esa-sceva/satcom-chunk-collection huggingface dataset:
# A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
## 2 Proposed EMD-BiGRU Architecture
### The Proposed Method  
The structural vibration signal has a fluctuating character due to many factors such as stiffness and damping temperature, etc. As a result, there is a degree of nonlinearity and non-smoothness in structural acceleration data. The predicted accuracy needs to be improved for traditional machine learning methods. Considering that the empirical mode decomposition technique can obtain more mode characteristics and BiGRU is powerful in modeling the time-series data, an EMD-BiGRU algorithm that predicts the missing data are proposed. Its specific modeling process is shown in Figure 4, and the flow of the method is as follows:  
Step 1: Using EMD method to decompose the raw structural response data \(X(t)\) into several IMFs and residual RES;  
Step 2: a feature including all IMFs and residual RES is inputted into the EMD-BiGRU model;  
Step 3: The set of features at the previous step is combined with the original structural response data \(X(t)\) to form a combined data set \(Con(t)\). Then, \(Con(t)\) is divided into a training set and a test set, where the training set is input to the BiGRU model for prediction model training, and then the test set is input to the prediction model to evaluate the predicted results.  
The inputs of the EMD-BiGRU model can be represented as follows:  
\[D=\{imf_{1},imf_{2},\ldots,imf_{n-1},res\} \tag{9}\]  
\[Con=\{X_{e},D\} \tag{10}\]  
where \(D\) denotes the decomposed feature data and \(imf_{i}\) is the \(i\)-th IMF components. \(res\) and \(n\) are residuals and a total number of decomposed IMF components, respectively. \(X_{e}\) denotes the historical structural response data and. \(Con\) represents the dataset combining the historical data with the feature data. The detailed process of the proposed method is briefly explained in Algorithm 1.  
Figure 4: Flow chart of EMD-BiGRU prediction.
# Modeling the Accuracy of Estimating a Neighbor's Evolving Position in VANET
## 1 Introduction
We observe that numerical results of the proposed model are located within the 95% confidential intervals of simulation results and confirm that our model is accurate (The numerical results of our model are calculated from Equation (5), which adopts the \(p_{s}\) derived in Section 2.3). It is noted that we can use statistics on the estimation error obtained from the proposed model in designing an algorithm for optimal parameter configurations. The contributions of this paper are as follows. * We establish the first analytic model for expressing an estimation error of a neighbor's evolving position to evaluate the accuracy of the estimation. * We validate the proposed model by comparing the numerical results of the model with the NS-2 simulation results. The remainder of this paper is organized as follows. In Section 2, we explain the mathematical model of estimation error of neighbor's evolving position. In Section 3, we validate the proposed model by comparing the numerical results of our model with those of the NS-2 simulation. This paper concludes with Section 4.
## 3 Results and Discussion  
### Assessing the Model's Adequacy  
Figure 3 presents the temperature distribution and degree of protein denaturation in goose breast meat samples at intervals of 0, 250, 500, 750, 1000, 1250, 1500, 1750, and 2000 s during the roasting process, and Figure 4 presents the mechanism of action of the heat transfer. Table 2 showcases the regression coefficients of the predicted quadratic polynomial models for the physical attributes of myosin, collagen, actin denaturation, and cooking loss. The adequacy of the prediction models has been thoroughly evaluated and confirmed, with all R\({}^{2}\) values ranging from 0.758 to 0.997. The adequacy of these models was evaluated based on their R\({}^{2}\) values and the P-values of lack of fit. The R\({}^{2}\) values for the myosin, collagen, and actin denaturation were impressively high, at 0.922, 0.982, and 0.997, respectively, indicating strong model performance and accurate predictions for these proteins. In contrast, the R\({}^{2}\) value for cooking loss was lower, at 0.758, suggesting a relatively weaker but still acceptable model fit. No cases of insufficient model fitting were detected, as evidenced by the \(p\)-values of lack of fit for myosin, collagen, actin denaturation, and cooking loss, which were 0.237, 0.228, 0.158, and 0.139, respectively.  
Standard deviation values ranged from 1.14 to 2.32, with the lowest standard deviation values observed for myosin denaturation level and actin denaturation level at 1.14 and 1.78, respectively. Collagen denaturation and cooking loss were described as standard deviations of 2.32 and 2.13, respectively. In optimizing the heat treatment process parameters for goose breast meat, 3 independent variables were taken into account: temperature, humidity, and fan rotation speed, representing the airflow speed within the oven chamber. The interactions between these variables and the responses were displayed in linear, intercept, and quadratic terms.  
