esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Model-Based Traffic Network Control Using Population Games
## 5 Traffic Simulator  
The implemented traffic simulator is shown in Figure 3. This integrates, through a COM server, the VISSIM platform, in which the traffic network is implemented, and MATLAB, where the control strategies described in Section 4.1 and the hybrid model described in Section 3.2 are implemented.  
The following are the main steps in the information flow between both simulator sections:  
1. First of all, the vehicular traffic network is implemented in VISSIM. Subsequently, MATLAB obtains all its properties, such as number of controllers, traffic lights, queue counters, number of links, cycle time (\(C_{p}\)), duration of yellow and red lights (intermediate times), and simulation time, among others.
2. The runtime environment, which is implemented in an object-oriented scheme, allows for the creation of class instances that represent the traffic network (implemented in VISSIM) and the virtual traffic controller associated with each node that is modeled by means of the hybrid dynamics described in Section 3.2.
3. The initial green times are loaded through class instances that represent the control strategies described in Section 4.1. These times, along with the cycle time (\(C_{p}\)) and the intermediate times (red and yellow), are used to generate the signaling plan for each controller, which is sent to VISSIM.
4. The update of the signaling plan, as well as the reading of the queue counters, are made in sample intervals \(\delta\). After this time, the queue lengths calculated in VISSIM are sent to the control strategy instance in order to recalculate the green times. This process is repeated until the simulation time expires.
5. Finally, the communication between both modules ends and the information related to performance parameters is stored in VISSIM.  
Figure 3: General structure of the implemented simulator.  
VISSIM includes a COM programming module that allows for an interaction with external programming languages and platforms such as MATLAB in a stable form. In our case, the traffic simulator architecture has an object-oriented scheme implemented in MATLAB, whose main classes are the following:  
* COMInterfaceVISSIM: Builds the VISSIM and MATLAB COM connection. It also contains methods to send information to VISSIM (cycle time, simulation time, performance parameters to be measured, etc).
* HybridController: Each instance of this class creates a \"virtual controller\" that interprets the signal plans and sends the instructions that the VISSIM controllers must follow.
* ControllerUserDefined: Used to define the control strategy to be used. It also generates the green times for each virtual controller generated.
* SignalPlan: Creates the signal plans from the information provided in the graphic interface and the green times given by ControllerUserDefined.
* MainSimulation: Main class in which the instances of the previously described classes are created.
###### Abstract  
One of the main causes of damage in hydraulic turbines is cavitation. While not all cavitation appearing in a turbine is of a destructive type, erosive cavitation can severely affect the structure, thus increasing maintenance costs and reducing the remaining useful life of the machine. Of all types of cavitation, the maximum erosion occurs when clouds of bubbles collapse on the runner surface (cloud cavitation). When this occurs it is associated with a substantial increase in noise, and vibrations that are propagated everywhere throughout the machine. The generation of these cavitation clouds may occur naturally or it may be the response to a periodic pressure fluctuation, like the rotor/stator interaction in a hydraulic turbine. Erosive bubble cavitation generates high-frequency vibrations that are modulated by the shedding frequency. Therefore, the methods for the detection of erosive cavitation in hydraulic turbines are based on the measurement and demodulation of high-frequency vibrations. In this paper, the feasibility of detecting erosive cavitation in hydraulic turbines is investigated experimentally in a rotating disk system, which represents a simplified hydraulic turbine structure. The test rig used consists of a rotating disk submerged in a tank of water and confined with nearby axial and radial rigid surfaces. The excitation patterns produced by cloud cavitation are reproduced with a PZT (piezoelectric patch) located on the disk. These patterns include pseudo-random excitations of different frequency bands modulated by one low carrier frequency, which model the erosive cavitation characteristics. Different types of sensors have been placed in the stationary and in the rotating parts (accelerometers, acoustic emission (AE), and a microphone) in order to detect the excitation pattern. The results obtained for all the sensors tested have been compared in detail for the different excitation patterns applied to the disk. With this information, the best location and type of sensor to detect the different excitations have been identified. This study permits improving the actual technique of detecting erosive cavitation in hydraulic turbines and, therefore, to avoid operation under these circumstances.  
