esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Laser Pulse Widths on Laser--Ag Nanoparticle Interaction: Femto- to Nanosecond Lasers
## 3 Numerical Simulation and Experimental Setup
### Setup for Laser Fragmentation  
The laser sources were an ultrafast laser (PHAROS; Light Conversion, Vilnius, Lithuania) and a nanosecond laser (YLP-1; IPG Photonics, Oxford, MA, USA) with an adjustable pulse width. The ultrafast laser beam was generated through mode locking and amplification of chirped pulses. The pulse width of the laser beam was 164 fs and 5 ps at a wavelength of 1070 nm. The nanosecond laser generated a laser pulse using a fiber resonance system, and the pulse width was 4, 32, 64, and 100 ns at a wavelength of 1070 nm. The laser-related process parameters are shown in Table 2. To assess the effect of the laser pulse width, it is important that the energy doses be as similar as possible, within the limits of the experimental setup. In this experiment, the fluence of the femtosecond, picosecond, and nanosecond laser beams was set in the range from 0.10 to 0.13 mJ/cm\({}^{2}\).  
The laser fragmentation setup is shown in Figure 3. The Ag target in distilled water was irradiated with a laser beam, which resulted in the production of nanoparticles. The emitted laser beam was controlled by a galvanometer scanner. The target was irradiated with the laser beam in a scanning pattern for 10 min. The laser beam had a Gaussian energy distribution and a spot size of 20 to 40 \(\upmu\)m. Ag nanoparticles were synthesized primarily during laser ablation, as well as by secondary laser fragmentation in the solution. Ag nanoparticles generated by laser fragmentation form clusters over time. The colloids thus generated can be transformed to stable nanoparticles by further laser irradiation; however, this was not performed in this study. Ag nanoparticles were analyzed at least one day after laser fragmentation. The nanoparticles were dried and visualized by transmission electron microscopy (TEM; JEM-2010; JEOL, Tokyo, Japan) and scanning electron microscopy (SU8010; Hitachi, Tokyo, Japan). In addition, the Ag nanoparticle size distribution was determined using a particle size analyzer (ELS-Z PLUS; Otsuka Electronics, Osaka, Japan); 300 to 500 nanoparticle samples were used per laser parameter.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pulse Width** & **Femtosecond Regime** & **Picosecond Regime** & **Nanosecond Regime** \\ \hline Fluence (mJ/cm\({}^{2}\)) & 0.13 & 0.13 & 0.10 \\ Repetition rate (kHz) & 200 & 200 & 500 \\ Delay time (\(\upmu\)s) & 5 & 5 & 2 \\ Pulse duration & 164 fs & 5 ps & 4, 32, 64, and 100 ns \\ Wavelength (nm) & 1070 & 1070 & 1070 \\ Process time (min) & 10 & 10 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Laser parameters.
## 6 Conclusions  
This paper presents a simulation of studying the lateral sand resistance on the compression side of monopile foundations after scouring, based on the state-dependent constitutive model for saturated sand. The soil strength that changes with depth in local scour conditions is explained. Based on the theory of the influence of dilatancy on friction angle proposed by [PERSON] [29], the p-y curves restricted in the deep horizon in the local scour condition are calculated. The conclusions are as follows:  
1. After the erosion depth increases gradually, which differs from the past lateral service capacity variation of remaining interface soil, the lateral soil resistance in the shallow horizon failed. The relative density increases by dilatancy and process the stress relief brings about the strength failure. The depth of the weakened area is about 0.6 times scour depth.
2. Unlike the general scour condition, the dilatancy of the shallow horizon and the restriction of remaining overburden soil surrounding the pile enhance the normal stress of the interface sand. Therefore, the lateral soil resistance in the deep horizon is ameliorated.
3. Having considered the effect of dilatancy on friction angle and the change in normal stress and relative density during the displacement of the pile, the p-y curves considering the state variation can evaluate the lateral resistance in the deep horizon in local scour conditions.  
Figure 15: The p-y curves consider the dilatancy in (**a**) no scour condition; (**b**) local scour condition.  
