esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities
## 4 Gradually Accelerating Environment Adaptive Consensus Algorithm  
In view of the high performance cost of traditional consensus algorithms, such as PBFT, this paper proposes an improved AE-PBFT algorithm based on a gradually accelerated credit management mechanism and an environment-adaptive consensus group dynamic division mechanism. As shown in Figure 2, the AE-PBFT algorithm has the following steps:  
(1) All nodes \(\mathcal{N}\) are initialized in the system. The client sends a transaction request \(\langle Request,o,t,c\rangle\) to the cluster master node, which contains operations and parameters;  
(2) The master node broadcasts the received request \(\langle Pre-Prepare,v,n,d\rangle\) to all backup nodes, which verify the legitimacy of the request and broadcast \(\langle Prepare,v,n,d,n\rangle\) to other backup nodes after confirmation;  
(3) After receiving a request broadcast by other backup nodes, a backup node verifies the legitimacy of the request. If the verification passes, the node enters the response phase;  
(4) All nodes entering the response phase verify the message and send the result to the master node;  
(5) When the master node receives more than \(2f+1\) identical confirmation messages, it believes that the system has reached a consensus, sends the confirmation result \(\langle Result,v,t,c\rangle\) to all nodes, and synchronously updates the node trust score. At this point, the consensus process ends.  
In view of the slow screening speed of the traditional fixed plus and minus trust model, this paper proposes a new trust model and consensus group division mechanism, which can quickly and effectively distinguish malicious nodes and divide consensus groups.  
Figure 1: Consensus process of PBFT algorithm.  
There are two core mechanisms in AE-PBFT: the node trust management model and the consensus group division mechanism.
## 2 Materials and Methods
### Data Collection
#### 2.5.2 In-Depth Interviews  
In-depth interviews were conducted with a subset of 25 farmers selected from the 250 sample. Five interviews per village were conducted. The interviews followed a semi-structured format, allowing for flexibility and an in-depth exploration of farmers' perceptions, beliefs, and experiences. Probing questions were used to delve into specific areas of interest, such as barriers and facilitators to adopting drought adaptation measures, and farmers' perceptions of the effectiveness of existing support mechanisms. The duration of the interviews varied depending on the participants and the depth of the discussion. However, each interview did not exceed 30 min.
## 3 Results and Discussion
### Soil Characterization  
The mine tailings had high heavy metal concentrations of 1354.50 \(\pm\) 2.26, 226.90 \(\pm\) 1.61, and 61.08 \(\pm\) 0.20 mg/kg for Pb, Zn, and Cd, respectively. At Point 1 (50 m from the mine tailings epicenter) in the top layer (5 cm) of soil close to the mine tailings, the highest concentrations of Pb, Zn, and Cd were 551.49 \(\pm\) 1.91, 245.74 \(\pm\) 0.49, and 19.23 \(\pm\) 0.02 mg/kg, and at a depth of 15 cm they were 368.73 \(\pm\) 0.40, 216.21 \(\pm\) 0.37, and 11.91 \(\pm\) 0.01 mg/kg, respectively (Figure 1). At Point 2 (200 m from the epicenter of the mine tailings), the concentrations of Pb, Zn, and Cd were 12.79 \(\pm\) 0.03, 48.18 \(\pm\) 0.08, and 14.28 \(\pm\) 0.02 mg/kg in the upper layer and 7.76 \(\pm\) 0.07, 8.25 \(\pm\) 0.07, and 5.40 \(\pm\) 0.04 mg/kg, respectively, in  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**HM** & **R\({}^{2}\)** &
\begin{tabular}{c} **Calibration Curve** \\ **Equation** \\ \end{tabular} & **Range (mg/L)** & **LOD (mg/L)** & **LOQ (mg/L)** \\ \hline Pb & 0.9999 & y = 0.9956\(\mathrm{c}\mathrm{c}\mathrm{a}\mathrm{e}\mathrm{a}\mathrm{e}\mathrm{a} \mathrm{e}\mathrm{a}\mathrm{e}\mathrm{a}\mathrm{e}\mathrm{a}\mathrm{e} \mathrm{a}\mathrm{e}\mathrm{a}\mathrm{e}\mathrm{a}\mathrm{e}\mathrm{a}\mathrm{a} \mathrm{e}\  
the lower layer. (Table 4). Our results were consistent with those of other researchers [1] in showing concentrations of 350.6, 54.3, and 11.8 mg/kg of Pb, Zn, and Cd, respectively, around 500 m from the mine tailings. Thus, the amount of Pb at Point 1 in the area adjacent to the factory was found to be 11.5 to 17 MPC. Zn concentration at Point 1 was 9 to 10 MPC. Cd concentrations were the highest at all sampled points in the mine tailings area at 2.5 to 20 MPC.  