erosive cavitation; hydraulic turbine; acoustic emission; high frequency; vibration Article
## 4 Conclusions  
This paper studies the influence of milling parameters on the residual stress of 7075 aluminum alloy and formulates a residual stress prediction model related to the parameters. It provides a calculation tool for obtaining milling-induced residual stress. First, the orthogonal experiment is designed to implement milling experiments on 7075 aluminum alloy. Through electrolytic polishing and X-ray diffraction, layer by layer, the workpiece residual stress distribution at different depths was obtained. Secondly, the residual stress characterization equations related to the milling parameters in end milling and side milling are formulated based on regression analysis. The sensitivity coefficient of each milling parameter to the residual stress is calculated. Thirdly, the prediction model of milling-induced residual stress is proposed based on experimental data, using a genetic algorithm and BP neural network. The findings indicate that milling-induced residual stress varies significantly along the workpiece depth direction. The residual stress depth is within 0.12 mm of the workpiece surface, and the overall distribution is in a \"spoon shape\". The proposed prediction model can calculate the residual stress under different milling parameters. The prediction error is between 0-14 MPa, roughly the same as the measurement accuracy of the residual stress analyzer. Therefore, the prediction model has high accuracy and powerful application potential. In addition, this study gives constant values to factors, such as cutting tools, workpiece materials, and cooling conditions, resulting in limitations in the prediction model. In the future, we will incorporate more factors affecting milling-induced residual stress into the prediction model to improve its generality. In addition, the reverse approach of the optimal machining plan under the specified residual stress will be our research focus.
## 3 Test Results
### Root-Soil Composite Shear Strength Characteristics  
The coordinates of the shear strength and fitting lines were plotted according to the Mohr-Coulomb theory, as shown in Figure 3. Accordingly, the internal friction angle \(\varphi\) and cohesion c were obtained as shown in Table 2. The corresponding linear regression equations and correlation coefficients R\({}^{2}\) are also given.  
It can be seen from Table 2 that the c and \(\varphi\) of the undisturbed bare soil are 33.2 kPa and 23.8\({}^{\circ}\), respectively. Comparably, the c of the root-soil composites during the different growth periods of C.d., M.m and G.s ranged from 34.1 kPa to 82.8 kPa, 49.5 kPa to 72.9 kPa, and 36.8 kPa to 99.8 kPa, respectively. The \(\varphi\) in the different growth periods of C.d., M.m and G.s ranged from 24.5\({}^{\circ}\) to 34.1\({}^{\circ}\), 19.7\({}^{\circ}\) to 29.9\({}^{\circ}\) and 22.4\({}^{\circ}\) to 39.1\({}^{\circ}\), respectively. Clearly, all c values of the root-soil composites were higher than bare soil, but the differences in \(\varphi\) were not as obvious. For the root-soil composites, c increased and then decreased initially with the increasing growth period. At 90 d and 120 d, the order of c was G.s > C.d > M.m. The maximum c of the G.s root-soil composite was 99.8 kPa at 90 d, which is about three times that of the bare soil. The c of the soil containing M.m roots was higher than that of the other two types of composites within growth periods of 30 d and 60 d. However, for the 120 d growth period, the \(\varphi\) values of the plants were similar to those of the bare soil. This indicates that the reinforcing effect of the roots depends more on the c and less on the \(\varphi\). Root systems help to improve the c of the soil, thereby increasing the shear strength of the soil.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Species** & **Regression Equations** & **R\({}^{2}\)** & **c/kPa** & \(\varphi\)\({}^{\circ}\) \\ \hline Bare soil & \(y=0.44158x+33.18\) & 0.99 & 33.2 & 23.8 \\ C.d 30 d & \(y=0.51340x+34.13\) & 0.96 & 34.1 & 27.2 \\ C.d 60 d & \(y=0.67590x+39.59\) & 0.92 & 39.6 & 34.1 \\ C.d 90 d & \(y=0.65382x+82.83\) & 0.99 & 82.8 & 33.2 \\ C.d 120 d & \(y=0.45572x+77.14\) & 0.94 & 77.1 & 24.5 \\ M.m 30 d & \(y=0.49388x+49.50\) & 0.89 & 49.5 & 26.3 \\ M.m 60 d & \(y=0.43958x+71.55\) & 0.96 & 71.2 & 23.7 \\ M.m 90 d & \(y=0.35808x+72.90\) & 0.87 & 72.9 & 19.7 \\ M.m 120d & \(y=0.57580x+55.95\) & 0.85 & 56.0 & 29.9 \\ G.s 30 d & \(y=0.76568x+36.82\) & 0.89 & 36.8 & 37.4 \\ G.s 60 d & \(y=0.81404x+38.02\) & 0.81 & 38.0 & 39.1 \\ G.s 90 d & \(y=0.58178x+99.78\) & 0.96 & 99.8 & 30.2 \\ G.s 120 d & \(y=0.41246x+86.61\) & 0.96 & 86.6 & 22.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The linear regression equation and correlation coefficients of the shear strength and normal stress in the different growth periods (R\({}^{2}\)).  
