esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
[27] investigated the joint optimization of cached recommendations for wireless edge computing, modeling the impact of recommendations on popularity through probabilistic transformation matrices and solving the optimization problem based on point estimation and Bayesian estimation methods. [PERSON] et al. [28] modeled the influence of a user's preference distribution and recommendation and then proposed a heuristic algorithm with low computational complexity to solve the joint recommendation and cache optimization problem. Deployment of joint caching and recommendation is becoming increasingly popular and has found significant performance improvements in various applications including video streaming [8; 9; 10] and content delivery [11; 12; 13; 14; 15; 16; 17; 18; 29]. A classification of major approaches for caching-recommendation joint optimization is shown in Table 1.  
Compared with the previous work, we do not assume that a recommender system is always \"honest\", i.e., our recommender system may make recommendations driven by factors such as product promotion rather than purely by user interests, which may make its recommended content deviate from cached hits. We study the joint optimization of caching and recommendation in CDNs in the general case without assuming the honesty of the recommender system and the tradeoff metric between caching and recommendations. In comparison with most existing studies that either focus only on cache optimization, or consider caching-recommendation joint optimization under special assumptions of their relationships, or provide only heuristics and deep reinforcement learning methods without proof of performance guarantee, our study synthesizes the general-case joint optimization problem of caching and recommendation, shows its NP-hardness in the simple case of a single user, and provides a solution with a performance guarantee of \(1-\frac{1}{e}\) from the optimal solution for the case when the recommended contents are selected only from the cached contents.
## 4 Results
### Step 2: Interpretive Structure Modeling (ISM) and MICMAC Results
#### 4.2.4 Model Validation  
The contextual relationship between lean implementation barriers is the basis for the ISM model development. The formulated model was reviewed and validated using the Delphi technique, as shown in Figure 4. There were three Delphi members, who were not part of the expert group; they were from the piston manufacturing unit, the connecting-rod manufacturing unit, and the cylinder-casting machining unit. They were approached to take part in the Delphi process, anonymously, and consented to do so.  
Figure 3: Classification of LBs using MICMAC analysis.  
Figure 2: ISM for LBs.  
The identity of the Delphi group was kept anonymous to avoid any biased decisions. The Delphi members were introduced to the lean implementation barrier identification and ISM methodologies separately. Detailed information on the ISM model with lean implementation barriers was sent to their respective units. Three rounds of Delphi covered validation of the short-listed LBs and ISM results. In round 1, the LB information sheets with the short-listed LBs were sent; a contextual relationship matrix to validate was also sent. In round 2, all team members were sent the contextual relationship matrix for verification. The feedback of the Delphi team was compared to that of the expert group. In round 3, all Delphi members were sent the final ISM model. All the Delphi members agreed to the ISM model, and a consensus was reached.
# RCBi-CenterNet: An Absolute Pose Policy for 3D Object Detection in Autonomous Driving
## 3 The Dataset and Learning Task  
### Dataset  
The dataset used in our study is derived from the ApolloSpace dataset [15] created by the Baidu Robotics and Autonomous Driving Lab (RAL) and Peking University. The dataset consists of more than 60,000 labeled 3D car instances from 5277 real-world images based on industry-grade CAD car models. All images are with high resolution and collected in real traffic environments with diverse driving conditions in four cities of China. Compared to KITTI, the ApolloSpace dataset contains more movable objects (11.7 vs. 4.1 average cars per image). There are some challenging environments in the dataset; for example, two extreme lighting conditions (e.g., dark and bright) appearing in the same image could be caused by the shadow of an overpass [52].  
The training data consist of 4262 images, and each comes with a line describing the 3D pose information of all vehicles annotated in the image. The pose information is given as a string with the following format: model type, yaw, pitch, roll, x, y, z,where (yaw, pitch, roll) gives the orientation/rotation information and (x, y, z) provides the position/translation information. The model type indicates the car model that comes with a 3D model, which can help determine the vehicle size and orientation but is not required as a part of the prediction result. Note that some vehicles are not of interest because they are too far/smalland are therefore removed from consideration for the task. In addition, the camera intrinsic parameters are provided to facilitate the coordinate conversion between the 3D world space and the 2D image space, as shown in Figure 2.  
