esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 5 Methodology  
This project delves into the impact of various attacks on the functionality and performance of mobile ad hoc networks (MANETs). The analysis focuses on three specific attack types: dropping attacks, which disrupt communication by discarding packets; sinkhole attacks, where a malicious node attracts and absorbs network traffic; and Jellyfish Delay Variance attacks, which introduce unpredictable delays into packet delivery. To comprehensively assess the potential damage, a combined scenario simulating a black hole attack, a fusion of sinkhole and dropping attacks, will also be investigated. These attack vectors were chosen due to their potential to severely compromise MANET operations, including disruption of critical communication, data loss, and degradation of network performance. By understanding the effects of these attacks, effective countermeasures can be developed to enhance the resilience and security of MANETs.  
Our simulations will utilize two network configurations, each consisting of 25 nodes. The first configuration will employ a random distribution, where node placement is random within the simulation area. The second configuration will use a uniform distribution, where nodes are spread evenly throughout the area. In both scenarios, only the attacking nodes will be mobile; see Figure 7.  
The specific order in which the simulations were conducted is documented in Table 1.  
For consistent and repeatable attacker movement across multiple simulation runs, the Bonn motion mobility model was employed [40]. This approach enables for the definition of both the speed and direction of attacker nodes within the simulator. The Bonn motion mobility model utilizes text files, commonly referred to as trace files, to define attacker movement within the simulator. These trace files are referenced by a configuration file (.ini).  
Each line in the trace file describes the motion of a single attacking node. The data are typically grouped into threes:  
* x: X-coordinate (specifies the node's horizontal position).
* y: Y-coordinate (specifies the node's vertical position).
* t: Time in seconds (represents the time at which the node should be at the specified coordinates). Optional  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{3}{c}{**Random Then Uniform Distribution**} \\ \hline
**Simulation** & **Attack** & **Number of Attackers** \\ \hline
1 & Baseline (No Attack) & \\ \hline
2 & Delay & One \\ \hline
3 & Sinkhole & Node \\ \hline
4 & Dropping & \\ \hline
5 & Combined (Black Hole) & \\ \hline
6 & Baseline (No Attack) & \\ \hline
7 & Delay & \\ \hline
8 & Sinkhole & Two \\ \hline
9 & Dropping & \\ \hline
10 & Combined (Black Hole) & \\ \hline \end{tabular}
\end{table}
Table 1: Simulation run order.  
Figure 7: Random node and uniform node distributed networks.  
* z: (Optional) height coordinate (specifies the node's vertical position in 3D simulations). This approach ensures consistent and repeatable attacker movement across multiple simulation runs, allowing for a reliable analysis of the attacks' impact.
# Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion
## 5 Conclusions  
This study introduces a monocular depth estimation approach that relies on dilated convolution and feature fusion. By integrating the DNET backbone with the dilated convolution feature fusion module and implementing a method to address class imbalance in the loss function, this methodology successfully accomplishes depth estimation from monocular images. The efficacy of our approach was validated on the NYU Depth-v2 Dataset and KITTI Dataset, with comparisons made against other state-of-the-art algorithms. Our method demonstrated a reduction in RMSE of over 10% and an improvement in accuracy metrics by over 5%. Despite the promising results obtained, the applicability of our method is limited by certain inherent limitations. The method is sensitive to sensor noise and faces challenges in integrating data from multiple sources, such as cameras and LiDAR. In future work, we will investigate the potential of updated depth estimation frameworks for practical applications. This will include the investigation of new algorithms and architectures with the objective of improving accuracy in a variety of conditions. The objective is to integrate these advances into operational systems, thereby enhancing their performance and reliability.  
Conceptualization, H.L. and S.L.; methodology, H.L.; software, H.L. and S.L.; validation, B.W. and Y.W.; formal analysis, S.L.; investigation, S.L.; resources, H.L.; data curation, H.L.; writing--original draft preparation, H.L. and S.L.; writing--review and editing, Y.W. and B.W.; visualization, B.W. and Y.W.; supervision, S.L.; project administration, H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
Figure 14: Depth estimation results of different approaches on the KITTI Dataset.
