esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Enhancement of Second Harmonic Generation via Noninvasive Modulation
## 3 Results and Discussion
### Enhancement of SHG with Scattering Effect  
Here, we studied the effect of different scattering mediums on the enhancement of SHG. Figure 4 demonstrated that the specific field distribution constructed by the medium of different scattering degrees has an impact on the modulation of the second harmonic signal. The coordination between the first and second order phases of the fundamental frequency light field reaches a preferable effect by wavefront modulation, which can obtain high intensity of SHG. Interestingly, compared with the case of no scattering medium, placing a 220-grit ground glass with 16 \(\times\) 16 modulated phase map can achieve higher enhancement of SHG, and the enhancement factor was 25 times. However, it was not always that the results of adding scattering would be better than that without scattering mediums. Figure 4a described the origin and optimized second harmonic light filed with different scattering mediums. Furthermore, a statistical histogram of multiple sets of data was displayed to explicitly represent the optimized results with different resolutions for different scattering mediums (Figure 4b). Obviously, employing the 220-grit ground glass got the best enhancement of SHG (the average of 22 times). According to the intensity distribution information of scattering mediums from Figure 3, the different character of scattering mediums can generate different speckle intensity distribution. Therefore, choosing an appropriate scattering medium can effectively enhance the conversion efficiency.  
Figure 3: Speckle pattern of different scattering mediums. (**a–d**) The light intensity distribution of the 514 th line of the speckle pattern with different scattering mediums. (**e–h**) The speckle pattern of different scattering mediums. Scale bar: 1.063 mm.
## 2 Background on Personality Research
### Personality Evaluation Tools
#### 2.2.1 Big Five Model  
_The Big Five model_ or the Five Factor Model [30] has often been described as the \"dominant paradigm\" in the field of personality research and as \"one of the most influential models\" in psychology [31]. Its origins can be traced to a list of 4500 terms relating to personality traits that were introduced by [PERSON] and [PERSON] [32]. Using factor analysis, this list was initially reduced to 16 traits, only to be narrowed down to the final five from which the model received its name. As such, it is a result of contributions from many different authors, with roots in the English lexicon [20]. The five traits, or factors, that make up the Big Five model are most frequently labelled as:  
1. _Openness_--measure of curiosity;
2. _Conscientiousness_--measure of efficiency;
3. _Extroversion_--measure of energy;
4. _Agreeableness_--measure of compassion;
5. _Neuroticism_--measure of sensitivity.  
When using the Big Five model to measure personality, a continuous value is assigned to person along each of these traits (Figure 3) with the exact scale of these numbers largely depending on the test used [7]. An example of this would be a person scoring \(85/100\) along the _Openness_ trait, which indicates they are less likely to be cautious when exploring new things.  
Figure 3: Graphical depiction of a sample Big Five test result.
## 3 Predicted Results and Experimental Validation
### Layered Cutting Force Coefficients  
For acquiring the layered cutting force coefficients, a group of slotting experiments is conducted on a VT850Z machining center, as demonstrated in Figure 7. The milling process parameters are listed in Table 3. According to the identification approach in Section 2.4, the layered cutting force coefficients for the selected combination of bull-nose end mill and workpiece material are calculated, as demonstrated in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline
**Spindle Speed** & **Feed Rate per Tooth** & **Axial Depth of Cut** \\ **(r/min)** & **(mm/tooth)** & **(mm)** \\ \hline
5000 & 0.008, 0.016, 0.024, 0.032 & 0.2, 0.4, 0.6, 0.8, 1.0, \\  & 1.2, 1.4, 1.6, 1.8, 2.0 \\ \hline \end{tabular}
\end{table}
Table 3: Machining parameters used in the slotting tests.  
Figure 6: Calculated CWEs for the following cut with a radial depth of cut of 6 mm and axial depth of cut of 0.5 mm. (**a**) Effect of lead angle on the CWE maps; (**b**) Three-dimensional CWE in the CCS with a lead angle of 0 degrees.  
Figure 7: Experimental setup for the slotting experiments.  