Given the transformed 2D position and orientation data, along with provided car models, the ground truth vehicle annotations can be visualized with a center point and the bottom rectangle of a 3D bounding box, as shown in Figure 3. Note that the bounding boxes are estimated and are only for visualization purposes. We can also plot a distribution of vehicle objects across the dataset, as shown in Figure 4.  
Figure 3: _Cont._  
Figure 2: (**a**) The camera perspective from the top of the autonomous vehicle. All images in the dataset are taken by this camera. The principal point (u, v), at the center of an image, is the point on the image plane onto which the perspective center is projected. With a given camera intrinsic matrix, we can obtain a conversion between a point’s 3D coordinate (x, y, z) in the world space and its 2D coordinate, denoted as (ix, iy), in the image space. (**b**) Six degrees of freedom (6 DOF) can be used to determine the absolute pose of a vehicle in the world space. Rotation around the side-to-side (X) axis is called pitch; rotation around the front-to-back (Y) axis is called roll; and rotation around the vertical (Z) axis is called yaw.
## 4 Results
### Development of Cost Contingency Prediction Models  
As described in Stage 2, seven ML prediction models were developed and evaluated, achieving Objective 1.  
Table 5 presents the evaluation outcomes of seven ML models for contingency predictions. The prediction performance was gauged with four metrics: \(R^{2}\), MAE, MSE, and RMSE. Linear regression and SVM yielded \(R^{2}\) values of 0.397 and 0.360, respectively. These values categorize both models as inaccurate as they fall below the \(R^{2}\) threshold of 0.5. According to [PERSON] [9], model performance is considered excellent if the \(R^{2}\) value is higher than 0.9, good if it is higher than 0.8, acceptable if it is above 0.5, and inaccurate if it falls between 0 and 0.5. Moreover, the decision tree model exhibited the lowest performance among the evaluated models, with an \(R^{2}\) of 0.084. The corresponding MAE, MSE, and RMSE values were HKD 156, 101, 470, and 318 million, respectively. These three algorithms were deemed inadequate as their \(R^{2}\) values did not reach the 0.5 threshold.  
In contrast, the random forest and gradient boosting models demonstrated substantial improvement in prediction accuracy, achieving \(R^{2}\) values of 0.725 and 0.782, respectively. These improvements highlight the advantage of ensemble learning techniques in capturing non-linear dependencies and reducing overfitting through multiple decision trees [36]. Of the tree-based methods, XGBoost achieved the best predictive performance, with an \(R^{2}\) of 0.795. The other three evaluation metrics (MAE = 86, MSE = 23876, and RMSE = 157) further supported XGBoost's effectiveness.  
The highest prediction accuracy, however, was achieved by the ANN model, which achieved an \(R^{2}\) of 0.808. This made it the highest-performing model among those tested. ANN's superior performance can be attributed to its capacity to learn complex, non-linear relationships between project characteristics and cost contingency, a capability that traditional regression-based and tree-based models lack [33]. Due to its superior performance, the ANN was selected for further analysis in the next phase of this study, which investigated the potential improvement in the model performance achievable through the integration of a complexity index.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Prediction** & **Metrics** & **Linear Regression** & **SVM** & **Decision Tree** & **Random Forest** & **Gradient Boosting** & **XGBoosting** & **ANN** \\ \hline \multirow{3}{*}{Materialized cost contingency} & \(R^{2}\) & 0.397 & 0.360 & 0.084 & 0.725 & 0.782 & 0.785 & 0.808 \\  & MAE & 156 & 141 & 156 & 94 & 86 & 86 & 93 \\ \cline{1-1}  & MSE & 66,709 & 70,870 & 101,470 & 30,412 & 24,095 & 23,876 & 21,236 \\ \cline{1-1}  & RMSE & 258 & 266 & 318 & 174 & 155 & 157 & 145 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance matrices for the first-stage prediction models.
