esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Methods: AOSR Heuristics
Similar to the product placement strategy, the location retrieval strategy also ensures it consolidates the racks by finding the minimum possible products to be fetched in order to clear a rack for upcoming products. If it is not possible to consolidate, it identifies the nearest possible location which the product can be picked to reduce the total activity-time. The _RetrieveLocation()_ function returns a failure notification, without crashing, only if the required product is not in stock in the desired quantity. ```
1:procedureRequest-Analyser-CRM
2:\(Initiate\) BeliefBuilder()
3:\(Initiate\) KnowledgeBuilder()
4:\(PlacementPlan[]\)\(\leftarrow\) KnowledgeThreadPlan[]
5:\(request\)\(\leftarrow\)\(\mathit{ACLmessageReceiver()}\)
6:\(P\)\(\leftarrow\)\(request.requiredProduct\)
7:\(Q\)\(\leftarrow\)\(request.requiredQuantity\)
8:\(C[]\)\(\leftarrow\)\(\mathit{Extract}\)-Characteristics(\(P\))
9:ifrequest is from CRMthen
10:if (\(P\)with\(Q\) inStock)then
11:\(PossibleLocations[]\)\(\leftarrow\)\(RetrieveLocation(C[]\), PlacementPlan[])
12:\(\mathit{FewestAvailable}[]\)\(\leftarrow\)\(\mathit{FindFewest}(PossibleLocations[])\)
13:\(\mathit{NearestAvailable}\)\(\leftarrow\)\(\mathit{FindNearest}(\mathit{FewestAvailable}[])\)
14:\(\mathit{ExtractPlacement(P,Q,NearestAvailable)}\)
15:\(\mathit{UpdateBeliefBuilder()}\)
16:\(\mathit{UpdateKnowledgeBuilder()}\)
17:\(\mathit{NotificationGenerator(SUCCESS,CRM)}\)
18:else
19:\(\mathit{NotificationGenerator(FAILURE,CRM)}\)
20:endif
21:\(\mathit{CheckUpdates}\)
22:endprocedure
```  
**Algorithm 4** Overview of Percept Builder-CRM Heuristics. Every location in an AOSR 2.0 recommended warehouse is given a unique name so that it can be easily identified and managed without any ambiguities. Every location has a placement code that is comprised of rack number, rack level, and characteristics (e.g., finished or raw, fast or slow, and hazardous or not). These configurations can be varied depending on the business need. An overview of _Placement Generator()_ is highlighted in Algorithm 5, which first identifies the available space using the heuristics of _SearchRack()_ and then extracts all details from the _Knowledge Base_. For testing purposes, we have explored the design mechanisms provided by several available tools but the features provided by JADE [32] were determined to be most suitable for the AOSR 2.0 strategy, e.g., JADE provides simple and flexible options for designing multi-agent scenarios with the ability to monitor overall agents' interactions via the sniffer agent module. Constraint-based experimentation has been applied to AOSR 2.0 in JADE to acquire results in comparison with some standard WMS approaches.
## 3 The Motion State Estimation Method
### Motion State Estimation
#### 3.4.2 Motion Vector Estimation  
After spatio-temporal clustering, the points of the same motion object on the intra-frame space and the points between different frames are aggregated into a class. Due to the sparse sampling property of the point cloud, the estimation of the motion vectors of individual points will have a large bias. Therefore, based on the rigid assumption of the motion object [27], the entire motion vector of the object is directly estimated. For motion vector estimation, due to the short time interval between neighboring frames, key frames are used with \(s\) frames between the key frames. The estimation of the motion state of a single object is equivalent to the estimation of the rotation \(R\) and translation \(T\) matrices between the particular point clouds (object points) in different frames, and thus, can also be achieved by point cloud matching. So in this paper, we use the ICP method to align the point clouds of a moving object at different times to obtain \(R,T\). Then, the motion vector \(V\) of the object can be obtained by matching the core points (the clustering center) from the previous section as, \(V=P_{center}-RP_{center}-T\). In order to obtain a more stable motion state, this paper uses a combination of the motion vector \(V_{k}\) between key frames and the superposed motion vector \(V_{s}\) of all frames within a key frame to estimate the final key frame motion vector \(V\). The schematic diagram is shown in Figure 8.  
\[V=\omega_{s}V_{s}+\omega_{k}V_{k} \tag{11}\]  
where \(\omega_{s}\) and \(\omega_{k}\) are weights. \(V_{s}\) is the accumulated motion vector of adjacent frames between two key frame.