Figure 4: The mechanism of action of the heat transfer.  
Figure 3: Temperature distribution and protein denaturation degree in the goose breast meat sample after 0, 250, 500, 750, 1000, 1250, 1500, 1750, and 2000 s of the heat treatment process.
## Materials and Methods
### Solid Phase Microextraction (SPME)  
Clean vials, lids, and septa were obtained for each sample. An 8.00 g \(\pm\) 0.05 g aliquot of reconstituted sheep milk, spiked with internal standard solution, was transferred by pipette to each sample vial. Samples were placed in a 32-vial tray (VT32-20) on a Gerstel MPS autosampler (Gerstel GmbH) and analyzed by GC-MS. An SPME fibre with a balanced polarity (50/30 \(\upmu\)m divinylbenzene/carboxoxen/polydimethylsiloxane (DVB/CAR/PDMS)) was chosen due to its ability to capture a wide range of volatile compounds from different chemical classes [(10; 40)]. SPME extractions were performed for 60 min at 35 \({}^{\circ}\)C and then desorbed in an Agilent split/splitless inlet at 240 \({}^{\circ}\)C for 2 min in splitless mode followed by a further 3 min with a purge flow of 50 mL/min. Based on preliminary experiments (data not shown), samples were not held on the autosampler longer than 7 h due to potential for microbial growth. Six sample replications were performed.
## 3 Results
### Comparison of Different Piezoelectric Transducers on the Platform  
An analysis was conducted of the transducers described in Section 3 on the same prototype platform. The piezoelectric transducers were placed in the grooves to be pressed by the rods. Since each side of the platform measured 30 cm, the number of transducers that could fit on each of the two rails varied depending on the size of the piezoelectric. In the case of the largest piezoelectric, 8.5 cm long, a total of six piezoelectric were placed (three on each side). Table 3 shows the voltage, current, and power outputs for the footsteps of a 70 kg person for the piezoelectric transducers.  
Figure 4 shows the power output for each transducer arrangement. The largest piezoelectric, M-8557-P1, generates the least power, mainly because the pressure is not transmitted uniformly throughout the entire piezoelectric because of its size. Another disadvantage is the smaller number of transducers that fit in the grooves in the platform. However, the MFC 0714 P2 piezoelectric, with a size that is close to the perimeter of the pressure rod, generates a higher voltage. In this case, the force applied by the rod to deform the entire piezoelectric is better distributed as compared with larger transducers, generating a higher voltage. In addition, its smaller size leads to more transducers distributed on the platform.  
Figure 3: Full bridge circuit (based on [29]).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **M-8557-P1** & **M-5628-P1** & **M-2814-P2** & **M-0714-P2** & **PZT Disk** \\ \hline Number of transducers & 6 & 10 & 18 & 32 & 20 \\ Output voltage (V) & 0.25 & 0.75 & 0.8 & 1.8 & 4.2 \\ Output current (mA) & 4.92 & 6.7 & 7.38 & 10.24 & 8.47 \\ Output power (mW) & 1.23 & 5.025 & 5.904 & 18.432 & 35.574 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Electric outputs of the platform for different piezoelectric transducers configurations for the footsteps of a 70 kg person.  
The piezoelectric PZT disk generates a greater amount of energy with even a smaller number of transducers than any of the MFCs. The configuration of the platform based on piezoelectric PZT disks generated 93% more power than MFC 0714-P2.
# Multi-Beam Focusing and Deflecting Characteristics of Liquid Crystal Optical Phased Array
## 2 Principles and Methods
### Creation of Composite Phase Distribution  
The multi-beam fan-out function can be obtained by using the GS algorithm, which has been studied in some papers [18; 19; 20]. However, in order to realize the function of multi-beam fan-out together with focusing, we need to additionally process the phase algorithm.  
