esa-sceva/satcom-chunk-collection huggingface dataset:
# Skyfall: Signal Fusion from a Smartphone Falling from the Stratosphere
## 3 Materials and Methods
### Station Timing, Orientation, and Location
#### 3.4.1 Timing  
There are various timelines available to a smartphone. When first booted, a smartphone internal clock does not know the time and would by default utilize the Unix epoch time zero of 1 January 1970 00:00 UTC. It will try to find the time via GNSS, cell, or Wi-Fi. The operating system time (OS time) will use any and all available combinations to update its internal clock. OS time will jump back and forth as needed to keep up with the various clocks at its disposal. GNSS time will also jump about depending on which satellites are available. A third timeline, the machine time, is initialized when the unit is booted. Although the machine time is monotonic, it is only as accurate as its internal oscillator. Accurately estimating true time is beyond the scope of this work; our aim is to obtain an estimate of timing errors for a given network configuration relative to a reference clock. In Skyfall, the reference clock is at Amazon Web Services and is only available when there is either cellular or Wi-Fi connectivity.  
The application uses well-established tri-message exchange algorithms [69, 70] for synchronization and relies on three assumptions: that the message propagation delay is nearly symmetrical, that errors of the timestamp itself have been mostly compensated, and that the clock's skew is negligible during the message exchange. The synchronization process starts when the reference server sends a message of its current timestamp to the device. The device then records the time of receipt and, after a delay, returns the message with its current timestamp. The server records the time of receipt, completing the synchronization message exchange. Using the timestamps, we can find the average latency to calculate the offset between the clocks. The best-fit offset is selected by finding the best minimal statistically significant latency to compute the offset.  
Timing correction is built into the RedVox SDK, and is presently only possible when communication is available to either a reference synchronization clock or GNSS satellites during data collection. Potential timing improvements (e.g., [71]) are under consideration.
## 3 Results and Discussion
### Experimental Results
#### 3.2.1 Particle Size  
Firstly, the influence of particle size on the OFI signal was investigated. The OFI microfluidic flowmetry simulations and measurements with 552 nm and 4.89 \(\upmu\)m diameter PS particles at the same concentration of 0.1% _w/v_ and flow rate of Q = 10 \(\upmu\)L/min are compared in Figure 5a (simulations) and Figure 5b (measurements). For the larger particle (blue solid lines in both figures), a low frequency peak _fd1_ corresponding to the forward scattered light reflection induced feedback enhancement appears, whereas the spectra of the smaller PS particle (red solid line) does not present such frequency peak. The _fd1_ value is calculated to be around 4 kHz considering the velocity distribution within the enhancement area at Q = 10 \(\upmu\)L/min. It is also interesting to note that the 552 nm particles produce a spectral signal level that is 9 dB higher than the 4.89 \(\upmu\)m particles at the frequency _fd2_ that correspond to the theoretical maximum Doppler shift. The signal level difference can be explained by the larger particle density (number in unit volume). Since the concentrations in weight per volume are the same, so that in the 4.89 \(\upmu\)m particles solution, the particle density is around 1000 times less than for 552 nm particles. As can be seen both in the measurement and in the simulation, the larger scattering cross-section of 4.89 \(\upmu\)m particles does not compensate the lower number of particles contributing to back-scattered light.
## 3 Materials and Methods
### Model Selection
#### 3.4.4 ARIMA-LSTM Hybrid Model  
The hybrid ARIMA-LSTM model is designed to capture the linear and nonlinear aspects of time series data. The time series must be stationary to apply the ARIMA model and predict future values. It should be checked with the Dickey-Fuller test to see if it is in place, and it should be performed if it is not already. Then, optimal parameters will be found to build the model, and finally, predictions will be made using the built model. LSTM works well for non-stationary parts of data as well as has relatively larger memory. The residuals obtained from ARIMA are fed into an LSTM model and trained to tap the pattern and predict the residuals for the next future period [35].  
These two models have been chosen for their ability to break down a time series into linear and nonlinear trends, as expressed by Equation (11). In this equation, \(\mathrm{L_{t}}\) illustrates the linear component of the time series at time step t. Also, \(\mathrm{N_{t}}\) defines the nonlinear component, and \(\varepsilon_{t}\) represents the error term in the \(\mathrm{x_{t}}\) time series [36].  
\[x_{t}=L_{t}+N_{t}+\varepsilon_{t} \tag{11}\]  
The reason for choosing different types of algorithms to conduct the research was to include the wide range of data found in cancer rate numbers and new case amounts and incidence rates with different qualities. LSTM networks provide flexibility in understanding long-term connected changes and complex patterns common in information. As a time-series analysis model, Prophet's ability to deal with unexpected values, missing data, and shifting trends is beneficial for the unpredictable nature of healthcare information, like cancer-related datasets. As a statistical analysis model, ARIMA is strong at examining and predicting linear sequences and is vital for ensuring that a complete analytical method can compare against more complex models.
