esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Verification of Sowing
#### 3.2.2 Sowing Simulation Experiment  
During the sowing simulation experiment, the materials of the simulation model were identical to those of the actual device. The simplified three-dimensional model of the seeder was loaded into EDEM, and the material parameters of the seeder components were configured according to Table 1. The total weight of the simulated Sesbania seed model was set to 0.2 kg. The simulation duration comprised 15 s at the low rotational speed (9.26 r/min) and 10 s at the high rotational speed (47.62 r/min). After the simulation, a grid block was added below the seed outlet in the EDEM post-processing module, in which the number of seeds falling was recorded every 0.01 s, as illustrated in Figure 10.  
The simulation was conducted three times. The replay-seeding and missed-seeding rates were calculated using Formulas (9) and (10), and the average values were subsequently determined. The final test results indicated a replay-seeding rate of 11.81% and a missed-seeding rate of 7.09%.  
\[D=\frac{n_{1}}{N}\times 100\% \tag{9}\]  
\[M=\frac{n_{2}}{N}\times 100\% \tag{10}\]  
where \(D\) is the replay-seeding rate; \(M\) is the missed-seeding rate; \(n_{1}\) is the number of instances where the number of dropped seeds exceeds 5; \(n_{2}\) is the number of instances where the number of dropped seeds is fewer than 3; and \(N\) is the total number of dropped seeds.
# Smart Waste Collection System with Low Consumption LoRaWAN Nodes and Route Optimization
## 2 State of the Art
### Wireless Sensors Networks
Multiple platforms based on the LoRaWAN specification are available in the market, like Actility [46], Loriot [47], LoraServer.io [47] and Senet [48], among others. NB-IoT [49] is an LPWAN new technology for the Internet of Things developed by the 3 rd Generation Partnership Project (3 GPP) [50]. NB-IoT technology employs a licensed network spectrum currently used by different Internet Service Providers (ISPs). The network can be deployed in two different ways: \"in-band\" or \"standalone\" deployment. The \"in-band\" deployment takes place in the spectrum allocated to Long Term Evolution (LTE), using resource blocks within the LTE carrier (even their guard-bands). The other deployment, the \"standalone\", implies the use of a dedicated spectrum. NB-IoT uses the Orthogonal Frequency-Division Multiple Access (OFDMA) modulation for the downstream channel and Single Carrier Frequency Division Multiple Access (SC-FDMA) for the upstream channel. Its main advantage, in terms of communication, over other LWPANs is that it is well suited for applications that need to have minimal latency and are required to communicate more frequently [35]. It is currently being deployed throughout Europe [51], but its availability is being reduced to urban areas where various functional tests are being carried out. These provide the network, and the connection is set through classic subscription services. Besides the technologies previously indicated, there are more technologies that are starting to become available in Europe [52], such as Random Phase Multiple Access (RPMA) by Ingenu [53; 54] and Weightless of Weightless SIG [55].
## 2 Related Work
In message ferrying, the source node evaluates all contacting nodes with the predictability of successful delivery by using defined functions and transmits messages to the node with the highest delivery rate. In order to reduce messages dropping and transmission delay in DTN, we try to make use of predictability and buffering to improve routing performance in this study.