Assume that the phase distribution of multi-beam fan-out obtained by the GS algorithm is \(\varphi_{F}(i)\), and the phase of lens focusing \(\varphi_{L}(i)\) can be expressed by Equation (1). Thus, the designed composite phase distribution \(\varphi_{FL}(i)\) of the multi-beam fan-out with focusing can be calculated by Equation (2) as follows.  
\[\varphi_{L}(i)=\frac{\phi}{\lambda f}\bigg{\{}d\bigg{(}i-\frac{N}{2}\bigg{)} \bigg{\}}^{2} \tag{1}\]  
\[\varphi_{FL}(i)=[\varphi_{F}(i)+\varphi_{L}(i)]\text{mod}(2\pi) \tag{2}\]  
where \(N\) is the number of electrodes of the LCOPA device, \(i\) is the serial number of electrodes, \(\lambda\) is the wavelength of the incident light, \(d\) is the distance between the centers of two adjacent electrodes, and \(f\) is the focal length of the lens. For the LCOPA device, the focal length \(f\) of the lens can be conveniently adjusted by programmable drive circuits.
## 3 Methods  
The general flowchart depicted in Figure 1 outlines the comprehensive steps taken to track drosophilas in a 3D environment using our proposed method. Below, we provide a step-by-step breakdown of the process.  
Data Acquisition: Utilizing two orthogonally positioned high-speed cameras, videos are captured in a controlled laboratory setting. This dual-camera setup ensures comprehensive spatial coverage, capturing all necessary angles for accurate 3D tracking.  
Image Processing: The raw footage obtained from both cameras undergoes background subtraction using the MOG2 algorithm. This step isolates the drosophilas from the static background, focusing only on moving entities within the recorded video.  
Detection: Post background subtraction, the detected foreground objects, which primarily include drosophilas, are processed through an improved Mask-RCNN model. This model, which is well-suited for handling partial occlusions, accurately identifies and segments each drosophila by producing segmentation masks. This process not only localizes the drosophilas but also delineates their shapes, which is crucial for the subsequent tracking phase.  
Figure 1: The general flowchart of our method. Each color in the resultant trajectories refer to a trajectory of a drosophila.  
Tracking via a Particle Filter: Once the drosophilas are detected, the tracking phase begins using an advanced particle-filtering technique. This particle filter is enhanced with a self-attention mechanism that dynamically updates the weights of the particles. This update is based on the kinematic features of the drosophilas, such as speed and direction, derived from the segmentation masks provided by the Mask-RCNN model.  
Trajectory Construction: The final output of the particle-filtering step is the construction of precise 3D trajectories for each tracked drosophila. These trajectories map out the complete movement paths of the drosophilas within the cubic container, illustrating their complex and dynamic flight patterns.  
Each of these steps is essential for the effective tracking of drosophilas, providing valuable insights into their behaviors and interactions in a controlled environment.
# Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) Systems
## 2 Reinforcement Learning
### Value Iteration vs Policy Iteration  
An important measure of value called the \"Q\" function \(Q_{\pi}(s,\mathbf{a})\) also depends on the next action \(\mathbf{a}=\pi(s)\):  
\[Q_{\pi}(s,\mathbf{a})=E_{\pi}\Bigg{[}\sum_{k=0}^{\infty}R_{t+k}|S_{t}=s,A_{t}= \mathbf{a}\Bigg{]}. \tag{7}\]  
Let \(Q^{*}(s,\mathbf{a})\) denote optimal \(Q\)-value at state \(s\) from following the optimal action \(\mathbf{a}=\pi^{*}(s)\):  
\[Q^{*}(s,\mathbf{a}) :=\max_{\pi}Q_{\pi}(s,\mathbf{a}), \tag{8}\] \[\pi^{*}(s) :=\arg\max_{\pi}Q_{\pi}(s,\mathbf{a}). \tag{9}\]  
The \(Q\)-function also satisfies a fixed-point equation:  
\[Q^{*}(s,\mathbf{a})=\max_{\mathbf{a}}E_{\pi}[R_{t}+\gamma Q^{*}(S_{t+1},A_{t+1 })|S_{t}=s,A_{t}=\mathbf{a}]. \tag{10}\]  
Both notions of value \(Q_{\pi}(s,\mathbf{a})\) and \(V_{\pi}(s)\) support two fundamentally different approaches in RL: value iteration and policy iteration. The actor-critic approach, associated with policy iteration maintains two parallel estimates: the \"actor\" is one in a family of policies \(\{\pi_{\theta}:\theta\in\Theta\}\) parameterized by \(\theta\) whereas the critic is an estimate of the value of the current policy \(\{\int(\theta):=E_{\pi_{\theta}}[V_{\pi_{\theta}}(s)]\), as depicted in Figure 2. Each sequence of updates cycles between the actor and critic, waiting for \(J(\theta)\) to stabilize before altering the current policy \(\pi_{\theta}\) to one better [13; 14].  
