esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling
### Finite Volume Method  
The finite volume method is used to discrete the Reynolds equation. Figure 4 represents a part of the film grid. Integrating Equation (8) within the shaded area in Figure 4, Equation (15) becomes:  
\[\Delta\hat{y}\frac{(p_{E}-p_{C})}{\delta\hat{x}}\left(\frac{{h_{p}}^{3}}{\mu} \right)_{\varepsilon}-\Delta\hat{y}\frac{(p_{C}-p_{W})}{\delta\hat{x}}\left( \frac{{h_{p}}^{3}}{\mu}\right)_{w}+\Delta\hat{x}\frac{(p_{N}-p_{C})}{\delta \hat{y}}\left(\frac{{h_{p}}^{3}}{\mu}\right)_{n}-\Delta\hat{x}\frac{(p_{C}-p_{ S})}{\delta\hat{y}}\left(\frac{{h_{p}}^{3}}{\mu}\right)_{s}=6S\big{(}h_{p} \big{)}\Delta\hat{x}\Delta\hat{y} \tag{15}\]Equation (15) can be written as:  
\[a_{W}P_{W}+a_{N}P_{N}+a_{E}P_{E}+a_{S}P_{S}=a_{C}P_{C}+6S\big{(}h_{p}\big{)}\Delta \hat{x}\Delta\hat{y} \tag{16}\]  
\[a_{W}=\frac{\Delta\hat{y}}{\hat{x}}\Bigg{(}\frac{h_{p}^{3}}{\mu} \Bigg{)}_{W},a_{N}=\frac{\Delta\hat{x}}{\hat{y}}\Bigg{(}\frac{h_{p}^{3}}{\mu} \Bigg{)}_{N},a_{E}=\frac{\Delta\hat{y}}{\hat{x}}\Bigg{(}\frac{h_{p}^{3}}{\mu} \Bigg{)}_{E},a_{S}=\frac{\Delta\hat{x}}{\hat{y}}\Bigg{(}\frac{h_{p}^{3}}{\mu} \Bigg{)}_{S}\] \[a_{C}=a_{W}+a_{N}+a_{E}+a_{S}\]  
Such a linear equation can be written for any grid point. These linear equations form a linear equation system, which is solved by the relaxation method under given boundary conditions (the outlet pressure and inlet pressure in the direction of \(\hat{y}\), and the continuity condition in the direction of \(\hat{x}\)).  
In the same way, Equation (13) can be discretized and solved. However, to avoid negative coefficient in the discretized equation, and ensure the convergence of the iterative solution scheme, the power-law scheme should be used [2].
## 6 Discussion  
### Single-Sideband Backscatter  
The frequency-shifting design creates copies of the main signal on both sides, one of which is undesirable, as demonstrated in Equation (2). This double-sideband backscatter signal may cause unnecessary interference in other frequency bands while also leading to a wastage of spectral resources. Therefore, a method to eliminate the unwanted sideband can be employed to achieve single-sideband backscatter. [PERSON] et al. [9] propose a design for a single-sideband backscatter tag. Specifically, we can integrate a single-sideband backscatter functionality into our tag hardware by incorporating a splitter. Each path of the splitter generates a double-sideband signal. In one path, we can generate a negative copy of  
Figure 13: BER analysis under varying conditions. (**a**) BER with error bars under different adaptive pulse-width square wave conditions and distance parameters. (**b**) Simulated BER versus SNR plot for the three-tag backscatter system.  
the signal on one sideband while maintaining an identical copy on the other sideband. Consequently, when we combine the signals from both paths, we effectively cancel out the signal on one sideband and amplify the signal on the other sideband. We believe that integrating this single-sideband backscatter functionality within the tags can further reduce signal interference between different frequency bands while ensuring harmonic interference elimination.
