esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Principles of the Scheme
### Voltage Stability Margin Estimation Function  
A common static voltage stability index is expressed by the voltage stability margin which shows how far the system is away from a possible instability event. In order to rapidly assess static voltage stability of a power grid, the local measurement-based methods have been presented in the works [25; 26]. The key idea of these techniques is provided here.  
To start with, consider a load at bus \(i\) connected to a complex power system, which can be simplified to a single-machine-infinite-bus system by an estimated Thevenin equivalent network as shown in Figure 4, where \(E_{i}^{th}\), \(Z_{i}^{th}\), and \(Z_{i}^{L}\) correspond to Thevenin equivalent voltage, Thevenin equivalent impedance, and load impedance in phasor representation at bus \(i\), respectively. When the power transmitted is maximum at bus \(i\), \(\left|Z_{i}^{L}\right|\) is identical to \(\left|Z_{i}^{th}\right|\). Based on the impedance match theory, tracking \(\left|Z_{i}^{L}\right|\) and \(\left|Z_{i}^{th}\right|\) plays an important role in real-time voltage instability detection.  
Using the approach above, the impedance based VSM index of bus \(i\) is defined as  
\[VSM_{i}=\frac{\left|Z_{i}^{M}\right|}{\left|Z_{i}^{L}\right|}=1-\frac{\left|Z_ {i}^{th}\right|}{\left|Z_{i}^{L}\right|} \tag{9}\]  
where \(\left|Z_{i}^{L}\right|\) and \(\left|Z_{i}^{th}\right|\)can be obtained by using the local measured voltage and current phasors which are available from the installed PMU [25; 26]. The static voltage stability margin for the entire power system is defined to be  
\[VSM=\min\left\{VSM_{i}\left|\,i=1,\,2,\,\ldots,\,p\right\}\right. \tag{10}\]  
where \(p\) represents the set of numbers of pilot buses. Note that the value of VSM is between 0 and 1. At the voltage-collapse point, the VSM is equal to 0. Although the approach proposed in [25] was employed to estimate VSM, it is noteworthy that any other measurement-based method of VSM analysis can be applied for this purpose, for example, the one proposed in [26].  
The index VSM incorporated with the voltage magnitudes at the monitored buses will be used as the triggering signals for activating the proposed ASVC approach. Figure 5 illustrates the proposed control strategy, where \(\varepsilon_{1}\) and \(\varepsilon_{2}\) are the threshold levels for voltage change and security margin, respectively. In Figure 5, if the power system is operated at the critical state with poor voltage profile or insufficient VSM, then the ASVC function will be automatically initiated to steer the power system away from the point which is prone to voltage collapse.  
Figure 4: Thevenin equivalent network at bus \(i\) with PMU installed.
# Detection of Waste Containers Using Computer Vision
###### Abstract  
This work is a part of an ongoing study to substitute the identification of waste containers via radio-frequency identification. The purpose of this paper is to propose a method of identification based on computer vision that performs detection using images, video, or real-time video capture to identify different types of waste containers. Compared to the current method of identification, this approach is more agile and does not require as many resources. Two approaches are employed, one using feature detectors/descriptors and other using convolutional neural networks. The former used a vector of locally aggregated descriptors (VLAD); however, it failed to accomplish what was desired. The latter used you only look once (YOLO), a convolutional neural network, and reached an accuracy in the range of 90%, meaning that it correctly identified and classified 90% of the pictures used on the test set.  
waste container; object detection; VLAD; convolutional neural networks; YOLO 2019  
* Research on approaches to object detection and classification;
* The employment of different approaches to object detection, and their implementation in a controlled test scenario; and
* An analysis of the results and the possibility of working with the different approaches in the future.  
The remainder of this paper is organized as follows. Section 2 describes the state-of-the-art methods used in this study. These descriptions explore the general concepts and procedure of the methods. Section 3 details the data and the technology used to serve as a benchmark in the case of replication, how the methods were implemented, and the results obtained from each method. The metrics used to evaluate the results are also pointed out, and the section ends with an analysis of the obtained results. Section 4 draws conclusions on the work presented in this document and discusses future work.