**Author Contributions:** Funding acquisition, J.L.; Investigation, N.J.; Methodology, N.J. and X.W.; Resources, J.L.; Writing--original draft, N.J.; Writing--review & editing, J.L. and X.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Outstanding Youth Foundation of Shandong province (ZR2021 YQ31), the National Natural Science Foundation of China (U2006225), the National Major Scientific Instruments Development Project of China (41627801) and Young Innovative Talents Introduction and Training Program of Colleges and Universities of Shandong Province.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Proposed Protocol
### Cluster Formation  
After the selection of the CHs, each final selected CH broadcasts the message that it becomes a CH within its communication range. The nodes that are not selected are treated as CMs (cluster members) and assigned to a particular CH with higher potential according to Equation (7):  
\[N_{p}=\frac{\alpha E_{ch}(i)}{Dis\big{(}S_{j},CH_{i}\big{)}} \tag{7}\]  
where \(N_{p}\) is the node potential, \(E_{ch}(i)\) denotes the residual energy of the respective CH, and \(Dis\big{(}S_{j},CH_{i}\big{)}\) represents the Euclidean distance between the \(CH_{i}\) and \(S_{j}\). \(\alpha\) is the proportionality constant. Since the node with higher residual energy has been selected as the CH in step 4.1.1, \(Dis\big{(}S_{j},CH_{i}\big{)}\) is regarded as a more important factor in this section. After a simulation comparison, we set \(\alpha\) to 0.3.
# Combining Post Sentiments and User Participation for Extracting Public Stances from Twitter
## 3 The Proposed Method
### Preprocessing and Feature Extraction  
First, we use Twitter Search API with selected topical keywords to retrieve topic-related short texts from social media. Specifically, we collected posts with emoticons as the training data for sentiment classification. Then, preprocessing tasks are needed to better represent the important features. Specifically, we filter out the URL links, hashtags, and emoticons from post contents. Also, stopword removal is performed to enable better feature representation. Then, we extract metadata such as the ID of the author, the posting time, and the number of retweets and likes. These will be used as additional features for classification.  
Due to the limitation of conventional bag of words model for very high dimensional space and the lack of contextual relations between words, we use word embedding models such as Word2 Vec [1] to better represent the limited content in short texts, and learn the word contexts in training data. Also, the fixed number of dimensions in word embedding model can facilitate more efficient computations. Among the two models in Word2 Vec, continuous bag-of-words (CBOW) and Skip-gram, we use word vectors trained via Skip-gram model as the inputs to the following stage of sentiment classification. This is due to the much better performance for skip-gram model in semantic analysis [1].  
After each post is represented using word embedding models, the sequence of word vectors are then used as inputs to the long short-term memory (LSTM) [10] neural networks one by one. The idea is that: we want to learn the longer distance dependency among sequence of words in different posts using LSTM. To comply with the sequential input of LSTM, we first convert posts into three-dimensional matrix M(X, Y, Z), where X is the dimension of Word2 Vec word embedding model, Y is the number of words in the post, and Z is the number of posts. To avoid a very long training time, we adopt a single hidden-layer neural network. The number of neurons in input layer is the dimension of Word2 Vec model, and the number of neurons in output layer is the number of classes, which is 2 in our case. By gradient-based back propagation through time, we can adjust the edge weights in the  
Figure 1: The system architecture of the proposed approach.  
hidden layer at each point of time. After several epochs of training, we can obtain the sentiment model for classifying sentiments in post contents in the following section.
# Seismic Ground Response Estimation Based on Convolutional Neural Networks (CNN)
## 4 Analytical Procedure
### CNN Model
#### 4.1.3 Comparisons  
With the earthquake dataset in site FKSH 18, CNN models A and B for the site were trained. Each model predicted the ground response at the surface and the results were compared with the baseline to decide main model. Among 20 predictions by each model, the response spectrum of a sample prediction is presented in Figure 8.  
It is found that model B outperforms model A. Not only the average errors of model B were lower than those of model A, but also data processing for model B was more convenient. Whereas the constitution of model A requires the tailored earthquake data with  
Figure 8: The sample prediction by models A and B and the measurement (FKSH18).  
Figure 7: Overview of model B.  
the same duration (120 s) and the same number of values (12,000), model B doesn't. As a result, the study decided to apply model B for the seismic ground response estimation. The study chose model B as a main model, and trained each CNN model for every selected site.
## 3 Experimental Results and Analysis
### Quantitative Analysis  
To verify the effectiveness of the improved anchor points, after the dataset was constructed, 500 randomly selected labeled images were used for training, and the remaining 60 labeled images were used for testing. The surface defect detection accuracies of automotive engine parts of the Faster R-CNN, Mask R-CNN, and proposed IA-Mask R-CNN models were tested. Due to the limitation of the GPU memory, the batch size in the experiment was set to one. The VGG16 and ResNet101 were first pre-trained on the COCO dataset that contained 80 image categories [22]. The networks were then fine-tuned using the pre-training weights and, finally, the Faster R-CNN was iterated 80,000 times and the Mask R-CNN and IA-Mask R-CNN were iterated 10,000 times. The quantitative results are shown in Table 3.  