## 2 Experimental Setup  
The experimental setup of the LS-APPJ is illustrated in Figure 1a, and it is mainly composed of a high-voltage pulse power supply, a plasma jet device, an electrical measurement system, and an optical detection system. The plasma jet device consists of a jet tube and a copper high-voltage electrode, as shown in Figure 1b. The jet tube is composed of quartz, with an outer diameter of 1.2 cm and a length of 35 cm. A spiral copper wire is coiled around the outer surface of the jet tube for 120 turns as the high-voltage electrode. The inductance of the floating spiral electrode is about 20 uH, which has a good matching effect with the pulse power supply. Remarkably, there is no ground electrode inside the jet tube. The plasma jet device is fixed on a PTFE shelf with the nozzle placed about 20 cm above the ground. A positive unipolar pulse voltage, negative unipolar pulse voltage, and bipolar pulse voltage with a rising time of about 20 ns are employed to drive the plasma jet device to produce a positive pulse plasma jet (PPPJ), negative pulse plasma jet (NPPJ), and bipolar pulse plasma jet (BPPJ), respectively.  
The waveforms of the voltage and current of the LS-APPJ are measured by a high-voltage probe (P6015A, 1000\(\times\), 75 MHz (Tektronix, Beaverton, OR, USA)) and a current probe (TCP312, 100 MHz (Tektronix, Beaverton, OR, USA)) and are recorded and displayed on an oscilloscope (TDS5054B, 500 MHz (Tektronix, Beaverton, OR, USA)). The plasma jet tube is long enough and contains no electrode to prevent the disturbance of the gas  
Figure 1: Experimental setup (**a**) and electrode structure (**b**) of LS-APPJ.  
flow, so that a smooth gas flow can be obtained when the gas flow rate of helium (99.999%) is kept at 3.5 L/min. In order to measure the length of the plasma jet, discharge images are captured by a digital camera (5D IV (Canon, Tokyo, Japan)) with an exposure time of 100 ms. To obtain the spatially resolved spectra emitted from the plasma jet, the head of an optical fiber is placed close to the reactor, which is adjustable in the vertical and horizontal directions via a 3D displacement platform. Then, the optical emission spectra (OES) from the discharge region are collected by a grating monochromator (SR-750i (Andor, Oxford, UK)) (grating grooves are 2400 lines mm\({}^{-1}\) and 1200 lines mm\({}^{-1}\) to acquire the spectra in the range of 300-480 nm and 575-800 nm, respectively). After the diffraction of the grating, the output spectral light can be converted into an electrical signal by an ICCD camera (iStar DH334T (Andor, Oxford, UK)).
## 6 Comparison
### Cost  
Cost is a critical factor in evaluating identity systems, with centralised and decentralised models showing stark differences. Centralised systems like LDAP, SAML, and OAuth are highly cost-efficient. Initial setup costs for small and medium organizations typically remain below USD 10,000, while operational costs are limited to infrastructure maintenance and periodic updates. Open-source tools and extensive vendor support further lower the total cost of ownership. LDAP's scalability and minimal hardware requirements reduce recurring costs, making it ideal for enterprises with fixed budgets [62].  
Decentralised systems, however, require significantly higher financial investments. Private blockchain networks like Sovrin and Hyperledger Indy necessitate validator node maintenance, with setup costs of USD 5000-USD 10,000 per node. Overall deployment for decentralised networks can range from USD 10,000 to USD 50,000 depending on scale and complexity. Public blockchain-based systems, such as uPort on Ethereum, incur operational costs tied to network activity, with gas fees averaging USD 1-20 per transaction during peak usage. These costs can escalate in high-frequency identity verification scenarios, making such systems less feasible for organizations with cost-sensitive operations. Additionally, decentralised systems require expertise in blockchain, forcing organizations to invest in retraining employees or hiring specialists, which further increases operational costs [17]. Consequently, decentralised systems are often adopted by large organizations with long-term strategies focused on decentralised control and compliance with privacy regulations.
