocols. Figure 18 shows the delay as a function of the number of vehicles. In Figure 18, the proposed scheme archives the lowest delay of the three protocols. This is because the proposed scheme can transmit data frames without contention in the allocated SCH interval. When the number of vehicles increases in the vehicular network, the congestion in the WAVE standard increases. The WAVE standard does not support the multi-hop communication. In the shadow zone between neighboring RSUs, the link disconnection frequently occurs, and the OBUs cannot receive service data frames from RSUs. Therefore, the delay in packet delivery is significantly increased. The OCBDF scheme is less affected by link disconnection, but the delay of the OCBDF scheme increases due to the congestion.
## 1 Introduction
### Motivations and Perspective
This technology can be applied for new breakwater port expansions, or it can be integrated into existing superstructures, which must be rebuilt due to the maintenance activities or upgraded due to climate change [26]. Given its concept, the system must meet both energy prerequisites and harbor safety requirements. Therefore, in order to reduce the overtopping at the rear of the structure, the upper crown wall is equipped with a reverse bevel consisting in an isosceles triangular parquet, also denominated \"nose\" (Figure 1). The effectiveness of the OBREC nose with a pre-fixed geometry was already confirmed by previous studies [19; 27]. A clear definition of the hydraulic performance of this nose under different overall dimensions and in the perspective of a SLR scenario is, however, still unknown. Previous studies focused on different configurations of the parquet instead of analyzing its hydraulic performance based on their geometrical characteristics and overall dimensions, indeed. Such a framework provides the main motivations and the path in which the experimental analysis has been conceived and carried out.
## 4 Experimental Results
### Results and Analysis
#### 4.2.5 More Visualization of Target Re-Identification  
Apart from classroom videos, our target re-identification model, TRTrack, also has excellent target re-identification results on publicly available SOT benchmark datasets. Figure 9 shows the target re-identification effect of TRTrack when some tracking objects leave the shots and reappear in the GOT-10k dataset, which reflects the target re-identification ability of TRTrack. It is worth noting that our model differs from other tracking algorithms in that when the target is identified as leaving the shot, tracking is no longer performed, and no prediction tracking bounding box is provided.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**TRTrack with**} & \multicolumn{3}{c}{**TRTrack with**} \\ \cline{2-7}
**Video ID** & \multicolumn{3}{c}{**Original YOLOv5-DeepSORT**} & \multicolumn{3}{c}{**Improved YOLOv5-DeepSORT (Ours)**} \\ \cline{2-7}  & **ET (Times)** & **ED (s)** & **TD (s)** & **ET (Times)** & **ED (s)** & **TD (s)** \\ \hline
001 & 7 & 17.53 & 15.44 & **2 \({}^{*}\)** & **2.72** & **2.64** \\
002 & 8 & 16.74 & 13.10 & **2** & **2.71** & **2.71** \\
003 & 7 & 11.98 & 10.56 & **4** & **5.27** & **5.27** \\
004 & 9 & 19.24 & 15.13 & **4** & **2.94** & **2.94** \\
005 & 14 & 22.87 & 19.06 & **3** & **3.01** & **3.01** \\
006 & 7 & 14.88 & 12.65 & **2** & **2.01** & **1.89** \\
007 & 4 & 5.43 & 3.29 & **0** & **0.00** & **0.00** \\
008 & 5 & 8.47 & 7.08 & **0** & **0.00** & **0.00** \\ \hline \hline \end{tabular} \({}^{*}\)The bold numbers in the table represents the optimal value in the evaluation metrics.  
\end{table}
Table 6: Results of improved YOLOv5-DeepSOT of boundary object detection module.
## 7 A case Study in Antarctica
### Analysis of Simulation Operation Results  
The power generation of the standalone renewable energy system in Zhongshan Station from 2014 to 2015 is presented in Figure 16.  
