esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Optical Conductivity
According to our results, the first peak at 0.8 eV in optics [22] is interpreted as electronic excitations within the \(x^{2}-y^{2}\) orbital from the LHB to a fully renormalized conduction band. The agreement between theory and experimental data could be further improved by tuning the on-site Coulomb interaction strength in the \(x^{2}-y^{2}\) orbital sector of MoO\({}_{2}\). On the other hand, the method presented here is clearly performing robustly even for a first guess of \(U\), which makes our approach suitable for a rapid scan and characterisation of battery materials candidates. Taken theory and experiment together, our work microscopically reconciles the most salient low-energy features seen in optical (Figure 5) and PES (Figure 4) experiments, showing the relevance of sizable electronic correlations (1.0 eV \(\leq U/W\leq 1.1\) eV, here \(W\approx 4.5\) eV is the one-electron LDA bandwidth) in MoO\({}_{2}\). Figure 5: Orbital resolved optical conductivity of monoclinic MoO\({}_{2}\) computed within LDA+DMFT. Notice the changes in the Drude-like peak below 0.5 eV and its evolution with increasing \(U\). For the \(x^{2}-y^{2}\) orbital also relevant is the energy position of the first optical conductivity peak at 0.72 eV for \(U=4.5\) eV which is in semi-qualitative agreement with experimental data taken from Ref. [22]. As discussed in the text, the two main peaks in optics are fingerprints of particle-hole excitations within the correlated monoclinic phase of MoO\({}_{2}\).
# Integration of Wavelet Denoising and HHT Applied to the Analysis of Bridge Dynamic Characteristics
## 4 Application Analysis
The observation sequences of the monitoring point in the \(x\) (lateral) and \(y\) (longitudinal) directions lasting for 1 h were selected to identify the first natural frequency of the bridge structure using the proposed method and to analyze the dynamic characteristics of the bridge structure. The obtained coordinate sequence was transformed from a geodetic coordinate system into a bridge axis coordinate system by using the same method as that in [11]. The time history curve of the averaged data from the GNSS monitoring point is shown in Figure 8. The accuracy analysis of the observation sequence in [16] indicated that the horizontal and vertical mean square errors in the entire time period were \(m_{x}=\pm 4.72\) mm and \(m_{y}=\pm 3.26\) mm. These values implied that no large gross errors existed amongst the observation values, and the accuracy of the GNSS dynamic observation data was reliable and suitable for dynamic frequency extraction. Figure 8: Time series diagram of the original observation data. Figure 7: Layout of the GNSS monitoring points on Sutong Bridge.
## 3 Results and Discussion  
### Morphology and Elemental Composition  
#### 3.1.1 Pure-HEO NPs and NFs  
Figure 1 shows the main results of the SEM and TEM analyses. CrMnFeCoNi-HEO prepared by SG consists of aggregates of NPs (Figure 1a); TEM analysis reveals that NPs exhibit rounded/polyhedral shapes (Figure 1j) and sizes distributed in the range of 70-500 nm, with an average value of 240 nm (Figure 1l). The electrospun CrMnFeCoNi-HEO NFs are featured by a porous architecture (Figure 1b), as frequently observed forelectrospun low-entropy oxides (LEOs) [37; 38], as well as for HEO NFs with different composition [29; 31]. The distribution of the NF diameters (Figure 1e) is centred at 0.66 \(\upmu\)m. TEM analysis shows that the NFs are built up by interconnected NPs with similar shapes as those obtained by SG method (Figure 1k). The sizes of the NPs forming the fibres vary between 30 and 400 nm, i.e., in a narrower range with respect to those prepared by the SG method (Figure 1l); in addition, the average NP size is smaller (200 vs. 240 nm). The results of the compositional analysis by SEM/EDX (Figure 1f,g) confirm the removal of all organic components during calcination and prove the formation of oxide NPs and NFs based on the nearly equimolar combination of Cr, Mn, Fe, Co, and Ni.
# Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks
## 2 Materials and Methods  
This section describes the optimization process that determines the weights to be assigned to each rule in the knowledge base of a fuzzy control system used for a clustering method in a WSN. The optimization process is performed by a PSO algorithm, which will be described later in this section. The section is structured as follows:  
* A brief description of fuzzy logic: some concepts related to fuzzy logic will be introduced, for example, input and output variables and knowledge base (see Section 2.1).