Figure 3: Fitting curves associated with the different plants. (**a**) C.d; (**b**) M.m; and (**c**) G.s.
## Results  
Profile of Extracellular Enzymes Produced by Fungi and Bacteria Isolated during the Composting Process  
A total of 105 fungal strains and 128 bacterial strains were initially isolated. However, in the case of fungi, only 40 could be maintained at laboratory conditions. Consequently, a total of 40 fungal and 128 bacterial strains were tested for hydrolytic and oxidative enzyme activities. The results obtained in the distribution of enzymatic activities are summarised in Figure 1. For fungi (Figure 1A), the most frequent secreted enzymes were amylase (72.5%), peroxidase (47.5%), polyphenol oxidase (42%), pectinase (42.5%), cellulase (32.5%), and protease (40%). In this case, the less frequent activities were tyrosinase and P-solubilising.  
Figure 1: Percentage of enzymes produced by (**A**) fungal and (**B**) bacterial isolates.  
In the case of bacteria (Figure 1B), the most frequent activity produced was polyphenol oxidase (95.3%) and peroxidase (99.2%). As well, ammonifying activity (52.3%) was present in more than a half of the bacteria isolated. Amylase was another frequent enzyme (45.3%) in bacterial isolates. The less common activities were cellulase, lipase, and P-solubilising.  
The distribution of the analysed enzymes along time is shown in Figure 2. In the case of fungi (Figure 2A), amyloid activity was predominant at the beginning of the process and in the maturation stage. An absence or decrease in the number of isolates producing enzymes between samples 2 and 8 days can be observed. From 14 days, there were more isolates producing enzymes like peroxidase, pectinase, hemicellulose, and tyrosinase, among others. Therefore, the fungal isolates from the initial part of the process and from the maturation stages showed a high number of extracellular enzyme activities.  
Concerning bacterial isolates, the highest number of activities was found in the isolates belonging to 8, 70 and 120 days, being peroxidase and polyphenol oxidase (Figure 2B) followed by ammonium enzymes and amylases y as the predominant enzymes. The highest number of strains with peroxidase and polyphenol oxidase was at 120 followed by samples at 8 and 70 days. Lignocellulolytic activities like peroxididases and polyphenol oxidase were found in a high number of fungal and bacterial isolates, being mostly isolated from the maturation phase, corresponding to the 70 and 120 day samples.
# Experimental Evaluation of Hybrid Fibre-Wireless System for 5G Networks
## 3 5G New Radio Hybrid MIMO-Based Fibre-Wireless System
### CNN Based Digital Pre-Distortion
#### 3.2.2 Training Algorithm  
The CNN DPD model training pseudocode is given below in Algorithm 1. Firstly, we train an emulated RoF CNN model that uses the input and output of the original RoF connection, and after this model training is achieved, we connect the CNN to the actual RoF link to this (post distorter) DPD block and commence the pre-distortion procedure when this training is completed (see Pseudocode 1). It is represented in the pseudocode that the CNN is established on \(I(\hat{x}(n))\) with updates on \(\hat{I}\) that is performed on the pre-distortion block used by the terms of \(\hat{I}^{-1}(x(n))\) as shown in Figure 2.  
```
\(\hat{x}(n)\gets x(n)\) for\(i\leq Z\)do \(y(n)\gets I(\hat{x}(n))\): //FI-Wi Transmission \(\hat{I}\leftarrow\) Train on \(\hat{x}(n)\), \(\frac{y(n)}{\hat{c}}\) //F-Wi Transmission CNN updates //Fixed NN weights of \(\hat{I}\) \(\leftarrow\) Train on \(x(n)\). //\(\hat{I}^{-1}(\{x(n))\}\) \(\hat{x}(n)\leftarrow\hat{I}^{-1}(x(n))\): //Pre-distort endfor
```  
**Algorithm 1****DPD Training with CNN**  
The loss evaluation function is mean square error (MSE), the optimization function is ADAM, the activation function is ReLu, and the values are monitored via backpropagation. Z number of iterations are carried out to enhance the performance.
