esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### FDTD Setup  
One-dimensional and two-dimensional FDTD schemes were used to compute normal and oblique incidence, respectively. Simulations were performed with 20 PPWL, considering an upper frequency of 4 k\(\surd\)2.  
Normal incidence evaluation is the first step in NEAM modeling algorithms. On one hand, its goal is to compare simulated absorption coefficients with measurements performed in an impedance tube, according to ISO 10534, parts I and II [28; 33]. On the other hand, the aim is to determine the equivalent coefficients that are to be used in 2D FDTD simulations (oblique incidence). The algorithm uses a summed multiple Ricker wavelet as input signal, which was developed to obtain a flat frequency response ranging from 125 Hz to 4 kHz octave bands. A signal with a summed ripple \(<\) 1.6 dB in the interest region is obtained. The FDTD air domain is computed with the linearized Equations (20) and (21). A quadratic PML was used on one side of the FDTD domain, and, on the other tube side a NEAM was placed with an impervious wall behind. One receiver captures the input and reflected pressure to and from NEAM, namely p\({}_{\text{I}}\) and p\({}_{\text{R}}\). Afterwards, incident and reflected components can be obtain by an adequate time windowing, acquiring then, via transfer function, the complex reflection factor as \(\text{R}=\text{FFT}\{\text{p}_{\text{R}}\}/\text{FFT}\{\text{p}_{\text{I}}\}\), where FFT is the Fast Fourier Transform acronym. Following that, normal incidence absorption coefficient is calculated as \(\alpha_{\text{n}}=1-|\text{R}|^{2}\)[34]. In addition, material surface impedance is extracted from FDTD results as the pressure to the particle velocity ratio at NEAM interface.  
A similar approach is conducted for the 2D FDTD procedure. In this case, oblique incidence is computed, obtaining, from individually angled simulations, the random incidence sound absorption thanks to Paris' integration [35]. The use of the oblique incidence in the 2D FDTD method was taken into account to quantify if the 1D virtual coefficients (normal incidence) could also settle the diffuse behavior. To evaluate the signal incidence angle, periodic boundary conditions were implemented at the top and bottom of the 2D domain using the sine-cosine method expressed in [31]. A PML layer is used in X-direction to prevent unwanted reflections at the domain beginning. Also, a NEXM is placed at the end of the domain with a rigid backing. Figure 2 shows the 2D domain simulation scheme. As the 2D model has periodicity in the Y direction, the NEXM behaves like an infinite material. Consequently, ISO 354 measurements [36], which are based in finite size samples, should not be used to compare absorption coefficient results. Instead, analytical calculations for infinity samples must be chosen. Since periodic boundaries are frequency dependent, the Ricker wavelet is no longer used. As an alternative, sinusoidal waves, centered at one third octave bands from 100 Hz to 5 kHz, are employed. Finally, the absorption coefficient is evaluated using the standing wave method expressed in ISO 10534-1.
# Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis
## 3 Classification of Lung Ultrasound Data
### Methods
#### 3.1.2 Frame-Based Models  
Our backbone neural architecture is a VGG-16 [70] that is compared to NasNET Mobile, a light-weight alternative [71] that uses less than 1/3 of the parameters of VGG-16 and was optimized for applications on portable devices. Both models are pre-trained on ImageNet and fine-tuned on the frames sampled from the videos. Specifically, we use two variants of VGG-16 that we name VGG and VGG-CAM. VGG-CAM has a single dense layer following the convolutions, thus enabling the usage of plain class activation maps (CAMs [72]), whereas VGG has an additional dense layer with ReLU activation and batch normalization. Considering the recent work of [57] on lung US segmentation and severity prediction for COVID-19, we investigated whether a segmentation-targeted network can also add value to the prediction in differential diagnosis. We implemented two approaches building upon the pre-trained model of [57], an ensemble of three separate U-Net-based models (U-Net, U-Net++, and DeepLabv3+, with a total of \(\sim\)19.5 M parameters). First, VGG-Segment is identical to VGG, however instead of training on the raw US data, we train on the segmented images from the ensemble (see example in Appendix B.1). Although it might seem unconventional, we hypothesized that the colouring entails additional information that might simplify classification. Secondly, in Segment-Enc the bottleneck layer of each of the three models is used as a feature encoding of the images, resulting in 560 filter maps that are fed through a two layer MLP with hidden layer sizes 512 and 256 respectively. The encoding weights are fixed during training. Both settings are compared to the other models that directly utilize the raw images. For more details on the architectures and the training procedure, please refer to Appendix B.