The results of Zhongshan Station load were previous monitoring data. The power generated by wind energy and solar energy was calculated by mathematical models of the standalone renewable energy system. If the power generated by the standalone renewable energy system cannot meet the requirement of load, the battery will act as an energy storage system to supply the load of the Zhongshan Station to maintain the normal operation. Accordingly, battery capacity can be also derived from the simulation. The observed wind speed data were selected to complete simulation calculation. The values of radiation intensity from NASA were used to calculate power generation by PV array. As can be seen in Figure 16, the monthly average load of Zhongshan Station remained basically stable within one year. The annual load of Zhongshan Station was 870,196 KW h. The minimum and maximum values of the load were 56,175 KW h in November and 87,743 KW h in July, respectively. The average value of the load of Zhongshan Station was 72,516 KW h. Wind power production dominated the power supply of the standalone renewable energy system and generated 747,858.4 KW h in a year, which was 86% of the load. The monthly average maximum and minimum values of wind power were 74,400 KW h in March, May, July, August, October and 8455.4 KW h in January. In March, April, October and November, only the energy generated by wind can meet the load of Zhongshan Station. The solar power was shown strong seasonal fluctuations owing to the polar night (June and July) in Antarctica. The annual solar power of Zhongshan Station was 97,361 KW h. The monthly average solar power of polar day in January and December were larger, which were 19,659.9 KW h and 21,779.7 KW h, respectively. The minimum value of monthly average solar power in a year appeared in June (0 KW h) and July (34 KW h). In January, February, May, June and July, wind and solar energy were less than the load required for the month, thus, the energy storage system also provided power to the load.  
Especially in August, the sum of the power of wind, solar, and battery (75,712.83 KW h) was less than the load (82,938 KW h), and fuel was used to complete the power supply (7225.17 KW h) to the load.  
Figure 16: The power generation of the standalone renewable energy system in Zhongshan Station from 2014 to 2015.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 3 Methods for Path Loss Prediction
### Comparison of Different Models
Then, these values were compared with the measured data and the prediction errors were computed. Multiple metrics including RMSE, MAPE, MAE, MaxPE, and ESD were used to evaluate the performance of the predictors, which were expressed as  
\[\text{RMSE}=\sqrt{\frac{1}{Q}\sum_{q=1}^{Q}\left(PL_{q}-PL_{q}{}^{\prime} \right)^{2}} \tag{9}\]  
\[\text{MAPE}=\frac{100}{Q}\sum_{q=1}^{Q}\left|\frac{PL_{q}-PL_{q}{}^{\prime}}{ PL_{q}}\right| \tag{10}\]  
\[\text{MAE}=\frac{1}{Q}\sum_{q=1}^{Q}\left|PL_{q}-PL_{q}{}^{\prime}\right| \tag{11}\]  
\[\text{MaxPE}=\max\left(PL_{q}-PL_{q}{}^{\prime}\right) \tag{12}\]  
\[\text{ESD}=\sqrt{\frac{1}{Q-1}\sum_{q=1}^{Q}\left(PL_{q}-PL_{q}{}^{\prime} \right)^{2}} \tag{13}\]  
Figure 4: Prediction performance of different predictors on the test dataset. The samples are from TD-SCDMA BS, with 80% of the samples as training dataset and 20% of the samples as test dataset. where \(q=1,...,Q\) is the index of the test sample, \(Q\) is the total number of test samples, \(PL_{q}\) is the measured data, and \(PL_{q}\)' is the predicted value of path loss. The prediction errors of different predictors are listed in Table 1. It is proved that the machine-learning-based models all have good performance and outperform the log-distance model. With selected hyperparameters, RF has the best performance in the measured scenario, followed by SVR, BPNN, and log-distance model.