## 3 Results and Discussion
### Drug Loading and In Vitro Release Studies
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline
**Conc.** & \multicolumn{3}{c}{**Zero-Order**} & \multicolumn{3}{c}{**[PERSON]**} & \multicolumn{3}{c}{**[PERSON]**} & \multicolumn{3}{c}{**[PERSON]**} \\ \cline{2-13}
**(\%, \(\textit{wt}\))** & \(\mathbf{k_{0}}\times\mathbf{10^{4}}\) & \(\mathbf{R^{2}}\) & **AIC** & \(\mathbf{k_{\mathrm{H}}}\times\mathbf{10^{2}}\) & \(\mathbf{R^{2}}\) & **AIC** & \(\mathbf{k\times 10^{2}}\) & \(\mathbf{n}\) & \(\mathbf{R^{2}}\) & **AIC** & \(\mathbf{k_{1}}\times\mathbf{10^{2}}\) & \(\mathbf{k_{2}}\times\mathbf{10^{4}}\) & \(\mathbf{R^{2}}\) & **AIC** \\ \hline
13 & 3.77 & 0.941 & \(-\)35.35 & 1.001 & 0.993 & \(-\)56.36 & 18.68 & 0.13 & 0.958 & \(-\)24.16 & 2.073 & \(-\)1.56 & 0.991 & \(-\)60.29 \\
22 & 4.82 & 0.969 & \(-\)36.41 & 0.810 & 0.986 & \(-\)54.03 & 1.534 & 0.43 & 0.992 & \(-\)30.93 & 1.72 & \(-\)1.24 & 0.999 & \(-\)84.96 \\ \hline \end{tabular}
\end{table} 
Table 1: Release parameters corresponding to AM-loaded Plx-\(g\)-CMP hydrogels of 13% and 22% copolymer concentration. Figure 8: Release dynamics of AM from loaded Plx-\(g\)-CMP hydrogels in PB (pH 7.4) at 37 \({}^{\circ}\)C: plots of zero-order (**a**), Higuchi (**b**), Ritger–Peppas (**c**) and Peppas–Sahlin (**d**) models. of [PERSON]'s second law of diffusion, which for small values of time (\(t<60\%\)) is given by Equation (9) [36]:  
\[\frac{M_{t}}{M_{\infty}}\ =\ 4\ \left(\frac{D_{E}\ t}{\pi\ H^{2}}\right)^{0.5} \tag{9}\]  
where \(M_{t}\) and \(M_{\infty}\) are the total mass of the diffusion compounds released from the layer after time \(t\) and infinite time, respectively. Since for most systems the diffusion coefficient (\(D_{E}\)) is dependent on drug concentration, as well as the concentration of the swelling agent (i.e., water), the slope of the plot of \(M_{t}/M_{\rm inf}\) as a function of \(t^{0.5}\) generates an average diffusion coefficient. The thickness of the hydrogel matrix (\(H\)) inside the vial was calculated from the volume of the Plx-\(g\)-CMP solution (i.e., 1 mL) and from the dimensions of the vial. \(D_{E}\) values obtained for the two concentrations of hydrogel from plotting \(M_{t}/M_{\infty}\) vs. \(t^{0.5}\) (Figure 8b) were 2.579 \(\times\) 10\({}^{-7}\) mm\({}^{2}\)/min for the 13% (\(w/v\)) sample and 1.692 \(\times\) 10\({}^{-7}\) mm\({}^{2}\)/min for the 22% (\(w/v\)) one. This reduction of the diffusion rate with the increase of the Plx-\(g\)-CMP concentration confirms our hypothesis that steric hindrances control the rate of drug release.
## 4 Results and Case Study
### Overall Comparison--Extrinsic UXDs Extraction Evaluation  
We chose UX experts from a group working with us on an ongoing research project. We performed an extrinsic evaluation by comparing the UXWE-LDA inferred topic with the gold-label topic assigned by the three UX experts. The UX experts annotated a total of  
Figure 11: Average TC of top words with different number of topics on on both datasets.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{2}{l}{**Electronics Products Dataset (TV-Screen)**} & \multicolumn{3}{l}{**Non-Electronics Products Dataset (Food)**} \\ \hline
**UXWE-LDA** & **WE-LDA** & **LDA** & **UXWE-LDA** & **WE-LDA** & **LDA** \\ \hline screen & image & screen & vegetable & flavor & popcorn \\ headset & video & **side** & cook & sweet & **functions** \\ microphone & end & **big** & meat & salt & **bag** \\ speaker & **microsoft** & **line** & pizza & **market** & potato \\ voice & logitech & **flat** & soup & fruit & healthy \\ sound & resolution & image & baked & **natural** & chicken \\ mic & top & **top** & delicious & spice & meat \\ audio & cd & lcd & chicken & **strong** & sweet \\ conversation & pc & resolution & bean & ingredient & **number** \\
**mike** & **skype** & sound & yummy & delicious & **machine** \\ loud & **chat** & **strap** & **winter** & bean & **basic** \\
**bottom** & picture & **substitute** & **simple** & **open** & spice \\ \hline \hline \end{tabular}
\end{table}
Table 3: Example topics generated by UXWE-LDA, WE-LDA, and LDA. The bold text shows an error.  
