esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantification of Microplastics in Plastic-Bottled Chinese Baijiu Using Micro-FTIR in Imaging Mode
## 2 Materials and Methods
### Quality Control Measures  
Various quality control procedures were employed to ensure the precision and accuracy of the findings. To assess the risk of contamination during the extraction and analysis processes, blank samples were prepared and evaluated alongside the Chinese baijiu samples. Moreover, a reference material having a known microplastic content was appraised to verify the precision of the micro-FTIR method. The instrument was calibrated on a daily basis with a standard polystyrene film. Additionally, the results were confirmed by conducting a repeat analysis of a subset of samples.
## 3 Results and Discussion  
We conducted a series of field trials under diverse environmental conditions (including night, bright daylight, rainy, and hazy weather) to investigate the acquisition of intensity images from scenes containing various typical noncooperative materials, such as buildings, metal, paint, etc. The LiDAR system was deployed on the rooftop for short-range measurements and on the Qinling Mountains (near Xi'an city, China) for long-range measurements exceeding 9000 m distance. Considering the potential risk to human ocular safety posed by the nonsafe wavelength of the 532 nm laser, we prioritized experimental safety, and consequently, directed our imaging efforts towards roofs and elevated structures. In addition, only an intensity image (not a 3D image) at a specific depth was reconstructed in the following tests.
# Classifying Four Carbon Fiber Fabrics via Machine Learning: A Comparative Study Using ANNs and SVM
## 3 Results and Discussion  
### Model Development  
Since machine learning models are developed based on the existing database acquired from experiments, here all the experimental results were used for the model training and testing. Statistical descriptions of the experimental results are shown in Table 2.  
Due to the powerful learning capacity of ANNs and SVM, we can define the corresponding quantified values by ourselves to classify the four different carbon fiber fabrics, respectively. Here, we define the samples numbers 1-4 as 200, 400, 600 and 800, respectively. Sample width, breaking strength and breaking tenacity were set as the independent variables, while the defined quantified classification for the four kinds of typical samples were set as the dependent values. Then, 85% of the data groups were set as the training set, while the remaining 15% were set as the testing set. ANNs were developed by NeuralTools(r) software (trial version, Palisade Corporation, Ithaca, NY, USA) [22; 23]. A general regression neural network (GRNN) [24; 25] and multilayer feed-forward neural networks (MLFNs) [26; 27] were used from the software. The SVM model was developed by Matlab software (Libsvm package [21]). The computer for model development was a Lenovo G480 (laptop). To find out the best results of the MLFNs, the nodes in the hidden layer were set from 2 to 50. To measure the performance of different machine learning models, root mean square error (RMSE) and required training time were used as indicators that could help us define the most suitable model. Model development results are shown in Table 3. Results show that the MLFN with 21 nodes  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Statistical Item** & **Sample Width (mm)** & **Breaking Strength (N)** & **Breaking Tenacity (MPa)** \\ \hline Minimum & 15 & 3867.3 & 1621 \\ Maximum & 30 & 22,618 & 5988 \\ Range & 15 & 1875.7 & 4367 \\ Average & 239 & 110,662 & 3087.61 \\ Standard deviation & 5.39 & 4011.17 & 1020.92 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical descriptions of the experimental results.  
Figure 3: Schematic structure of an SVM (support vector machine) [22].  
(MLFN-21) has the lowest RMSE for the testing process (36.03), while the SVM and other ANNs have comparatively higher RMSEs and lower classification accuracies. The change regulation of MLFNs with different numbers of nodes (Figure 4) also shows that with the increase of the node numbers, the required training times of the MLFNs gradually increase with a fluctuation. Though the training time of MLFN-21 is slightly longer than those of SVM, GRNN and other MLFNs with lower numbers of nodes, it is still acceptable because the training time will decrease with a high-performance computer. Therefore, the MLFN-21 can be rationally considered as the best model for classifying the four different carbon fiber fabrics in our experiments.