## 4 Hop-By-Hop Worm Propagation in Mobile Sensor Networks  
In this section, we describe a hop-by-hop worm propagation with carryover epidemic model in a mobile sensor network. Note that this model is also used as an assumption for worm detection in our previous work [16].  
Many researchers have adopted an epidemic model [3] in order to model the infection quota, which is defined as the number of nodes that a worm infects per time slot in a network. This is because the epidemic model is useful at the quantification of infection quota in a series of time slots. The infection quota in the discrete time version of a simple epidemic model [3] is calculated in units of time slots by the following equation:  
\[I_{k}=(1+\theta N)I_{k-1}-\theta I_{k-1}^{2} \tag{1}\]  
where \(\theta\) is a pairwise infection rate [3] and \(N\) is the total number of nodes in the mobile sensor network. \(I_{0}\) indicates the number of worm originators. We define \(I_{k}\) as the cumulative infection quota from the \(0\)th time slot to the \(k\)th time slot. As a consequence, the infection quota in the \(k\)th time slot is set to \(I_{k}-I_{k-1}\).  
Since the epidemic model only deals with the infection quota, there should be a model to describe how worm is spread through the mobile sensor network. To meet this need, we consider a a hop-by-hop worm dissemination strategy in mobile sensor network. Under this strategy, we assume that a sensor has _susceptible_ or _infectious_ state. All sensor nodes are initially set to the susceptible state except infectious worm originators. Each infectious node spreads worm to its one-hop neighboring susceptible nodes. Once a susceptible node is infected by a worm, it changes its state to infectious.  
Although the epidemic model is good at modeling the infection quota, it may not be useful in the scenario in which infectious nodes in a time slot could have fewer neighboring susceptible nodes than the infection quota due to the network topology. To pacify this problem, we slightly change the discrete time version of simple epidemic model in such a way that we carry over the infection quota deficit in the current time slot to the next time slot, charging the total infection quota over the entire time period. More specifically, we denote \(q_{k}\) the infection quota deficit in the \(k\)th time slot. The infection quota in the first time slot is calculated as \(I_{1}-I_{0}\) and the infection quota in the \(k\)th time slot is accordingly computed as \(I_{k}-I_{k-1}+q_{k-1}\) (\(k\geq 2\)). We call this modified version of epidemic model the _carryover epidemic model_. In the next section, we provide a quantitative analysis of the carryover epidemic model.
## 4 Discussion
Therefore, we think that our approach could be combined with these methods to address the domain shift issue, which represents an advantage of our approach that will be further investigated in future work. Although our approach was based on a U-net implemented with Monte-Carlo dropout and a recently developed CKC algorithm, this work differs from other cardiac image segmentation methods developed to tackle a similar issue with higher performance for LV segmentation and biomarker quantification. In addition, the promise of our approach was demonstrated in the context of a U-net, which has been widely used for numerous applications, suggesting the generalizability of our framework for a broad range of segmentation tasks that involve a U-net. The improved segmentation generalizability may stem from the combination of the advantages of deep-learning and machine-learning methods without a deep architecture. As a result, both deep and shallow image features can be learned or employed, and the power of data-driven and rule-based segmentation methods was aggregated, potentially mitigating the limitations of the individual methods. However, further investigation of this is warranted. Efforts that can further improve the performance of our proposed approach including: (1) applying the CKC algorithm only to the fine-tuning subjects with problematic segmentation; (2) automatically selecting the datasets with acceptable CKC segmentation for CNNfine-tuning; and (3) adding a few more new unlabeled datasets for each iteration. We think that these strategies may be optimized and implemented in parallel to for potentially greater robustness. Regardless, the results realized here suggest that our approach provides a way to improve deep-learning segmentation generalizability without increasing the difficulties of algorithm interpretability, a major concern facing the community [42], and may facilitate broader use and translation of deep-learning techniques for research and clinical care. We acknowledge several study limitations. First, the segmentation accuracy of our approach is lower than CNNs trained and tested on the same datasets. However, here we focus on adapting a trained CNN for segmentation of previously unseen cardiac MRI datasets, which is particularly challenging and requires urgent solution. Importantly, we achieved segmentation accuracies higher than two state-of-the-art segmentation methods (a combined method that employed style-intensity augmentation and DeepLabV3+) and LV function measurements that were strongly correlated with manual results. We note that the basal and apical slices of the heart are difficult to segment due to poor image qualities and the complexity of cardiac structures, which represent some of the major challenges facing the community. In addition, the proposed algorithm was validated on a retrospective dataset and the effectiveness of this approach warrants a prospective evaluation with datasets from different centers, MR scanners, imaging protocols, and disease phenotypes.