## 3 Road Surface Grade Identification
### Acquisition of the Vehicle's Vibrational Response  
Deep learning demands extensive data for network training, yet acquiring comprehensive field vehicle measurements is challenging. Therefore, different road surface roughness signals are generated using filtered white noise, and the network is trained on the vertical acceleration output from the suspension model simulation. A simulation study on ride comfort was conducted using Matlab R2023b, leveraging the previously developed vehicle vibrational model and stochastic road surface model. The experiment was designed to test six distinct vehicle speeds across six varied road surface classifications (A through F). The simulation ran for 3600 s, with a sampling rate of 200 Hz, to capture the vehicle's vibrational responses under these conditions. In road surface grade reverse identification based on vehicle responses, directly extracting features from the vehicle's response signal can result in complex and difficult-to-handle features. To simplify the analysis, the vehicle's response data are divided into 3600 equal-length sequences, each with a 1 s interval. The constructed vehicle vibrational response dataset is randomly split into two sets, with 70% used for training and 30% used for testing. The former trains the model; the latter tests it.
# Classification of Children's Sitting Postures Using Machine Learning Algorithms
## 2 Related Work
### Sitting Posture Classification
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No. of Pressure Sensors and Location** & **No. of** & **No. of Sitting** & **Algorithm** & **Accuracy** & **Reference** \\
**of Installation** & **Participants** & **Conditions** & **Algorithm** & **Accuracy** & **Reference** \\ \hline \hline Seat (5) & 10 & 8 & Density-based clustering & 94.2\% familiar & [PERSON] et al. [28] \\ \hline Seat (8 \(\times\) 8) & 10 & 9 & SVM & 93.9\% unfamiliar; 98.9\% familiar & [PERSON] et al. [29] \\ \hline Seat (16 \(\times\) 16) & 25 & 7 & Dynamic Time Warping & 85.90\% & W. [PERSON] et al. [26] \\  & & & & (DTW) & 85.90\% & W. [PERSON] et al. [26] \\ \hline Seat (4) & 9 & 7 & SVM & 97.20\% & [PERSON] et. al. [33] \\ \hline Seat (4) and Backrest (1) \({}^{*}\) Kinetic & 7 & 6 & kNN & 92.7\% unfamiliar & [PERSON] et al. [8] \\ sensor (3) and temperature sensor (1) & 7 & 9 & combines several Naive & 82.3\% unfamiliar & [PERSON] et al. [32] \\ \hline Seat (6 \(\times\) 8) and Backrest (2 \(\times\) 8) & 7 & 9 & Bayes classifiers & 82.3\% unfamiliar & [PERSON] et al. [32] \\ \hline Seat (2) and Backrest (1) & 20 & 6 & \begin{tabular}{c} DT; SVM; Multilayer \\ Perception \\ \end{tabular} & \begin{tabular}{c} Decision tree (99.5\%); SVM (81.5\%); \\ MLP (99.7\%) \\ \end{tabular} & [PERSON] et al. [30] \\ \hline Customized device: Seat (240): a total of 96 out of 240 sensor elements have & & & & Customized: only seat sensor: 55\%; (with back sensor: 51\%) & [PERSON] et al. [9] \\ \hline \begin{tabular}{c} Commercial device: Seat (2 \(\times\) 32), \\ Backrest (1) \\ \end{tabular} & 9 & 11 & Naive Bayes classifier & \
\begin{tabular}{c} Customized: only seat sensor: 55\%; (with back sensor: 51\%) \\ (with back sensor: 84\%) \\ \end{tabular} & [PERSON] et al. [9] \\ \hline Seat (42 \(\times\) 48) and Backrest (42 \(\times\) 48) & 1 (single); 30 (multi) & 14 & PCA-based Algorithms &
\begin{tabular}{c} single-user system: over 95\%; \\ multi-user system: 96\% (lamiliar \\ user), 79\% ( unfamiliar user) \\ \end{tabular} & [PERSON] et al. [31] \\ \hline \end{tabular} Note.: \({}^{*}\)Additional sensors other than pressure sensors.  
\end{table} 
Table 1: Detail information of previous studies on classification of sitting postures.
# Physicochemical Characterization and Antioxidant Activity of Jara Honey Produced in Western Georgia
## 3 Results and Discussion  
### Moisture Content (%)  
One of honey's most crucial properties is its water content because it affects the substance's viscosity, specific gravity, maturity, crystallization, flavor, preservation, shelf life, and palatability [44]. It is a crucial element in determining the quality of honey [46]. It depends on several variables, including bee species, floral supply, honey harvesting season, honey maturation level (total dehydration), and meteorological factors [16; 27].  