## 2 Materials and Methods
### Data Processing
MATLAB R2024a (MathWorks, Natick, MA, USA) was used to filter and smooth the kinematic data to reduce high-frequency noise. During the pre-impact and post-impact phases, a 4 th-order Butterworth low-pass filter was applied with cut-off frequencies of 15 Hz and 30 Hz, respectively. For angular velocity data, a 6 th-order Butterworth filter with a 50 Hz cut-off frequency was used. During the impact phase, the Woltring smoothing algorithm with a \"lowess\" method and a 10-frame window was applied, effectively handling rapid signal changes by performing localized nonlinear regression. The data was then exported, and the following kinematic variables were extracted in MS Excel (Windows 365, Redmond, WA, USA). For the hip joint, the extracted variables included the adduction-abduction range of motion (ROM), peak adduction angular velocity, peak abduction angular velocity, flexion-extension ROM, peak flexion angular velocity, peak extension angular velocity, internal-external rotation ROM, peak internal rotation angular velocity, and peak external rotation angular velocity. For the knee joint, the extracted variables included the flexion-extension ROM, peak flexion angular velocity, and peak extension angular velocity. For the ankle joint, the extracted variables included the plantarlexion-dorsiflexion ROM, peak plantarflexion angular velocity, and peak dorsiflexion angular velocity. Both the ROM and peak angular velocity were calculated over the entire kicking phase. ROM is calculated by subtracting the minimum angle from the maximum angle during the kicking phase. Peak angular velocity was direction-specific and defined as the maximum angular velocity value for each movement direction.
# Software-Defined Named Data Networking in Literature: A Review
## 3 Overview and Background
### Named Data Networking
#### 3.1.3 NLSR  
NLSR [15] propagates two types of LSAs: adjacency LSA and prefix LSA. The adjacency LSA advertises active links between an NDN router and its neighbors, including the router's name, active links, and link costs. It is advertised at the router startup and when there is a status change in a router's links.  
Figure 3: NDN forwarding process.  
The prefix LSA advertises a name prefix registered with a router, containing a validity flag and the name prefix. Each router may announce multiple prefix LSAs if it has multiple name prefixes. When a name prefix is deregistered, the NLSR updates the corresponding prefix LSA by setting the validity flag to 0 and disseminating the new LSA to other router nodes.  
Obsolete LSAs are removed from the Link State Database (LSDB) when their lifetime expires. Therefore, if a router crashes, its neighbors will update the status of their LSAs, and traffic will not be directed over those broken links [16].  
The NLSR ensures that all routers have a consistent view of the network topology and name prefix reachability through an LSDB synchronization process. This process begins with the computation of a hash tree over all data. The root hash is then exchanged between neighboring routers to detect inconsistencies.  
If inconsistencies are found, the routers engage in the process of exchanging hash values of data on the next tree level. This continues until they identify the specific data causing the discrepancies. Once identified, the routers exchange the data to reach consistency.  
The LSDB synchronization process is summarized in the following nine steps, which are illustrated in Figure 4[16]:  
1. Routers periodically send out a root advise interest that contains the hash value of its LSDB to their neighbors to check if their databases match. The neighboring routers reply only when they detect any differences.
2. When Router A's NLSR creates a new LSA, it writes the LSA in the CCNx repository (REPO), which changes Router A's LSDB hash value.
3. Router A then detects the difference from Router B's LSDB hash value and replays the root advise interest sent in Step 1.
4. The routers continue exchanging hash values at the next tree level until they find the exact data causing the mismatch. After that, Router B sends a content interest packet to retrieve the data.
5. Router A then sends the requested data (LSA) to Router B.
6. Once Router B's REPO receives the data (LSA), it sends a notification to its local NLSR with the data name.
7. Following that, Router B's NLSR requests the LSA from the REPO.
8. Subsequently, Router B's REPO replies with the LSA.
9. Lastly, Router B's NLSR updates its LSDB with the new LSA.  
Figure 4: LSDB synchronization process.
## 1 Introduction  
InGaN-based light-emitting diodes (LEDs) have been attractive and widely applied in the field of general lighting, backlighting for displays, automotive lighting and so on due to the wide emission spectra range covering near-ultraviolet to red. [1; 2; 3] The efficiency droop, which is one of the main challenges of InGaN-based LEDs since internal quantum efficiency (IQE) reduces by increasing the injection currents, is still extensively investigated. [4] Many theories have attempted to explain such observations, including Auger recombination [5; 6; 7], electron leakage, [8; 9] lack of hole injection, [10; 11] and carrier delocalization [12; 13].  
