esa-sceva/satcom-chunk-collection huggingface dataset:
# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
## 2 Background
### Sharding Technology  
Faced with the challenge of data processing in the big data era, the MongoDB database provides sharding technology as the solution to scale out [22]. Sharding technology is a database cluster system used to horizontally expand massive data, and the data is split and stored in different data nodes to handle greater data loads. Applying sharding technology can reduce the pressure on single machine performance caused by high data volume and high throughput applications, and improve random access performance under a large data volume. When storing massive data, one server might not be enough to store the data and provide acceptable read-write throughput. By establishing a cluster, the data can be divided and stored on multiple machines, so the database system can store and process more data to meet the processing needs of the large growth of data. Figure 1 presents the overall sharding cluster architecture of MongoDB.  
The MongoDB cluster architecture mainly includes three parts: the shard server, the router server, and the config server. The actual data are stored in the shard server, which can be a replica set or a server. The router server is the entry that clients request the database and is used to address and locate the requests from these clients. All these requests need to be handled by mongos and forwarded to the corresponding shard server. The config server is used to store the configuration information of the router and the shard, which is set up at first and does not need significant space and resources.  
When implementing sharding in the collection, one or more fields should be referred to as split data, which is also called the shard key. After the shard key is specified, the data are split into small data chunks, and different chunks are stored in corresponding sharding machines. There are three kinds of shard keys: ascending key, random key, and combined key. The values of the ascending key (such as object id and time) will grow steadily over time, and the latest insert documents will be assigned to the max chunk. The values of the random key have no specific rules to follow, such as name, MD5, and password, as they are absolutely random. As more data are inserted, all the data can be distributed evenly in different chunks. When there is no suitable shard key in the database,a combined key can be considered. The selection of the shard key can affect the performance of the system, such as its scalability, so it is very important to choose an appropriate shard key [23].
## 3 Performance Evaluation of RAN Network Slicing
### Relationship between RAN Network Slicing and QoS  
To demonstrate the impact of application type on RAN network slicing, a test scenario was created for different applications without changing the numerology. In Test 1, six different application traffic types and correspondingly six different QoS configurations were created, and the average throughput was examined. In Test 2, the results were compared using the same QoS configuration for each application. Table 2 shows the test scenario created and the average throughput results obtained after the simulation.  
According to the simulation results, when different QoS is assigned to each flow of six UEs, the average throughput is measured as 102.34 Mbps. When the same QoS is assigned to these flows, the average throughput is measured as 95.58 Mbps. The impact of QoS types on throughput is shown in Figure 4. Analysing the simulation results obtained, it can be seen that to implement RAN network slicing, it is necessary to use QoS algorithms specific to each network slice to meet the requirements of different application characteristics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Numerology** & **Subcarrier [kHz]** & **Slot [ms]** \\ \hline
0 & 15 & 1 \\
1 & 30 & 0.5 \\
2 & 60 & 0.250 \\
3 & 120 & 0.125 \\
4 & 240 & 0.0625 \\ \hline \hline \end{tabular}
\end{table}
Table 1: 5G Frame Structure by Numerology [16].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**UE** & **Traffic Type** & **Test 1 QoS Type** & **Test 2 QoS Type** \\ \hline UE-1 & AR/VR & 1 & 3 \\ UE-2 & Voice & 2 & 3 \\ UE-3 & Video & 3 & 3 \\ UE-4 & Game & 4 & 3 \\ UE-5 & V2X & 5 & 3 \\ UE-6 & Video TCP & 6 & 3 \\ \hline \hline \multicolumn{3}{c}{Average throughput} & 102.34 Mbps & 95.58 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 2: QoS Type Test Scenario and Results.
## 3 Results and Discussions
### Nanocomposite Regeneration  
The excellent adsorbent not enough to have excellent adsorption capacity but also must be regenerated according to the industrial, practical, and economic point of view. The adsorbent's reusability ability led to a decrease in the total cost of materials and adsorption processing. For the adsorption/desorption study, the nanocomposite was separated after each adsorption study using a magnet and regenerated again by immersing in 0.2 M HCl to be used again. The nanocomposite MnFe\({}_{2}\)O\({}_{4}\)/GO was studied for reusability toward removing Pb\({}^{2+}\) ions and NR dye from the water for up to five cycles. The reusability study of MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite toward capturing Pb\({}^{2+}\) ions and NR dye was shown in Figure 9. The results showed that the first cycle has the highest removal efficiency for Pb\({}^{2+}\) ions and NR dye removal due to the intact pores and active sites. After that, the removal efficiency was slightly decreased due to the damaged sites after the adsorption/desorption cycle. The reusability study indicates that Pb\({}^{2+}\) ions and NR removals from water are efficient with low cost over the synthesized MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Pollutant** & \multicolumn{3}{c}{**Freundlich**} & \multicolumn{3}{c}{**Langmuir**} \\ \hline  & \(\mathbf{K_{F}}\) & \(\mathbf{N}\) & \(\mathbf{R^{2}}\) & \(q_{m}\) & \(\mathbf{K_{L}}\) & \(\mathbf{R^{2}}\) \\ \hline Pb\({}^{2+}\) & 300.0 & 6.523 & 0.986 & 636.94 & 0.168 & 0.998 \\ \hline NR dye & 11.11 & 0.160 & 0.859 & 46.08 & 0.065 & 0.996 \\ \hline \end{tabular}
\end{table}
Table 1: Freundlich and Langmuir adsorption isotherms different parameters for Pb\({}^{2+}\) ions and NR dye adsorption over MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite.  
