esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Preparation and Recovery of Magnetic Precipitates  
An experimental design was developed to investigate the potential reuse of magnetic precipitates generated from the ferrite treatment of PVA wastewater. To enhance experimental reproducibility and controllability, NaOH was employed as the pH regulator instead of commercial cleaning solution. A 2 \(\times\) 2 factorial design was implemented to study the effects of reaction system scale and pH on magnetic precipitate formation. Specifically, high-concentration PVA solution (37.8 g/L) was used to establish two reaction system scales: 300 mL and 350 mL. In the smaller system (300 mL), 55.5 mL PVA solution was precisely measured and mixed with 244.493 mL NaOH solution for initial pH adjustment. For the 350 mL system, 64.8 mL PVA solution and 285 mL NaOH solution were used accordingly. The pH values were adjusted to different target levels through H\({}_{2}\)O\({}_{2}\) pre-oxidation: Series A experiments were adjusted to 11.1, while Series B experiments ranged from 10.9 to 12.3. Under continuous stirring, Fe solution was added sequentially: for the 300 mL system, initial addition of 14.4 mL followed by 8.2 mL; for the 350 mL system, 16.8 mL followed by 9.5 mL. This sequential addition strategy was designed to optimize magnetic precipitate formation. The final PVA concentration was maintained at 7 g/L across all treatment groups to ensure comparability. This precise experimental design facilitated understanding of how reaction system scale and pH influence magnetic precipitate formation while providing essential experimental foundation for developing precipitate recovery and reuse strategies. Through rigorous control of reaction parameters, particularly staged pH adjustment and sequential Fe ion addition, the process aimed to produce stable precipitates with desirable magnetic properties.
# Multi-Objective BiLevel Optimization by Bayesian Optimization
## 4 Experiments
### Noise-Free Experiments
#### 4.1.2 Performance Measures  
The multi-objective algorithms are assessed in terms of convergence to the Pareto-front and with respect to the diversity of the obtained solutions. In order to examine the proposed algorithm's performance, we compare it with the OMOPSO-BL and H-BLEMO algorithms in terms of the hypervolume (HV) indicator and the inverted generational distance (IGD). We present our results in terms of function evaluations and compare them with DBMA and H-BLEMO. We use the same number of final sample sizes of the Pareto-optimal solution set to compare HV and IGD values with the other algorithms. HV measures the volume of the space between the non-dominated front obtained and a reference point. It is a common metric for comparing the performance of the obtained solutions with the true Pareto-front published for the problems. IGD calculates the sum of the distances from each point of the Pareto-front to the nearest point of the non-dominated set found by the algorithm. More details can be found for the metric in [42]. The IGD values of the DBMA algorithm are not reported [21] but we compare the results with the HV metric. Both HV and IGD measure the convergence and the spread of the obtained set of solutions.
# Interpretation of Frequency Response Analysis for Fault Detection in Power Transformers
## 3 Fault Extent Methodology  
This paper proposes three steps, or phases, for interpreting the FRA measurement, as shown in Figure 7. In phase 1, the response is divided into three frequency regions: the low-frequency region (LF < 2 kHz), the mid-frequency region (2 kHz < MF < 20 kHz) and the high-frequency region (HF > 20 kHz). Phase 2 determines the level of variation between the measured response and the reference signature by calculating the correlation coefficient (CC) that results in three categories. In [35; 36; 37], three CC limits are suggested to identify the transformer condition. The transformer is considered in good condition for a CC more than 0.98. The transformer health state is considered marginal for a CC in the range (0.96-0.97), and the transformer calls for further investigation for a CC less than or equal to 0.96. These limits are categorized based on the variation between the measured  
Figure 5: The resistance, inductance, and capacitance (RLC) ladder network for single disc wind— [30].  
Figure 6: (**a**) Resistance, inductance, and capacitance (RLC) circuit simulated response, (**b**) effect of resistance, (**c**) effect of inductance, and (**d**) effect of capacitance.  
and reference responses, which can be categorized into no variation, small variation, and large variation. The limits or range for the CC variation are given in Table 2.  