HM concentrations in polluted soils exceeding the MPC were determined according to the regulations. MPC values (mg/kg) for HMs were 32.0 for Pb, 23.0 for Zn, and 2.0 for Cd [37].  
The territories adjacent to Shymkent City are characterized by ordinary greyem [38]. These are soils with a low-moisture, low-humus surface, an arid moisture regime, and the absence of permafrost within 200 cm of the surface [39]. As these are alkaline soils, with a pH of 7.5-8 and absolute dominance of oxidative processes, they do not have a high capacity to accumulate Pb, Zn, or Cd directly, but Pb processing wastes, as shown in Figure 2, have contaminated the area for more than 70 years. [1]. In Figure 3, the distribution of Pb, Zn, and Cd is illustrated using color interpolation. The areas of distribution of certain HM concentrations are highlighted by isolines and displayed at the scale of the sampling points and the adjacent mine tailings territory.
## 4 Experiment Evaluation
### Experimental Analysis
#### 4.3.2 Drift Detection Performance Analysis
Overall, in the context of sudden drift experiments, all three drift detectors demonstrate excellent performance. Table 6 illustrates the performance of different drift detectors on the gradual drift dataset. In the LED dataset, regardless of whether the base classifier is NB or HT, VFDDMs achieve the optimal detection delay, surpassing the MWDDM algorithm with increased weighting factors. This indicates that the Bernstein test significantly helps to enhance the speed of drift detection in datasets with small variances. Moreover, VFDDMs outperform MWDDM in terms of accuracy. Additionally, VFDDMs exhibit the highest true positive rate, demonstrating the advantages of our variance feedback strategy. It is worth noting that MWDDM also achieves top detection speed in gradual drift datasets. This indicates that increasing the instance weighting strength improves drift detection speed in both abrupt and gradual drift environments, although at the cost of higher false positive rates, as shown by the performance of HDDM_W and MDDM. The performance of BDDM reveals that its advantage in detection speed is less pronounced in gradual drift environments, suggesting that the Bhattacharyya distance is more suitable for detecting concept drift in abrupt drift scenarios. In the Circle dataset, FHDDM, MDDM, WMDDM, and VFDDMs demonstrate good performance in terms of false positive rates. In the Hoeffding test, VFDDM_H simultaneously achieves low false positive rates and detection delay rates. The McDiarmid test improves the speed of drift detection while maintaining the same false positive rate as MDDM.
# Ten Years of TerraSAR-X Operations
## 1 Introduction
An important proof of technology was the acquisition and processing of data in TOPSAR mode, which--like the ScanSAR mode--acquires data of several sub-swathes in parallel, but also performs an along-track azimuth steering, and thus overcomes severe ScanSAR limitations, like the scalloping effect. The new bi-directional SAR mode BiDiSAR was developed. It shapes an antenna pattern with a forward and a backward oriented lobe, and thus records two time-shifted images of the same scene on ground into only one receiving channel. In summary, we describe in this paper the TerraSAR-X ground segment extensions and service improvements, as well as the evolution of the SAR product portfolio since the TerraSAR-X mission operations started in 2008. We report on dedicated mission campaigns necessitating specific ground segments adaptations on the one hand, on the other hand opening new research opportunities by providing additional products. Details of selected radar experiments performed within the last decade are given. Specific challenges to be met by the satellite operations are discussed. The orbit control philosophy and the operational experience is described. System performance and mission utilization aspects are addressed.