# An Improved Approach to DNS Covert Channel Detection Based on DBM-ENSec
## 3 Methodology  
To approach the issue outlined in Section 1, this paper establishes a DCC attack environment, from which individual traffic features are extracted and subsequently transformed into grayscale images. These images serve as inputs to the detection model, thereby improving both its performance and interpretability. The input grayscale images are processed using an improved Deep Boltzmann Machine to extract deep features. These features are then synthesized and trained through an integrated classifier, which enhances the accuracy of DCC detection. The overall process of this study is shown in Figure 1. The model consists of two primary components: data collection and processing, and DNS covert channel detection.
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 5 Testbed Implementation  
The main objective of this work is to implement a system that uses WSN infrastructure to measure located mobile objects using RSSI. The application involves scattering a given number of fixed nodes over an observation region or area of interest; these nodes work together to determine where the mobile node in the network will be located.  
The mobile node, designated as the object to be monitored, moves within the monitored region and communicates with the fixed nodes in the network, allowing the system to stabilize the node's position with reasonable accuracy. The interaction between the mobile node and fixed nodes is crucial to measuring mutual distances (based on the RSSI index), which is then used to determine the position of the mobile node using location algorithms. Then, the different algorithms are compared to determine which one is more efficient and accurate in estimating distance.  
The system nodes consist of the ESP32 module DevKitMv1 developed by Espressif Systems, a semiconductors company located in 690 Bibo Road, Shanghai, Shanghai 201203, CN. This module is depicted on left side of Figure 11, and it represents an evolution of the ESP8266 module, widely used in the IoT field.ESP32 is a low-cost, low-power microcontroller series based on the ESP-WROOM-32 chip with integrated Wi-Fi and dual-mode Bluetooth and uses a 32-bit dual-core Tensilica Xtensa LX6 microprocessor.  
MCU Specifications:  
Tensilica LX6 dual core 32-bit microprocessor;  
Working frequency settable between 80 MHz and 240 MHz;  
ROM: 448 KB for booting and CPU;  
SRAM: 520 KB for data and instructions;  
RTC slow and fast SRAM: 16 KB for data storage and coprocessor access during deep-sleep mode.  
WiFi: 802.11 b/g/n/d/e/i/k/r up to 150 Mbps;  
Bluetooth v4.2 BR/EDR and BLE (Bluetooth low-energy) support.  
Figure 11: ESP32 DevKit v1 and communication protocol.  
The ESP32 devices have been programmed using the Arduino IDE version 1.8.19, a cross-platform application written in Java, provided with an easy-to-use and intuitive C/C++ software library and derived from the IDE created for the processing programming language.  
It should be specified that for the ESP-WROOM-32 chip, the output power at the antenna reaches 20.5 dBm; this is because Equation (4), on which the calculation of the RSSI is based, takes into account the power of the signal transmitted by the emitter (in Watts). All the source codes implemented to perform the measurements described in the following sections is available on github [[https://github.com/maudarie/sensorLocalization](https://github.com/maudarie/sensorLocalization)]([https://github.com/maudarie/sensorLocalization](https://github.com/maudarie/sensorLocalization)) (accessed on 1 April 2025).
## 3 Case Studies
### Rocker Arm
#### 3.1.2 Generative Design Approach  
As well as for the TO approach, also in the case of GD the design process started by using a CAD system in order to define functional surfaces and obstacle geometry (Figure 1). Unlike the TO, a starting shape was not adopted. Subsequently, the same constraints and loading conditions adopted for the TO analysis were defined. While, the objective functions were to maximize stiffness, safety factor of 2, and target mass of 150 g. Additionally, in this case, the Ergal and titanium materials were taken into account. This generative design study produced 10 design solutions. In order to perform a direct comparison, with the same material and minimum FoS, with the resulting designs of the different approaches, the solution of the generative design process was considered that presented a minimum FoS of 2.4 which has a mass of 140 g and a maximum displacement of 0.30 mm. Furthermore, since the field of application is that of racing cars, the solutions showing the best results in terms of performance were also considered. As a consequence, the following figures depict,  
Figure 6: Stages of the TO approach for the redesign of the rocker in titanium.  