## 2 Materials: Case Studies from Bi-Level Optimization Competition
### Track 1: Bi-Level Optimization of Bidding Strategies in LEM  
The case considers an LEM where multiple agents interact to maximize system transactions. Agents that interact in the problem include consumers, producers, and prosumers. The main concern is to maximize the transactions locally and interact, if required, with the power grid back-up system. The problem is formulated using a bi-level problem formulation. The lower level maximizes the internal transactions; the upper level looks for agents' profit maximization.  
The solution of the problem does not allow independent optimization for both levels. The solutions must guarantee that the clearing price (\(cp\)) is reached in the lower level, and the solution affects the interaction with the wholesale market. \(cp\) is affected by agents' strategies for selling and buying energy. The bidding process uses merit order for the allocation of generation and load offers.  
The system profit maximization follows Equations (1)-(3):  
\[\max\sum_{j}\frac{P_{j}}{N_{p}}-\sum_{i}\frac{C_{i}}{N_{c}} \tag{1}\]  
\[P_{j}=\sum_{t}cp\cdot x_{j,t}+c^{t}E{\it{E}}{\it{s}}_{j,grid}-c^{m}\cdot G_{j} \tag{2}\]  
\[C_{i}=\sum_{j}cp\cdot x_{j,t}+c^{t}E{\it{buy}}_{grid,i} \tag{3}\]  
where:  
\(P_{j}\): Producers' profits  
\(C_{i}\): Cost for consumer agents  
\(cp\): LEM clearing price  
Figure 1: Local market and grid as a back-up.  
\(x_{j:i}\): Energy sold by the agent  
\(c^{F}\): LEM clearing price  
\(Esell_{j,grid}\): Energy sold by the agent  
\(c^{m}\): Marginal price of generation unit  
\(G_{j}\): Energy produced by generation unit  
\(c^{G}\): Grid price  
\(Ebuy_{grid,i}\): Energy bought by agent \(i\)
## 3 Results
### Findings from the First Pilot Study  
The first pilot was launched in 2019 and was completed successfully in January 2020. The pilot validated the following:  
* A robust, consistent, and systematic approach to modelling items in a non-standardised format can be achieved. This approach also helped in highlighting ambiguity in data definitions, which were otherwise difficult to identify.
* Using modelling tools provided the benefits of following a disciplined manner of structuring data with its attributes, cardinalities, associations, and values. This rigour in data representation benefited downstream tools such as EDC and reporting tools, providing consistent implementations.
* Modelling in a tool-based environment enabled viewing data in multiple dimensions such as tabular and graphical views to suit the user preference. This resulted in more engagement in reviews and discussions, which ultimately helped in richer and unambiguous data models, as shown in Figure 10 below:  
* Transformations to other standard formats including CDISC ODM can be achieved reducing the burden of manual mapping and human errors arising from such a process.
* Representing the data dictionary in the form of models and then exporting them to industry standards such as CDISC ODM enabled various EDC systems, e.g., REDCap, and used them to construct new data entry forms.  
Figure 10: Modelled data dictionary item of oxygen saturation.
# Meander Line Super-Wideband Radiator for Fifth-Generation (5G) Vehicles
## 3 Antenna Design  
A microstrip patch antenna's basic structure and dimensions are proposed in Figure 1. The antenna comprises a meander line patch, dielectric substrate, and continuous ground plane. The dimensions of the substrate are 34 mm \(\times\) 20 mm \(\times\) 1.6 mm. The top substrate has a meander line constructed according to the vital mathematical expressions in Section 2: 5G FR2 from Equations (8)-(10). The dielectric material of the substrate has a relative permittivity of \(\epsilon=4.4\) and a thickness of 1.6 mm and is made of FR4 epoxy. A 50 \(\Omega\) microstrip line feeds this antenna. The meander slot has three vertical widths of 4.7 mm with a height of 5.2 mm and two horizontal widths of 5.22 mm with a height of 4.7 mm. It is placed symmetrically on either side of the microstrip line. The continuous ground plane is set below the radiating patch to improve the antenna's bandwidth.