###### Abstract  
This study aims to evaluate the effect of chitosan (CS) scaffold, alone, and the potential synergistic effect when impregnated with simvastatin (SIM), on immortalized human bone-marrow mesenchymal stem cells (hbMMSCs) compared to CollaCote (CL). CS scaffolds were fabricated and seeded with immortalized hBMMSCs. Samples were divided into control groups (negative with no added material and positive with CL added) and four experimental groups: CS alone, CS/SIM 0.01, 0.03, and 0.05 mg, respectively. Cell viability, osteoblastic differentiation and calcium deposition were investigated via AlamarBlue, alkaline phosphate activity assays and alizarin red S staining at 1 and 14 days, respectively. At day one, no significant difference was noted between the groups regarding cell viability. However, all CS/SIM groups showed significant cutback at day 14 in cell proliferation compared to CS alone and CL groups (\(p<0.001\)). All groups supported osteoblastic differentiation with no significant difference. Alkaline phosphate activity increased in both time periods in the CS/SIM 0.05 mg group compared to the other SIM groups, with no significant difference among the experimental groups. Chitosan scaffold is a bioactive compatible material capable of regenerative potential of hBMMSCs and a promising material to be used for perforation repair.  
chitosan; differentiation; osteoblast; scaffold; simvastatin 2021 11 5336. [[https://doi.org/10.3390/app11125346](https://doi.org/10.3390/app11125346)]([https://doi.org/10.3390/app11125346](https://doi.org/10.3390/app11125346))
## 3 Training Signal Optimization
### Worst-Case MSE Minimizing Training Structure
Finally, it is important to note that the number of real-valued variables to be optimized is reduced from \(2(M_{t}L+M_{t}^{2}+M_{r}^{2}+L^{2})\) to \(\min\{M_{t},L\}+M_{t}+L\) with the proposed structures in (15) and (16). **Remark 2**. : _When the interference is temporally white and its variance information is perfectly known, the result (15) becomes \(\mathbf{P}^{*}=\mathbf{U}_{T}\mathbf{D}_{P}^{*}\), or equivalently, \(\mathbf{P}^{*}\mathbf{P}^{*H}=\mathbf{U}_{T}\mathbf{\Lambda}_{P}^{*}\mathbf{U}_ {T}^{H}\), where \(\mathbf{\Lambda}_{P}^{*}=\mathbf{D}_{P}^{*}\mathbf{D}_{P}^{*T}\), which is consistent with the result derived in [19]._  
**Remark 3**. : _When \(\mathbf{Q}\) is perfect, but \(\mathbf{T}\) is imperfect, which is the case considered in [20], the training structure in (15) becomes \(\mathbf{P}^{*}=\mathbf{U}_{T}\mathbf{D}_{P}^{*}\mathbf{V}_{Q^{\prime}}^{H}\) where the columns of \(\mathbf{V}_{Q}\) consist of the eigenvectors of \(\mathbf{Q}\)._
# Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer
## 4 Model Introduction and Application
### Evaluation Indices  
To quantitatively evaluate and compare the predictive accuracy of each model, this study employs three evaluation indices: the root mean square error (RMSE), mean absolute error (MAE), MAPE (Mean Absolute Percentage Error) and the Coefficient of determination (R\({}^{2}\)).  
(1) Root mean square error (RMSE)  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{t=1}^{N}(Y_{\text{O}}-Y_{P})^{2}} \tag{13}\]  
(2) Mean absolute error (MAE)  
\[\text{MAE}=\frac{\sum_{t=1}^{N}\lvert Y_{O}-Y_{P}\rvert}{N} \tag{14}\]  
(3) MAPE (Mean Absolute Percentage Error)  
\[\text{MAPE}=\frac{100\%}{n}\times\sum_{i=1}^{N}\left\lvert\frac{\dot{y}_{i}-y _{i}}{y_{i}}\right\rvert \tag{15}\]  
(4) Coefficient of determination (R\({}^{2}\))  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{N}(\dot{y}_{i}-y_{i})^{2}}{\sum_{i=1}^{N}( \overline{y}_{i}-y_{i})^{2}} \tag{16}\]  
The root mean square error (RMSE) measures the square root of the average squared differences between predicted and actual values, providing a sense of scale for prediction errors, which can be expressed as Equation (13). Mean absolute error (MAE) represents the average magnitude of prediction discrepancies, emphasizing the absolute error magnitude, which can be expressed as Equation (14). If MAPE = 0%, the model is a perfect model, and MAPE greater than 100% indicates an inferior model. The smaller the value of MAPE, the better the accuracy of the prediction model, which can be expressed as Equation (15), MAPE \(\in[0,+\infty]\). The coefficient of determination (R\({}^{2}\)) evaluates the proportion of variance in the dependent variable that the model explains. The closer the value of R\({}^{2}\) is to 1, the better the fit of the regression line to the observed values, which can be expressed as Equation (16), R\({}^{2}\in[0,1]\).