Figure 7: Redesigned rocker in titanium validated through FEM analysis.  
respectively, the best outcomes related to the adoption of the Ergal (Figure 8), with a mass of 146 g, a maximum displacement of 0.36 mm, and a minimum FoS of 3; and titanium (Figure 9) materials, with a mass of 149 g, a maximum displacement of 0.50 mm, and a minimum FoS of 2. Furthermore, the aluminum rocker can be manufactured by means of conventional and AM technologies while the titanium component requires the adoption of AM technologies.
# A Heterogeneous Battlefield Situation Information Sharing Method Based on Content
## 6 Experiment Results and Analysis
The results of the total experiment is showed in Figure 14.  
Before the predefined information about targets was modified, the information matching degree of each terminal was one, i.e., each terminal could obtain the required information completely and correctly based on the information sharing platform. After the predefined information about targets was modified randomly, the information matching degree of each terminal showed some fluctuations. That is, some items of the obtained information were mismatched with the predefined information. The main reason was that the semantic integrity of the information contained in the message was not fully considered when updating the unified information space. For example, there was an incidence relation between \(a_{1}\) and \(a_{2}\), and the update of their information should maintain the consistency of time. However, due to the limited length of the message, a message may only contain the information of \(a_{1}\) or \(a_{2}\). If updating the unified information space with the content of this message, then a mismatch in the information of \(a_{1}\) and \(a_{2}\) occurred with respect to the time. That is, the information of \(a_{1}\) or \(a_{2}\) was not the latest information, which correspondingly caused the mismatch of the obtained information and the predefined information when computing the information matching degree of terminals. Thus, the verification of the information integrity needs to be further improved. Figure 14: The information matching degree of terminals. Figure 13: Information about targets, the concept-logic tree, and entity relationships used during the experiment.
# A Hybrid Marker-Based Indoor Positioning System for Pedestrian Tracking in Subway Stations
## 2 Related Work
### Marker-Based Indoor Localization
In this study, each user was assigned a unique color and users displayed it on a smartphone, which was attached to their shoulders. A method of measuring the user's location was used by recognizing colors through a camera attached to the ceiling and converting the location into a location matrix. Meanwhile, [PERSON] et al. [23] proposed a new marker and introduced a positioning system that applied it. The new marker added encoded information based on the concept of binary necklaces to the existing WhyCon circular marker. In the process of recognizing the marker, the ID of the marker and the rotated angle could be established simultaneously. These studies highlighted advantages such as high accuracy and low computational cost, but exhibited limitations such as computational overhead and high installation costs due to the decoding process. In addition to marker-based positioning for pedestrians, various studies using markers have also been conducted in robotics [24; 25]. [PERSON] et al. [24] proposed an indoor positioning system for mobile robots based on a newly designed marker. In the proposed system, the robots moved and captured markers attached to the ceiling. Subsequently, the captured image was processed to obtain location and direction information, and a marker map was constructed based on this. The generated marker map was optimized through a graph-based algorithm, and the graph edge was updated using the Bayes estimation method. In this study, markers could be installed somewhat freely, and the accuracy and recognition rate of the marker maps were improved by utilizing the G20 framework. However, when the proximity of the lighting was close to the robot, the recognition rate was reduced. Meanwhile, researchers in [25] proposed a robot localization system based on a QR code. When the robot moved and recognized a QR code, the robot decoded it and delivered the information to the local server, and the server performed positioning in a way that informed the shortest route to the destination based on the transmitted information. At this stage, if the proximity sensor built into the robot detected an obstacle, the robot avoids it and returned to the original path. In this study, security was improved by using an encrypted network. Furthermore, because the robot itself could avoid obstacles, its dependence on the server was reduced, and the server computational cost could be reduced. However, because the QR code is arranged to match unique coordinates, the initial cost may be high. It is also difficult to estimate the performance of this system as the study did not provide any experimental methods or results.