# Propagation Mechanism of Pressure Waves during Pulse Hydraulic Fracturing in Horizontal Wells
## 4 Results and Discussion
### Accuracy Verification  
For the sake of the computation, it is assumed that the fracture fluid flow rate at the wellbore is injected in a sinusoidal manner. The horizontal well consists of a 1500 m vertical section and a 1500 m horizontal section. At time zero, the fracture fluid has already filled the entire pipe column. The problem is solved using the characteristic line method and the finite difference method, and the results of the two solution approaches are compared. The comparison results are shown in Figure 6.  
The two methods yield nearly identical solutions for pressure transmission, with differences remaining within 1%. In terms of solution speed, the characteristic line method is faster than the finite difference method. However, due to the omission of the convection term in the solution process of the characteristic line method, its accuracy is inferior to the finite difference method when there is significant convection in the fracturing fluid within the pipe column. In the subsequent calculations, the finite difference method will continue to be used to study the problem.  
As illustrated in Figure 7, when the sinusoidal waveform method is employed for fluctuating injections of the fracturing fluid, the wellhead pressure exhibits an immediate approximate sinusoidal variation. Due to the time required for pressure wave transmission, pressure fluctuations throughout the pipe column exhibit a certain degree of lag, with the lag time being directly proportional to the distance. As the distance increases, the amplitude of the pressure fluctuations decreases gradually due to the influence of friction losses along the pipe.  
Figure 6: Comparison of results between finite difference method and characteristic line method.  
Figure 7: Pressure variation at different locations within the column.
# Railway Track Stress-Strain Analysis Using High-Precision Accelerometers
## 7 Discussion
Its implementation in practice is associated with expansion and diversity of types of sensors used, giving preference to the easiest to use, cheapest, and most reliable analogs. With this in mind, the currently widely used mobile laboratory cars should be supplemented with sensors placed directly on the track sections, which allow for regular monitoring, regardless of high train traffic. Typical strain gage transducers are suitable for these purposes, but are difficult to install and operate, especially with large temperature fluctuations. Using high-precision accelerometers is preferable in terms of operating factors, but requires a transition from measuring rail track deviations to stress-strain analysis. As shown above, a specifically developed finite element model of a railway track provides the means for such a changeover. Our experimental results showed coincidence of observed deviations with the corresponding vertical and side forces acting on the rail under various axial loads.
# New Method for Simultaneous Arsenic and Selenium Speciation Analysis in Seafood and Onion Samples
## 2 Results and Discussion
### Speciation Analysis of Water-Soluble Arsenic and Selenium by HPLC-ICP-MS
It is well known that selenium is an essential micronutrient for humans, and seafood is one of the major selenium sources [40]. Moreover, its toxicity and bioavailability strongly depends on its chemical form and concentration [50]. In seafood water extracts in the present study, in contrast to data for onion samples, the Se(VI) was the predominant form. However, higher concentration was marked for Se-cystine and SeMet forms. In seafood samples, the Se(IV) was marked only in white blanched shrimp. [PERSON] et al. (2016) determined the SeMet was found in fish muscle and liver tissues after enzymatic hydrolysis in the range 1.0-5.4 mg\(\cdot\)kg\({}^{-1}\), which is much higher compared to this study [53]. Lower concentration (from 0.12-4.0 mg\(\cdot\)kg\({}^{-1}\)) of SeMet was obtained by [PERSON] et al. (2018), who confirmed that SeMet was the major Se species in muscle of Atlantic salmon fed both basal diets and diets supplemented with selenised yeast [54]. These findings are in agreement with the earlier statement that SeMet is more readily available than inorganic Se species [54], which can be explained by higher data for SeMet compared to Se(VI) in the present study. Taking into account the sum of all forms of selenium in water extracts, the marked range 0.13-1.47 mg\(\cdot\)kg\({}^{-1}\) was at the same level as the obtained total content of selenium in fish and shellfish (range 0.12-1.27 mg\(\cdot\)kg\({}^{-1}\)) [40]; we also found a selenium level of 0.567 mg kg\({}^{-1}\) in tuna, 0.346 mg kg\({}^{-1}\) in scads, and 0.279 mg kg\({}^{-1}\) in swordfish and in the range from 0.470 to 0.950 mg kg\({}^{-1}\) in fish and mollusks [25].