The calculated specific coefficients \(F_{t},F_{r}\), and \(F_{a}\) are fitted to three cubic polynomials. The corresponding functions are expressed as follows:  
\[\left\{\begin{array}{l}K_{t}(z)=-4.064z^{3}+14.767z^{2}-15.829z+8.955\\ K_{r}(z)=-6.887z^{3}+24.386z^{2}-25.779z+11.173\\ K_{a}(z)=1.279z^{3}-4.338z^{2}+4.347-0.364\end{array}\right. \tag{48}\]  
Taking the height \(z\) as a variable, the layered cutting force coefficients associated with each cutting disk element can be obtained.
## 2 Materials and Methods
### Analysis of Phenolic Compounds by HPLC-DAD  
The phenolic profile of the NEP extracts obtained under the optimal extraction conditions was analyzed with the 1100 HPLC-DAD system from Agilent (Agilent Technologies) described in Section 2.7 according to the previously published method with some modifications [45]. Separations were performed with a porous-shell fused-core Ascents Express C18 analytical column (150 mm \(\times\) 2.1 mm, 2.7 \(\upmu\)m) with an Ascents Express C18 guard column (0.5 cm \(\times\) 2.1 mm, 2.7 \(\upmu\)m), both from Supelco (Bellefonte, PA, USA). The mobile phases consisted of both (A) water with 0.5% formic acid and (B) acetonitrile with 0.5% formic acid in a gradient elution analysis, programmed as follows: 0 min, 5% (B); 0-15 min,20% (B); 15-25 min, 30% (B); and 25-50 min, 80% (B) with 15 min of post-time for column conditioning at a flow rate of 0.3 mL/min. The column temperature was set at 50 \({}^{\circ}\)C, and the injection volume was 5 mL of the extract. The detection wavelengths employed were 210, 254, 260, 280, 320, 360, and 510 nm.
## 4 Discussion  
### About the Correlation Between fx and fy  
When comparative simulations were carried out using and representing a certain value of CS, in the same manner as represented in Figure 10, no significant differences in the distribution of fx and fy plots were observed when the ship's length was changed. What is more, a similar precision of the polynomial regression would be obtained. Therefore, when designing the hashing arrangement, without considering the absolute value of the fx and fy values, the disposal of the arrangement would be similar on ships of different lengths. Although this similar representation on a single chart is interesting, these polynomial  
Figure 19: 3D maps for the obtained fy model. (**a**) fy vs. CS; LSP for VSP = 3 (tween deck) and fx = 0.204; (**b**) fy vs. LSP; VSP for CS = 200 kN and fx = 0.204.  
Figure 20: 3D maps for the obtained fx model. (**a**) fx vs. fy; VSP for LSP = 0.6 and CS = 200; (**b**) fx vs. CS; LSP for VSP = 3 kN (tween deck) and fy = 0.794.  
regressions cannot be considered models because they only respond to a very specific situation.  
Regarding the correlation between the fy and fx values when 100 kN < CS < 400 kN, a weaker relationship is observed as the ship's length increases. This result can be related to the lower accelerations registered, which primarily means that the longitudinal hashing requirements will be much lower.  
Finally, the ship's loading condition, expressed in the relationship between B/GM, is noted that the lower the B/GM ratio, the greater the required values of fy. Therefore, for the same ship's beam and from a lashing requirements point of view, the fact of reaching a very high GM (a very'stiff' ship) is not recommendable. However, it is also relevant to mention that if the ship has a very low GM, the natural rolling period would be high, so the lashing lines will be working an excessive time during the rolling motion from one side to the other.