# Building Wireless Control Applications with XBee and LabVIEW
## 4 Experimental Tests
### Discussion  
This section analyzes the use of XBee wireless technology for control applications programmed with LabVIEW. A Single Input Single Output (SISO) system was considered, in which both the sensor and actuator were connected wirelessly, by means of XBee modules. Several experimental tests were carried out in order to explore the use of these technologies. Namely, the experiments carried out involved analyzing--(1) the performance of the Analog-to-Digital Converter (ADC) included at the XBee device; (2) the offset and message dropout for different distances and sampling periods; (3) the message delay at different distances and sample periods; and (4) the presence of Wi-Fi interferences in a very unfavorable scenario. The results of these tests should show if the combination of XBee and LabVIEW was adequate to building some non-critical WNCS  
The first conclusion drawn from these experiments was that the dropout rate was not so high, for the measured distances (up to 5 m) and sampling periods (up to 30 ms). For the tests carried out, the dropout was always around 2%. During the tests, it was also proven that the flexibility of using XBee antennae was very high, since wireless technologies allow to easily move the devices, at different distances.  
Nonetheless, there were also weak points. A higher delay was created. This delay was very sensitive to the baud-rate of the XBee configuration. However, the authors believe that a higher speed could increase the dropout. Additionally, it was confirmed that Wi-Fi might cause serious problems of interference with XBee, since both were using the 2.4 GHz band. For this reason, the authors evaluated the use of XBee modules that work at different frequency bands, such as 868 MHz or 900 MHz. Another finding was that the ADC used by the XBee module did not provide good results. It was improved by connecting an Arduino Uno board for sampling the signal. However, for some high precision applications, this approach could still be poor. For these cases, more precise technologies should be analyzed.  
In general, it could be concluded that for non-critical systems or for monitoring purposes only, the analyzed combination could have become a viable alternative, even though it had some limitations. However, for more critical applications several factors must be improved, either by more precisely tuning the XBee configuration and, if possible, reducing the environmental interferences.
# Integration of Deep Learning and Collaborative Robot for Assembly Tasks
## 3 Implementation
### Object Classification System  
The assembly part classification system enables the effective detection and storage of the various elements of the assembly. To achieve this, the system follows a three-stage process: image acquisition, image preprocessing, and image classification.  
A Logitech(r) C910 camera (Lausanne, Switzerland) was used as the input for the system, as shown in Figure 5. Figure 5a exemplifies the position in which parts were photographed for classification, as well as the setup used for both the training and classification images. Figure 5b shows the robot retrieving the object after it was properly classified. Once the image was captured, data processing was carried out simultaneously in a Python3 script as follows. The images were captured using the OpenCV library. Subsequently, the images were resized to the desired dimensions of 224 \(\times\) 224 pixels, which were designated for the design of data pipelines. And finally, contrast and brightness were modified from the captured images. These preprocessing steps allowed us to enhance the differences between objects, enabling the identification of small differences that distinguish the parts.  
Figure 4: Flow diagram of the robot routines composed of the different subsystems involved.  