# Blast Loading Prediction of Complex Structures Based on Bayesian Deep Active Learning
## 4 Experimental Results and Analysis
### Prediction Accuracy and Uncertainty Quantification Results Evaluation
#### 4.2.2 Scatter Diagram  
To further analyze the prediction performance of the BDAL model for blast loading, a scatter plot was drawn comparing the actual peak overpressure values from numerical simulation with the predicted values from the BDAL model, as shown in Figure 4. The dots in the figure represent 7200 test sample points (36 \(\times\) 200), which are arranged in ascending order based on their true values. The horizontal axis (\(x\)) represents the actual peak overpressure of each sample point, whereas the vertical axis (\(y\)) indicates the corresponding model's prediction value. The red solid line in the center signifies where the true value equals the predicted value; the proximity of each point to this line reflects the accuracy of the model's peak overpressure predictions. The two red dashed lines serve as envelope lines, indicating a relative error of \(\pm\)30%. Additionally, the color of the dots indicates the density of the measurement points, where a redder color indicates a higher number of samples in that interval, and a bluer color denotes fewer samples. The panel on the left of Figure 4 contains all 7200 test samples, while the four subgraphs on the right are randomly selected from the 36 groups of examples in the test set, with each group containing 200 samples.  
As shown on the left side of Figure 4, most of the sample points are located inside the two red dotted lines, indicating that the BDAL model is more accurate at predicting the peak overpressure using the entire test set. In the four subgraphs on the right of Figure 4, the sample points are basically located in the two red dotted lines and are relatively close to the red solid line in the middle. This further demonstrates that the BDAL model has good prediction performance for different explosion sources.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **R2** & **MAPE** & **NMPIW** & **PICP** \\ \hline Training & 0.986 & 0.043 & 0.01 & 0.850 \\ Test & 0.974 & 0.061 & 0.03 & 0.731 \\ \hline \end{tabular}
\end{table}
Table 4: Peak overpressure prediction results.  
\begin{table}
\begin{tabular}{c c} \hline
**Network Structure** & **14-200-200-200-200-1** \\ \hline Activation Function & Relu \\ Learning Rate & 0.004 \\ Epoch & 2000 \\ Optimizer & Adam \\ Dropout Rate & 0.2 \\ \hline \end{tabular}
\end{table}
Table 3: Training and structural parameters of the BDL model.
## 4 Simulation and Analysis
### Simulation Results
#### 4.2.2 Path II  
The reference trajectory of \(\infty\) was Equation (34), the initial status \(\xi_{start}\) was \(\left[1.1,0.6,0\right]^{\text{T}}\), and the range of weight ratio \(q\) was \(q\in\left[9,15\right]\). The weight ratio \(q\) of a traditional MPC was 15. The simulation results are shown in Figures 15-20.  
\[\left\{\begin{array}{l}X=1.1+0.7\sin(\frac{2\pi t}{30})\\ Y=0.9+0.7\sin\left(\frac{4\pi t}{30}\right)\\ \varphi=0.5\end{array}\right. \tag{34}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\Delta l\) & \(\Delta\varphi\) \\ \hline traditional MPC & 6.712\% & 5.670\% \\ MPC based on ANFIS & 2.841\% & 1.569\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Maximum fluctuation difference with disturbance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\Delta l\) & \(\Delta\varphi\) \\ \hline traditional MPC & 10.938\% & 7.693\% \\ MPC based on ANFIS & 3.707\% & 3.085\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The peak difference between the up and down fluctuations with disturbance.  
Figure 15: Trajectory comparison without disturbance.  
Figure 16: Trajectory comparison with disturbance.  
Figure 17: \(\Delta l\) without disturbance.  
Figure 18: \(\Delta\varphi\) without disturbance.  
Compared with the traditional MPC with disturbance, MPC based on ANFIS reduced the maximum fluctuation difference of \(\Delta I\) by 73.326%, and all the \(\Delta\varphi\) approached 0. The stability of \(\Delta I\) was increased by 78.148%. The error curve was much flatter and stabilized faster compared with the traditional MPC with disturbance. Moreover, the robot could respond more quickly when there was disturbance.  
Experiments showed that MPC based on ANFIS had superior performance compared with a traditional MPC.
# A Source Anonymity-Based Lightweight Secure AODV Protocol for Fog-Based MANET
## 4 Source Anonymity-Based Lightweight Secure AODV Protocol
### Source Anonymity-Based Lightweight Secure AODV Protocol  
#### 4.1.1 Source Anonymity Algorithm  
In SAL-SAODV, the pseudonym scheme is that in which the virtual prime IDs corresponding to the false IDs are generated to hide the nodes' true ID with the nodes ID. In general, the node's ID of MANET is 4 bytes, which ranges from 1 to 4,294,967,296 (0 is generally not used). The number of prime numbers within 1 billion is 50,847,534, which is able to meet the requirement of the pseudonym. The pseudo-code of the prime queue (false ID queue) generation algorithm is described in Algorithm 1. Although the number of loops is executed more frequently during the generation of primes, the work only needs to be performed once in this algorithm.  