No variation means that the compared FRA responses are identical with a calculated CC of more than 0.98, as shown in Figure 8a. The small variation refers to cases when the CC ranges between 0.96 and 0.97. For large variation, the difference between the measured and baseline responses results in a CC less than 0.95, as shown in Figure 8b. In phase 3, the fault is estimated based on the CC levels for each frequency region.  
Equation (3) is used to calculate CC. The value approaches 0 or 1 when the two responses are uncorrelated or identical, respectively. In Equation (3), \(n\) is the number of frequency points, and \(x(i)\) and \(y(i)\) are the two compared responses.  
\[CC_{(x,y)}=\frac{\sum_{i=1}^{n}x(i)\times y(i)}{\sqrt{\sum_{i=1}^{n}\left[x(i) \right]^{2}\times\sum_{i=1}^{N}\left[y(i)\right]^{2}}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c} \hline
**CC Value** & **Variation Level** \\ \hline
0.98–1.00 & No Variation \\
0.96–0.97 & Small Variation \\ \(\leq\)0.95 & Large Variation \\ \hline \end{tabular}
\end{table}
Table 2: CC benchmark limits and level of variation.  
Figure 7: The proposed methodology for fault extent using the correlation coefficient (CC).
## 5 Discussion
This reduction in complexity not only optimizes resource utilization, but also facilitates faster convergence, as the model processes fewer parameters while maintaining its capacity to capture essential patterns. Importantly, this approach ensures that the integrity and robustness of the analysis are not compromised, achieving a balance between computational efficiency and predictive performance. The proposed approach, while effective in many contexts, is not without limitations that merit acknowledgment. Firstly, the method may struggle to adequately capture periodic or harmonic features within the data, potentially resulting in the loss of critical information in scenarios where such patterns are significant. Secondly, its capacity to model long-term dependencies or time-varying patterns in signals may be limited, restricting its applicability in cases requiring a thorough understanding of complex temporal dynamics. These limitations underscore the need for future research aimed at refining the methodology, particularly through the incorporation of advanced techniques designed to enhance the modeling of periodicity and long-term dependencies. The works presented under the related works heading generally utilize methods that involve complex or computationally intensive feature extraction techniques. This work aims to address the problem in a simpler and more efficient way. Our aim is to present a method that achieves high performance in the prediction process by determining an efficient feature selection process. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **SoH Classification** & \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Performance Metrics (\%)**} \\  & **Technique** & & & & \\ \hline \multirow{3}{*}{[53]} & \multirow{3}{*}{Two-class} & \multirow{3}{*}{NA} & Optimized SVM without & Accuracy: 90.48\% \\  & & & feature extraction & Recall: 77.47\% \\  & & & and selection & Precision: 100.00\% \\ \hline \multirow{4}{*}{Proposed method} & \multirow{4}{*}{Two-class} & \multirow{4}{*}{15 TD} & SVM-RFI: 10 TD features & Accuracy: 96.34\% \\  & & & & Recall: 96.14\% \\ \cline{1-1}  & & & & Precision: 96.48\% \\ \cline{1-1} \cline{3-5}  & & & & Accuracy: 99.06\% \\ \cline{1-1}  & & & & Recall: 99.13\% \\ \cline{1-1}  & & & & Precision: 98.54\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The NASA battery dataset (room temperature) literature results.