300 online reviews, where each sentence is label based on the provided UX dimension list. Sentences with mutual agreement from all three annotators were considered as gold-label for the performance evaluation. We employed the topic-wise performance metrics (recall, precision, and F1 score) for comparison with LDA baseline algorithms. Precision means the percentage of correct classifications of that topic among all gold-label reviews sets, where the UXWE-LDA model predicts that topic. Recall for a topic is the portion of correct classifications of that topic out of all the cases of that topic in the gold-label reviews. The F1 score of a topic is the harmonic mean of recall and precision of that topic and is given in Equation (8).  
\[\text{F1 Score}=2\times\frac{precision_{k}\times recall_{k}}{precision_{k}+ recall_{k}} \tag{8}\]  
where a higher F1 score indicates that the model performs well for classifying the test data as shown in Table 4.  
Figure 12 has shown that UXWE-LDA achieves 3% improvement on average as compared with LDA due to the incorporation of semantic domain knowledge.
## 3 Experimental Result
### Classifier Structure  
The proposed DL architecture was constructed by combining the pre-trained DAEs encoding layer and fine-tuned of DNNs part with the fully connected layer. In the classifier part used ReLU as the activation function, Cross-entropy as the loss function and Adam as the optimization method with the learning rate is set from 0.1 and gradually decreased to 0.0001. Adam optimization allows the use of adaptive learning rates for each parameter. The result of learning rate used is that the learning rate about 0.001 produces a good result in the loss value. Figure 1 shows the details of the proposed structure for the fully connected classifier. We have used 10 experiments to select the best model structure of deep learning (see Table 2). All parameters were tuned to obtain the smallest loss value to avoid overfitting, high accuracy, high sensitivity, high specificity, high precision, and high F1-Score. However, the deep structure must produce a short processing time and small memory usage. Table 2 shows that DL performance improved when using a ReLU activation function, compared to the Sigmoid activation function. Moreover, ReLU activation function was a more straightforward computation than sigmoidal functions, and it has been proven that ReLU works better than sigmoidal functions [31]. Based on all performances, model 8 was selected as the best model with three hidden layers in pre-training and three hidden layers of fine-tuning used. In our work, model 8 is the proposed DL structure in Table 3. Table 3 shows the proposed structure of the deep learning method used in the present study.  
Figure 3: Sample of Auto Encoder reconstruction results.
# YOLOv8n-CBAM-EfficientNetV2 Model for Aircraft Wake Recognition
## 3 The Proposed Method
### YOLOv8n-CBAM-EfficientNetV2 Network Design
#### 3.2.2 Neck Network and Head Network  
The neck network of the YOLOv8n-CBAM-EfficientNetV2 model is primarily comprised of a convolutional layer, an upsampling layer, a connection layer, and a module derived from the CBAM. The Conv module is employed for the extraction of wake features and the reduction in dimensionality. The Upsample module is utilised for the scaling of low-resolution feature maps to a size commensurate with that of the high-resolution feature maps, i.e., to recover the spatial resolution of the feature map. The Concat module enables the feature fusion of feature maps with different resolutions or at different layers, thereby providing richer information to the subsequent network layers. The C2f module is a residual block that enables the network to learn the mapping relationship between inputs and outputs by introducing residual connections, thereby optimising the training process of the network. Furthermore, the incorporation of the CBAM module serves to augment the feature extraction capabilities of the YOLOv8n-CBAM-EfficientNetV2 model.  
The detect module in the head network is primarily responsible for classifying the targets present in the input image, locating the detection frames, and calculating the confidence level score. To generate the final detection results, a non-maximal value suppression algorithm was employed to eliminate superfluous detection results and select the detection frame with the highest confidence level score as the output result. This was achieved by screening multiple overlapping detection frames. Ultimately, the detect module can extract and generate predicted frames based on the information obtained from the neck network, including the target location, category, and confidence level.
# A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer
## 2 Materials and Methods
### Statistical Analysis
#### 2.5.1 Classification Models  
The transformed and scaled dataset containing the metabolomics results was randomly divided into two equal parts, using computer assisted randomization, such that each subset contained the same number of cases and controls. One set was used to train a given classification model and the other was used to evaluate the diagnostic performance of each model. Nine different classification models were built and optimized using the training dataset containing observations where class membership (e.g., case or control) was known a priori: Partial Least Square Discriminant Analysis (PLS-DA), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), k-nearest neighbor (k-NN), Artificial Neural Network (aNN), Support Vector Machine (SVM), Logistic Regression (LR), and Deep Learning (DL). Models were built according to [PERSON] et al. [14].  