## 1 Introduction  
Microgrid distribution networks are the next generation of electric power. The purpose of smart distribution networks is to overcome the problems of traditional electrical energy. Traditional energy systems are generally used to transport power from a central generator to customers [1; 2]. In contrast,a smart grid utilizes bi-directional flows of electrical energy and information to create an advanced and automated power distribution network. Table 1 presents a brief comparison between the existing grid and the smart grid. In general, a smart grid can respond more efficiently to events that occur anywhere on the network through acting on appropriate strategies during electrical energy production, transmission, distribution and consumption.  
Using a communication networks ensures full coordination between generation and protection. The reliable flow of real-time information among all network components is critical to the success of the smart grid process. The process can be reliable and efficient with the use of modern communication [3]. A wireless sensor network (WSN) is distributed between the generation station and the load, where many sensors are wirelessly connected to communicate with one another. These electronic applications are known as detectors or sensors, which detect physical units. Sensors have the capability to detect many physical units, such as electric current, voltage, resistance, magnetic flux density and so on. Different types of sensor nodes are available in the market, such as micaZ (San Jose, CA, USA), TelosA (Palo Alto, CA, USA) and IMote2 (San Jose, CA, USA), which are able to generate WSNs (Chicago, IL, USA) [4; 5].  
Many wireless standards are used in industrial applications [6; 7; 8; 9; 10; 11]. ZigBee is one of the most useful technologies in WSNs, which is based on the IEEE 802.154 standard created in 2003. The ZigBee system is different from other communication standards, such as WiFi, Bluetooth and Worldwide Interoperability for Microwave Access (WiMAX), and has the advantages of small data transmission and low power consumption. In addition, the ZigBee system is more cost-effective than other wireless communication standard sensors because it provides flexible bandwidth performance, scalability and self-organization [12].  
This paper is structured as follows: Section 2 compares the communication infrastructure between traditional power grids and smart grids. Section 3 provides a general description of the communication, which can be implemented in the smart grid. Section 4 analyses the proposed method of the protection model and the use of the OPNET simulator. Section 6 analyses the results of the simulation and three scenarios applied to the results. Section 6 presents the results of the study.
# GUI for Analysis of Parameters, Accurate Design and Optimization of Microstrip Filters
## 3 Considerations on Filter Designing  
### Phisical Limits in the Width \(W_{L}\) and \(W_{\mathrm{C}}\) of Microstrips  
The impedance of the microstrips is related to their width. Starting from \(W_{0}\), which represents the input impedance line, the lines wider than \(W_{0}\) are capacitive lines with width \(W_{C}\), while the lines narrower than \(W_{0}\) are inductive lines with width \(W_{L}\). One aspect that logically must exist but is not typically addressed in textbooks is that there is a lower limit for \(W_{\mathrm{C}}\) and an upper limit for \(W_{L}\).  
The computation of the physical lengths of microstrip lines, according to the method used in this work, involves three steps:  
1. Define the widths of the capacitive and inductive lines, \(W_{C}\) and \(W_{L}\), then the parameters \(Z_{0\mathrm{C}}\) and \(Z_{0\mathrm{I}}\). can be computed.
2. Compute the first approach of the physical lengths, according to Equations (9) and (10).
3. Apply the balance of the physical lengths, forming a set of equations based on (12) and (13). This last step re-computes the physical lengths to satisfy the set of equations formed.  
Figure 4: (a) Microstrip low-pass filter. (b) Its network representation with cascaded sub-networks for network analysis. (c) Equivalent circuits for the subnetworks.  
In the process of solving the set of equations, some \"small values of \(W_{\text{C}}\)\" or \"large values of \(W_{\text{L}}\)\" will cause the solution of the set of equations, in step 3 to yield complex values for some lengths. This means that, there are some boundary values for \(W_{\text{C}}\) and \(W_{L}\) at which the following:  
* A \(W_{\text{C}}<W_{Clim}\) will give complex values in physical lengths of capacitive lines.
* A \(W_{L}>W_{Llim}\) will give complex values in physical lengths of inductive lines.  
Choosing a \(W_{\text{C}}\) or \(W_{L}\) out of the bounds will make unfeasible the realization of the filter. Hence, it is important to identify those limit values. Algorithm 3 finds these boundary values.