# Battery Management System Hardware Concepts: An Overview
## 2 Battery Management System Requirements
### Galvanic Isolation  
Usually, battery packs are divided into a high voltage and a low voltage part. Those two need to be galvanically isolated, which means digital signals need to be decoupled by either optical, inductive or capacitive means. All thermal sensors need to be galvanically separated, in order to avoid high voltage faults to be allowed in the battery pack's low voltage sections. The reason is to block high energy surges to be forwarded into the low voltage system that is usually connected to the rest of the system e.g., for communication and defined as safe to touch by the user or maintenance personnel, which is comparable to the concept of power distributing \(\Pi\)-net layouts.
## 5 Characteristics of MMW Fuze Echo Signal under Rainfall Conditions
Meanwhile, under a rainfall rate of 50 mm/h and an operating frequency of 60 GHz, the bottom noise in the beat signal spectrum diagram for a distance of 12 m is close to the target signal amplitude, indicating that with increasing rainfall rate and frequency, the detection of slightly distant targets will be affected by the rain-induced bottom noise. Figure 5: Beat signal diagram at 9 m without rain: (**a**) time domain; (**b**) frequency domain. The beat signal spectrum at a distance of 9 m, with a rainfall rate of 50 mm/h and an operating frequency of 60 GHz, is simulated for 3 dB beam widths of 120\({}^{\circ}\), 60\({}^{\circ}\), and 30\({}^{\circ}\), as shown in Figure 8a-c. The amplitude of the target signal significantly increases as the 3 dB beam width decreases. This increase reflects a higher concentration of transmitted energy due to beam focusing, which enhances target detection performance. Additionally, backscattering noise gradually decreases with a reduction in the 3 dB beam width, thereby reducing interference from the rain signal. This effect is attributed to the corresponding  
Figure 6: Beat signal spectrum at rainfall of 16 mm/h and operating frequency of 35 GHz: (**a**) 6 m, (**b**) 9 m, and (**c**) 12 m. The beat signal spectrum at rainfall of 50 mm/h and operating frequency of 35 GHz: (**d**) 6 m, (**e**) 9 m, and (**f**) 12 m. The beat signal spectrum at rainfall of 50 mm/h and operating frequency of 60 GHz: (**g**) 6 m, (**h**) 9 m, and (**i**) 12 m.  
Figure 7: Amplitude of (**a**) the target signal; (**b**) rain backscatter noise. decrease in the number of raindrops within the field of view. Comparison of performance across the three different beam widths shows that the bottom noise remains relatively stable, with no significant fluctuations. This stability strongly indicates that within the beam width range of 30-120\({}^{\circ}\), the bottom noise of the echo signal under rainy conditions is not directly related to the 3 dB beam width.
systems. They can be used for the installation and maintenance 0f infrastructure such as pipes and cables [4], salvaging debris and sunken objects, mineral exploration [5], and biological samples gathering [6].  
Even with the technological advances in UUVs and UVMS, they still face several limitations that prevent deep sea exploration. For instance, AUVs are limited to long-distance maneuvering at middle depths, while the use of ROVs is challenging in surface waters with strong waves and disturbances caused by currents. ROVs and UVMS also require constant communication with the operator, which becomes harder during seabed tasks due to the limitations of tethered communication. Some proposed solutions involve remote satellite communication [7]. In addition, the use of rigid manipulators is undesired for gathering delicate marine samples such as coral reefs, as they are hard to handle and can be damaged by the manipulators.  
All these rigid robots have limited flexibility and adaptability to their environment. By contrast, soft robots offer more adaptability due to their compliant nature [8]. Such adaptability is exhibited in their ability to deform and change their shape according to the surrounding environment with which they are interacting. For example, soft robots made from compliant materials can achieve shrinking and bending motion that allow them to navigate within narrow areas. They also offer the possibility to grasp and manipulate various objects by adapting to their different shapes and structures. The evolution of soft robots led to advances in the pursuit of biomimicry of living creatures. The ability of soft robots to deform, change their shapes, exhibit infinite degrees of freedom, and perform complex motion, makes them a suitable candidate for the basis of biological emulation, especially that of underwater creatures, which are one of the sources of biomimetic inspiration for robotic and engineering systems. The exploitation of theses advantages offered by soft robots would help solve some of the challenges facing deep sea exploration. For instance, the use of soft manipulators allows safe handling of delicate coral reef samples [9; 10], whereas imitating fishes' and marine creatures' propulsion methods can offer more efficient solutions for exploration and maneuvering at depth or in perturbed surface waters [11; 12; 13; 14].  