###### Abstract  
The influence of small groups in evacuation cannot be ignored. However, the current simulations and experimental studies have oversimplified the settings of small groups and evacuation scenarios. A significant disparity exists between the findings of existing studies and real-world scenarios. This paper compiled data on the number and size of small groups and the location of small group members in built environments. Subsequently, a scenario with intricate functions and shapes was established, and finally, these data was employed in agent-based simulations. The data encompassed 50 small groups comprising a total of 111 members. These groups, ranging from 2 to 4 members each, exhibited spatial separations between members spanning from 1 m to 23 m. Simulation outcomes indicated a detrimental effect of small groups on overall evacuation. A significant and positive correlation was observed between the distance separating small group members and the escalation in evacuation time, total jam time, and evacuation distance. The data provides a foundation for configuring initial scenarios in small group evacuation experiments and simulations. The simulation results can provide a basis for hospital safety evacuation management.  
small group data; large public building; outpatient area; fire evacuation; Pathfinder +
Footnote †: journal: Medical  
The context of evacuation scenario settings has garnered considerable attention. However, there remain two primary research challenges in this area. Firstly, real-life evacuation scenarios often involve diverse small groups composed of occupants with varying mobility capabilities, such as elderly individuals accompanied by adults in commercial complexes, and teenagers and children accompanied by adults in gymnasiums. Current research seldom incorporates heterogeneous small groups with differing walking abilities into simulated scenarios. Secondly, evacuation of building spaces characterized by intricate functions and shapes presents heightened complexities. Existing studies predominantly focus on analyzing evacuation dynamics within singular functional spaces like pedestrian zones, classrooms and offices, and gymnasiums, typically characterized by simpler spatial layouts such as rectangular, L-shaped, or circular designs. Consequently, there is a dearth of research on evacuation in buildings featuring complex functions and shapes, thereby posing novel challenges and opportunities for small group evacuation studies.  
Due to the lack of strategic level data and the simplification of simulation scenarios, the impact of small groups on evacuation remains an open question.  
Therefore, our research objectives are as follows: (1) to collect strategic level data to enhance the realism of evacuation experiments and simulations; (2) to set more intricate simulation scenario; and (3) to assess the influence of small groups on evacuation.
## Abbreviations  
* Autocorrelation function ADF Augmented Dickey-Fuller ANN Artificial neural network ARIMAX Autoregressive integrated moving average with exogenous variables DBN Deep belief network DoE Design of experiment KPI Key performance indicator LSTM Long short-term memory MAE Mean absolute error PACF Partial autocorrelation function PdM Predictive maintenance
# The Impact of Nonlinear Mobility Models on Straight Line Conflict Detection Algorithm for UAVs
## 4 Conflict Detection Algorithm (SLIDE)  
In [6], the authors proposed a mathematical model for the 3D conflict detection and a new algorithm called \"SLIDE\". \"SLIDE\" is a straight line conflict detection and alerting algorithm for a group of drones to share a common airspace. SLIDE views the drones' trajectories in the very near future as straight lines and depends only on the position and velocity information which are exchanged periodically between the drones. It does not require a priori knowledge of each drone's trajectory; the drones can dynamically change their trajectories based on the flight situations and the surrounding environment. Additionally, it does not require any remote visual inspection (RVI) tools such as high-resolution cameras.  
To ensure safe navigation in the 3D airspace, each drone is surrounded with a virtual spherical area called the protected zone. The protected zone for any drone should not overlap with any other drone's protected zone. SLIDE predicts a conflict between two drones if their protected zones overlap at any time (t) within its prediction time (also called look ahead time) \(T_{l}\). SLIDE is a distributed algorithm; each drone individually uses its state information along with the states received from its neighbor drones to decide whether it is going to lose the safe zone during the prediction period. Each drone periodically sends (in each broadcast cycle \(T_{b}\)) its state information message, which contains its current position, velocity, and the radius of its protected zone. More specifically, SLIDE works as follows:  
* Input: \(T_{b}\) and \(T_{l}\).
* Output: Whether the drone will encounter conflicts during the \(T_{l}\), and the timing of the conflicts (if any).
* Process: Each drone broadcast its information periodically every \(T_{b}\) in a STATE message that contains the following:  
* Current position;
* Current speed;  
Figure 3: Drone trajectory in 2D using EGM.  
The protected zone radius.  