Next, the classification models were ensembled using a voting scheme weighted by the classification accuracy of each model. Ensemble learning algorithms were used to generate a set of classifiers that assigned a weighted vote of each model's predictions. Ensembling of all models was performed using RapidMiner Studio version 9.4 (RapidMiner, Boston, MA,USA) while PLS-DA and the voting scheme was conducted using R (version 3.5.3) [19] and subsequently integrated in the data mining algorithm.  
Class imbalance was managed by means of Metacost algorithm [20] according to [PERSON] et al. [16]. Metacost introduced a misclassification cost matrix coherent with the class size.
###### Abstract  
FWI is a nonlinear optimization problem; significant discrepancies between the initial and true velocity models can lead to solutions converging to local optima. To address this issue, we proposed a PIRNN-based FWI method with first-arrival time constraints. Physics-informed recurrent neural networks (PIRNNs) integrate the physical processes of seismic wave propagation into recurrent neural networks, offering a novel approach for full-waveform inversion (FWI). First, the physical processes of seismic wave propagation were embedded into the recurrent neural network, enabling finite-difference solutions of the wave equation through forward propagation. Second, first-arrival time differences between synthetic and observed records were calculated, which then guided the selection of appropriate seismic traces for FWI loss computation. Additionally, the spatiotemporal gradient information recorded during the forward propagation of the recurrent neural network was utilized for backpropagation, enabling nonlinear optimization of FWI. This method avoids the local optima caused by waveform mismatches between the observed and synthetic records resulting from inaccurate initial velocity models. Numerical experiments on the BP and Marmousi velocity models demonstrated that the proposed method accurately reconstructed subsurface velocity structures even when the initial model significantly deviated from the true model, and maintained a degree of reconstruction accuracy in the presence of considerable noise, thereby validating its low sensitivity to the initial model and its robustness against noise.  
acoustic full waveform inversion (AFWI); vertical seismic profiling (VSP); physics-informed recurrent neural network; first-arrival constraint +
Footnote †: journal: Applied sciences
## 5 Control Performance Evaluation
### Sensitivity Analysis Under Detuning Situations  
In practical application, the target optimal parameters for the TLIMD may be deviated from the optimization due to the manufacture errors of device, ageing of the spring and damper, and frequency change in the primary structure in service, directly resulting in the detuning of the TLIMD and deteriorating the control performance. Hence, the sensitivity analysis is essential to estimate the performance of the TLID once the detuning occurs.  
Previous studies, in general, assume that the frequency and damping ratios of the damper device are varied in a range normalized to the optimal values to obtain the ratio of these maximum amplitudes of the primary structure to the optimized values using a widely used tool contour plot. However, an issue may arise: the comparison of the sensitive performance between different kinds of dampers is difficult if they have a similar sensitivity. Additionally, one contour plot only represents the one specified mass ratio or inertance mass ratio. It cannot roundly evaluate which kind of damper is insensitive to parameters variation, and a new index needs to be introduced. Therefore, the above question is discussed in this subsection through the sensitivity comparisons among TID-TMDI- and TLIMD-controlled systems. The current discussion is focused on two critical parameters, i.e., the frequency ratio \(\beta\) and the damping ratio \(\zeta_{2}\), where their deviation value in TLIMD/TMDI/TID are set to \(\beta\) = (0.8-1.2)\(\beta_{opt}\) and \(\zeta_{2}\) = (0.8-1.2)\(\zeta_{2,opt}\)  
Herein, an assessment index called the root mean square error (RMSE) is introduced, defined as  
\[RMSE=\sqrt{\sum_{i=1}^{M}\sum_{j=1}^{N}\frac{\left[I_{ij}(\beta,\zeta_{2})-I_ {H,opt}(\beta_{opt},\zeta_{2,opt})\right]^{2}}{MN}} \tag{32}\]  
where \(M\) and \(N\) represent the sum of deviation points for frequency and damping ratios, respectively and \(i\) and \(j\) are the number of \(M\) and \(N\).  
Figure 9 shows the RMSE value versus the mass ratio \(\mu\). It can be observed that the TLIMD exhibits the minimal value for all the selected cases, especially under the large mass ratio condition (such as \(\mu\) = 0.1). Furthermore, the increase in mass ratio \(\mu\) and the inertance ratio \(\mu_{b}\) can diminish the RMSE of all these dampers, and thus somehow enhance the robustness of the damper performance.  