```
1:Set nodes' maximum number (\(N_{MAX}\)) in MANET, Initialize the prime queue \(P_{i}=0\), Set \(P_{1}=2\)
2:for (Set counter \(A=1\); \(A<=N_{MAX}\); \(A=A+1\))
3:for (Set counter \(B=2\); \(B<=\) [\(A\) square root] + 1; \(A=A+1\))
4:if\(A/B=0\)then
5: Exit this loop
6:else
7:\(P_{A}=B\)
8:endif
9:endfor
10:endfor
```  
**Algorithm 1** Prime queue generation algorithm.  
According to the false ID queue generated by Algorithm 1, the node can generate a false ID for itself. The false ID corresponds to the true ID in the false ID queue from the prime number sequence in the sequence of prime numbers, which is a single mapping in Figure 2. The _pseudonym_ is generated by Formula (1).  
\[Pseudonym=Rand(FalseID,FalseID+\Re) \tag{1}\]  
where, \(Rand(n\), \(n+m)\) is a function, which can generate a random number between \(n\) and \(n+m\). \(\Re\) is a random number that is updated regularly. At this point, the false ID can be replaced with the generated pseudonym.  
The process of index generation can be distributed in the initial stage of the network, by the signaling interaction between the sink node and each node, or by the operation of the node itself. In addition to the single mapping, the group mapping and block mapping and other tactics can be used to index and generate themselves false IDs, or a false ID set, and even the encryption can be implemented further. The specific mapping can be determined according to the actual requirements. In addition, the false ID can also be changed in the network operation process via the signaling interaction.
## 4 The Impacts of Microalgae/Cyanobacteria and Their Extracts on Crops' Production
### Crops' Protection
#### 4.2.2 Protection against Abiotic Factors  
Besides biotic factors, plants' exposure to extreme environmental conditions (e.g., very cold and hot temperatures, high salinity conditions and drought) can negatively impact crops' production and development [9; 124]. In the case of extreme temperatures, low temperatures may reduce the metabolic activity of higher plants and damage the cell membranes, as the phospholipid layer that constitutes these membranes become unstable. Exposure to high temperatures may also be harmful for cell membranes and influence the synthesis and activity of plants' proteins and enzymes [124]. High salinity levels are commonly associated with the osmotic stress in plants, which affects the nutritional composition of plants (typically, salt-stressed plants exhibit lower chlorophyll contents than the non-stressed ones) and, hence, plants' metabolism and growth [9; 124; 125]. Drought is also responsible for the osmotic stress in plants. However, it also affects plants' ability for gas exchange, significantly influencing the photosynthetic and transpiration rates and, there fore, the plants' yield [9; 124]. Considering the negative impacts of extreme environmental conditions on plants' growth and development, increasing the tolerance of higher plants to these harsh conditions is crucial for the improvement of agricultural productivities. For this purpose, the use of biostimulants can be a very promising option, as these compounds are able to [124; 126]: (i) promote the accumulation of antioxidant compounds, thus increasing the plants' tolerance to stress conditions; and (ii) improve the overall performance of higher plants, thus stimulating their growth. Although several biological compounds are known for their stimulatory activity, the use of secondary metabolites produced by microalgae and cyanobacteria has attracted researchers worldwide, due to their wide variety and to their ability to induce several plant response mechanisms and metabolic pathways. In fact, the use of algal-based products as biostimulants has been successfully applied in food crops under temperature, salt and drought stress conditions [34; 113; 114; 115; 116; 117; 118]. In the study performed by [PERSON], et al. [34], the polysaccharides excreted by \(D\). _salina_ induced the jasmonic acid pathway, a metabolic pathway involved in plants' response to stress, reducing the salt stress damages in tomato cultures. Moreover, this algal-based treatment promoted an improvement in roots and shoots' length. Similarly, the use of seaweed extracts from _Ulva rigida_ and _Fucus spiralis_ in bean plants under drought stress increased the tolerance of this plant to drought and improved its growth [116].