When a drone receives the STATE message, it will calculate if there is a potential conflict during the \(T_{l}\), assuming that the two drones will continue in a straight line path. Each drone maintains a conflict table to record all information about conflicts, such as the identifiers of conflicting drones and the starting and ending time of the conflict. The start time of a conflict is when the overlap between the protected zones is expected to begin. The end time of a conflict is when the overlap between the protected zones is expected to finish. The conflict times are calculated using the drone's local time, so there is no need for synchronization between the drone's clocks.  
Back-off time \(T_{off}\) is used to avoid losing STATE messages when several drones try to send simultaneously. Each drone will wait for some random number chosen randomly and uniformly distributed from the interval \([0,T_{off}]\). The maximal \(T_{off}\) should be selected based on the network density. For high density, \(T_{off}\) should be increased to avoid simultaneous broadcasts.
## 3 Hardware Design
### Power Management Module  
The bottom-layer interchangeable module, named _Yellowstone_, is a battery-based power management system designed to regulate and monitor the power rails of the Marmote SDR platform. The main purpose of _Yellowstone_ is to power the entire Marmote SDR stack and to measure and log current draw, along with battery status.  
The _Yellowstone_ power management module has three possible sources of power, a 5 V wall adapter, a USB connector and a Li-Ion battery. The former two are used both to power the voltage regulators and to charge the battery with a charging profile tailored to the attached 6,000 mAh Li-Ion battery. A step-down regulator controls the 1.5 V rail, while a low-dropout regulator is used on the 3.3 V rail, primarily supplying the core and the I/O blocks of the SmartFusion SoC on the mixed-signal _Teton_ module, respectively. Both power rails are available for the upper layer modules, along with the unregulated external 3.6 V-5 V rail, if further voltage levels are needed. The power management module also monitors the current of both the analog and digital 1.5 V and 3.3 V power rails via current sense circuitry and counts the battery charge using a battery gauge. Both the analog current sense outputs and the digital battery gauge output are connected to a low-power microcontroller that measures and optionally logs these data through USB or to a memory card.
## 3 Seismic Damage Model
### Model Verification and Discussion
By comparing the calculation damage indexes of the three damage models presented, it was concluded that the M-Usami damage model can better represent the experimental phenomena and the actual damage of the test specimens, and thus can be potentially used as a bidirectional seismic damage model for RC bridge piers. The M-Usami damage model integrates the effect of seismic energy dissipation on damage level by setting a square of the ratio of displacement load in each loading cycle to the ultimate displacement under monotonic loading. This is a judicious practice, as the cumulative energy dissipation contributes parabolically to the damage propagation with displacement load: the larger the displacement load, the higher the energy dissipated by the pier with a parabolic increase. The square of the ratio of displacement load to the ultimate displacement can thus essentially reflect the impact of cumulative energy dissipation on damage propagation. Given the importance of seismic energy dissipation in decreasing seismic damage suffered by structures, a specimen design with different energy dissipation capacity should be considered. For example, our study shows that with a higher slenderness ratio, the structure can deform much larger at the same damage level. In addition, tall pier specimens can be reinforced with sufficient longitudinal steels and confinements to prevent mid-height plastic response and shear failure, as shown in this study and others, such as [PERSON] et al. [4]. This study focuses on the pseudo-static tests on tall hollow RC bridge piers, while the earthquakes in the field have dynamic excitation waves with much higher frequencies, initiating the contributions of the inertial forces of the piers to the variations of seismic response, such as the position of plastic hinges [6]. Note the plastic deformation formed at the pier base in this pseudo static experiment, as shown in Figure 7. In addition, the pier bottom was bolted into the underground without considering the soil-foundation-structure interaction in this study. Future studies can thus be conducted to understand the seismic performance of hollow RC bridge piers under biaxial dynamic loading with consideration of soil-foundation-structure by using shake table tests. Presently, there is no current design method suitable for designing hollow RC bridge piers subjected to multi-dimensional seismic excitations; the quantitative assessment of seismic damage suffered by structures in this study could still provide insights into the development of performance-based seismic design codes for hollow RC bridge piers.