## 3 Measurement, Control, and Communication Architecture
### Custom-Designed Hardware Components  
The physical implementation of the proposed system relies on a set of custom-designed hardware components integrated through open-source embedded platforms. As shown in Figure 2, the hardware subsystem includes dedicated sensors for electrical and environmental monitoring, a microcontroller for data acquisition and control, and an embedded system for high-level communication.  
At the core of the measurement and control layer is the Arduino(r) MEGA 2560 (Arduino SA, Lugano, Switzerland), which acts as the main microcontroller. It acquires analog signals from the custom voltage and current sensing board and the wind speed sensor, and it also generates the Pulse Width Modulation (PWM) control signal used to drive the DC-DC boost converter. These operations are performed in real time, enabling dynamic regulation of the turbine's power output.  
Measurement and control data is transmitted to a Raspberry Pi(r) 3 Model B+ (Raspberry Pi Foundation, Cambridge, UK) via UART serial communication. The Raspberry Pi(r) functions as the embedded host system, handling communication with the supervisory interface and relaying commands received from the user.  
The Arduino(r) MEGA 2560 was specifically selected due to its ability to handle multiple analog sensor inputs and generate precise PWM control signals, while maintaining deterministic timing crucial for energy regulation. The Raspberry Pi(r) 3 Model B+ was chosen for its multitasking capabilities, Python support, and built-in connectivity, making it ideal for implementing lightweight, asynchronous communication services on resource-constrained systems. While these specific models were used in the proposed platform, the overall system design remains compatible with alternative microcontrollers and single-board computers that meet minimum requirements for analog acquisition, PWM generation, and basic network communication.
## 4 Numerical Analysis
### Critical Buckling Load Affected by the Length/Width Geometry Parameter  
Except for the dynamic equivalent effective stiffness of the honeycomb reinforced composite laminate, the critical dynamic buckling is directly influenced by the length and width of the honeycomb reinforced composite sandwich structure at the same time. The geometry shape of the composite structure is determined by the length-width ratio \(k_{a}\). Accordingly, the critical dynamic buckling is affected by the geometrical parameter \(k_{a}\). In this section, the critical dynamic buckling of the honeycomb reinforced laminated composite structure is analyzed numerically. In this section, \(t_{1}=t_{3}=2.5\) mm, \(t_{2}=4t_{1}\), \(d_{t}=1.5t_{1}\), \(k_{n}=1\) and the buckling loads affected by \(k_{a}\) are shown in Table 2. The results are the same as those shown in Figure 4; the geometry dimensions of the composite laminate affect its buckling sharply by increasing the length and width of the composite structures, and the structure's buckling is decreased significantly. However, it can be observed from Figure 5 that the structure's buckling loads are increased by raising the geometry parameters, and a larger \(k_{a}\) can always provide a bigger one when the other parameters are defined. To analyze the rate changes in the buckling load on the geometrical parameters, Figure 6 is provided. It can be seen in this figure that the honeycomb reinforced composite structure's buckling load sharply increases when \(k_{a}\) is smaller than five, but it slowly increases when \(k_{a}\) is larger than five in the considered ranges.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{\(a\)} & \multicolumn{1}{c}{**0.1**} & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.8** & **0.9** & **1** \\ \hline \multirow{8}{*}{\(k_{n}\)} & \(-2\) & 921.69 & 230.42 & 102.41 & 57.61 & 36.87 & 25.60 & 18.81 & 14.4 & 11.38 & 9.22 \\  & \(-1.5\) & 582.62 & 145.66 & 64.74 & 36.41 & 23.30 & 16.18 & 11.89 & 9.10 & 7.19 & 5.83 \\  & \(-1\) & 395.41 & 98.85 & 43.93 & 24.71 & 15.82 & 10.98 & 8.07 & 6.18 & 4.88 & 3.95 \\  & 0 & 307.70 & 76.93 & 34.19 & 19.23 & 12.31 & 8.55 & 6.28 & 4.81 & 3.80 & 3.08 \\  & 1 & 198.55 & 49.64 & 22.06 & 12.41 & 7.94 & 5.52 & 4.05 & 3.10 & 2.45 & 1.99 \\  & 1.5 & 153.49 & 38.37 & 17.05 & 9.59 & 6.14 & 4.26 & 3.13 & 2.40 & 1.89 & 1.53 \\  & 2 & 49.170 & 12.29 & 5.46 & 3.07 & 1.97 & 1.37 & 1.00 & 0.77 & 0.61 & 0.49 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Buckling loads vs. \(k_{n}\).  