In order to keep a high IQE at a high injection current, reducing the carrier density in the emitting quantum wells (QWs) is a suggesting primarily remedy to minimize the Auger recombination as well as the carrier overflow loss [14; 15]. A maximum quantum efficiency above 200 A/cm\({}^{2}\) has been achieved with a 9 nm wider well thickness for blue InGaN/GaN double-heterostructure LED by [PERSON] et al. [16], however, InGaN/GaN heterostructure LEDs show a low external quantum efficiency (EQE) due to the lack of the electro confinement effect [17; 18; 19; 20; 21]. [PERSON] et al. showed that the efficiency droop was alleviated by enlarging the QW thickness due to a smaller carrier overflow [17; 18]. Nevertheless, this theory is insufficient since too many factors were involved, such as the change of the piezoelectric field, the electro-hole wavefunction overlap and the carrier density. [PERSON] et al. showed that both the electron and hole dwell times can be significantly increased with a wide QW thickness by theoretical calculation, but the effect on the efficiency droop is also unclear [19]. [PERSON] et al. realized InGaN blue LEDs with a high quantum efficiency and low-efficiency droop at a high current density of 400 A/cm\({}^{2}\) on a semipolar (20-2-1) plane by employing a thick well of 12 nm, which was due to the low operation carrier density [20]. However, growing InGaN blue LEDs on the semipolar bulk GaN is too expensive and remains impractical for commercialization. Since the majority of carrier recombination happens in the last quantum well (LQW) due to the low mobility of the hole and fast mobility of the electron [21], we investigated the effect of the LQW thickness on the efficiency droop behavior for the c-plane polar InGaN LEDs based on the experimental design and theoretical calculation based on the ABC model.
## 3 Displacement Estimation Method
### Coupled Strain Separation
Based on the geometric relationship between points 2 and 3, the transverse bending strain at point 3 can be expressed in terms of the corresponding strain at point 2:  
\[\varepsilon_{m23}=\frac{y_{2}-d_{3}}{y_{2}}\varepsilon_{m2}, \tag{8}\]  
where \(d_{3}\) represents the distance between points 2 and 3. The relationship between points 2 and 3 is shown in Figure 2c. As indicated in Figure 2a, \(y_{2}=\frac{d_{3}}{2}\), which can be substituted into Equation (8) as follows:  
\[\varepsilon_{m23}=-\varepsilon_{m2} \tag{9}\]  
Because point 1 lies on the neutral axis z, \(y_{1}=0\), the transverse bending strain at point 1 is  
\[\varepsilon_{m21}=0. \tag{10}\]  
Finally, Equations (6), (8)-(10) can be substituted into Equation (3) to establish the following:  
\[ \begin{cases}\varepsilon_{1}=\varepsilon_{N}^{\prime}+\varepsilon_{my1}\\ \varepsilon_{2}=\varepsilon_{N}^{\prime}+\frac{z_{1}-h}{z_{1}}\varepsilon_{ my1}+\varepsilon_{m22}\\ \varepsilon_{3}=\varepsilon_{N}^{\prime}+\frac{z_{1}-h}{z_{1}}\varepsilon_{ my1}-\varepsilon_{m22}\\ \end{cases}  \tag{11}\]  
Solving Equation (11) yields \(\varepsilon_{N}^{\prime}\), \(\varepsilon_{my1}\), \(\varepsilon_{m22}\). The vertical bending strain and transverse bending strain can be calculated at the three points using Equations (6) and (10). This enables the coupled strain monitored at the three points to be decomposed into three distinct directional strains.