## 3 Multi-Objective Optimization of System Construction
### A Multi-Objective Optimization Method for System Construction Based on a Genetic Algorithm  
#### 3.2.1 NSGA-II Algorithm  
NSGA-II is a genetic algorithm used to solve the multi-objective optimization problem. This method improves the sorting method of the basic genetic algorithm and puts forward the fast non-dominated sorting method, which enables the genetic algorithm to solve the multi-objective optimization problem and reduces the complexity of the non-inferior sorting genetic algorithm. It has the advantages of fast running speed and good convergence of the solution set. It is an algorithm widely used in the field of multi-objective optimization. The process of the NSGA-II algorithm is shown in Figure 4.  
The basic implementation steps of the NSGA-II algorithm are as follows:  
1. The initial population is randomly generated, and the size was \(N\). The non-dominated queuing is performed on the initial population, and the selection, crossover and mutation operations are carried out on the initial population by the genetic algorithm to obtain the first-generation progeny population.
2. The combination of parent population and child population in the second generation, and the rapid non-dominant sequencing is carried out, and the crowding degree of individuals in each non-dominant layer is calculated, and the appropriate individuals are selected to form a new parent population according to the non-dominant relationship between individuals. Starting from the second generation, the following generations are treated in the same way.
3. The genetic algorithm is used to select, cross and mutate the parent population to generate a new offspring population.
4. Repeat steps (2) and (3) until the constraints of the algorithm are met.
## 3 Experiments
The sampled baseband FOFDM signal was processed by an offline algorithm, which was surrounded by a dashed rectangle in Figure 1. The baseband FOFDM signal had 8 pilot symbols and  
Figure 3: (**a**) Power vs. current for an LD working at 25 \({}^{\circ}\)C. (**b**) Optical spectrum of the LD working at bias current of 65 mA and temperature of 25 \({}^{\circ}\)C.  
Figure 2: Photograph of the experimental setup. 256 FOFDM payload symbols in every signal frame. For the offline algorithm, symbol synchronization identifying the start of each frame was first performed. Following the serial-to-parallel (S/P) conversion, a discrete Fourier transform (DFT) with a length of 512 points was performed to obtain the frequency domain signal. Next, the channel was estimated using the pilot symbols, and single-tap equalization was carried out. After IDCT de-precoding and parallel-to-serial (P/S) conversion, the decision was made to recover the binary bit sequence. The error bits in the recovered binary bit sequence were counted directly, then BER was calculated.
# Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields
## 3 Results
### Calibration Sampling  
In situations similar to that analyzed, where there are a large number of saplings to analyze, it is advisable to perform a calibration process before the sampling method. Through this process, we can know which areas of the nursery present with an increased risk of infection. This process can help us to propose sampling methods that can improve sample selection.  
The calibration process begins when dividing the nursery into 9 horizontal bands, each consisting of 500 points depending on the coordinate values of \(x\) for the sapling \(i\) with \(i=1,\ldots,n=\) 10,920. Figure 4 illustrates the configuration of the bands schematically.  
Figure 3: First (**a**) and third (**b**) quartile for \(\pi_{i}|Y\) for the complete dataset.  
To find the probabilities in all locations of the nursery (point observed, prediction point), we fit the proposal modeling under the INLA-SPDE approach, and we use the mesh or triangulation shown in Figure 1. Thanks to the projection that we built on triangulation, it is possible to study the spatial process as a continuous process. In Figure 4, we can observe that the greatest risks are located in bands 1 and 9 of the nursery.  
Once we conclude the calibration process, we can determine which sampling methods provide the greatest benefits in terms of sample size and prediction errors. For this reason, we calculated the measures of discrepancy between the probabilities obtained throughout the nursery (projection based on triangulation defined in Figure 1) and those obtained under different sampling methods. The projection points in each sample are defined on the triangulation defined for all the nursery (Figure 1). This way, we can compare probabilities at the same points of projection.  
The discrepancy measures or the prediction errors are obtained for each random sampling from successive simulations. The measures defined are: mean square error (MSE), absolute error (ABSE) and coeficient of variation (CV).