* The clustering algorithm developed for the present approach is detailed (see Section 2.2.)
* The energy model for radio communications between nodes in the WSN is described (see Section 2.3).
* The simulation scenarios used for the optimization process (area dimensions, number of nodes, mode of deployment, etc.). This subsection also includes a brief description of the network model used in the simulations (see Section 2.4)
* The optimization process based on PSO is described (see Section 2.5).  
It should be noted that the optimization process simulates different WSN deployments in three scenarios to obtain the final weight of the rules. Consequently, these simulations need an energy model (see Section 2.3) to evaluate the energy consumed by the nodes in their communication and internal processing (e.g., data aggregation).
# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
## 2 Materials and Methods
### Evaluation Methodology Indicators  
In this experiment, Pixel Accuracy (PA), Mean Pixel Accuracy (mPA), and Mean Intersection over Union (mIoU) are used to measure the segmentation of pixel points.  
Pixel Accuracy, which indicates the number of correctly segmented pixels as a percentage of all pixels, can be used to evaluate the global accuracy of the model. For \(k+1\) class segmentation targets, where the target to be segmented is class k, and when the background is class 1. The formula \(p_{ii}\) denotes the number of correctly classified pixels; \(p_{ij}\) denotes the number of pixels whose class \(i\) is predicted to be class \(j\). The pixel accuracy calculation formula is shown in Equation (5).  
\[PA=\frac{\sum_{i=1}^{k}p_{ii}}{\sum_{i=1}^{k}\sum_{j=1}^{k}p_{ij}} \tag{5}\]  
Mean Pixel Accuracy, which represents the average of all category pixel accuracies, is the sum divided by the number of categories, as shown in Equation (6).  
\[mPA=\frac{1}{k+1}{\sum_{i=0}^{k}\frac{p_{ii}}{\sum_{j=0}^{k}p_{ij}}} \tag{6}\]  
Mean Intersection over Union, which calculates the arithmetic mean of the intersection ratios in each category, is used to measure the pixel overlap of the dataset. Mean Intersection over Union is shown in Equation (7).  
\[mIoU=\frac{1}{k+1}{\sum_{i=0}^{k}\frac{p_{ii}}{\sum_{j=0}^{k}p_{ij}+\sum_{j=0} ^{k}p_{ji}-p_{ii}}} \tag{7}\]
# Classic Discrete Control Technique and 3D-SVPWM Applied to a Dual Unified Power Quality Conditioner
## 3 Design of Controllers
### Voltage Controller  
The control strategy selected for the shunt APF consists of PID actions with two poles and two zeros. One of the poles is placed at the source to reduce the speed error and improve noise rejection, the second is positioned at a higher frequency to attenuate high-frequency noise. On the other hand, the zeros are positioned at the same frequency of the poles of the power plant to reduce their effects. The discretized transfer function of the voltage controller in the shunt APF (\(H_{v}\)) is given by Equation (24).  
\[H_{v}(z)=\frac{30.37\mathrm{z}^{2}-59.63\mathrm{z}+29.31}{z^{2}-z+1.12*10^{-3}}. \tag{24}\]
# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
## 4 Results and Discussion
### Damage Detection
#### 4.4.4 Structural Damage Comparison  
In Table 7, the top predictions for ResNet, MobileNet, and EfficientNet correctly classify the object as _structural damage_, and MobileNet actually achieves the lowest confidence score of 42.85% for that predicted class. ResNet and EfficientNet achieve similar confidence scores of 68.11% and 67.96%, respectively, for their top prediction. Although the bounding box location in Figure 9b predicted by MobileNet more accurately encompasses the entire _structural damage_ area in comparison to the bounding box location in Figure 9a predicted by ResNet and Figure 9c predicted by EfficientNet, the second and third predictions made by ResNet and MobileNet are inaccurately classified as either a _damaged wall_ or a _damaged roof_, very likely due to the similar features of _structural damage_ in this image that the model has learned to extract in predicting the _damaged roof_ and _damaged wall_ classes as well. The same applies to the third prediction made by EfficientNet of the _flood damage_ class.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**ResNet**} & \multicolumn{2}{c}{**MobileNet**} & \multicolumn{2}{c}{**EfficientNet**} \\ \hline  & **Score** & **Type** & **Score** & **Type** & **Score** & **Type** \\ \hline \#1 & 68.11\% & structural damage & 42.85\% & structural damage & 67.96\% & structural damage \\ \#2 & 13.43\% & damaged wall & 11.74\% & damaged roof & 25.56\% & structural damage \\ \#3 & 12.26\% & structural damage & 6.30\% & damaged roof & 18.86\% & flood damage \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of confidence scores and associated object categories for the top three predictions made on the image of _structural damage_ for each of the three models.  