## 1 Introduction  
Over the last two decades there has been considerable progress in the development of fluoride glass fiber lasers doped with lanthanide ions with operating wavelengths larger than 2500 nm. So far, the longest operating wavelength achieved using fluoride fiber laser technology is 3900 nm, which was realized using liquid nitrogen cooling of ZBLAN fiber doped with holmium ions [1]. At room temperature, the longest operating wavelength achieved so far is 3680 nm [2], which was realized using erbium ion doped ZBLAN fiber and a two pump lasing scheme [3]. The output power under continuous wave (CW) operation erbium ion doped ZBLAN fiber lasers has reached 24 W in a cavity configuration using bulk optics elements [4], whilst in an all fiber, passively cooled configuration, 20 W was reached [5]. The slope efficiency of lanthanide ion doped fluoride fiber lasers has so far reached 73% [6]. Also, fluoride fiber lasers, with a very large wavelength tuning range covering the mid-infrared wavelength range up to 3400 nm, have been realized [7]. Pulsed operation of lanthanide ion fluoride glass fiber lasers has been achieved using both gain switching and Q-switching. The highest average power obtained so far using Q-switching is 12 W [8], whilst the highest peak power is 10 kW [9]. Gain switched dual pump erbium doped fluoride fiber lasers have achieved an operating wavelength of 3550 nm with a peak power of 204 W and a repetition frequency of 15 kHz [10].  
The progress in practical realization of lanthanide ion fluoride glass fiber lasers was accompanied by a development of design and modelling tools. Most of the fiber laser models developed thus far rely on solving the rate and propagation equations for the pump and signal waves in the time domain, and rely on the application of the method of characteristics (MOC) [11; 12; 13; 14; 15; 16; 17]. When using MOC, the spatial and temporal step ratio has to remain fixed, to preserve the physical meaning of the results obtained. More recently, an alternative approach has been introduced which relies on the application of the method of lines (FD-MOL) [18], and the concept of the Extended Taylor Series (ETS) [19; 20], to derive second order finite difference (FD) approximations for spatial derivatives. In this contribution, FD-MOL is used to model the Q-switched operation of the fluoride glass fiber laser doped with erbium ions. It was shown that inclusion of the spontaneous emission phenomenon is essential for obtaining correct results.  
The paper is divided into four sections. After an introduction, the model details are presented in the second section. In the third section, a discussion of the results is provided, which is followed by a concise summary in the last section.
# Application of Gas Foil Bearings in China
## 3 Challenges and Prospects
### Design Criteria of Gas Foil Bearing  
Due to the diversity of industrial applications, higher requirements are put forward for the variety and performance of high-speed turbo machinery. Therefore, design criteria for gas foil bearings and appropriate foil structure parameters are crucial for their actual application; for example, the trade-off relationship between foil stiffness and Coulomb damping. In some cases, a large Coulomb damping is formed due to the high foil flexibility, which is beneficial for improving the stability of the rotor bearing system and lessening the whirl vibration amplitude. However, the large flexibility will cause a large deformation of the foil and rupture the gas film. In addition, the dynamic analysis method of the foil rotor bearing system also needs to be improved. Efforts are being made to increase the operating DN value of the foil bearing to \(4.5\times 10^{6}\) mm \(\cdot\) r/min, in China.