The image classification was performed using a CNN (Convolutional Neural Network) trained with the TensorFlow DL library. The architecture used for the model was a modification of the \"AlexNet\" first proposed by [PERSON] et al. [47]. The CNN consists of five convolutional layers, one layer for normalization after the first convolutional layer, max pooling layers after the second to fifth convolutional layers, two fully connected layers, and a drop-out layer.  
Figure 5c illustrates the structure of each layer in the CNN, including the window sizes used. To train the CNN, a data pipeline was implemented in Python3 using a dataset obtained from the same setup as the one used for the application.  
Each of the seven classes (one for each object observed in Figure 3) was trained with approximately 150 preprocessed images. The dataset was split into 70% used for training, 20% for validation, and 10% for testing. The trained model achieved an accuracy of 0.98 on the test data. The system was integrated into a Python3 script using an ROS node structure. This node subscribed to the camera topic to receive RGB images and published the observed class. The images were classified in 10 consecutive instances within the central node to achieve a precise classification. The mode of these measurements was then used as the final class. This approach ensures accurate classification and prevents the capture of undesired frames.  
Figure 5: Setup used for object classification. (**a**) The object is positioned for image capture. (**b**) The CNN classifies the object and the cobot retrieves the object for storage. (**c**) Structure and window size of CNN for object classification.
## 2 Material and Methods
### Measurement Principle and Device Architecture  
The measurement principle of using the proposed HR to measure analyte concentration is shown in Figure 6. The proposed HR is linked with a circular duct, where one end is equipped with an acoustic power source and the other end is defined as an anechoic end. Two microphones are used to obtain the \(P_{1}\) and \(P_{2}\) in Equation (5) before and after the neck inlet of the HR. Using the same HR dimensions as the analytical example, the architecture consisting of a duct (\(L_{d}=140\) mm, \(R_{d}=15\) mm) linked with an HR was fabricated by 3D printing. The material of 3D printing was ABS (acrylonitrile butadiene styrene) resin. The walls were solid and wall thickness was 3 mm. A wireless speaker was selected as the acoustic power source, and two acoustic pressure sensors were placed to measure the acoustic pressures. The polyester fiber was used as an acoustic absorption material to cover the other end of the duct in order to make it into an approximately full acoustic absorption boundary. The absorption coefficient in the frequency range of 100-1000 Hz is larger than 0.92. The fixed fluid was selected as air  
Figure 4: Acoustic finite element model and the 1 st mode. (**a**) Acoustic finite element model; (**b**) pressure distribution of the 1 st mode.  
Figure 5: Spectrum of transmission loss obtained by acoustic response analysis using Finite Element Method (FEM).
## 3 Results
### Copula-Based Joint Distribution Optimization  
Table 2 presents the goodness-of-fit evaluation results for the bivariate Copula functions fitted to SPEI-3 and STI-1. Based on the criterion that a smaller squared Euclidean distance indicates a better fit; the Gaussian Copula function demonstrates the optimal performance with the lowest value of 0.1422. Therefore, the Gaussian Copula was selected to construct the CDHI.  
Figure 2: (a) Pearson correlation between different drought indices and STI-1; (b) Spearman correlation between different drought indices and STI-1.
## 1 Introduction  
Optimization is a fundamental process underlying the performance of numerous scientific and engineering problems [1]. The effectiveness of an optimization method directly determines the success of its applications, as these algorithms aim to identify the best solution to a problem, assessed by a criterion known as the fitness or cost function. The primary goal in optimization is to maximize or minimize this function [2] (pp. 1-2).  
A major application of optimization today lies in training machine learning models, where algorithms optimize the weights of neural networks. Among the most widely used optimization techniques for neural networks are gradient-based algorithms, including Gradient Descent (GD) and its variants, such as Momentum, Adagrad, and Adam [3]. Despite their longstanding use, these algorithms remain robust and efficient, often outperforming many newer alternatives, particularly in high-dimensional applications such as machine and deep learning [4] (p. 2). Their enduring popularity stems from their computational efficiency and adaptability to diverse problems.  