Alongside the great potential of soft robots, many challenges need to be overcome in order to reach the full functionality of bioinspired soft intelligent systems. Key challenges vary from design and fabrication to modeling and control. These challenges and the current state of research in solving them are discussed in this review.
# Enforcing Optimal ACL Policies Using K-Partite Graph in Hybrid SDN
## 4 Proposed Solution
### Network Policy Representation  
Through a 3-tuple <_Source IP_, _Access Policy_, _Destination IP_>, we represent a network-wide policy in our system. In this tuple, _Source IP_ represents the policy for a packet originated from _Source IP_, Access Policy describes the specific actions (like drop (not allowed) or forward (allowed)) for the packet, and _Destination IP_ represents the policy for the packet with destination address _Destination IP_. _Source IP_ and _Destination IP_ can be a particular IP address (like 10.1.1.1, a range 10.0.1.1-10.0.1.3, or wild card 10.0.1.*). Access Policy can be Allowed or Not Allowed. For example, a 3-tuple <10.0.1.1-10.0.1.2, Allowed, 10.0.4.1-10.0.4.2> means that packets originated from IP address 10.0.1.1-10.0.1.2 are only allowed to access devices with IP 10.0.4.1-10.0.4.2 and from any other subnet 10.0.4.1-10.0.4.2 cannot be accessed.  
\(P1=\textbf{<10.0.1.1-10.0.1.2,Allowed,10.0.4.1-10.0.4.2>}\)  
\(P2=\textbf{<10.0.6.1-10.0.6.2,Allowed,10.0.9.1-10.0.9.2>}\)  
\(P3=\textbf{<10.0.12.1-10.0.12.2,Allowed,10.0.13.1-10.0.13.2>}\)  
\(P4=\textbf{<10.0.17.1-10.0.17.2,Allowed,10.0.18.1-10.0.18.2>}\)
# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 4 Results and Discussion
### Proposed Neural Network Architecture  
The proposed neural network architecture was designed to perform sleep stage classification in EEG recordings using a combination of CNNs, BiLSTM, and self-attention mechanisms. It consists of five main components.  
* 1D Convolutional Layers: Five layers with increasing filters (from 128 to 1024), each with ReLU activation, maximum pooling operations, and batch normalization. The choice of five layers adds depth to capture complex EEG features, while max pooling enables hierarchical extraction and dimensionality reduction, allowing the efficient processing of long sequences.
* Bidirectional LSTM Layers: Three BiLSTM layers with 512, 1024, and 2048 memory cells, respectively. The use of three layers helps to capture complex long-term temporal dependencies in both directions, supporting the modeling of the sequential nature of sleep stages.
* Self-Attention Layers: Placed after each BiLSTM, these dynamically weight the hidden states over time. This allows the model to focus on the most relevant temporal features, complementing BiLSTM's ability to capture dependencies and improving the discrimination of transient or subtle sleep stage patterns.
* Fully Connected Layers: Two dense layers with 2048 and 5 outputs, respectively, with ReLU activation. The first dense layer reduces the high-dimensional LSTM output to 2048 units for feature consolidation, while the second generates classification scores for the five sleep stages.
* Softmax Output Layer: A final softmax output layer that maps the outputs to probabilities for each of the five sleep stages: N1, N2, N3, R, and W.  
The number of layers and units was empirically selected based on the validation performance in multiple configurations. Although increasing the complexity of the model generally improved the performance, the upper limit was constrained by the available computational resources. To efficiently process long EEG sequences and handle large volumes of data, the model leverages hierarchical convolutional feature extraction and max pooling to reduce the dimensionality. This reduction, combined with dense layers, enables scalability to extended recordings despite computational limitations. In addition, attention mechanisms were incorporated to explore their potential impact on the capture of important temporal features in EEG signals and to selectively emphasize informative EEG segments relevant to each stage of sleep. The scheme of the proposed model architecture is shown in Figure 11, which illustrates the arrangement of the layers involved in the learning process. This architecture yielded the best classification performance among several alternatives and was used in subsequent experiments.