# Efficient Tensor Sensing for RF Tomographic Imaging on GPUs
## 3 Description of the Tensor Sensing Problem  
[PERSON] et al. [4] proposed to use the transform-based tensor model to formulate the RF tomographic imaging as a tensor sensing problem. Then, they utilized a fast iterative algorithm Alt-Min to solve the tensor sensing problem. Here, we briefly summarize their approach including the Alt-Min algorithm in order to map it onto the GPU architecture.
# A Unified Database Solution to Process BIM and GIS Data
## 3 Results
### Implementation of the IFC Model in a Relational Database  
The first step of the experiment was to create a database structure corresponding to the IFC model. The database model corresponds exactly to the IFC model, which makes it possible to avoid numerous complications in the processes of data writing and reading by application software. It should be remembered that the procedure of processing the data stored in IFC into a relational model is quite complex. The IFC model is not relational and has a number of quite specific features (e.g., multiple local coordinate systems and complex internal references between objects). In order to enable editing operations on the BIM data saved in a relational database, the mechanism of stored procedures was applied. For each class of IFC objects, four stored procedures were prepared for write, read, update, and delete operations. For the purpose of the experiments, the stored procedures enabling writing, reading, modification, and deletion of the data of IFC objects were prepared for more than 100 classes of IFC objects. The procedures were implemented in SQL and PL/pgSQL (Procedural Language/PostgreSQL) languages on the PostgreSQL platform. For comparison, stored procedures for several selected classes of IFC objects in PL/SQL (Procedural Language for SQL) in the Oracle database and T-SQL (Transact SQL) in the Microsoft SQL Server database were also prepared. The purpose of this part of the experiment was to check whether it was possible to create stored procedures that could be triggered in a similar way regardless of which database platform was used. As a result of the experiment, it was confirmed that the triggering of the prepared stored procedures operating on tables in PostgreSQL, Oracle, and SQL Server databases was very similar. Thus, the proposed solution can be used with any of the above relational database management systems.
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
## Appendix A  
In a practical scenario, \(R_{o}\) and \(h_{i}^{o}\) are both positive. In addition, the range of elevation is \(0<\varphi_{i}^{o}<\pi/2\), so \(\cos(\varphi_{i}^{o})>0\) and \(\sin(2\varphi_{i}^{o})>0\) are valid. Based on the definition of \(c_{i,1}\), \(c_{i,2}\), and \(c_{i,3}\), we have \(c_{i,1}>0\), \(c_{i,2}>0\), \(c_{i,3}<0\). Therefore, the discriminant of (21) is  
\[(c_{i,2})^{2}-4c_{i,1}c_{i,3}>0 \tag{21}\]  
Thus, the two solutions of (21) can be given as  
\[x_{1}=\frac{-c_{i,2}+\sqrt{c_{i,2}^{2}-4c_{i,1}c_{i,3}}}{2c_{i,1}},x_{2}=\frac {-c_{i,2}-\sqrt{c_{i,2}^{2}-4c_{i,1}c_{i,3}}}{2c_{i,1}} \tag{22}\]  
Therefore, \(x_{1}>0\) and \(x_{2}<0\) are valid. \(x_{1}>0\) is the only positive solution of (21).
# Malware Analysis in IoT & Android Systems with Defensive Mechanism
## 5 Android Vulnerability Detection
### Policy Enforcement and Device-Based Solutions  
The authors [36] proposed a privacy tool labeled Paranoid Android, which efficiently identifies several malicious access mechanisms on remote servers hosting another replica of the user's device. The design is unique in its role of proving compatibility with the cloud environment. A log of all actions is maintained on the user's device, as well as on the cloud, to match the sequences in terms of fraudulent access. The designed system is used for various kinds of analysis such as instant and variable analysis, anti-viruses, memory scanners, and system call-based analysis. The benefit of this is the capacity to perform multiple detection mechanisms simultaneously to achieve a better detection rate. However, it has been identified that the battery life is drained due to the transmission of data from one mode to the other while also retrieving the encrypted versions.  