Figure 8: Freundlich adsorption isotherm (**a**); Langmuir adsorption isotherm (**b**) for Pb\({}^{2+}\) and NR dye adsorption over MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite.
## 4 Simulation and Discussion  
Based on the above theoretical analysis, we have verified the effectiveness of the designed ANFTSMC algorithm in coaxial HAUV control at the theoretical level. The main purpose of this section is to verify the effectiveness of the designed controller through simulation. Next, we conducted a simulation of HAUV motion control via Simulink and selected the two most typical working conditions (water-exit and water-entry).  
To verify the effectiveness of the ANFTSNC controller designed for the coaxial HAUV, we developed a simple feedforward PD controller as a reference comparison item. The feedforward PD controller is designed as follows:  
\[\begin{array}{ll}U_{2 pd}=-k_{pp}\epsilon_{1}-k_{dq}e_{2}+\ddot{\phi}_{d}+ \big{(}k_{s}k_{\phi\omega}+k_{sa}k_{\phi a}\big{)}\Big{|}\dot{\phi}\Big{|}\dot{ \phi}\\ U_{3 pd}=-k_{pp}e_{3}-k_{dq}e_{4}+\ddot{\theta}_{d}+(k_{s}k_{\theta\omega}+k_{ sa}k_{\phi a})\Big{|}\dot{\theta}\Big{|}\dot{\theta}\\ U_{4 pd}=-k_{pp}e_{5}-k_{dq}e_{6}+\ddot{\psi}_{d}+\big{(}k_{s}k_{\psi\omega}+k_ {sa}k_{\phi a}\big{)}\Big{|}\dot{\psi}\Big{|}\dot{\psi}\\ v_{xpd}=-k_{px}e_{7}-k_{dx}e_{8}+\ddot{\lambda}_{d}+(k_{s}k_{xxw}+k_{sa}k_{xa}) \Big{|}\dot{x}\Big{|}\dot{x}\\ v_{ypd}=-k_{py}e_{9}-k_{dy}e_{10}+\ddot{y}_{d}+\big{(}k_{s}k_{y\omega}+k_{sa}k_{ ya}\big{)}\big{|}\dot{y}\big{|}\dot{y}\\ v_{zpd}=-k_{pz}e_{11}-k_{dz}e_{12}+\ddot{z}_{d}+(k_{s}k_{zw}+k_{sa}k_{za}) \big{|}\dot{z}\big{|}\dot{z}\end{array} \tag{43}\]  
The controller and adaptive law parameters used in our simulation are presented in Table 3.
# Clone Node Detection Attacks and Mitigation Mechanisms in Static Wireless Sensor Networks
## 4 Mitigation of the Replication Attacks in Static Wireless Sensor Network
### Key Pre-Distribution Techniques Used for the Mitigation  
One of the most regular techniques for restricting the attacker's capability of producing new nodes' ID, is by attaching each legitimate node's ID (at the initial phase of the WSN establishment) with a unique feature it holds. An example of a unique feature can be a hashed text. Based on this fact, the WSN must use a key pre-distribution scheme. Briefly, this scheme entails that a node's ID could have corresponded to the secret keys it shares with its neighbors. Thus, the node's ID is given by the hashed text of its secret keys. Since the attacker's illegitimate nodes were not part of the group (i.e., one of the neighbors of the shares) and do not hold any knowledge about the secret keys, then, the hash will make the attacker's intention difficult to accomplish. This is one of the reasons that it is strongly recommended to implement this strategy while building the wireless sensor network. Therefore, it will be necessary for the attacker to capture physically a legitimate node (one of the neighbors) to proceed with the extraction and clone that captured legal node.  
Using the key-establishment procedure, the communication between two or more nodes is protected. All the sensed information sent between the participants could be verified, thus protected. Therefore, this level of security (key-establishment) minimizes the eavesdropping action where an attacker may passively sit in the middle. It also helps in case the attacker wants to perform an active attack, such as inject illegitimate sensor data into the network.  
