esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 7 Fault Location Finding Methods
### Wide-Area FL Approach  
Conventional fault location techniques are not able to trace faults when either of the monitoring devices installed at end terminals of TL fail to record changes in voltage and current profiles. Wide-area FL methods can be a possible solution [PERSON] et al. (2016) for this kind of scenario. In wide-area FL methods, a replica of each application/algorithm runs at different transmission substations, as shown in Figure 9 [PERSON] et al. (2016), to avoid overloading the available computation and communication resources of that particular station. Thus, fault can be located even with less number of devices installed at different end-terminals of transmission links. Optimization-based synchronized algorithms are proposed for fault location [PERSON] et al. (2016); [PERSON] et al. (2016). Traveling-wave (TW) methods are also applied for fault location finding with single-end data and two-end data, respectively [PERSON] et al. (2016). The linear least square (LLS) method is employed to locate the fault position. Synchronized voltage based non-iterative substitution algorithm proposed for fault location estimation in [PERSON] et al. (2016). This method is based on the positive and negative sequence impedance matrix obtained via network topology. The matching degree factor is equal to zero in a positive sequence network, represents the fault point location [PERSON] et al. (2016). Thus, the matching degree is used to point out the faulty bus in the entire system. In [PERSON] et al. (2016), a hierarchical routine based on impedance is used to locate faulted zone, line and point.  
Figure 9: Wide-area fault location method: the local site is where the application request originates and is responsible for coordinating the remote replicas.
# An Efficient One-Dimensional Texture Representation Approach for Lung Disease Diagnosis
## 3 Methods
### Statistical Feature Extraction  
In our approach, Figure 2 shows the 1 DTR method that can diagnose lung diseases using 1D data for a short time processing. Thus, to achieve the intended goal of the study, we first collected a widely used dataset, the Guangzhou Women and Children's Medical Center dataset [25], and the COVID-19, pneumonia, and normal chest X-ray PA dataset [27]. These datasets were then converted to a grayscale image to simplify the image data, reducing the computational complexity and focusing on essential features for analysis. Once the datasets are simplified, we extract statistical features. Instead of using window-sized downsampling to obtain statistical features, our method extracts the statistical features vertically, reducing the image dimensions into a 1 DTR. This vertical extraction provides several benefits, such as computational efficiency, maintaining key features of the disease's white areas, and getting a more comprehensive representation of the classes.  
This step is essential as the mean gives information about the average intensity. It can normalize the image intensity, as chest X-rays can vary in contrast and brightness due to the equipment and the acquisition environment. Moreover, different tissue densities, such as bones, can affect pixel intensities. They typically appear brighter as they absorb more X-rays, unlike the lungs, which appear darker with a lower intensity as they are filled with  
Figure 2: The process involves extracting features from an input image by calculating its mean, standard deviation, and binary pattern. Interpolation is used to ensure all features are the same length. The 1D data are then preprocessed using a standard scaler, and the string labels are converted into integers using a label encoder. Finally, the preprocessed data are passed to an FFNN model.  
air. The mean provides a consistent average value and distribution for the pixel, ensuring that relatively high pixel variables do not shadow the crucial features.  
Choosing standard deviation came from the fact that it adds information about intensity distribution and variability. In pneumonia, the affected lung regions usually have a more diverse patch appearance with varying intensity, unlike healthy lungs with homogeneous intensity patterns. Accordingly, the higher standard deviation indicates a more significant variation, which can be interpreted as pneumonia. The mean and standard deviation of the pixel intensities can provide meaningful texture descriptions, such as homogeneity, contrast, and smoothness. These can capture the small patterns and changes between viral pneumonia, bacterial pneumonia, and normal lungs and help quantify the changes among them, which is essential for efficient training of the DL model.