## 4 Analysis and Discussion  
The track monitoring was conducted for two years, demonstrating the applicability of the monitoring system and accumulating plenty of field monitoring responses. The responses excited by one type of vehicle was selected to reduce influential factors due to the different vehicle types in the subsequent analyses. This type of vehicle is characterized by eight carriages with two bogies and four wheelsets in each carriage. The axial distance between two wheelsets is 2.5 m, the central distance of two bogies is 17.5 m, and the whole length is 208.8 m. Generally, responses excited by a total of at least 39 trains of this type were recorded every day, and the passing-by speeds range from 71 km/h to 273 km/h. Before the subsequent analyses of these data, de-noising process of raw signals was performed. The'sym5' wavelet function for multi-layer threshold de-noising [57] was employed in this paper. A typical raw signal before and after de-noising is shown in Figure 7. Figure 7b is an enlargement of the inset in Figure 7a. The blue and the red curves represent the raw signal and the de-noised signal, respectively. The correlation coefficient between raw signals and their de-noised signals was more than 0.95, so de-noised signals could be used for subsequent analyses. The analysis of historical data was performed at first to evaluate the reasonability of choosing the two segments as the mutual instantaneous baseline for each other. On this basis, comparative analyses between current features of Segment 1 and those of Segment 2 were conducted to identify the effective features which can distinctly differentiate interlayer debonding from the normal state.
###### Abstract
[34] used close-range photogrammetry, engineering leveling, and displacement sensors to determine the deflection curve of a reinforced concrete beam. [PERSON] et al. [35] used the photogrammetric load traffic vibration measurement of three bridges to see the vibrational magnitudes well within the design standards of the American Association of State Highway Transportation (AASHTO). [PERSON] et al. [36] used an image-based 3D measurement equipment for the structural dynamics of a suspension bridge. [PERSON] and [PERSON] [37] utilized the photogrammetric technique to measure the 3D displacement of a bridge span. [PERSON] and [PERSON] [38] proposed a technique for computing the sensor orientation parameters of the camera using only a photogrammetric board, and they measured the instantaneous deformation of a railroad bridge within an error of 2 mm in three dimensions. The present study measured the deformation of a steel railway bridge for the cyclic loads of three types of rail cars, using the sequential photogrammetric technique. Conventionally, interior and exterior orientation parameters (IOPs and EOPs) of the camera are required, for example through the bundle adjustment. These are obtained from control points (CPs) with 3D precise coordinates that have been surveyed using a terrestrial instrument, such as total stations. However, in this implementation we proposed a relative orientation method that uses coplanarity and scale adjustment using the feature points on the bridge to automate the photogrammetric. The objective of the study is application of relative orientation-based efficient sequential image processing for 3D displacement measurement of a plate girder. The method is proposed to compensate some limitations of a conventional laser displacement sensor. A laser sensor is often installed in a limited number for points of interests because the sensor accurate but costly. The proposed method is designed applicable to any point where a cheap target is placed. The photogrammetric targets installation is very easy and a lot of targets can be used for denser displacement monitoring. In addition, the proposed method can measure 3D displacement while the laser sensor measures one-direction displacement. However, it still needs a target that can be a limitation to a point of inaccessible area. The paper is organized as follows. Section 2 introduces the proposed photogrammetric method with the relative orientation and sequential image processing for 3D displacement measurement. Section 3 illustrates experimental results and is followed by the conclusion in Section 4.
## 4 Case Study
### Working Mode Identification and Parameter Selection
#### 4.1.2 System Parameters  
To meet the condition monitoring, the acquisition parameters of FOSS were as shown in Table 1. The parameter ranges in the table were used to filter outliers.  
All of the data described in the following sections were from the real ship operation parameters (from a training ship). The data set consisted of two parts:  
(1) Dataset A:  
A system maintenance cycle from March 2019 to September 2019 was selected from the database of the AMS. During this period, the main factor that dominated the health of the system was the performance degradation of the system components. When the system ran for about 5300 h, the system was out of function. The change of each state parameter in the system is shown in Figure 6a.  