Key revocation: When the administrator of the network decides to terminate a sensor, or in a situation where a sensor is lost, that sensor should in no way be allowed to make use of its credentials, which it stores to connect to networks.  
Re-keying: This mechanism involves the ability of an executive node to appropriately update the credentials of a node without the interference of the back-end system. This action can be undertaken for reducing the communication interactions and management, which rely on the back-end system.
## 3 Results and Discussion
### Performance of Pixel-Level FM Classification  
Table 2 shows the pixel-level FM detection rates on training and testing with the VNIR and SWIR models. The pixel-level detection rates on the training set varied between 62-71% (VNIR) and 72-81% (SWIR), while the performance on the test set was significantly decreased down to 10-33% (VNIR) and 38-59% (SWIR), respectively. The decrease was partially due to the mixed pixel effect at the border of the large FMs. The average size of ROIs in the training set was around 180 pixels with a rectangular shape close to square. Therefore, the dimension of one side was around 13-14 pixels. A strip of about two pixels along the boundaries of FM objects had mixed spectra from meat and FM. The average area size of ROIs in the test set was about 40 pixels with a rectangular shape close to square as well. In addition, the dimension of one side was around 6-7 pixels. Our calculation indicated that only around 75% of the pixels from the center could have pure FM spectra in the training set, whereas the expected pure FM pixels in the test set was around 50%. The results implied that the detection limit of a pixel-level spectral detection model was about 1 mm (3-4 pixels in length) and a higher resolution HSI system would be required to  
Figure 6: Selection of key wavelengths for discriminating chicken fillet from other materials. (**a**) VNIR range muscle and fat and (**b**) SWIR range muscle and fat related key wave bands.  
detect smaller than 1 mm length samples. In the case of metal, the pixels were sometimes classified as glare in both the VNIR and SWIR models due to the specular reflection from smooth metal surfaces. In the case of wood, small samples with light color similar to meat color were difficult to detect with the VNIR model. Overall, the SWIR model performed better than the VNIR model.
# Application of External Torque Observer and Virtual Force Sensor for a 6-DOF Robot
## Materials and Methods
### Machine Learning-Based Robot Dynamic Model  
#### 3.2.1 Robot Dynamic Model  
The dynamic equations primarily calculate the torque exerted on the motor driver based on dynamic parameters, such as the position, velocity, and acceleration of the robotic arm. The commonly used methods for derivation are the Euler-Lagrange equation and the Newton-Euler equation. This paper utilizes [PERSON]'s equation of motion to solve the dynamics, as depicted in Equation (1). In this equation, \(\tau\) is the calculated ideal torque, \(q\) is the angle of each axis of the motor, \(\hat{q}\) is the angular velocity, \(\bar{q}\) is the angular acceleration, \(M\) represents the inertia matrix of the robotic arm, \(C\) represents the torque vector due to Coriolis force, \(G\) represents the torque vector due to gravity force, and \(F\) represents the torque vector due to friction force.  
\[\tau=M(q)\bar{q}+C\big{(}q,\dot{q}\big{)}+G(q)+F(q) \tag{1}\]  
Building a mathematical model for dynamics requires several internal parameters of the robotic arm, including the mass of each axis, the position of the center of mass relative to each axis coordinate system, and the inertia tensor matrix. Most of the information mentioned above consists of parameters considered confidential by the manufacturer. Therefore, regression analysis is commonly used in the literature for system identification. By designing various stimulation trajectories, it aims to analyze unknown parameters, which necessitates intricate calculations and results in low accuracy. The robotic arm used in this paper has internal parameters provided by the manufacturer that are relevant to the study. Therefore, both the dynamics equation and machine learning techniques can be employed to establish the dynamic model of the robotic arm. A comparison will be conducted between the two approaches.  
Figure 2: Architecture of Raspberry Pi-based system.
## 4 Results and Discussions
### Microstructure Analysis  
The surface microstructure of the RCA is shown in Figure 12. The results indicate that the surface porosity of the RCA is repaired after SCA modification, resulting in a tighter bond between the RCA and the interface. In terms of the surface microstructure of the RCA, without SCA modification, there are a large number of mortar pores on the RCA surface, which is the main reason for the high water absorption and poor adhesion performance of the RCA to asphalt. After SCA modification, the mortar pores of the RCA surface are significantly reduced. The reason for the reduction in surface pores of the RCA is that SCA first undergoes hydrolysis to form silanol, which then undergoes a condensation reaction with the hydroxyl groups on the cement mortar surface, resulting in the coverage of the cement mortar surface with alkyl-terminated siloxane, forming chemical adsorption.  