## 3 Optimization Model
### Constraints
#### 3.2.1 Power Balance Constraints  
If load shedding is to be avoided, the system should meet the electrical demand completely. When BESS is charging, the electrical power balance should be:  
\[P_{D,i}+P_{EV,i}-P_{FC,i}-P_{W,i}-P_{PV,i}-\frac{P_{B,i}}{\eta_{ch}}-P_{U,i}=0 \tag{15}\]  
and in the discharging interval of the BESS, the electrical power balance should be:  
\[P_{D,i}+P_{EV,i}-P_{FC,i}-P_{W,i}-P_{PV,i}-\eta_{dch}P_{B,i}-P_{U,i}=0 \tag{16}\]  
where  
\begin{tabular}{l l} \(P_{D,i}\) & Electrical demand at interval \(i\) (kW) \\ \(P_{W,i},P_{PV,i}\) & Wind, PV powers at \(i\)-th interval (kW) \\ \(P_{B,i}\) & BESS charging or discharging power at \(i\)-th interval (kW). \\ \(P_{EV,i}\) & EV charging power at \(i\)-th interval (kW) \\ \(\eta_{ch},\eta_{dch}\) & Charging, Discharging efficiencies of the BESS \((p.u)\) \\ \end{tabular}  
Here \(P_{B,i}\) is negative in the charging interval of BESS and positive otherwise.  
The thermal power is provided by FC and auxiliary boiler, therefore, following constraint must be satisfied to balance the thermal power requirements of the SH.  
\[H_{D,i}-H_{FC,i}-H_{BL,i}=0 \tag{17}\]where \(H_{\text{FC},i}\) is the heating produced by FC (kW) and \(H_{BL,i}\) is the auxiliary boiler output (kW) at interval \(i\). \(H_{D,i}\) is the thermal demand of the SH.
## 1 Introduction
The main contributions of this paper are summarized as follows. (1) We proposed a low-complexity MSD algorithm with a reduction in matching branches. The proposed algorithm first reduces the number of matching branches in the MSD process by averaging the local signals corresponding to the target symbol group, which includes the observed symbol and its preceding and succeeding symbols. Leveraging the phase symmetry property of PCM/FM signals, the proposed algorithm further minimizes the storage requirements for local reference signals and reduces the computational complexity of correlation operations. The simulation results demonstrate that the proposed algorithm achieves almost the same detection performance with only one-eighth computational complexity compared to the original MSD algorithm. (2) We further optimize the above algorithm by multiplexing FIR filters. When the algorithm is implemented on a Field Programmable Gate Array (FPGA) platform, the correlation operations are implemented using filters structure. Limited by hardware resources, the infinite impulse response (IIR) filter does not converge easily and is not conducive to hardware implementation [17] when the coefficients are imprecise. In contrast, the finite impulse response (FIR) filter has lower complexity and higher stability; therefore, this paper adopts the structure of FIR filters to perform the correlation operations. In the proposed algorithm, a multi-channel, multi-coefficient optimization design was applied to the FIR filters to further reduce hardware resource consumption.
# Path Tracking Control Strategy Based on Adaptive MPC for Intelligent Vehicles
## 8 Future Work and Limitations  
While the proposed adaptive MPC controller demonstrates significant improvements in path tracking accuracy and stability, several limitations and future research directions warrant discussion.
## 4 Reliability, Availability and Fault Tree Analysis
### Component Importance
#### 4.4.1 [PERSON]'s Measure  
[PERSON]'s measure \(I^{B}(i|t)\) is a metric that describes the reliability importance of a component [45]. This measure is defined as the partial differentiation of the system reliability with respect to the reliability of component \(i\), as follows  
\[I^{B}(i|t)=\frac{\partial R(t)}{\partial R_{i}(t)}\ \ \ \text{for}\ \ i=1,2,\ldots,n \tag{9}\]  
If \(I^{B}(i|t)\) is large, a small variation in the reliability of component \(i\) will result in a major change in the reliability of the system. A component \(i\) is considered _critical_ for the system, if when the component \(i\) fails, the system also fails. Thus, the [PERSON]'s measure can also be interpreted as the probability of component \(i\) being critical for the system at time \(t\)[42].
## 3 Dependability
### Safety  
Safety is critical for MCAs, especially in use cases where human lives are at danger, such as autonomous driving and telesurgery. The IEC 60601 [29], which is a technical standard for the safety and performance of the medical electrical equipment, defines safety as the avoidance of any hazards due to the operation of a device under normal or single-fault conditions. However, this definition can be broadened to cover non-medical domains, thereby including faulty conditions such as wrong lane selection in autonomous driving, or task offloading failure affecting the information given to the end user, or creating distractions in an augmented reality application. The current trend in communication networks is to simplify safety through the development of bug-free software or through an AI-based optimization problem. It is necessary to study the interaction between the composing cyberphysical systems (CPS) and the environment of each use case [30]. In [31; 32], telesurgery safety considerations from the medical point of view are given. It also mentions their experience with different surgical robots and elaborates on some comparisons.