By quantitatively comparing the results of the three detection models, it was observed that, after adding the mask branch, the Mask R-CNN and IA-Mask R-CNN performed better in detecting surface defects of automotive engine parts than the Faster R-CNN and required a shorter training time. In addition, the number of iterations had decreased, and a higher precision was achieved with the support of the ResNet101. After improving the anchor scales, the IA-Mask R-CNN did not require additional training parameters compared to the Mask R-CNN. Under the same pre-training model, training time, and iteration number, the improved network achieved the best accuracy among all the detection models, thus validating the effectiveness of the improved anchor scales.
## 3 Results and Discussion
### Characterization  
#### 3.2.1 FTIR  
Figure 8 displays the FTIR spectrum of the hybrid flocculant, revealing characteristic absorption peaks at wavenumbers 3020 cm\({}^{-1}\) (C-H bond vibration), 1704 cm\({}^{-1}\) (C=O bond vibration), 1471 cm\({}^{-1}\) (C-H bond vibration), and 1408 cm\({}^{-1}\) (C-N bond vibration). For the hybrid flocculant, there was no absorption peak in the range of 3500-3200 cm\({}^{-1}\), indicating that there was no hydroxyl group in the substance. Notably absent was any characteristic absorption peak corresponding to C=C double bonds within the spectrum's range from 1675 to 1500 cm\({}^{-1}\), indicating that the reactants undergo free-radical-initiated polymerization resulting in covalent bonding between the organic and inorganic components.
# An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions
## 2 Autonomous Vehicles Sensors Ecosystem
### Gnss  
GNSS is the most widely used technology for providing accurate position information on the surface of the earth. The best-known GNSS system is the Global Positioning System (GPS), which is a U.S. owned utility that provides users with positioning, navigation, and timing (PNT) services. The free, open, and dependable nature of GPS made it an essential element of the global information infrastructure that affects every aspect of modern life.  
The GPS was developed by the U.S. Department of Defense (DoD) in early 1970 and is divided into three segments: the space segment, the control segment, and the user segment. The GPS system's space and control elements are developed, maintained, and operated by the US Air Force [17]. The space segment consists of 31 operational satellites of which at least 24 are available 95% of the time. These satellites fly in medium earth orbit (MEO) at an altitude of 20,200 km, and each satellite orbits the earth twice daily. Its configuration allows any receiver located on the earth's surface to receive signals in the L-band and some of the S-band frequency range, from 6-12 satellites. The control segments to a global network of ground facilities that track GPS satellites, analyze their broadcasts, perform analysis, and provide orders and data to the constellation; and the user segment refers to the process of dividing into distinct groups based on shared characteristics.  
The operating principal of the GNSS is based on the ability of the receiver to locate at least four satellites, to calculate the distance to every single one of them, and then uses this information to identify its own location by using a process called trilateration. It is worth mentioning that GNSS signals suffer from several errors that degrade the accuracy of the system, such as the following: (1) timing errors due to differences between the satellite atomic clock and the receiver quartz clock, (2) signal delays due to propagating through the ionosphere and troposphere, (3) multipath effect, and (4) satellite orbit uncertainties. In order to improve the accuracy of current positioning systems on vehicles, data from  
Figure 8: Applications of ultrasonic sensors in vehicles (Image source: newelectronics).  
satellites are merged with data from other vehicle sensors (e.g., inertial measurement unit (IMU), LiDARs, RADARs, and cameras) to achieve reliable position information.
## 2 Background
### Feature Selection  
Feature selection is a crucial step in machine learning that involves selecting a subset of the most relevant features from the original set. This process aims to improve model performance by reducing overfitting, enhancing generalization, and decreasing computational cost. Feature selection methods can be categorized into three main types: filter, wrapper, and embedded.  
The filter method is advantageous for its simplicity and computational efficiency. This method evaluates features independently and selects those with the highest correlation with the target variable, which, in our case, is the emotional state.  
The embedded method directly incorporates feature selection into the model training process. This method combines filter-based and wrapper-based feature selection. Features with nonzero coefficients were selected to train the model with lower complexity and optimize the training procedure.  
The wrapper method iteratively evaluates different feature subsets by training and validating the classification model. Our approach explores the feature space comprehensively, potentially uncovering the complex interactions between features that enhance classification performance. However, this process can be computationally intensive, especially with large feature sets.  
In our study, feature selection was driven by the need to explore various approaches that could effectively capture the patterns in EEG brainwave data related to emotional states. We chose three methods: the filter method (SelectKBest with ANOVA \(F\)-test), the embedded method (least absolute shrinkage and selection operator (LASSO)), and the wrapper method (GA).
# Congestion Control Mechanism Based on Backpressure Feedback in Data Center Networks
## 3 Congestion Detection and Identification  
Congestion detection is a crucial step in flow control. Only after detecting congestion can we further identify the flow affected by congestion and then adjust the transmission rate of the flow. In this section, we observe changes in the switch buffer and design a congestion detection and congested flow identification scheme.