Figure 10 shows the maximum amplitude ratios of primary structure with various frequency and damping ratios deviated from the damper optimal design values. The mass ratio \(\mu\) for the TLIMD and TMDI is set to 0.01, and the inertance ratio for the TMDI and TID is set to 0.1. It can be seen that the contour lines for the TMDI and TID looks the same while the sensitive performance of the TLIMD may be better than the others due to its small value. Figure 10d further ensures this conclusion, in which the TLIMD provides the minimal indices \(I_{H}\) value.  
Figure 9: The RMSE value versus the mass ratio.
# Ultra-Wideband Compact Fractal Antenna for WiMAX, WLAN, C and X Band Applications
## 2 Antenna Design
### Effect of the position of Lg1  
To further improve the performance of the antenna, a slit in the ground plane was inserted. The change in slot position in the partial ground has a significant effect on the wideband operation of the monopole antenna. A decrease in bandwidth is observed when shifting from the suggested value of 'Lg1', as shown in Figure 5. It is clear that Lg1 = 12.25 mm is the best.  
Figure 4: Parametric analysis of radius (R).  
Figure 5: Parametric analysis (Lg1).
###### Abstract  
The computer information service industry is closely related to the fourth industrial revolution and stands at the core of the global value chain. It has become an essential engine for developing industries in various countries, and its scale is constantly expanding. In the critical period of global economic transformation and development, the use of mathematical models to predict its international competitiveness will help scientifically evaluate the development level of the industry and accelerate the adaptation to the needs of the fourth industrial revolution. In this article, a prediction model is proposed for the international competitiveness of the computer information service industry. First, we used the Revealed Comparative Advantage (RCA) index to measure the international competitiveness of the computer information service industry. Furthermore, based on the characteristics of the industry and high-quality development theory, we constructed the evaluation indicator system of influencing factors and used the grey relational analysis method to screen key indicators. Then, we combined the Grey model and BP neural network algorithm to construct the GM-BP prediction model. Finally, China is used as an example to predict the international competitiveness of its computer information service industry, and suggestions are made for industrial development. The results show that the grey relational analysis method can genuinely reflect the impact of different aspects on the international competitiveness of China's computer information service industry and better determine the key indicators of influencing factors. The GM-BP model has minor errors and excellent simulation results and can accurately predict the future status of international competitiveness. The applicability and reliability of the model are reasonable.  
computer information service industry; international competitiveness; BP neural network; grey relational degree; high-quality development Article
# A Tunable Self-Offloading Module for Plantar Pressure Regulation in Diabetic Patients
## 1 Introduction
This can significantly reduce cost, and depending on the design, it can make the overall shoe design close to the normal daily shoes. Therefore, it can serve both of the mentioned needs, comprising user experience, as well as affordability. There are existing passive approaches, such as offloading [30; 31], graded stiffness [32], and cushioning [33], with the capability of PP distribution, but these approaches lack offline adjustments and are not compatible with detecting/acting against peak PP within shoes. Works such as [34] provide a pre-defined PP distribution profile, but because of the larger exposed area, local occurrences may not be effectively handled. The device in [35] allows PP redistribution more locally with an array of regular hollow hexagons featuring an edge spacing of 6.5 mm and a thickness of 0.5 mm, but for a given device, it lacks threshold limit tunability and offloading profile consistency. An offline tunability feature with reliable performance in such a device would be of additional benefit as the PP profile can be pre-controlled, depending on the given situation of the diabetic foot and/or the patient. Local handling and redistribution of the excess pressure were also considered in our previous work [29]; therefore, we aimed to keep a similar exposed area for individual modules in this work. We aimed to replace the active offloading module of [29] with a passive alternative so that the energy dependency, cumbersome electronics, and cost can be reduced. Sensing assessment studies [36] suggest that plantar pressure may go higher than 740 kPa for an exposed area smaller than 100 mm\({}^{2}\). However, depending on the individual requirements, the threshold pressure may be much lower (100 kPa or lower). Keeping such a requirement under consideration, the device in the present work aimed to exhibit tunability to cover 100-740 kPa as the threshold limit. This work proposes the design, development, and experimental validation of a passive module with offline tunability of its threshold feature with the advantage of a smaller exposed area of the foot, leading to an effective local handling of PP. The corresponding system design (Section 2) and numerical simulation (Section 3) are initially presented. Section 4 discusses the experimental setup and validation of the device working. The conclusion of the work and future perspectives are discussed in Section 5.