Figure 2: Buckling loads affected by axial force parameter.  
Figure 4: Buckling loads affected by geometrical parameter \(k_{a}\).  
Figure 5: Buckling loads affected by geometrical parameter \(k_{a}\) and the dimensions.  
Figure 3: Buckling loads affected by axial force parameter. (**a**) \(\mathrm{k_{n}}\) vs. Buckling loads, and (**b**) \(\mathrm{k_{n}}\) ration vs. Buckling load ratio.
## 4 Experiments and Results  
### Data Set  
Due to the lack of publicly available customs datasets, in this paper, a customized customs clearance goods dataset (HSD) is used to validate the model's performance. This dataset is compiled from historical customs clearance data provided by our group, along with publicly available customs clearance data from an import and export trade website. It includes high-frequency customs clearance goods such as timber, spare parts, rubber products, and other frequently traded items. After processing and removing anomalous data, a total of 320,360 entries were obtained, encompassing 227 unique HS codes. Each sample in the dataset consists of goods description texts and their corresponding HS code. To effectively evaluate the model's performance, the dataset is split into training, validation, and test sets in a ratio of 0.80:1.0.1. The detailed statistics of the dataset are provided in Table 1.
## 3 Results and Discussion  
### Color  
The color results of the parasnip cultivars 'Warrior' and 'Albion' were shown in Table 1. The degree of lightness, represented by the L* values, was 90.70 \(\pm\) 0.21 for 'Warrior' and 77.52 \(\pm\) 0.57 for 'Albion', while the redness, represented by the a* values, was 0.45 \(\pm\) 0.42 for 'Warrior' and \(-\)0.48 \(\pm\) 0.29 for 'Albion'. The yellow color, represented by the b* value, was 28.72 \(\pm\) 0.91 for the 'Warrior' and 24.84 \(\pm\) 1.63 for the 'Albion'. The 'Warrior' cultivar showed significantly higher L*, a*, and b* values than the 'Albion'. Parsnips have a color range from cream to white, depending on the cultivar, and both 'Warrior' and 'Albion' have a white appearance. Therefore, the L* value, which is a measure of lightness, is high for both cultivars. Carotenoids found in fruits and vegetables provide them with their yellow and orange colors and are among the phytochemicals that include \(\beta\)-carotene. Parsnips contain various carotenoids, including \(\beta\)-carotene, which give them their yellow color based on their concentration. The cultivars 'Warrior' and 'Albion' had greater b* values for yellow than a* values for red, suggesting that the \(\beta\)-carotene content of parsnips contributed to their yellow color (2016).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cultivar** & \begin{tabular}{c} L* \\ **(Lightness)** \\ \end{tabular} & \begin{tabular}{c} a* \\ **(Redness)** \\ \end{tabular} & \begin{tabular}{c} b* \\ **(Yellowness)** \\ \end{tabular} \\ \hline \
\begin{tabular}{c} ’Warrior’ \\ ’Albion’ \\ \end{tabular} & 90.70 \(\pm\) 0.21  & 0.45 \(\pm\) 0.42  & 28.72 \(\pm\) 0.91  \\
\begin{tabular}{c} ’Albion’ \\ \end{tabular} & 77.52 \(\pm\) 0.57  & \(-\)0.48 \(\pm\) 0.29  & 24.84 \(\pm\) 1.63  \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hunter L*, a*, and b* color values of parasnip (_Pastinaca sativa_).