## 2 Materials and Methods  
### Photos of Bubston Clusters  
In Figure 1, we exhibit a pattern of the track of a continuous wave (CW) low-intensity laser radiation with a wavelength of \(\lambda\) = 532 nm, scattered at 90 degrees in deionized water. In addition, the liquid samples were filtered by forcing the liquid through a highly hydrophilic polymer track membrane (purchased from Sartorius, Germany), which is made of high-quality polycarbonate. The capillary structure of the pores of these membranes is uniform; the pores are also evenly distributed over the membrane surface, allowing the best retention of particles. The pore size was 300 nm; thus, individual bubstons with a radius of 100 nm pass through the membrane, but bubston clusters larger than a micron in size are filtered out by such a membrane. Note that immediately after filtering, the scattered radiation track is absolutely homogeneous, so we do not present this pattern here. After filtration, the liquid sample was settled for 24 h in a cell in a dust-free room; i.e., an access of solid impurity particles to the sample was excluded. In Figure 1, we present a snapshot of the track after such settling.  
The radiation was focused with a lens of long focal length \(F\) = 100 mm. In this case, the beam radius in the lens waist is approximately 20 microns, and the waist length is about 2 mm; this is exactly the length of the white bar in Figure 1. Note that the track of scattered radiation contains local bright dots, whose size should exceed the wavelength \(\lambda\) = 532 nm; i.e., these are the so-called Mie scatterers, see, for example, monograph [41]. Since fine filtration does not remove individual bubstons from the liquid, but removes bubston clusters, the appearance of large-scale scatterers can only be associated with the coagulation of bubstons (the formation of clusters) in the process of settling.  
Bubston clusters can also be observed with a conventional optical microscope (see Figure 2); the photograph was taken in the dark field of a microscope. A 100 \(\upmu\)m layer of water was uniformly illuminated with CW laser light at wavelength \(\lambda\) = 532 nm. Bright inclusions with vague boundaries are clearly seen against a dark background. It is obviously due to the fact that the optical density of these inclusions is less than the optical density of water. Indeed, according to the results of [14; 30], the refractive index of bubston clusters in the visible range is \(n_{b}\) = 1.26, while for water the refractive index is \(n\) = 1.33. An important feature of bubston clusters is the absence of a clear interphase boundary between the cluster and the surrounding liquid, in contrast to micron-sized gas bubbles. Indeed, as was shown in [32], bubston clusters are fractal objects; therefore, there is no geometric concept of an interface for such objects. This is why the diffraction patterns of visible light on the bubston clusters cannot be obtained.
## 1 Introduction
### Research Significance  
Although the above-mentioned results show similarities to those in this paper, experiments at elevated temperatures with such a material combination and such a complex geometry cannot be found in the literature. There is some research in the mechanical behaviour of metal-CFRP hybrids with two different materials and a simple geometry (mostly single or double lap joints) regarding only the mechanical data. The significance of the here-conducted work lies in the consideration of a more complex geometry and material combination and the involvement of NDT techniques to obtain further information about the underlying damage mechanisms. The exact behaviour of the CFRP hybrids under these conditions cannot be predicted, and further investigation is needed.
## 3 Results and Discussion  
### Morphology and Structure  
The morphology and structure of the as-synthesized FeNi-Pd@CN catalyst were firstly characterized by SEM and HRTEM images. It can be seen from Figure 1 that the particles with several micrometers were the stack of broken layers. The specific surface area (S\({}_{\text{BET}}\)) of FeNi-Pd@CN was 9.18082 m\({}^{2}\)/g, calculated by the nitrogen adsorption-desorption isotherm. The isotherm curves were characteristic of a typical IV isotherm with an H3 hysteresis loop, indicating the slit-shaped pores formed by the plate-like particles aggregation (Figure S1). Palladium deposition on the surface of FeNi-Pd@CN could be observed in Figure 1b,c, as its crystalline interplanar spacing of 0.224 nm was assigned to Pd (111) plane [19]. Further exploration found that tiny particles with typical (104) planes of \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) were embedded in the carbon nitride matrix (Figure S2). Elements mapping also shows a small amount of aggregate Fe in Figure 1d,h. No obvious clusters related to Ni monocrystal species could be identified from HRTEM images, and Ni was evenly distributed in g-C\({}_{3}\)N\({}_{4}\). The atomic contents of Fe, Ni, and Pd were 0.72, 0.91, and 0.03 at%, respectively (Table S1). Low content of Pd (0.27 wt%) ensured low prices for the catalyst preparation.