The SeqDroidtool, assigning an identity to every device being sold on the market, enables us to trace the device in the event of theft or loss attempts. This sequencing varies from one model to another and is further encrypted to prevent malicious access by hackers. The user purchasing a particular handset should digitally sign a certificate that acts as a source during the transmission of ownership and also to prevent fraud. The representation of data samples using mathematical models is of utmost interest to the researchers to provide an effective understanding of the design via a set of flow models or equations. It is also essential to preserve the consensus across all the participating entities to ensure validity and transparency in terms of log access from a third-party virtualized environment. However, the application of mathematical models should be justifiable across a varied set of operations to identify the malicious attempts made to access the data samples. This may come with the integration of several domains and increase the maintenance costs. According to experts in [37], billions of users throughout the world install Android apps on a daily basis, granting access to a vast array of sensitive personal data. Over time, several methods have been developed to better understand how apps protect or compromise the privacy of their users. These findings, on the other hand, have come from a variety of research disciplines and approaches to privacy, resulting in a growing but dispersed body of information. To close this gap, researchers conducted a thorough mapping study to offer practitioners and scholars an overview of the state-of-the-art techniques for assessing privacy in Android apps, released between 2016 and 2020. They highlight the most important discoveries, identify and analyze the most critical gaps, and outline promising research. A summary of the proposed techniques is presented in Table 3.
## 3 ERA-MADDPG Intelligent Routing Solution
### ERA-MADDPG Intelligent Routing Interacts with the Environment
The link weight \(W_{i}\) is dynamically calculated based on multiple network features, including link delay \(d_{ij}\), available bandwidth \(b_{ij}\), and packet loss rate \(l_{ij}\). Specifically, \(W_{i}\) is defined as a weighted sum of these features. This design ensures that the link weight reflects the comprehensive cost of data transmission, guiding the routing algorithm to select optimal paths. The link weight vector is defined as shown in Equation (18). \[w\_link=\langle W_{1},W_{2},\cdots W_{i},\cdots,W_{n}\rangle \tag{18}\]  
where \(W\) represents the node-link weight, the weighted shortest path from the source node to the target node is calculated based on the path weight. By continuously updating the distance array and selecting the node with the shortest distance, the shortest path from the source node to other nodes can be efficiently calculated. (3) Reward value mapping: The reward value is used to provide feedback on the quality of the current action and calculate the reward value based on network performance indicators. The reward value is set to \(R=M\) (throughput, delay, loss), and the performance indicators include parameters such as throughput, link delay, and packet loss rate. Adjust the corresponding reward value weight according to the actual traffic demand, and the reward calculation equation is defined as (19), where \(R\) is a dimensionless metric calculated by normalizing and weighting throughput, delay, and loss. The weights \(n_{1}\), \(n_{2}\), and \(n_{3}\) are adjusted according to traffic demand to balance the impact of performance indicators on multi-agent learning. In sensitive network environments where reliability is prioritized, increasing \(n_{3}\) can effectively reduce packet loss by penalizing it more heavily during training. \[R=n_{1}\bulletthroughput+n_{2}\bullet\frac{1}{delay}+n_{3}\bullet\frac{1}{loss},n_{1},n_{2},n_{3}\in[0,1] \tag{19}\]
## 3 Results
### Identification of Volatile Organic Compounds by GC-IMS  
#### 3.3.1 Comparison of GC-IMS Topographic Plots  
As an emerging technique for food flavor analysis, GC-IMS offers significant advantages, such as no sample pre-treatment and rapid analysis, making it highly promising for the detection of trace volatile compounds. To better observe the content differences of VOCs among the various fermentation groups, a two-dimensional spectrum was generated (Figure 2A). In this plot, each bright spot represented a compound, and the color intensity directly reflected the concentration level of volatile compounds: the closer the color was to white, the lower the content; the closer it was to red, the higher the concentration [26; 27].  
Figure 2: The volatile fingerprints of the different fermentation groups obtained from gas chromatography–ion mobility spectrometry (GC-IMS). (**A**) Topographic plots; (**B**) difference comparison plots. The red box in (**B**) represents the primary regions of the difference comparison plots of the distinct fermentation groups.  
The GC-IMS results showed that the retention time of the volatile components in the GAB mostly ranged from 0 to 1200 s, with a drift time between 1.0 and 1.7 s. To clearly observe intergroup differences, the EC-1118 fermentation group was selected as the reference, and differential spectra were generated by comparing it with other fermentation groups (Figure 2B). Red indicated VOCs higher than the control, while blue indicated lower concentrations, with the intensity representing the degree of difference. Especially in the area marked by the red box, the SY fermentation group displayed the most red points, suggesting a higher concentration of VOCs compared to the other three groups. Notably, NM-8 showed a greater number of dark blue spots, indicating lower VOCs concentrations in these regions compared to the other fermentation groups.