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
### Motivation and Contribution  
In VANETs, the connected vehicles and nodes of the VANETs environment contain road-side sensing and transmitting modules, traffic monitoring by the government, infotainment systems, and control systems etc. These devices share sensitive data with different protocols. VANETs are highly vulnerable to internal and external security attacks. For example, attackers can use the communication to steal personal information such as passenger details, travel times, tracked routes and destinations, range, and main locations. Furthermore, this data can be used to create selected personal profiles to launch multiple attacks. These attacks can be directed towards specific users or interfere with the whole transportation system, hence blocking the flow of traffic over a vast area. For example, movements can track or disrupt through spying on the victim's location data [8]. Therefore, location privacy in VANETs is an important design challenge to be considered.  
Many survey papers are available to focus on security and the location privacy aspects of VANETs. However, to the best of our knowledge there is no comprehensive survey that explores and critically analyzes the existing state-of-the-art solutions based on its operational and security concerns for location privacy in VANETs. Our survey is different in terms of classification and categorization of existing threats to location privacy. Moreover, we emphasize mostly on the location privacy of VANETs so that can help researchers and developers to differentiate and choose best schemes during deployment of security measures against violation of location privacy in VANETs. Motivated by this, the contribution of this survey paper can be outlined as follows:  
* First, we overview and explore state-of-the-art solutions available for location privacy in VANETs, and also discuss its safety and operational concerns.
* Secondly, we critically analyze the most obvious security attacks, identity thefts, manipulation and other techniques poised to location privacy in VANETs.
* Thirdly, we compare these state-of-the-art solutions based on various operational and security boundaries such as alleviation of execution, security performance efficiency, trade-offs, and service conveyance and present them in tabular form.
* Finally, this survey will help researchers to develop a deeper understanding of the location privacy in VANETs and the state-of-the-art solutions and the challenges present in its design and development.  
The rest of the paper is structured as follows, Section 2 provides background overview, security and privacy requirements, present privacy threats to VANETs environment and specifically brief explanation of location privacy. In Section 3 of this paper, we discussed the related work some adversary models, privacy preserving schemes. Analysis and discussion in Section 4. Finally, Section 5 concludes the paper.
## 4 Simulation Experiment and Analysis
### The Comparison of this Model with Other Models in the Article  
In this set of experiments, the cascading failure model of the air traffic control network, based on the theory of interdependent networks proposed in this article, was compared with other single-layer and multi-layer network models.  
As shown in Figure 11a, literature [31; 32] modeled the airport network and found that the largest connected subgraph of the airport network is approximately inversely proportional to the attack ratio when subjected to random attacks. Literature [32] modeled the airport network, route network, and sector network, based on domestic flight data in China. Under random attacks, the robustness of the network, from weak to strong, is: route network, sector network, and airport network. However, in the actual operation of the air traffic control network, the elements of each node, such as route points, sector nodes, and airport nodes in the route network, sector network, and airport network, are not operated independently. There are certain internal connections between them. The models in literature [31; 32; 33] did not consider the dependency between the various node elements in the network, greatly underestimating the size of the cascading failure of the air traffic control network.  
As shown in Figure 11b, literature [34] modeled the air traffic control network in North China, based on data from the region, and constructed a three-layer interdependent network consisting of airport network, route network, and control network, adding bidirectional and unweighted connections outside to simulate the dependency between networks. Due to its simple failure rules that do not fully describe the actual dependency relationships, the North China air interdependent network model largely underestimates the size of the cascading failure of the air traffic control network. The schematic diagram of the air traffic control network in North China is shown in Figure 12. The interdependent air traffic control network model was used to model the North China air traffic control network. Compared with the North China air interdependent network model in literature [34], the interdependent air traffic control network model takes into account the heterogeneity of the network and defines the complex interaction relationship between the control coordination network and the air route facility network. The model defines control dependency and service dependency, clearly reflecting the propagation rules of cascading failures in the network. Compared with the models in literature [31; 32; 33; 34], the interdependent air traffic control network model performs better and can predict more node failures and better predict changes in cascading failures in the network.  
Figure 11: Comparison of the interdependent air traffic control network model with other models: (**a**) Comparison with single-layer airport network, route network, and sector network models; (**b**) Comparison with interdependent air traffic control network model in North China.