# Considerations about Parameters Estimation into a Minimum Variance Control System
## 4 Conclusions  
For nonlinear processes, the on-line estimation of the parameters of a linearized model around an operating point is an essential step in many adaptive control strategies. Considering a minimum variance control system, the present paper demonstrates, both analytically and by simulation for a concrete case study that, in steady-state closed-loop (under control system constraints), although parameters estimates differs from the real ones in the open-loop (without constraints), the process gain estimates are similar in both situations. Therefore, the parameter estimator rather allows an accurate estimation of the process gain factor and not of the real parameters of the linearized model used to implement the control law.  
In order to validate by simulation, the analytical demonstration, a particular case study considering as controlled process an induction generator integrated into a wind energy conversion system was analyzed. The minimum variance control system aims to maintain a constant voltage at terminals, under the action of two types of disturbances: mechanical torque variation (due to wind speed variation) and electrical load/unload (by connecting/disconnecting electrical consumers). For both disturbances, the performed simulations accurately validated and confirmed the analytically demonstration regarding the equality (in steady-state) of the two estimated gain factors, both in open-loop and closed loop. Moreover, in the same operating point, a simple retuning of the controller (by changing the control penalty factor) affects the parameters estimates values, but the process gain (computed based on them) remains unchanged-identical to the real one estimated in open-loop.  
As a final conclusion, the novelty of the paper consists in demonstrating that into a closed-loop control system (in steady state regime), the parameters estimator allows an accurate estimation of the real process gain based on the process parameter estimates, although these estimates are different in closed-loop from the real ones computed in open-loop. In fact, as the paper demonstrates both analytically and by simulation, a proper operating of the control system does not require the real values of the process parameters (open-loop linearized model parameters), but only an accurate estimation of the process gain.  
[PERSON] and [PERSON] designed the analysis strategy of the estimation and control process. [PERSON] and [PERSON] elaborated the case studies. [PERSON] performed the simulation and analyzed the obtained results. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Steel Surface Defect Detection Based on YOLOv8-TLC
## 4 Experimental Results and Analysis
### Analysis of Realization Results
#### 4.3.2 Horizontal Comparison Experiment  
To validate the effectiveness of the TSP module constructed in this paper, we designed a horizontal comparison experiment with similar modules, SPPF and SPPCSPCP [39]. As shown in Table 4, the TSP module can effectively enhance the detection accuracy of the model. The best results of each experimental indicator are displayed in bold in the table.  
To better demonstrate the advantages of the LSK-Attention introduced in this paper, we conducted experiments by embedding different attention mechanisms in the same location, including Global Attention Mechanism (GAM) [42], Convolutional Block Attention Module (CBAM) [43], Gather-Excite (GE) [44], Explicit Visual Center (EVC) [45], and LSK-Attention. Heat maps of patch defects generated by each attention mechanism were compared, as shown in Figure 10. The darker the red color in the heatmap, the greater the contribution of the region to the final detection result, and the higher the attention to this part. The green and yellow parts have the second highest attention, and the blue part indicates that the feature has a smaller impact on object detection and recognition. Table 5 illustrates that incorporating the LSK module enables the model to more accurately identify various defects and effectively extract defect features on the steel surface. To visually showcase the superiority of the LSK module, we compared the defect heat maps generated by each attention mechanism. As shown in Figure 10, the LSK module better focuses on defect information and effectively extracts steel surface defect features.  
To validate the effectiveness of introducing DSConv to improve the C2f module, this experiment compared several other convolution modules, including Receptive-Field Attention Convolution (RFAConv) [46], Omni-Dimensional Dynamic Convolution (ODConv) [47], Dynamic Snake Convolution (DYSnake) [48], and Deformable Convolution v2 (DCNV2) [49]. As shown in Table 6, only the C2f-DS module improved with DSConv was able to enhance the model's feature representation capability while maintaining low computational complexity. It did not significantly increase the computational cost of the model, thus improving performance while preserving efficiency.