# Pedestrian Behavior in Static and Dynamic Virtual Road Crossing Experiments
## 2 Materials and Methods
### Analysis
#### 3.2.1. Percentage of Crossings  
The percentage of crossings was also examined through a three-way repeated-measures ANOVA. The initial speed, vehicle speed pattern, and the experimental approach were the considered factors. Some main effects were found for the initial speed, F(1, 58) = 41.82, \(\eta^{2}\) = 0.42, \(p<0.01\), and speed pattern, F(2, 116) = 146.18, \(\eta^{2}\) = 0.72, \(p<0.01\), but not for the experimental approach F(1, 58) = 0.03, \(\eta^{2}<0.01\), \(p\) = 0.87. A significant effect for the initial speed \(\times\) speed pattern interaction was also found, F(2, 116) = 49.33, \(\eta^{2}\) = 0.46, \(p<0.01\).  
Post hoc tests using Bonferroni were conducted to compare the different speed patterns and the initial speed within the speed profiles. In general, there was a significant increase in the crossing percentage from constant to slowing down (\(p<0.01\)) and from constant to stopping (\(p<0.01\)), but not from slowing down to stopping (\(p=1.00\)). Contrasts within  
Figure 11. Participants’ responses ordered by average of Crossing Start Time, for the initial speed of 30 km/h, per speed pattern and experimental approach: (**a**) dynamic; and (**b**) static.  
each speed pattern show differences involving 20 and 30 km/h for the speed constant (\(p<0.01\)) and for slowing down (\(p=0.03\)) but not for stopping (\(p=0.28\)).  
The experimental approach \(\times\) speed pattern \(\times\) initial speed interaction also had a significant effect on the percentage of crossings, F(2, 116) = 4.72, \(\eta^{2}=0.08\), \(p=0.01\). Bonferroni post hoc tests which were conducted to compare the initial speed within the experimental approaches with each speed pattern showed that only in the dynamic approach and in constant speed patterns (\(p<0.01\)), and in the static approach in constant (\(p<0.01\)) and slowing down patterns (\(p=0.04\)), there were significant differences in the percentage of crossings when compared with both initial speeds.  
The results indicate that participants crossed significantly more when the vehicle slowed down or stopped (see Figure 12). They also showed that the participants crossed more when the vehicle approached at 20 km/h than at 30 km/h. Still, this difference was only significant when the vehicle approached at a constant speed or slowed down. Importantly, no differences were found between experimental approaches in the participants' crossing decision when comparing the different vehicle speed patterns of approach to the crosswalk.
## 3 Mathematical Description of the Medical Rescue Operations after Mass Casually Incident
### Rules for Qualifying Injured to Medical Priority Groups  
According to START [13; 29; 40; 41] (Simple Triage and Rapid Treatment), there are four medical priority injured groups in the MCI. During medical rescue operations, these people are marked with colors or numbers. Each color corresponds to the priority of the victim'smedical care [4]. Based on the assessment of the health condition of the injured person, qualification to the appropriate group is made.  
Let \(g\) characterizes the priority of the victim's medical care:  
\[g=\overline{1,4}, \tag{30}\]  
where:  
\(g=\ 1\) means immediate medical operations upon arrival of emergency services.  
\(g=\ 2\) means urgent medical operations with a possible delay.  
\(g=\ 3\) means medical operations with a delay of up to several hours.  
\(g=\ 4\) means resignation from medical care. This is the priority group of injured people who are not expected to survive the next 24 h.  
\(\mathbf{L}_{g}\) is a set of numbers of people injured in a mass casualty incident classified to the \(g\)-th group of medical treatment priority:  
\[\mathbf{L}_{g}=\Big{\{}l:d_{g}^{group}(l)=1,\ l\in\mathbf{L}\Big{\}},g= \overline{1,4}, \tag{31}\]  
where  
\[d_{g}^{group}(l)=\left\{\begin{array}{ll}1,\ \text{if the $l$-th injured person is qualified to the $g$-th group},\\ 0,\ \text{otherwise}.\end{array}\right. \tag{32}\]  
where \(\underset{g,h\in\mathbf{G}}{\forall}:g\
eq h\Rightarrow\mathbf{L}_{g}\cap \mathbf{L}_{h}=\varnothing\).  
The detailed conditions for assigning casualties to priority groups are omitted due to the lack of article space.