## 7 Discussion  
We tested the LSTM and EMD-LSTM models in terms of their ability for Dst index prediction. Our results prove the effectiveness of deep learning methods, which can be used to improve the prediction accuracy of the model by changing the model parameters and length of the training data, processing the training data, replacing the training data. Thus, this method is worthy of deeper study in the future. The newly launched MSS-1 satellite [29], which provides high accuracy and nearly east-west oriented (inclination angle of 41\({}^{\circ}\)) magnetic field data, can lay a solid foundation for studying the removal of external field interference and even for studying short-term changes in the ionospheric field and the magnetospheric ring current.  
There are also some shortcomings in this study. The models only use one kind of data for training and prediction and lack the necessary physical constraints. Thus, the next step will be to screen other space weather indices and select indices with higher correlation with the Dst index, such as the magnetospheric ring current index (RC index) or the geomagnetic perturbation index (Ap index), for constraints and co-estimating, using multiple input parameters for training and predicting. In this way, realistic prediction results can be obtained by adding physical constraints.  
Conceptualization, Y.F.; methodology, J.Z. ([PERSON]) and Y.F.; software, Y.L. and J.Z. ([PERSON]); validation, Y.F.; writing--original draft preparation, Y.F.; writing--review and editing, Y.F.; supervision, Y.F; funding acquisition, Y.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (42250103, 41974073, 41404053), the Macau Foundation and the pre-research project of Civil Aerospace Technologies (Nos. D020308), and the Specialized Research Fund for State Key Laboratories.  
Not applicable.  
All measurement data will be made available upon request.  
We acknowledge the High-Performance Computing Center of Nanjing University of Information Science & Technology for their support of this work. We also thank the reviewers for their constructive comments and suggestions.  
The authors declare no conflict of interest.
# Audio Steganalysis Estimation with the Goertzel Algorithm
## 4 Statistical Coefficient Results
### Audio Quality Metrics Comparative Results
#### 4.2.2 Estimates in the Frequency Domain  
The following estimates are made in the frequency domain:  
Log-likelihood ratio (LLR): The LLR measure is also referred to as the Itakura distance measure. The LLR is based on the discrepancy between the all-pole models of the host audio vector (clean) and the stegoaudio vector [8; 30; 31; 32].  
Log Area Ratio (LAR): The LAR measure is an LPC technique that depends on LP reflection coefficients [8; 33].  
Itakura-Saito Distance (ISD) Measure: The ISD measure is the discrepancy between the power spectrum of the potential audio vector and that of the host audio vector [8; 30; 31; 32; 34].  
COSH Distance Measure: The COSH distance is a symmetric version of the Itakura-Saito Distance [8; 34].  
Cepstral Distance Measure (CDM): The CDM is the distance between the cepstral coefficients of the original and distorted signals [8; 30; 31; 32; 35].  
Spectral Phase and Spectral Phase-Magnitude Distortions: This metric allows the phase and magnitude differences between the host audio vector and the possible stegoaudio to be assessed [8; 36].  
Short-Time Fourier-Radon Transform Measure (STFRT): This metric provides time-localized frequency information for situations in which the frequency components of a signal vary over time [8; 36].
## 4 Methods for EIT Data Reduction and CNN Model Compression for Efficient Edge Deployment
### Dynamic Post-Training Quantisation  
Dynamic quantisation after training is a technique aimed at reducing the size of the neural network models by converting the weights and activations of the floating-point representation into a low-precision representation. This process reduces the computational complexity and memory footprint of the model [35].  