# Identification of Water Hammering for Centrifugal Pump Drive Systems
###### Abstract  
Water hammering is a significant problem in pumping systems. It damages the pipelines of the pump drastically and needs to identify with an intelligent method. Various conventional methods such as the method of characteristics and wave attenuation methods are available to identify water hammering problems, and the predictive control method is one of the finest and time-saving methods that can identify the anomalies in the system at an early stage such that the device can be saved from total damage and reduce energy loss. In this research, a machine learning (ML) algorithm has used for a predictive control method for the identification of water hammering problems in a pumping system with the help of simulations and experimental-based works. A linear regression algorithm has been used in this work to predict water hammering problems. The efficiency of the algorithm is almost 90% compared to other ML algorithms. Through a Vib Sensor app-based device at different pressures and flow rates, the velocity of the pumping system, a fluctuation between healthy and faulty conditions, and acceleration value at different times have been collected for experimental analysis. A fault created to analyze a water hammering problem in a pumping system by the sudden closing and opening of the valve. When the valve suddenly closed, the kinetic energy in the system changed to elastic resilience, which created a series of positive and negative wave vibrations in the pipe. The present work concentrates on the water hammering problem of centrifugal pumping AC drive systems. The problem is mainly a pressure surge that occurs in the fluid, due to sudden or forced stops of valves or changes in the direction and momentum of the fluid. Various experimental results based on ML tool and fast Fourier transformation (FFT) analysis are obtained with a Vib Sensor testbed set-up to prove that linear regression analysis is the less time-consuming algorithm for fault detection, irrespective of data size.  
Keywords:centrifugal pump; regression analysis; water hammering; machine learning; variable frequency drive +
Footnote †: journal: Applied Sci. 2020, 10, 2683; doi:10.3390/app10082683
## 3 Experimental Section
### Desorption Experiments  
The desorption process from the porous lignite material follows the general principle of applying gas flow (high purity argon Ar) in order to entrain the gases that are adsorbed on the surface and the interior of the lignite, i.e., CO\({}_{2}\), CO, CH\({}_{4}\), etc. The release of these gases from the surface and the interior of the porous lignite material is achieved by the pressure of the gas flow overcoming the forces that hold the gases in the lignite pores (van der Waals forces).  
Desorption tests were conducted under low and high temperatures to investigate the methane desorption from lignite in order to evaluate temperature effects on gas desorption [36; 37]. The desorption process of the three lignite samples from the southern field of the Ptolemaida basin was evaluated at four different temperatures: 25 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, 100 \({}^{\circ}\)C, and 150 \({}^{\circ}\)C at standard atmospheric pressure (1 atm). The pressure of the desorption process is equal to the atmospheric pressure because, at these temperatures, any increase in pressure could cause the ignition of the methane produced. The desorption tests were performed using a fixed-bed quartz reactor with an internal diameter of 0.9 cm and a total height of 40 cm, under ambient pressure. A schematic representation of the equipment used is given in Figure 5. For the desorption experiments, 3.0 g (dry weight) of each lignite sample was used to fill the reactor up to a height of 7.1 cm. The bed of the packaging material (called desorbent bed) was supported on both sides of the reactor by inert quartz. The bed geometry (height/diameter) was 7.88. The temperature of the reactor was measured by a type K thermocouple located in its center. The temperature of the reactor furnace could reach a wide range of operating temperatures (up to 800 \({}^{\circ}\)C) and was monitored by a type K thermocouple.  
Figure 4: Crushed coal samples.  
Figure 5: Schematic representation of the experimental equipment used for the desorption experiments.  
Desorption experiments at 25 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, 100 \({}^{\circ}\)C, and 150 \({}^{\circ}\)C were performed using a gas flow at 200 mL/min of high purity Ar (5.0). All samples were subjected to an 8 min degassing process, channeling 1.6 L of Ar gas. The gas flow was controlled through a stainless-steel dosing valve provided by PARKER. Airflow was carefully measured using a bubbler gauge prior to the start of each experiment. The compositions of the reactants and gas flow products were continuously monitored using an analytic system that included an analyzer (FTIR) (Gasmet DX 4000) for the analysis of H\({}_{2}\)O, CO, CO\({}_{2}\), CH\({}_{4}\), C\({}_{3}\)H\({}_{6}\), and C\({}_{3}\)H\({}_{8}\).