Excessive water content in honey can lead to the growth of microbiological reactions and the development of the fermentation process, which negatively affects the qualitative characteristics of honey [3]. The moisture level in this inquiry ranged from 14.3% to 18.6%. (Table 2). Specifically, the average rate in the Keda samples was 17.78%, in the samples of Shuakhevi it was 16.47%, and in the samples of Khulo it was 15.9%. The water content in the honey samples taken for analysis was less than 20%, which is within the norm stipulated by the standard (International Honey Commission). This also indicated the maturity of the analyzed honey samples.
## 6 Conclusions  
This study investigates the bearing characteristics of foundations under horizontal monotonic loading through a combination of model experiments and numerical simulation. Utilizing PIV (particle image velocimetry) image acquisition and analysis techniques, the following conclusions are drawn:  
Figure 16: Displacement vector field under different pile-bucket diameter ratios. (**a**) Pile-bucket diameter ratio d/D = 0.417; (**b**) Pile-bucket diameter ratio d/D = 0.357.  
Figure 17: The displacement vector field of soil surface under different pile-bucket diameter ratios. (**a**) Pile-bucket diameter ratio d/D = 0.417; (**b**) Pile-bucket diameter ratio d/D = 0.357.  
Under identical pile-to-bucket diameter ratios, the horizontal load and displacement curves of the composite foundation exhibit consistent variations across different relative density conditions, dividing into stages of elastic deformation and elastoplastic deformation. Additionally, increased relative density results in a larger horizontal load borne by the bucket foundation in the composite foundation;
2. Within the same relative density level, the diameter of the bucket foundation in the composite foundation has a significant impact on its horizontal bearing performance. When the pile-bucket diameter ratio is between 0.357 and 0.5, the effect of bucket diameter on the ultimate bearing capacity of the larger composite foundation is relatively minor and follows a linear pattern. However, as the pile-bucket diameter ratio decreases further (\(\leq\)0.357), the influence of bucket diameter on the ultimate bearing capacity of the composite foundation becomes more substantial;
3. Across different relative density levels, the shape of the influence range of the surrounding soil in the composite foundation is generally similar. Under loose sand conditions, the influence range extends more to the sides compared to dense sand conditions. A raised area forms on the front soil surface of the composite foundation, while a depression area forms on the rear soil;
4. Under varying pile-bucket diameter ratios, the depth of influence of the surrounding soil in the composite foundation significantly decreases. As the pile-bucket diameter ratio decreases, the influence of the pile foundation top on the soil in the composite foundation also diminishes.  
Conceptualization, X.Z.; Methodology, D.Y. and K.Z.; Software, A.Z. and D.Y.; Writing--original draft, D.Y.; Writing--review and editing, X.Z. and M.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by grants from the Natural Science Foundation of China (Grant No. 41803033 and 41903034).  
Not applicable.  
The data presented in this study are available in the article.  
We thank [PERSON], [PERSON], and [PERSON] for their assistance in model testing and data analysis.  
The authors declare no conflicts of interest.
# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 3 Results and Discussion  
The problem addressed in this paper is the need for the efficient identification of feature selection methods in modern data processing systems. As the volume of data generated by information systems increases, ML techniques are often employed to extract meaningful information. However, the large size of these datasets may lead to slow performance, making it necessary to remove redundant features from the data during pre-processing. Feature selection is a crucial step in modern data processing systems, but it is a challenging task due to the complexity and size of the datasets. To address this problem, the authors propose the use of WSA metaheuristics for feature selection and compare its performance with seven other metaheuristics.
## 3 Results
### Effects of Antibiotics on D. melanogaster
#### 3.1.1 Antibiotics Induce Strong Mortality in Developing _D. melanogaster_ Larvae  
To test for effects of antibiotics on _D. melanogaster_, I exploited the well-described developmental dynamics of the fruit fly [107]. Timings of the developmental changes from embryos over pupae to the adult stage were already proven to be highly sensitive towards nutrient quality of the diet [109], and these dynamics therefore should allow picking up subtle effects of antibiotic treatments. Embryos were reared for 14 days on a solid nutrient source containing any of the three antibiotics at three concentrations with a drug-free condition as a control. Under these conditions--where antibiotics were confirmed to remain reasonably stable (Figure 1A)--dechorionated, axenic larvae feeding on antibiotics displayed a strongly increased mortality compared to their conventional counterparts or untreated controls (Figure 1A). The average mortality of \(\pm 30\%\) across all antibiotics and concentrations in the axenic groups strongly exceeded the mortality of the untreated axenic group and of the treated conventional groups that were statistically insignificant compared to the mortality of the untreated conventional larvae (Figure 1A). Antibiotic-treated, axenic larvae that reached the pupal stage developed successfully into adults and did not suffer any additional lethality during this stage (Figure S1B).  