# A Discriminative-Based Geometric Deep Learning Model for Cross Domain Recommender Systems
###### Abstract  
Recommender systems (RS) have been widely deployed in many real-world applications, but usually suffer from the long-standing user/item cold-start problem. As a promising approach, cross-domain recommendation (CDR), which has attracted a surge of interest, aims to transfer the user preferences observed in the source domain to make recommendations in the target domain. Traditional machine learning and deep learning methods are not designed to learn from complex data representations such as graphs, manifolds and 3D objects. However, current trends in data generation include these complex data representations. In addition, existing research works do not consider the complex dimensions and the locality structure of items, which however, contain more discriminative information essential for improving the performance accuracy of the recommender system. Furthermore, similar outcomes between test samples and their neighboring training data restrained in the kernel space are not fully realized from the recommended objects belonging to the same object category to capture the embedded discriminative information effectively. These challenges leave the problem of sparsity and the cold-start of items/users unsolved and hence impede the performance of the cross-domain recommender system, causing it to suggest less relevant and undistinguished items to the user. To handle these challenges, we propose a novel deep learning (DL) method, Discriminative Geometric Deep Learning (D-GDL) for cross-domain recommender systems. In the proposed D-GDL, a discriminative function based on sparse local sensitivity is introduced into the structure of the DL network. In the D-GDL, a local representation learning (i.e., a local sensitivity-based deep convolutional belief network) is introduced into the structure of the DL network to effectively capture the local geometric and visual information from the structure of the recommended 3D objects. A kernel-based method (i.e., a local sensitivity deep belief network) is also incorporated into the structure of the DL framework to map the complex structure of recommended objects into high dimensional feature space and achieve an effective recognition result. An improved kernel density estimator is created to serve as a weighing function in building a high dimensional feature space, which makes it more resistant to geometric noise and computation performance. The experiment results show that the proposed D-GDL significantly outperforms the state-of-the-art methods in both sparse and dense settings for cross-domain recommendation tasks.  
recommender systems; deep learning; cross domain; geometric deep learning; non-Euclidean domain +
Footnote †: journal: Applied sciences
## 4 Signal Preprocessing
### Range Fast Fourier Transform  
FMCW-transmitted chirps are characterized by the frequency \(f\), bandwidth \(b\), and duration \(T_{chirp}\). The reflected IF signal is parsed to determine the radial range between the radar and the target. The frequency of IF signal \(f_{IF}\) is proportional to the radial distance \(r\) and is denoted as:  
\[f_{IF}=\frac{s2r}{c}, \tag{1}\]  
However, the radial distance between the radar and target is estimated as:  
\[r=\frac{f_{IF}c}{2s}, \tag{2}\]  
where \(c\) represents the speed of light \(3\times 10^{8}\) m/s and \(s\) is the chirp frequency slope, which is calculated as (\(s=\frac{b}{T_{chirp}}\)). The range-fast Fourier transform (FFT) is applied to each chirp of the radar data cube to convert the time domain IF signal into the frequency domain. The peak of the resulting frequency spectrum determines the range of each target. The distance can be calculated by averaging the distance collected by all the chips in a frame and the number of chirps in the frame.  
Figure 3: The workflow of signal processing and cloud point generation.  
Figure 2: The proposed framework.
## 3 Results
### Transmit Subsampling
#### 3.2.3 FISTA and the Dictionary Normalization  
FISTA requires tuning the regularization parameter as well as number of steps for the scenario at hand. We empirically determined that \(\beta~{}=~{}0.01\) as well as running 20 steps of FISTA is suitable for our application and kept these values constant for all investigations. To justify the dictionary normalization introduced by the matrix **W** in Equations (9) and (10) the performance of the algorithm is compared with and without the weighting. The comparison is carried out using four transmit elements that were determined by the proposed selection scheme. The resulting B-scans are displayed in Figure 8.  
Figure 8: B-scan views of FISTA reconstructions for four transducer elements comparing the weighting scheme given in Equation (7) with a uniform weighting, i.e., \(\textbf{W}~{}=~{}\textbf{I}\), for (**a**) a defect at \(z~{}=~{}13\) mm and (**b**) \(z~{}=~{}15\) mm, respectively. All plots are normalized such that the colormap spans the values from zero to the maximum amplitude of the respective scatterer.  