Unlike static quantisation, which uses pre-calculated statistics from a representative dataset, dynamic quantisation calculates quantisation scales in real-time during inference. The process can be broken down into several theoretical stages [36]:  
**(1) Weight quantisation**  
Weight quantisation, as presented in (4), begins with the determination of the quantisation scale [37], calculated as the ratio between the maximum absolute value of the floating-point weights and the largest integer representable with \(n\) bits:  
\[\Delta_{w}=\frac{\max(|w|)}{2^{n-1}-1} \tag{4}\]  
where \(w\) is the floating-point weight and \(n\) is the number of bits used for quantisation.  
Weights are then quantised such as in (5), by dividing floating-point weights by the quantisation scale and rounding to the nearest integer:  
\[q_{w}=\text{round}\bigg{(}\frac{w}{\Delta_{w}}\bigg{)} \tag{5}\]  
**(2) Quantification of Activations**  
The next step involves the calculation of activation limits for each batch of data: \(a_{\text{max}}\), \(a_{\text{min}}\), then the determination of activation quantisation scale which is calculated following (6), as a function of the maximum absolute value of observed activations, to normalise activations within the range of quantifiable values:  
\[\Delta_{a}=\frac{\max(|a|)}{2^{n-1}-1} \tag{6}\]  
where \(a_{\text{max}}\) and \(a_{\text{min}}\) are the maximum and minimum values of observed activations.  
Activations are quantified by dividing the floating-point activations by the quantification scale and rounding off the result, as shown in (7):  
\[q_{a}=\text{round}\bigg{(}\frac{a}{\Delta_{a}}\bigg{)} \tag{7}\]  
Activations are then dequantised to recover floating-point values. To obtain the actual value of activations after quantisation, these are multiplied by the quantisation scale and adjusted by adding the minimum value of activations, as presented in (8):  
\[a=q_{a}\cdot\Delta_{a}+a_{\text{min}} \tag{8}\]
# Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre
## 4 Forecasting Model
The data obtained from the Port Isabel recording station was leveraged as part of the initial investigation into the feasibility of predicting storm surges with the LSTM and CNN architectures both separately and combined. As part of a pilot, the Port Isabel recordings were used to iterate models by tuning the hyperparameters and their architecture to find the best performance. The best-performing architecture obtained from the Port Isabel pilot models was used in the first iterations of the final model trained on the synthetic water surface elevation data. Subsequent iterations changed the size of the model, integrated mixed precision, and modified training parameters to find the best performance for the final CNN+LSTM model. The second set of data used to train the final storm surge model comes from the execution of numerical model simulations corresponding to each Atlantic hurricane season from 2008 to 2020. To force the simulations, the forecasted weather information was used. This provides a training dataset for the surrogate model to learn and replicate the performance of the numerical simulations. To evaluate the machine learning model, a set of 10 virtual buoy stations were selected from the numerical model finite element mesh. These virtual stations correspond to points of interest in the Lower Laguna Madre. The set of virtual buoy stations and their location can be seen in Table 2.  
To better visualize the location of the stations, a map of the Lower Laguna Madre with the virtual stations can be seen below in Figure 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Latitude** & **Longitude** & **Numerical Model Mesh ID** \\ \hline South Padre Island (SPI) & 26.0854 & \(-97.1562\) & 42195 \\ \hline Laguna–SPI & 26.0862 & \(-97.2007\) & 59162 \\ \hline Ship Channel & 26.0423 & \(-97.2071\) & 61388 \\ \hline Laguna Heights & 26.0854 & \(-97.2518\) & 55382 \\ \hline Laguna Vista & 26.1007 & \(-97.2815\) & 52698 \\ \hline Port Isabel & 26.062 & \(-97.215\) & 60551 \\ \hline Port Mansfield Inside & 26.5588 & \(-97.4201\) & 5684 \\ \hline Port Mansfield Outside & 26.564 & \(-97.2593\) & 10629 \\ \hline Arroyo Colorado Inside & 26.3616 & \(-97.3266\) & 29783 \\ \hline Arroyo Colorado Outside & 26.3814 & \(-97.1979\) & 18356 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Table detailing the locations of virtual buoy stations used in the study. Figure 3: Map for the location of the 10 virtual buoy stations in the study.