Figure 8: Inference carried out on the image of _flood damage_ for each of the three models. The white bounding boxes correspond to the correct predictions of _flood damage_ from the first row of Table 6.
## 4 Case Studies: Part I: Simulation Analysis
### Hyperparameter Settings and Performance Evaluation Metrics  
To optimize the overall performance of the classification algorithm, hyperparameter tuning is required for the deep learning model. This tuning process is carried out by observing the performance metrics on the validation and test sets. Regarding the hyperparameter settings, the initial learning rate is set to 0.0001, the number of training epochs is 50, and the batch size is 64. The training process adopts the cross-entropy loss function for loss computation and employs the Adam optimizer to update the model parameters.  
To comprehensively evaluate the overall performance of the proposed algorithm in PQD classification tasks, in terms of the accuracy metric, we adopt the average classification accuracy (Acc) for performance evaluation. In terms of the efficiency metric, we adopt floating-point operations (FLOPs), the number of model parameters (Params), model size (Size), and average time as evaluation indicators. The calculation formula for Acc is as follows:  
\[\text{Acc}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}} \tag{5}\]  
where TP and TN represent the number of correctly classified positive and negative samples, respectively; and FP and FN represent the number of misclassified positive and negative samples, respectively.  
The computations performed in a CNN primarily consist of FLOPs, which are widely used to assess model complexity [18]. The FLOPs, \(F_{s}\), for traditional convolutional layers, grouped convolutional layers, depthwise separable convolutional layers, and fully connected layers are respectively formulated as follows:  
\[F_{\text{s1}}=2H_{out}D_{out}C_{in}K_{h}K_{d}C_{out} \tag{6}\]  
\[F_{\text{s2}}=2H_{out}D_{out}C_{in}K_{h}K_{d}C_{out}/s \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configuration** & **Parameter** & **Configuration** & **Parameter** \\ \hline OS & Win10 64 bit & Python & 3.9 \\ CPU & Ryzen 3970X & Pyts & 0.12.0 \\ GPU & RTX 3090 & Pytorch & 1.12.1 \\ RAM & DDR4 128G & CUDA & 11.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the experimental environment.  
\[F_{83}=2H_{out}D_{out}C_{in}(K_{h}K_{d}+C_{out}) \tag{8}\]  
\[F_{84}=2C_{in}C_{out} \tag{9}\]  
where \(H_{out}\) and \(D_{out}\) represent the height and width of the output feature map, respectively. \(C_{in}\) and \(C_{out}\) represent the number of input and output channels, respectively. \(K_{h}\) and \(K_{d}\) represent the height and width of the convolution kernel, respectively. \(s\) refers to the number of groups in the grouped convolution layer.
# S-Band Doppler Wave Radar System
## 3 MORSE Radar System
### Algorithm Process
First we define the n th order moment \(M_{n}\):  
\[M_{n}=\int_{0}^{\infty}f^{n}E(f)df \tag{16}\]  
then \(H_{s}\) (by empirical formula):  
\[H_{s}=4\sqrt{M_{0}} \tag{17}\]  
Significant wave period \(T_{s}\):  
\[T_{s}=\sqrt{M_{-2}/M_{0}} \tag{18}\]  
Peak period \(T_{p}1\), which corresponds to non-directional wave height spectrum peak frequency:  
\[T_{p1}=1/f_{p1} \tag{19}\]  
Dominant wave length \(\lambda_{p1}\):  
Figure 5.0  
Dominant wave phase velocity \(V_{p1}\):  
Figure 5.1  
Average wave period \(T_{av}\):  
Figure 5.2  
The whole data processing approach is demostrated in Figure 5.3  
Figure 5.4 Doppler frequency shift spectra of all cells and Doppler velocity of all cells with range resolution of 7.5 m. (**a**) Doppler frequency shift spectra of all range cells. Data used are acquired with No.4 antenna at 04:05 25 March 2015. (**b**) Directional velocity of all range cells corresponding to figure a.