Comparing the developmental dynamics, I detected the frequently reported, slower development of larvae with a diminished microbiome [107; 108] as the axenic group was delayed compared to the conventional group without any antibiotics (Figure S1C). Likewise, as also expected from their high overall mortality (Figure 1A), the antibiotic-treated, axenic groups displayed a retarded development compared to the treated, conventional groups and the untreated axenic group (Figure S1C). However, upon disregarding the mortality in the larval stage, I could not detect any additional development delay in the treated, axenic groups compared to the untreated axenic control, while, globally, an axenic stateremained delayed compared to the untreated, conventional group (Figure S1D). In other words, antibiotic treatment seems to increase mortality only in axenic groups while, with regards to development dynamics, the greatest delay is caused by an axenic status. Of note, most ampicillin- and amikacin-treated, conventional groups showed a strongly diminished microbiome in the adult stage at the end of the experiment (determined qualitatively by comparing streaks of homogenized flies), resembling that of the axenic state obtained by dechorination. Agreeing with this, these groups also displayed a small increase in development time (Figure S1C,D). Together, these data suggest that the direct effect of antibiotics in the axenic groups results from an interaction between high antibiotic concentrations and the fragility of the dechorionated embryos.
## 1 Introduction
Therefore, LSTF is more suitable for realistic fields like water quality prediction assessment, so this paper combines the above-mentioned classical deep learning models: MLP, RNN, Transformer, and LSTM with the field of long-range time series prediction, and explores the prediction ability of these models on LSTF problems while comparing with the long-term time series prediction models FEDformer [13] and Informer [14] for a comparison of each prediction performance. In this case study, we have made three main contributions aimed at providing corresponding assistance to subsequent researchers in the field of water quality:  
1. For the particular study area of Chaohu Lake, the variables affecting the water quality index WQI are grouped to find the best prediction effect and the corresponding weighting proportion. 2. The perspective of the field of water quality forecasting from short sequence to the new field of long sequence time-series forecasting proved that LSTF has more excellent control ability in the relationship between multiple variables, and LSTF can solve the actual long-term water quality forecasting problem, proved the degree of suitability of long series forecasting and water quality forecasting. 3. Compare the prediction results of LSTF model informer and several popular deep learning models for long sequence, find out the best long sequence forecasting model under one specific field of water quality prediction, and provide effective modern prediction tools and tools for water quality prediction.
# Matheuristics for the Design of a Multi-Step, Multi-Product Supply Chain with Multimodal Transport
## 4 MILP Model and Matheuristics
### Matheuristics
#### 4.2.2 Model of Reduced SCND  
The variables of the model that become parameters are the following:Parameters  
\[z_{xq}=\left\{\begin{array}{ll}1&\text{if it is decided to open a warehouse $w$ with a capacity level $q$,}\\ &(w\in W;q\in Q)\\ 0&\text{other case}\end{array}\right.\]  
\[u_{uc}^{t}=\ \ \left\{\begin{array}{ll}1&\text{if it is decided to open a warehouse $w$ with a client $c$ by mode of transport $t$,}\\ &(w\in W;c\in C;t\in T)\\ 0&\text{other case}\end{array}\right.\]  
The parameters that will no longer be used in model 2.2 with respect to the original are the following:  
\begin{tabular}{|c|l|} \hline _Parameters not used_ & \\ \hline _SQUP_ & Storage capacity required to store a unit of \(p\), \((p\in P)\) \\ \hline \(\overline{SQ}_{xqy}\) & Maximum storage capacity of warehouse \(f\) with level of capacity \(q\), \((w\in W;q\in Q)\) \\ \hline _SQ_xqy} & Minimu production capacity used by factory \(f\) with level of capacity \(q\), \((w\in W;q\in Q)\) \\ \hline \end{tabular}  
The SCND model defines the remaining decision variables of the original model, taking as parameters, the decision variables resolved by both the heuristic and the 2.1 allocation model, removing the constraints that are not necessary.  