## 2 Materials and Methods
### EEM-PARAFAC Model  
The PARAFAC analysis was performed employing MATLAB2018b, with a free graphical user interface based on the drFEM toolbox, EEMlab [31]. The dataset consisted of 88 EEMs, 6 blanks, and 6 Raman scans. Due to the low absorbance of the samples, the inner filter effect correction was not considered (the samples' absorbance within the range 250-600 nm were always below 0.05). Intensity standardisation was carried out by using the water Raman scatter peak at 350 nm excitation wavelength [32]. The modelling methodology was analogous to that of previous works [26; 33]: (i) scattering was corrected (first-order Rayleigh scattering was eliminated with missing values, whereas second-order Rayleigh and Raman first- and second-order scattering were corrected by interpolation, respectively) and outliers were detected and eliminated (mostly EEMs from I site due to their negligible fluorescence); (ii) EEMs were normalised to their total signal to give the same leverage to each fluorophore; (iii) an exploratory modelling was carried out, observing that the model of three components was the one that, tentatively, could better describe the dataset; (iv) the model obtained was refined and afterwards validated based on the core consistency diagnostic (CORCONDIA) and split-half analysis [23; 34]; (v) normalisation was reversed to obtain the scores corresponding to each component from the respective EEM on the dataset.
## 2 Current Studies and Technologies for V2X Services
### V2X Communication Modes  
A vehicle can interact with its environment through various types of communication as specified in [15]:  
1. Vehicle-to-Vehicle (V2V): A type of communication, in which User Equipements (UEs) (such as vehicles) communicate using V2V services.
2. Vehicle-to-Pedestrian (V2P): A type of communication, in which both UEs (vehicle, pedestrian) communicate using V2P services.
3. Vehicle-to-Infrastructure (V2I): A type of communication, in which one part is a vehicle- capable user equipement (VUE) and an RSU entity, both communicating using V2I services.
4. Vehicle-to-Network (V2N): A type of communication, in which one part is vehicle-capable user equipment (VUE) and the other part is a V2X application server on the cloud for instance, both communicating using V2N services. As shown in Figure 1, V2N relates to any communication between vehicles and computing infrastructures such as RSU deployed either with eNodeB or like a standalone stationary UE [15].  
Figure 1: 3 GPP Release 14 [16] for V2X services using direct communication over side link PC5 and LTE-Uu.
# A Lateral-Directional Control Method for High Aspect Ratio Full-Wing UAV and Flight Tests
###### Abstract  
To solve the lateral-directional control problem of the high aspect ratio full-wing unmanned aerial vehicle (UAV) without an aileron and rudder, a control method is proposed that uses the differential thrust of propellers as the control output and the yaw angle as the controlled attitude angle. Meanwhile, simulation analysis and experimental verification are carried out. First, a lateral-direction mathematical model and a differential thrust of propeller model of the full-wing drone are established. The influence of the aerodynamic derivative \(\mathrm{C}_{Y\beta}\) on the lateral-direction mode is analyzed. Second, based on nonlinear dynamic inversion (NDI) and active disturbance rejection control (ADRC) theories, a yaw angle controller that uses the differential thrust of propellers as the control output is designed. Finally, the vector field (VF) method is improved to obtain the straight-line trajectory tracking method satisfying different speeds, and the logic of waypoint switching is given. The research shows that \(\mathrm{C}_{Y\beta}\) has a great influence on the dutch roll damping of the drone. For the full-wing configuration, it is feasible to use the yaw angle as the controlled attitude angle without considering the roll angle. The simulation and experimental results show that the designed lateral-directional control method for the high aspect ratio full-wing UAV has a good control effect and disturbance rejection ability. Meanwhile, the control method has less parameters to adjust and less calculation, which is very suitable for engineering applications.  