# Air Traffic Simulation Framework for Testing Automated Air Traffic Control Solutions +
## 2 Proposed Architecture Framework  
### Architecture Requirements  
The research gaps identified in Section 1.3 inform the high-level requirements for the conceptualization of the simulation framework. These requirements are defined to support the independent development, testing, and validation of algorithms--such as those modeling the refined aircraft or controller behavior. The core architectural requirements are as follows:  
* Integration of real-world traffic data representative of the characteristics of a selected airspace;
* Use of an agent-based modeling approach within a modular architecture that clearly separates functional components;* Provision of well-defined interfaces to ensure comprehensive control and visibility over data flows throughout the simulation;
* Inclusion of basic traffic flow visualization capabilities to facilitate real-time monitoring during simulation runs;
* Support for flight plan-based navigation, implemented at the agent level rather than the core simulation layer, to enable realistic trajectory handling without reducing framework flexibility.
## 3 Methods
### Enhancing Conveyor Belt Monitoring with IoT-Based Alert Systems
Techniques such as Gaussian filtering and region of interest (ROI) selection are used to eliminate noise and to focus on critical areas to improve image processing accuracy.
## 2 Materials and Methods
### Air Mass Back-Trajectories and Analysis  
Three-day back-trajectories were obtained from NOAA's Hybrid Single-Particle Lagrangian Integrated Trajectory model with an arrival height of 500 m above the ground once every hour over the 24 h periods of interest (5 July and 26 September 2015) discussed in the subsequent section. A K-means clustering algorithm [32; 33] was then applied to the twenty-four air mass back-trajectories to group them based on similar wind directions and speeds.
## 2 Materials and Methods
### Experimental Setup  
We consider the eNTERFACE'05 audiovisual emotion dataset as an example, given within a speaker-dependent scenario described in Section 1.1. The dataset encompasses six distinct emotions: anger, disgust, fear, happiness, sadness, and surprise [38]. It comprises recorded utterances from 42 individuals representing 14 different nationalities. Although the utterances in this dataset were recorded in the English language, the involvement of people from different cultural backgrounds increases the complexity of the experiment as it is well known that emotion semantics show both cultural variations and universal structure [39].  
During the experiment, the participants were presented with short stories carefully designed to elicit specific emotions. Subsequently, the participants were required to respond with a predefined set of utterances corresponding to the emotions they experienced. The collected reactions were then evaluated by two experts, who discarded any ambiguous responses.  
To facilitate the federated learning experiment and simulate different clients, we divided the eNTERFACE'05 dataset into three separate non-overlapping parts, each one containing an equal number of speakers. This means that each client consisted of utterances recorded by 14 different speakers. The aim of such an experimental setup is to emulate distinct data sources or clients in a federated learning scenario by leveraging diverse speaker data from multiple sources.
## 4 Discussion  
### Accuracy of Jamming Power Estimation  
In order to verify the rationality of \(P_{t,array}\), the jamming power results are compared between (23) and (17). The carrier frequency is 400 MHz, the number of nodes is 16, which are distributed in the _xoy_ plane, and the array radius is 5 m. The distribution of the jamming power is shown in Figure 8. The wave-superposition method could be regarded as the true value of the jamming power; the curves of the two algorithms are basically the same, and the estimated jamming power at every azimuth angle is almost equal to the true value.  
Figure 7: Equivalent transmitted power distribution in the horizontal direction.  
In order to further evaluate the accuracy of the estimation, the relative errors between the two algorithms are shown in Figure 9. The relative errors are all less than 1.4%, showing good accuracy of the proposed method. As a result, the jamming power evaluation could be simplified using the array-based algorithm.
# Compact Broadband Antenna with Vicsek Fractal Slots for WLAN and WiMAX Applications
## 2 Antenna Geometry Design
### Parametric Analysis of the Suggested Antenna
#### 2.3.2 The Ground Plane Length \(L_{g}\) Variation Effects  
Similar to \(L_{f}\) and \(W_{f}\), \(L_{g}\) is also varied, as depicted in Figure 6. By changing the length \(L_{g}\) from 13 to 16 mm, the bandwidth increases from 1.5 GHz to 4.35 GHz. Moreover, it is seen that the optimal value of the length of the ground plane is \(L_{g}=15\) mm, for which the suggested antenna achieves the desired broadband with good impedance matching.  
Figure 5: Parametric studies for the variation in the feed line length \(L_{f}\) and width \(W_{f}\).