## 2 Materials and Methods
### Excitation Modelling
In the case of the static behavior of a structure, i.e., under the assumption that the ground displacements are applied infinitely slowly, the mass and damping matrices in Equation (1) and the dynamic component of motion in Expression (2) disappear, which leads to the quasi-static displacements determination  
\[\mathbf{x}_{s}^{d}=-\mathbf{K}_{ss}^{-1}\cdot\mathbf{K}_{sg}\cdot\mathbf{x} _{g}. \tag{3}\]Substituting Equations (2) and (3) to Formula (1) leads to the following equation:  
\[\mathbf{M}_{ss}\ddot{\mathbf{x}}_{\mathrm{s}}^{d}+\mathbf{C}_{ss}\dot{\mathbf{x}}_ {\mathrm{s}}^{d}+\mathbf{K}_{ss}\mathbf{x}_{\mathrm{s}}^{d}=(\mathbf{M}_{ss} \cdot\mathbf{K}_{ss}^{-1}\cdot\mathbf{K}_{sg}-\mathbf{M}_{sg})\cdot\ddot{ \mathbf{x}}_{g}-(\mathbf{C}_{ss}\cdot\mathbf{K}_{ss}^{-1}\cdot\mathbf{K}_{sg}- \mathbf{C}_{sg})\cdot\dot{\mathbf{x}}_{g} \tag{4}\]  
Eurocode 8 [26] allows for the omission of the second component of the right-hand side of Equation (4) as being significantly smaller than the first one, resulting in the following equation of motion of a structure under kinematic excitation:  
\[\mathbf{M}_{ss}\ddot{\mathbf{x}}_{\mathrm{s}}^{d}+\mathbf{C}_{ss}\dot{\mathbf{ x}}_{\mathrm{s}}^{d}+\mathbf{K}_{ss}\cdot\mathbf{x}_{\mathrm{s}}^{d}=-(\mathbf{M}_{ ss}\cdot\mathbf{K}_{ss}^{-1}\cdot\mathbf{K}_{sg}+\mathbf{M}_{sg})\cdot\ddot{ \mathbf{x}}_{g} \tag{5}\]  
It is clearly visible from Equations (2) and (5) that the total displacements of the structure depend on the ground acceleration vector (\(\ddot{\mathbf{x}}_{g}\)), containing accelerations attributed to the structure's supports. In the case of dynamic analyzes of multi-support structures, ground movement at all support points must be taken into account. Since ground accelerations (or velocities) are usually registered by the seismological station at only one point, a model of kinematic excitation, which takes into account the spatial variability of ground movements during a quake, should be introduced to determine the motion of different structure's supports. In this paper, the authors recommend a new advanced model of kinematic excitations resulting from mining tremors.
## 4 Materials and Methodology
### Data Preparation and Preprocessing  
#### 4.3.1 Data Augmentation  
To address the constraints of our current dataset size, a widely adopted strategy in training deep learning models involves augmenting the dataset using various techniques. Research indicates that data augmentation can notably enhance the classification accuracy of deep learning algorithms. As discussed earlier, CNNs tend to perform better with larger datasets. Following normalization, our augmentation process included rotating chicken meat images by 30 degrees.  
During image acquisition, we first placed the chicken samples on a conveyor table, adjusting the height to ensure proper visibility within the camera's field of view. Subsequently, we obtained images categorized into four groups labeled C1, C2, C3, and C4, totaling 878 images. These categories represent the different types of chicken pieces captured in this study. After obtaining the images, we split the chicken meat image dataset  
Figure 3: Schematic diagram of the testing process for classification of C1, C2, C3, and C4.  
into 67% for training data and 33% for testing data. This division provided us with a total of 588 images for training and 290 images for testing. After dividing the data, we applied data augmentation techniques exclusively to the training set. As part of these techniques, we increased the number of images in each class by adding 1000 additional images. Thus, we obtained 2138 images for 'yellow wing (C1)', 2168 images for 'white wing (C2)', 2146 images for 'yellow thigh (C3)', and 2136 images for 'white thigh (C4)'. In total, the training dataset comprised 4588 images.  
Additionally, during the data augmentation process, we applied the cropping process three times, resulting in images of different sizes: 400 \(\times\) 400 pixels, 800 \(\times\) 800 pixels, and 1200 \(\times\) 1200 pixels. These transformations introduced additional variations to the original images, thereby enhancing the resilience of the machine learning model by exposing it to diverse conditions and input data variations.  
Figures 4 and 5 showcase examples of images from the chicken meat dataset utilized in this study, applying the rotation and image cropping data augmentation methods.