high aspect ratio; full-wing; lateral-directional control; without anileron; differential thrust of propellers; nonlinear dynamic inversion; active disturbance rejection control; vector field path following; flight test +
Footnote †: journal: Applied Sciences
## 4 Conclusions, Limitations and Future Research
### Limitations and Future Research  
Like all investigation, this is also has some limitations. Firstly, our sample focuses on a single demographic area, that is, Dehradun, which can be used as a basis for further research in other demographic setups. Secondly, this investigation was stated to know the effects of WhatsApp on academic performance of management students, which is a limitation. Thirdly, many other variables such as socioeconomic status, residential area, daily hours devoted to study, accommodation, academic background, family background, and age, among others, which influence the academic performance of a students, can be explored. This is another limitation of this research and can be addressed in further works.
# Vision-Based Path Guidance to Achieve Dies-Free Roller Hemming Process
## 3 Verification and Results
### Hemming Experiment and Results  
In order to understand how much positional deviation of the sheet metal work piece exists without a specific die, experiments using three car hood sheet metal parts of the same model were conducted. In each test, as illustrated in Figure 16, the sheet metal part was uploaded, aligned, and fixed on the universal platform, followed by manipulation of the robot arm to allow for the sensing module to scan three positions on the two edges of the sheet metal. Positions and orientations of the six points, as shown in Figure 16, were measured and are listed in the \"Robot Pose\" columns in Table 1. Here, X, Y, and Z are the absolute positions of the robot's sensor frame with respect to the world frame and W, P, and R are the sensor frame's orientation values. These measured values were used to estimate the hemming positions of the car hood sheet metal parts, that is Part A, B, and C as indicated in Table 1. Through this estimation, the desired sheet metal hemming positions were obtained, thereby the deformation and displacement of the sheet metal could be estimated. Through this experimental investigation, the position errors denoted as dX, dY, and dZ from Parts A, B, and C, respectively, were derived, and each of them corresponded to the maximum offset among Parts A, B, and C of the car hood along its Cartesian axis. The displacement offset error, the distance error \(\epsilon\) in Table 1, is defined as the square root of the sum of squared dX, dY, and dZ. It is observed from the experiment that the offset error was found to be up to 3.695 mm, and the distance error \(\epsilon\) was up to 4.461 mm, both of which far exceed the general sheet metal tolerance requirement of within 0.5 mm when fixed or when a specific die was used to support the sheet metal parts. On the other hand, should such offsets and distance errors be well controlled, they can be treated as calibration information for error compensation. The scanned data and the derived accuracy errors, namely the displacement offset and the distance error, for each of the car hood sheet metal parts are tabulated in Table 1.  
Figure 15: Steps for the verification and values of the accuracy of the visual sensor. Figure 16: Workflow (**a**) and photographs illustrating the manipulation of the robot arm for the sensing module to capture positions and orientations at the six points of the car hood sheet metal (**b**).
# Agreement Between Tele- and Face-to-Face Assessment of Neuromotor Development in High-Risk Children
###### Abstract  
Background: Early interventions in high-risk children seek to improve prognosis, minimize developmental delays, and prevent functional deterioration. The objective of this study was to evaluate the level of agreement between the face-to-face assessment and tele-assessment of neuromotor development in high-risk children between 0 and 18 months of age. Methods: Forty-five children at high risk of developmental delays were included in this study (33% female, mean gestational age of 35.31 \(\pm\) 4.03 weeks). The patients were included in a face-to-face and a tele-assessment using the Alberta Infant Motor Scale (AIMS) and the level of motor evolution (_Niveaux d'Evolution Motrice_, NEM) assessments. Results: The analysis showed excellent interrater reliability (\(p\geq\) 0.99) for the AIMS. The NEM assessment showed almost perfect reliability (kappa \(\geq\) 0.81) for most items. Seven of them showed substantial reliability (kappa = 0.61-0.80), one moderate reliability (kappa = 0.568), and one fair reliability (kappa = 0.338). Conclusions: This study reveals an excellent/substantial interrater reliability for most of the items assessed. The results are promising to increase the accessibility to a clinical diagnosis and a rehabilitation approach to minimize the development of neuromotor delays in children at high risk.  
assessment; child; child development; physical therapy; telerehabilitation Article