The interface microstructure of the RCA/asphal is shown in Figure 13. In terms of the interface structure between the RCA and asphalt, without SCA modification, the presence of a large number of pores on the RCA surface leads to defects in the RCA/asphal interface structure. After SCA modification, the bond between the RCA and asphalt interface becomes denser. This is mainly because one end of the hydrolysis product of SCA is tightly adsorbed on the RCA surface, while the organic functional groups extend into the asphalt, tightly connecting the RCA and the asphalt. It acts as a \"molecular bridge\" between the RCA and asphalt interface, tightly connecting the RCA and asphalt together and improving the interface properties between the asphalt and RCA.
## 3 Numerical Simulation
### Analysis of Results under Different Conditions
#### (1) Relationship between Displacement and Water Level Changes  
The relationship between displacement and water level changes is illustrated in Figure 4. From the simulation results, it can be observed that the groundwater level significantly influences both the X and Z displacements of the surrounding rock of the tunnel, exhibiting an approximately linear trend. As the groundwater level increases, the X displacement of the surrounding rock of the tunnel shows an approximately linear decrease, while the Z displacement exhibits an approximately linear increase.
# Network Forensics Method Based on Evidence Graph and Vulnerability Reasoning
## 3 The Proposed Method  
Our proposed method combines evidence graph and vulnerability reasoning. To ensure that the method follows certain procedures, we first propose a new network forensics framework that conforms to the general principles of network forensics. The framework is shown in Figure 1.  
The functionality of each component in the framework is briefly described below:  
* Data collection module collects digital evidence (network program logs and network packets) from heterogeneous nodes deployed in the network in which network packets are the primary sources and network program logs are the accessorial ones.
* Data preprocessing module adds digital signature and timestamp to digital evidence to ensure the integrity of the data.
* Data storage module stores collected data into a database to ensure data authority and non-repudiation.
* Data standardization module extracts the common fields from the collected data, converts them into a unified format and presents the results as an event vector. In addition, SVM is used to filter out benign packets and suspicious packets so as to reconstruct accurate evidence graph using the suspicious packets.
* Evidence reasoning module uses the vulnerability evidence reasoning algorithm (VERA) to infer attacked nodes as well as the attacking routes. Primary data can also be backtracked to provide more and clearer evidence.
* Evidence presentation module uses the evidence graph to show final evidence, which would include the intrusion nodes as well as the operations between nodes.  
Figure 1: A Framework for Network Forensics.  
Note that core to our analysis method are the data standardization module, the evidence graph construction module and the evidence reasoning module. The evidence graph construction module constructs the evidence graph based on preprocessed data and updates graph attributes. Afterwards, the evidence reasoning module performs attack scenario inference based on the graph structure. In the following, we will describe these main modules in more details.
# Multi-Factor Highway Freight Volume Prediction Based on Backpropagation Neural Network
## 4 Case Analysis of Highway Freight Volume Forecasting Based on Neural Network  
### Necessity Analysis  
We analyzed the characteristics of the data, the limitations of resources and time, and the existing research. Therefore, considering the characteristics of the data studied in this paper and compared with the traditional prediction methods, the highway freight volume prediction based on BP neural network has the advantages of dealing with nonlinear relationships and complex data structures, and more accurately describing the relationship between highway freight volume and various influencing factors. Secondly, it has strong generalization ability and can adapt to different scenarios and conditions. The prediction can improve the prediction accuracy and stability through continuous learning and optimization and is very good in the accuracy of prediction. Before the research method of this paper is determined, we need to clearly define the problems we want to solve and the expected goals and consider the complexity and scale of the problem, as well as the type and quantity of data. For the deep learning algorithm that has been popular recently, it needs a lot of data support. However, the data of this study are limited (the reasons for the limited data are explained in detail below). Compared with the deep learning algorithm, the multi-factor analysis algorithm is more suitable for the case where the sample size is small and the variable relationship is more complex. The selection of multi-factor analysis or deep learning needs to be considered comprehensively according to the nature of the problem and the causal relationship between variables, and the multi-factor analysis algorithm is finally selected as the algorithm used in this paper.  
For the prediction of the mode of transportation, we generally consider a variety of factors, usually considering the nature of the goods, such as whether they are perishable goods considering the time distance and cost of transportation. For the measurement of the volume of goods, we can directly measure the length, width, and height of the goods to calculate their volume. We can also use the water level method (put the goods in water, use the rise of the water level and the height to measure the volume of the goods) to measure the volume of the goods indirectly. For the prediction of cargo volume and transportation mode, a variety of factors need to be considered comprehensively. For the influencing factors of highway freight volume selected in this paper, highway freight turnover, highway freight volume, and highway diesel consumption are mainly measured by weight. The population is mainly measured by the number of people, and the gross domestic product is mainly measured by price. Their prediction can play an auxiliary role in the model data source and provide support for the data source.