Q-learning by contrast, associated with value iteration, only maintains an estimate of the \(Q\)-function \(Q_{\pi}(s,\mathbf{a})\)[15]. The policy \(\pi_{g}\) that explores the state-space is the \"greedy\" policy:  
\[\pi_{g}(s):=\arg\max_{\mathbf{a}}Q_{\pi}(s,\mathbf{a}). \tag{11}\]  
The unusual feature of (11) is that the greedy policy \(\pi_{g}\) is different from the \(\pi\) of \(Q_{\pi}(s,\mathbf{a})\) (which generates \(\pi_{g}\)); \(Q\)-learning does not wait for the estimate \(Q_{\pi}(s,\mathbf{a})\) to stabilize before altering the policy to \(\pi_{g}\). Nevertheless, there is mathematical proof that \(\pi_{g}\) converges to \(\pi^{*}\) if it is sufficiently \"exploratory\" (i.e., samples a large enough subset of the state-space infinitely often); the \"max\" operation in (11) is an infinity-norm with a telescoping property in the policy space [16]. The literature often asserts that \(Q\)-learning has shorter convergence times due to this aggressive update strategy.
# The Influence of Capacitance and Inductance Changes on Frequency Response of Transformer Windings
## 1 Introduction
At present, the FRA method is one of test methods commonly used in the group of advanced diagnostics of transformers, therefore any help in understanding the relationship between the results and the tested object leads to improvement of interpretation quality. Research on interpretation of FRA results has been conducted for years, and there are different experimental [11; 12] or modeling techniques [13; 14; 15] used to help find correlations between visible changes in the frequency response curve and actual mechanical or electrical problems in transformers related to capacitance or inductance changes. This paper takes under consideration the influence these two parameters have on transformer frequency response (FR). The analysis is performed with a computer model of a transformer with introduced modifications resulting in capacitance and inductance changes. Results obtained from the model, in which changes of parameters are fully controlled, allow for good understanding of the relationship between curve shape changes and object properties. These results are free from additional interferences and effects, as is usually the case in industrial measurements, therefore providing direct relationships between introduced changes and obtained conclusions. In the next step there are some experimental results presented, showing the influence of capacitance and inductance changes on the FR of real transformers. It was achieved by measuring the frequency response for different on-load tap changer positions (inductance change) and by adding an additional capacitance to the bushing in the phase that was not under measurement (capacitance change). The main aim of the paper is to provide analysis on correlations between changes in capacitive and inductive parameters and their effect on the FR curve shape. With this knowledge it will be easier to identify measurement mistakes (like wrong tap position).
# Aerial Robotic Solution for Detailed Inspection of Viaducts
###### Abstract  
The inspection of public infrastructure, such as viaducts and bridges, is crucial for their proper maintenance given the heavy use of many of them. Current inspection techniques are very costly and manual, requiring highly qualified personnel and involving many risks. This article presents a novel solution for the detailed inspection of viaducts using aerial robotic platforms. The system provides a highly automated visual inspection platform that does not rely on GPS and could even fly underneath the infrastructure. Unlike commercially available solutions, our system automatically references the inspection to a global coordinate system usable throughout the lifespan of the infrastructure. In addition, the system includes another aerial platform with a robotic arm to make contact inspections of detected defects, thus providing information that cannot be obtained only with images. Both aerial robotic platforms feature flexibility in the choice of camera or contact measurement sensors as the situation requires. The system was validated by performing inspection flights on real viaducts.  
Keywords:inspection; maintenance; UAV; aerial robotics; aerial robotic manipulation; viaduct; LIDAR; photogrammetry; contact +
Footnote †: journal: Applied Sciences
## 4 Experiment Settings
### Evaluation of Classification Methods  
Evaluating the proposed framework requires considering (1) _classification performance_, which assesses how effectively the model distinguishes between positive and negative instances, (2) _calibration quality_, which measures how closely its predicted probabilities match actual outcome frequencies, and (3) _UQ_, which ensures that CP sets meet desired coverage and efficiency levels. This section details the metrics used for these three perspectives.  