It can be seen that without the weighting, i.e., a uniform \(\mathbf{W}\ =\ \mathbf{I}\), the level of the noise compared to the maximum amplitude of the defect in Figure 8a is higher. This is due to the rows of \(\mathbf{M}\) corresponding to scatterers in that region having a larger norm, as the region is visible over a wider aperture (compare also to Figure 4). Consequently, FISTA yields a less sparse solution for scatterers being predominantly in that region, as a slight change to values of \(\mathbf{i}_{FISTA}\) associated with that region introduces a large change to the data fidelity term \(\big{|}|.|\big{|}_{2}^{2}\) but a small change to the regularization term. For the defect at \(z\ =\ 15\) mm in Figure 8b, the weighted reconstruction has a better defect to front wall separation. A likely cause for this is the uniformly weighted reconstruction favoring the backwall reflection much more than the weighted reconstruction, leading to a more faint defect image.  
Concluding, without the introduction of \(\mathbf{W}\) as given in Equation (7), the FISTA shows more noise, preferably near the inner pipe radius. This is due to the non-uniform effect of the L1-norm regularization term, which is meant to promote sparsity, for non-normalized dictionaries. In praxis, the proposed weighting scheme is capable of countering that effect.
## 2 SPT-Based Determination Method for Constitutive Parameters  
In order to improve the accuracy, simplicity, and practicability of numerical modeling, this study proposes an SPT-based method for selecting the parameters of the constitutive model. Figure 1 is the computational flowchart of the method. Three main steps are involved: firstly, the key parameters of the constitutive model are obtained through the sensitivity and correlation analyses. Then, the optimal ratios between the key parameters and the rest of the important parameters are obtained by using the multi-parameter optimization method. The inverse analysis using the monitoring data of braced excavation is then carried out to obtain the optimized constitutive parameters, and finally the optimized parameters are empirically related to the SPT blow counts. The proposed method in this study is a step-by-step inverse analysis method. The method is proposed to reduce the number of optimized parameters and improve the efficiency of parameter optimization.
# Interconnecting Haptic Interfaces with High Update Rates through the Internet +
## 5 Simulation Results
### Jitter  
System instability is vulnerable to high values of jitter. Based on Table 1, haptic applications are more sensitive to jitter than other multimedia.  
Figure 10 illustrates the jitter of the protocols for an network bandwidth 20 Mbps and delays from 5 to 60 ms. At this high bandwidth no congestion occurs. Almost all of the protocols show very small jitter, lower than 0.6 ms.  
Figure 8: Throughput vs bandwidth.  
Figure 10: Jitter for internet bandwidth 20 Mbps.  
Figure 9: Throughput for internet bandwidth 5 Mbps and delay 40 ms.  
Figure 11 shows the jitter for network bandwidth 1 Mbps. It is a fully congested network with a lot of packets lost. The jitter is now much higher than in Figure 10. The protocols with the highest jitter are the DCCP, the TCP and the SCTP. RTP, UDP and ETP protocol have smaller jitter than 6.7 ms, with the ETP exhibiting the best performance with a jitter smaller than 2.6 ms. According to Table 1, the jitter should be lower than 2 ms, a goal that most of the protocols could not achieve, due to the congestion of the network. An interesting observation is that the protocols TCP, SCTP, and DCCP lower their jitter as the delay of the Internet bandwidth increases. All these protocols almost have the same tcp-like congestion control algorithm.  
Figures 12 and 13 depict the jitter of the protocols when the Internet bottleneck bandwidth varies from 1 to 20 Mbps. The average Internet delay is near 40 ms. All the protocols show a different behavior when the Internet bandwidth is 1 Mbps and the Network is heavily congested. For higher bandwidth values all protocols present almost the same behavior. As the Internet bandwidth increases, the congestion and as a consequence the jitter effect, are decreasing.