(2) Dataset B:  
Another system maintenance cycle between May 2020 and December 2020 was selected from the database of the AMS. During this period, in addition to the dominant degradation, the health condition of the system was affected by double pump switching; the degradation process changed and the system life increased. When the system ran for about 6195 h, the output pressure and flow of the whole system were insufficient due to the filter. This data sample was used to compare the prediction results of different prediction processes.  
Figure 5: System structure diagram of FOSS: (**a**) structural diagram of real ship system; (**b**) system-schematic diagram.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Parameter Descriptions**} & \multirow{2}{*}{**Unit**} & \multicolumn{4}{c}{**Benchmark Data**} \\ \cline{3-5}  & & & **Lower Limit** & **Optimal Value** & **Upper Limit** \\ \hline
1 & SP outlet pressure & MPa & 0.3 & 0.5 & 0.8 \\
2 & SP inlet pressure & m & 0 & 3 & 3 \\
3 & CP outlet pressure & MPa & 0.6 & 0.8 & 1.2 \\
4 & CP inlet pressure & m & 0 & 3 & 3 \\
5 & Fuel oil viscosity & cSt & 7.0 & 12.0–14.0 & 20.0 \\
6 & AF pressure differential & MPa & 0.02 & 0.02 & 0.08 \\
7 & DF pressure differential & MPa & 0.02 & 0.02 & 0.08 \\
8 & MB pressure & MPa & 0.3 & 0.45 & 0.6 \\
9 & Service oil tank level & m & 0.3 & 2.12 & 2.66 \\
10 & Three-way valve position & & & 0/1 & \\
11 & Main engine fuel oil inlet pressure (Function) & MPa & 7 & 8.5 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of sensor data sets.  
Figure 6: Illustration of raw signals from two datasets: (**a**) Dataset A; (**b**) Dataset B.
# Enhancing Weather Forecasting Integrating LSTM and GA
## 3 Data and Methods
### Evaluation Metrics  
The coefficient of determination \(R^{2}\) was used to evaluate the regression models, and the MSE, root mean square error (RMSE), and mean absolute error (MAE) were considered to evaluate the LSTM models.  
The statistical measure \(R^{2}\), where \(0\leq R^{2}\leq 1\), assess the goodness of fit of the model and measure the predictive power of the independent variables, Equation (8). The higher the \(R^{2}\) value, the better the model fits the data.  
\[R^{2}=1-\frac{SS_{res}}{SS_{tot}} \tag{8}\]  
where \(n\) is the sample size, \(SS_{res}\) is the sum of squared residuals, \(SS_{res}=\sum_{i=1}^{n}(y_{i}-y_{i}^{\prime})^{2}\), \(SS_{tot}\) is the sum of squared differences between the observed values and the mean of observed values, \(SS_{tot}=\sum_{i=1}^{n}(y_{i}-y)^{2}\), \(y_{i}\) is the observed value, and \(y_{i}^{\prime}\) is the predicted value.  
The MSE measures the average of the squared differences between the observed and predicted values:  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-y_{i}^{\prime})^{2} \tag{9}\]  
The MAE measures the average of the absolute differences between observed and predicted values:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-y_{i}^{\prime}| \tag{10}\]  
Finally, the RMSE takes into account the square root of the differences between the observed and predicted values:\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-y_{i}^{\prime})^{2}} \tag{11}\]  
These metrics therefore play an important role in the case studies that have been developed, as they make it possible to compare the different results obtained. They were calculated considering the forecast and observed data from the testing dataset for the time range of predictions.  
Figure 2 shows the methodology applied in this paper. Considering the two locations, the datasets were pre-processed. After that, regression models were used to estimate the missing values for Quinta de Santa Barbara between 2010 and 2022, considering the data obtained from Pinhao. After obtaining the new dataset for Quinta de Santa Barbara, an LSTM network was considered to forecast short- to medium-term weather variables. A GA was also considered to optimize the hyperparameters of the network. Finally, the proposed evaluation metrics allow us to evaluate and compare the forecast results of each proposed network.  
The following cases have been proposed to evaluate the accuracy of weather forecasting using LSTM networks for short- and medium-term horizons:  
* Case I: Short- and medium-term forecasting using LSTM.
* Case II: Short- and medium-term forecasting using an optimized LSTM with a GA.