However, gradient-based algorithms typically assume a convex cost function with a single optimal solution [5]. This assumption often fails in real-world applications [6] (p. 33), where non-convex functions exhibit multiple local minima and maxima. While local optima may suffice in some cases, this limitation can significantly impact performance in others [7] (pp. 6-7). Additionally, algorithms like GD and Adam are sensitive to initial conditions [8], restricting their ability to explore the solution space thoroughly.  
To overcome these limitations, researchers have explored multi-start methods, which initiate optimization from multiple starting points to increase the likelihood of finding better solutions. These approaches, however, often incur high computational costs and require careful resource allocation [9] (p. 13), and they still may not guarantee finding the global optimum [10] (p. 455).  
Swarm-based algorithms, such as Particle Swarm Optimization (PSO) [11], have demonstrated strong global optimization capabilities. Nevertheless, these methods face challenges such as the curse of dimensionality [12] and high computational overhead [13] (p. 112), which limit their applicability in complex domains like deep learning [14] (pp. 14093-14094). Hybrid approaches that combine the global exploration of swarm algorithms with the local refinement of gradient-based methods, often termed swarm-based gradient methods [15] or gradient-based swarm algorithms [16], show promise but require further validation in high-dimensional settings [17] (p. 34).  
In this paper, we present Multi-Agent Gradient Descent (MAGD), a novel multi-start algorithm designed to address the limitations of traditional gradient-based methods. MAGD provides an efficient multi-start framework with a simple implementation. By simultaneously updating multiple agents and dynamically removing underperforming ones, MAGD reduces computational costs while enhancing performance. Additionally, it integrates the Adam updating rule, accelerating convergence in complex landscapes.
# Improving Robotic Bin-Picking Performances through Human-Robot Collaboration
## 3 Collaborative Human-Robot Bin-Picking  
Bin-picking is a challenging task to fully automate, and failure rates are still high for practical industrial applications. Rather than focusing on decreasing the rate of the typical bin-picking failures described in Section 2.3, we focused on improving the fault tolerance of an already existing workcell through human-robot collaboration, combining the best features of both operators and robots.  
The diagram representing human-robot collaboration when resolving bin-picking failures is depicted in Figure 1. The green box represents successful bin-picking task execution, which consists of cyclically repeating 3D map acquisition with a 3D vision sensor, object recognition, pose estimation, path planning, and subsequent pick and place of the objects until the bin is empty. The use of a collaborative robot ensures that this sequence of operations can be performed by the robot while a human operator works by its side sharing the same working environment. Safe and fluent human-robot interaction can also be enhanced by implementing collision avoidance algorithms.  
Regular bin-picking task execution can be interrupted either due to one of the failures described in Section 2.3 or due to autonomous human intervention. In a non-collaborative bin-picking scenario, when a fault occurs, the line needs to be paused, and a human operator needs to enter safety cages, clear the fault, for example, by removing interlocked objects, and restart the line. Instead, in a collaborative bin-picking scenario, when one of the possible faults occurs (orange box in Figure 1), an operator is already working side by side with the cobot and can interrupt his or her work and quickly clear the fault (light blue box in Figure 1).  
The use of a collaborative robot also enriches possible applications, as human intervention is not limited to the separation of entangled objects or to their being discarded. For example, when a perception failure occurs, the cobot can seek help from the human operator, who can intuitively hand guide the robot to the pick position. The cobot then proceeds to pick the part and restore bin-picking task execution.  
Moreover, bin-picking task execution can also be interrupted by human intervention. The human operator working alongside the cobot can autonomously predict and prevent bin-picking failures: if the operator notices that two objects are entangled, he or she can intervene and disentangle them. In addition, if the operator notices that an object cannot be picked because of constraints in robot motion, he or she manually removes it from the bin and places it where it needs to be placed. Finally, the operator can deposit more items inside the bin when it is almost empty: in this case, the cobot must behave accordingly, for example, by moving away from the bin.