The model uses constraints (2) to (4), (6) to (8), (11) to (13), (15) to (19), and (23) to (24).  
\[\sum_{q\in Q}z_{fq}\leq 1 f\in F \tag{23}\] \[u_{od}^{t}\in 0,1 (o,d)\in\{(S,F)\cup(F,W)\},t\in T \tag{24}\]  
The objective function is used the same as the original model, although some terms do not contain decision variables but parameters, with the aim of comparing the matheuristic solutions with those of the MILP.
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **Publish Date** & **Mine Type** & **Main Method** & **Input Parameters** & **Output** \\ \hline [PERSON] et al. [9] & 2007 & Open-pit mine & ANN & DMBS, \(Q_{\text{max}}\) & PPV \\ \hline [PERSON] et al. [10] & 2010 & Open-pit mine & ANN & \(Q_{\text{max}}\), DMBS, HSC, & PPV \\ \hline [PERSON] et al. [11] & 2010 & Open-pit mine & \begin{tabular}{c} MLPNN, GRNN, \\ RBFNN \\ \end{tabular} & \(Q_{\text{max}}\), DMBS, NHR & PPV \\ \hline [PERSON] et al. [12] & 2011 & Open-pit mine & RFR, SVM & \(Q_{\text{max}}\), \(Q_{\text{total}}\), HD, ED, & \begin{tabular}{c} PPV, first VE, \\ duration time of \\ first VF \\ \end{tabular} \\ \hline [PERSON] et al. [13] & 2012 & Open-pit mine & ANN & \begin{tabular}{c} RMR, DMBS, DMBS, \\ HoD, HoDle, SBB, \\ Qmax, \(Q_{\text{total}}\), Ny, \\ \end{tabular} & PPV, VF \\ \hline [PERSON], [PERSON] et al. [14] & 2014 & Open-pit mine & ANN, MLR & \(Q_{\text{max}}\), DMBS, SD, & PPV \\ \hline [PERSON] et al. [15] & 2015 & Open-pit mine & ANN & \(Q_{\text{max}}\), DMBS & PPV \\ \hline [PERSON], [PERSON] et al. [16] & 2015 & Open-pit mine & \begin{tabular}{c} DE algorithm \\ theory \\ \end{tabular} & HoDe, B, S, CL, \(Q_{\text{max}}\), & PPV \\ \hline [PERSON] et al. [20] & 2021 & Open-pit mine & \begin{tabular}{c} AGPSO-ELM, \\ PSO-ELM \\ \end{tabular} & \begin{tabular}{c} B, \(Q_{\text{max}}\), HS, DMBS, \\ PF, HoDe \\ \end{tabular} & PPV \\ \hline [PERSON] et al. [21] & 2021 & Open-pit mine & \begin{tabular}{c} GEP, ANFS, \\ SCA-ANN \\ \end{tabular} & \begin{tabular}{c} DMBS, \(Q_{\text{max}}\), RD, SRH \\ \end{tabular} & PPV \\ \hline [PERSON] et al. [22] & 2021 & Open-pit mine & \
\begin{tabular}{c} MVO-ELM, ELM, \\ MLP, USBM \\ \end{tabular} &
\begin{tabular}{c} C, \(Q_{\text{total}}\), HD, VD, B, \\ PPR, DT, RMI, f, a, \\ VoD \\ \end{tabular} & PPV \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of PPV prediction in metal mines.
## 2 The Applications in Smart Grid Based on Time Synchronization
### Lightning Correlation  
A lightning strike on a line or supporting structure can lead to both physical damage and electrical faults. Often, a line will trip following a lightning strike but will reclose several seconds later, remaining energized. However, lightning may still cause damage to insulators or conductors, making it advisable to inspect the area where the strike occurred [32].  
The U.S. National Lightning Detection Network (NLDN) supports such assessments by deploying over 100 sensors across the contiguous United States (CONUS) with an average baseline spacing of 300-350 km. The NLDN identifies signals from impulsive lightning events within the very low-frequency/low-frequency range, utilizing time-of-arrival and magnetic direction-finding methods to precisely determine the location of each strike. For each event, the NLDN provides detailed data including the time, location, event type, polarity, peak current estimate, and field waveshape parameters (such as rise time and peak-to-zero time), as well as confidence ellipses for location accuracy [33].  
Lightning strikes are timestamped to within one millisecond, allowing utilities to compare strike data with fault inception times obtained from point-on-wave timestamps recorded by DFRs or relays. This comparison helps utilities pinpoint inspection locations and anticipate potential damage based on the strike's intensity. An accuracy of one millisecond relative to absolute time is sufficient for reliable lightning fault correlations [32].