Let \(\mathcal{D}_{\text{test}}=\{(\mathbf{x}_{i},y_{i})\}_{i=1}^{N}\) be the held-out test set, where \(y_{i}\in\{0,1\}\). For each instance \(i\), the model \(M\) produces a probability \(\hat{p}_{i}=M(\mathbf{x}_{i})\). A hard classification label \(\hat{y}_{i}\) follows from applying a threshold \(\tau\):  
\[\hat{y}_{i}\ =\ \begin{cases}1,&\text{if }\hat{p}_{i}\geq\tau,\\ 0,&\text{otherwise.}\end{cases} \tag{25}\]  
Define the confusion matrix counts:  
\[\begin{split}\text{TP}&=\sum_{i=1}^{N}\mathbb{I}(\hat{y} _{i}=1\wedge y_{i}=1),\quad\text{TN}=\sum_{i=1}^{N}\mathbb{I}(\hat{y}_{i}=0 \wedge y_{i}=0),\\ \text{FP}&=\sum_{i=1}^{N}\mathbb{I}(\hat{y}_{i}=1\wedge y_{i}=0), \quad\text{FN}=\sum_{i=1}^{N}\mathbb{I}(\hat{y}_{i}=0\wedge y_{i}=1).\end{split} \tag{26}\]  
Based on these counts, we employ:  
Area Under the ROC Curve (AUROC).The Receiver Operating Characteristic (ROC) curve plots  
\[\text{TPR}(\tau)\ =\ \frac{\text{TP}(\tau)}{\text{TP}(\tau)+\text{FN}(\tau)}, \quad\text{FPR}(\tau)\ =\ \frac{\text{FP}(\tau)}{\text{FP}(\tau)+\text{TN}(\tau)} \tag{27}\]  
for all thresholds \(\tau\in[0,1]\). The AUROC is the integral of TPR with respect to FPR. Values near 1.0 indicate strong discrimination, whereas 0.5 implies random guessing.  
Area Under the Precision-Recall Curve (AUPRC).When the positive class is rare, the Precision-Recall (PR) curve is often more informative. It plots  
\[\text{Prec}(\tau)\ =\ \frac{\text{TP}(\tau)}{\text{TP}(\tau)+\text{FP}(\tau)}, \quad\text{Rec}(\tau)\ =\ \frac{\text{TP}(\tau)}{\text{TP}(\tau)+\text{FN}(\tau)} \tag{28}\]across thresholds. AUPRC integrates these values, with higher scores indicating better identification of the minority class in imbalanced scenarios.  
_Matthews Correlation Coefficient (MCC)._ The MCC accounts for all four confusion matrix elements in a single coefficient:  
\[\text{MCC}=\frac{\text{TP}\times\text{TN}-\text{FP}\times\text{FN}}{\sqrt{( \text{TP}+\text{FP})(\text{TP}+\text{FN})(\text{TN}+\text{FP})(\text{TN}+\text {FN})}}. \tag{29}\]  
It ranges from \(-1\) to \(+1\). Values near \(+1\) correspond to perfect classification, \(0\) indicates random performance, and negative values imply an inverse relationship between predictions and true labels.
## 4 Results and Discussion
### Capillary Porosity
#### 4.2.2 Effect of Alkali Equivalent on Capillary Porosity  
Figure 9 shows the capillary porosity of RPSG with different alkali equivalents. As can be seen from Figure 9, the capillary porosity of RPSG does not have a simple linear relationship with the alkali equivalent. The capillary porosity of specimen RP-S30-M1.3N6 was reduced by 48.90% compared to that of specimen RP-S30-M1.3N3, while the capillary porosity of specimens RP-S30-M1.3N9 and RP-S30-M1.3N12 remained essentially unchanged compared to that of specimen RP-S30-M1.3N6. Meanwhile, the capillary porosity of RP-S30-M1.3N15 increased by 50.5% compared with that of RP-S30-M1.3N12. The silica and aluminum components in RP need to be dissolved and polymerized in an alkaline environment to form NASH hydration gel. The active CaO in slag also needs an alkaline environment to dissolve and polymerize to form CSH and CASH gels. Properly increasing the alkali equivalent and improving the alkaline environment of the geopolymer reaction can promote the formation of more NASH, CSH and CASH gels, thereby improving the compactness of the specimen. When the alkali equivalent exceeds a certain threshold, the strong alkali environment will not only inhibit the dissolution of the silicon-alumina components in the RP, but also guide the active CaO in slag to form precipitation and adhere to the particle surface, hindering the progress of the RPSG hydration reaction [65]. Therefore, continuously increasing the alkali equivalent cannot continuously improve the compactness of the specimen and may even damage the compactness of the specimen.  
Figure 8: Capillary porosity of RPSG with different slag contents.