## 2 System Model  
We consider an OTFS modulation system with \(M\) symbols and \(N\) subcarriers. The transmitter and receiver are equipped with a single antenna. The OTFS modulation frame is shown in Figure 1.
## 2 Background and Preliminaries
### Federated Learning Algorithm  
In federated learning, an aggregation algorithm refers to a technique implemented for consolidating the outcomes of training numerous intelligent models on the clients' devices, utilizing their respective local datasets. This algorithm plays a crucial role in combining the results derived from the local client training processes and subsequently updating the global model [25]. Two such algorithms are:  
1. Federated Stochastic Gradient Descent (FedSGD) averages the locally computed gradient at every step of the learning phase.
2. Federated averaging (FedAVG) averages local model updates when all the clients have completed training their models. Before moving forward, we shall introduce some terms:  
* Round: A round in federated learning is an iteration of the federated learning process. In each round, a subset of clients is selected to participate in the training process.
* Clients: k randomly selects a subset of K clients to participate in the current epoch.
* Non-IID dataset: This stands for a non-independent and identically distributed dataset. For an image classification problem, this means we may have some classes which exist for some clients but do not exist for another client. Non-IID poses a challenge to deep learning models, as it can lead to biased or unreliable models, resulting in low accuracy and incorrect results.
* IID Dataset: This stands for independent and identically distributed dataset. For an image classification problem, it means that each image has a similar probability distribution as the others, and all are mutually independent.
## 4 Communication Standards for Wireless Sensor Networks
### Zigbee  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Major Challenges** & **Description** \\ \hline Severe ecological conditions & Wireless sensor nodes can be subjected to harsh environmental condition which may cause fault in wireless sensor node. \\ \hline Various network topologies & Heterogeneous network topologies in energy distribution network due to various features and failure of sensor nodes may cause technical challenges in design of sensor nodes. \\ \hline Limited capability & Restricted processing and memory capabilities cause various challenges in design and deployment of wireless sensor networks. \\ \hline Bit errors & In communication systems, high bit error rates are observed due to high noise level. This calls for various error detection and correction schemes. Detection and correction of errors require greater memory and processing facilities which make the design of sensor network challenging. \\ \hline Security of sensor networks & Security of wireless sensor network is an indispensable and decisive requirement. The sensor nodes must be secured from physical tampering to hacking for smooth functioning of various smart grid applications. \\ \hline Quality of service necessities for smart grid environment & The parameters like high data rates, latency, reliability and authenticity are vital for quality of service necessities of smart grid applications. Wireless sensor networks must fulfill these criterions for successful implementation of various applications. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Challenges of WSN.  
Zigbee is based on IEEE 802.15.4 standard. It is an energy proficient short range wireless communication technology. It functions in the ISM (Industrial, scientific and medical) band which is allocated for industrial, scientific and medical applications. Zigbee operates in the band of 2.4 GHz, 868 and 928 MHz with full duplex wireless data transmission. IEEE 802.15.4 standard describes physical layer and media access layer and Zigbee Alliance has expanded the configuration of an application layer and network layer. The maximum throughput achievable by Zigbee is 250 Kbps [51]. In the area of power automation, it is applicable for smart meters, power system monitoring and measurement of various electric parameters. Smart Grid integrates information and communication technology with an existing power system to improve the power grid network with the capabilities of self-healing, disaster recovery, interoperability, compatibility, energy efficiency and security [52; 53; 54; 55]. Zigbee can play an imperative role in operation and maintenance of power grid, data accumulation, parameter measurement, security, monitoring and consumer interface [59].
# Manipulation Planning for Large Objects through Pivoting, Tumbling, and Regrasping
## 6 Simulation and Experiments
### Experiment 2: Object Orientation  
Experiment 2 tests a combination of regrasping and pivoting during the rotation of the piano around the z-axis in the world frame (see Figure 17). The initial pose of the piano is shown in Figure 17a and the support vertices are marked as yellow dots. The robot grasps the piano, pivots it around one of its support vertices, and places it down in a pose where a rotation of \(-45\) degrees in the z-axis is performed (see Figure 17a-c). Further, the robot changes the grasp poses to different contact surfaces as depicted in Figure 17d,e to avoid self-collision of the robot and maintain the EEF in a reachable pose. After regrasping, the robot rotates the piano by another \(-45\) degrees around the \(z\)-axis and places it down (see Figure 17f). In Figure 17g,h, a new regrasping motion is performed after which the robot pivots the piano to the target pose (see Figure 17i,j).  