## 2 Theoretical Analysis of Reflection Error Correction for Ambient Radiation of Turbine Blade
### Three-Dimensional Dynamic Reflection Model of Turbine Blades
\begin{array}{c}\mathbf{X_{1}}(k-1,l)-\mathbf{X_{1}}(k,l)\\ \mathbf{Y_{1}}(k-1,l)-\mathbf{Y_{1}}(k,l)\\ \mathbf{Z_{1}}(k-1,l)-\mathbf{Z_{1}}(k,l)\end{array} \right] \tag{8}\] \[k=1,2,3,\ldots,61\ \ l=1,2,3,\ldots,171\] \[\mathbf{b_{1k,l}}=\left[ \begin{array}{c}\mathbf{X_{1}}(k,l+1)-\mathbf{X_{1}}( k,l)\\ \mathbf{Y_{1}}(k,l+1)-\mathbf{Y_{1}}(k,l)\\ \mathbf{Z_{1}}(k,l+1)-\mathbf{Z_{1}}(k,l)\end{array} \right]\] (9) \[k=1,2,3,\ldots,61\ \ l=1,2,3,\ldots,171\]  
The unit normal vector \(\mathbf{n_{1k,l}}\) of the panel should be perpendicular to the vector \(\mathbf{a_{1k,l}}\), \(\mathbf{b_{1k,l}}\) and satisfy the right-hand rule, and the normal vector of the panel should point to the outside of the blade to calculate the angle factor. The unit normal vector \(\mathbf{n_{1k,l}}\) is expressed as  
\[\mathbf{n_{1k,l}}=\frac{\left(\mathbf{a_{1k,l}}\times\mathbf{b_{1k,l}}\right)}{\left|\mathbf{ a_{1k,l}}\times\mathbf{b_{1k,l}}\right|} \tag{10}\]  
The area of the panel is  
\[s_{1_{1,l}}=\left|\mathbf{a_{1k,l}}\times\mathbf{b_{1k,l}}\right| \tag{11}\]  
The cosine of the angle between the direction vector \(\mathbf{d_{10k,l}}\) from the panel to the target point and the normal vector \(\mathbf{n_{1k,l}}\) of the panel is expressed as  
\[\cos\theta_{10_{k,l}}=\frac{\mathbf{d_{10_{k,l}}}\cdot\mathbf{n_{1k,l}}}{\left|\mathbf{d_ {10_{k,l}}}\right|} \tag{12}\]  
The cosine of the angle between the direction vector \(\mathbf{d_{01k,l}}\) from the target point to the panel and the normal vector \(\mathbf{n_{0}}\) of the target point \(P_{0}\) is  
\[\cos\theta_{01_{k,l}}=\frac{\mathbf{d_{01_{k,l}}}\cdot\mathbf{n_{0}}}{\left|\mathbf{d_{01 _{k,l}}}\right|} \tag{13}\]  
where \(\mathbf{d_{10_{k,l}}}=\mathbf{p_{1_{k,l}}}-\mathbf{p_{0}}\), \(\mathbf{d_{01_{k,l}}}=\mathbf{p_{0}}-\mathbf{p_{1_{k,l}}}\); \(\mathbf{d_{10_{k,l}}}\) and \(\mathbf{d_{01_{k,l}}}\) are the direction vectors between the panel and the target point \(P_{0}\), and the two vectors are in opposite directions. The angle factor between a panel on the guide vane and the target point \(P_{0}\) can be expressed as  
\[F_{01_{k,l}}=\frac{cos\theta_{10_{k,l}}cos\theta_{01_{k,l}}}{\pi\left|\mathbf{d_ {01_{k,l}}}\right|^{2}}s_{1_{k,l}} \tag{14}\]  
Figure 5: Schematic diagram of face element vector of: (**a**) guide vane; (**b**) target blade. Then, the angle factor of the entire guide vane to the target point \(P_{0}\) is  
\[F_{01_{total}}=\sum_{k=1}^{k=61}\sum_{l=1}^{l=171}\frac{\cos\theta_{10_{k,l}}\ \cos \theta_{01_{k,l}}}{\pi\left|d_{\mathbf{0}_{k,l}}\right|^{2}}s_{1_{k,l}} \tag{15}\]  
Equation (15) was used to calculate the radiation angle factor between all the adjacent blades and a certain point on the target blade to obtain the radiation angle factor distribution of the whole target blade. Combined with the measured total radiation emittance of the adjacent blades, the reflected energy of the target blade surface can be calculated.