Experiment 2 shows that to perform a relatively large change in the object orientation, the robot needs to contact different surfaces of the object; with a combination of regrasping and pivoting, the robot successfully manipulates the object to the target pose.
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 3 Research Methodology
### Research Questions  
To delve deeper into the penetration path planning problem, this section presents relevant research questions (RQs) to review previous findings and summarize the penetration path planning modeling approach and planning methodology in automated PT. This paper will address these four RQs sequentially: Section 4 will answer RQ1, Section 5 will answer RQ2, Section 6 will answer RQ3, and Section 7 will discuss RQ4, concluding with a summary of the answers to all RQs in Section 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Research** & **Focus Area** & **Key Contributions** \\ \hline
[23] & The development and comparison of automated penetration testing techniques. & Highlights efficiency gains, cost reduction, and simplified operations through automation. \\  & The application of RL in penetration path planning. & Provides a comprehensive overview of RL-based penetration path planning methods, highlighting their effectiveness in dynamic and uncertain network environments. \\  & Development and analysis of models to simulate penetration testing. & Summaries the complexities of constructing penetration testing planning models at different levels of abstraction to enhance the realism of simulated penetration testing. \\  & RL in cyber offense and defense. & Summaries intelligent decision-making models and RL methods in cyber offense and defense. \\
[14] & Classification of automated penetration testing techniques. & Provides a detailed classification and summary of automated penetration testing techniques. \\  & In-depth review of penetration path planning with emphasis on new methodologies. & Provides a detailed analysis of existing studies from three key aspects: penetration path planning models, penetration path planning methods, and simulation environments. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of related review research.  
1. RQ1: How can the penetration path planning problem be modeled?
2. RQ2: What are the current methodologies for solving the penetration path planning problem, and what are the recent research developments in this area?
3. RQ3: What simulation environments are available for training penetration path planning?
4. RQ4: What are the existing challenges in penetration path planning techniques, and what future research directions should be pursued?
## 4 Results
### Signalling Data Traffic Characterisation
#### 4.1.2 Latency and Jitter  
Latency measurements indicate an average message transfer delay of 610 ms, in which 70 ms is dedicated to the network delay (see Figure 5). This means that the prototype provides a reduced network latency of 66.1% compared to the complete communications establishment. Moreover, as Figure 5(b) shows, the network can provide a delay lower than 150 ms 99% of the time. As a result, the overall signalling response time is lower than 800 ms 99% of the time, as shown in Figure 5(c).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Theoretical Value** & **Average Measured Value** \\ \hline \multicolumn{3}{c}{**Bit Rate**} \\ \hline Overall bit rate & 3.8 kbps & Interlocking: \\ Trailing tests & & Trailing: 2.8 kbps \\ Supervision tests & & Supervision: 3.3 kbps \\ Signalling comms failure & & Failure: 7.5 kbps \\  & & Object Controller: \\  & & Trailing: 2.2 kbps \\  & & Supervision: 4.8 kbps \\  & & Failure: 2.5 kbps \\ \hline \multicolumn{3}{c}{Time} \\ \hline Signalling layer round trip time & \textless{}1800 ms & 601 ms \\ Network delay (Latency) & \textless{}500 ms & 70 ms \\ Messages period (Jitter) & 300 ms & 318 ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network KPIs. Average values measured in the application layer: signalling critical services.  
Figure 5: Signalling and network round trip time.  
Figure 6: Cumulative density function of network KPIs.  
In terms of jitter, the signalling system sends information every 318 ms (communication heartbeat) in UDP. For this reason, if the network jitter exceeds this value, the packets received by the signalling equipment will be out of order, which could cause impairment or even failure in the communication process due to UDP not providing congestion control or reception confirmation. In this case, it has been found that the prototype offers a jitter of less than 40 ms in 99% of cases, which indicates that the packet reception variation margin may still be 86.7% higher than measured, as Figure 6a describes.  
In short, the most restrictive network parameter for establishing communications is latency since the signalling equipment requires a message transfer period lower than 1.8 s in a round-trip measurement, as described in Table 1.