# An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment
## 1 Introduction
Turtlebot2 is a small, low-cost, fully programmable, ROS (Robot Operating System) based mobile robot with simple operation and strong scalability. HERO is the heterogeneous extensible robot open platform provided by Intel [15]. It has high-quality features such as low power consumption, high performance and small size (18 cm \(\times\) 14 cm \(\times\) 7.5 cm). The whole of the HERO platform is composed of the host system and the external FPGA acceleration card. In the whole hardware system, CPU has rich IO (Input/Output) functions, provides various high-speed and low-speed interfaces, and its performance is enough to meet that of the simple algorithms. FPGA integrates abundant logic and computing units, storage and routing resources, which ensures the performance acceleration of FPGA in neural network inference. Besides, there is an 8-channel PCIe Gen 3 interface that provides a bandwidth of 7.88 GB/s to perform transmission and communication [15]. The main contributions of this work are summarized below:  
1. The quantization strategy of combining convolution and batch normalization into one operation and grouping convolution filters with different data distribution is proposed to solve the problem of the large model and long execution time of a semantic segmentation network. A function configurable pipeline design method and a multi-level memory access optimization scheme for the convolutional Encoder-Decoder architecture of semantic segmentation networks is put forward. 3. The high energy-efficient hardware acceleration solution of the DS-SLAM algorithm on a heterogeneous computing platform is proposed, which can be utilized for the functional module to provide mobile robots with autonomous localization. The remainder of this paper is organized as follows--Section 2 provides an overview of the current implementation of different SLAM algorithms on the embedded platform. In Section 3, we give a brief description of DS-SLAM algorithms and the SegNet semantic segmentation network, as well as our proposed quantization strategy. Section 4 presents the framework of our FPGA accelerator and some optimization methods. Subsequently, Section 5 provides the experimental results of the proposed energy-efficient DS-SLAM implementation. Finally, a brief conclusion is provided in Section 6.
# Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm
## 2 Materials and Methods  
### Data Collection  
It is necessary to use the slice program to calculate material consumption and fabrication time according to different raster angles of 3D models created in the computer environment. In this study, the Zaxe XDesktop commercial slicing program was used, and the training data required for ML was created. Pieces with different geometries (rectangle, cylinder, and sphere, etc.) were used as models (Figure 1). Data were created by changing seven different raster angles (0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), and 90\({}^{\circ}\)) and three infill densities (30%, 60%, and 90%) for the Zaxe Z1 Plus open-source 3D printer.  
Process time (min), weight of the material (g), and length of the material wire (m) were calculated for each model according to the change in these two parameters. While process time refers to the time required to produce the model, the weight of the material defines the amount of material used during fabrication. Finally, the length of the material wire refers to the length of the filament consumed relative to the amount of material used. Other printing parameters were kept constant in the slicing program. These parameters are layer thickness 200 \(\upmu\)m, nozzle temperature 220 \({}^{\circ}\)C, wall thickness 5 mm, and printing speed 60 mm/s.
## 3 Experimental Results and Analysis  
### Strength and Deformation Properties  
Based on the recorded data from uniaxial compression tests, the curves of stress versus strain of these rock samples can be easily plotted, which are presented in Figure 4a. It is observed that these curves were concave-upward at the initial loading stage, indicating the gradual shut of native cracks and pores in the samples under the action of compressive loads. When the elastic deformation stage was reached, the curves turned into straight lines; that is, the stress increased linearly with the increase of the axial strain. Afterwards, the curves became convex. This suggests the specimens experienced plastic deformation resulting from the initiation and development of cracks. After the peak point, the \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Detection** & **Sampling** & **Sampling** & **PTT/\(\mu\)s** & **PTT/\(\mu\)s** & **HDT/\(\mu\)s** & **HLT/\(\mu\)s** \\
**Threshold/dB** & **Rate/Msps** & **Time/k** & **PTT/\(\mu\)s** & **PDT/\(\mu\)s** & **HDT/\(\mu\)s** & **HLT/\(\mu\)s** \\ \hline
45 & 10 & 5 & 256 & 50 & 20 & 300 \\ \hline \end{tabular}  
* Notes: Ptf, PDT, HDT and HLT mean the pre-trigger time, peak definition time, hit definition time and hit lockout time.  
\end{table} 
Table 2: AE acquisition parameters for uniaxial compression tests. specimens lose their bearing capacity quickly, showing significant brittleness. Therefore, the whole deformation process contains four typical phases: native defects closure, elastic deformation, plastic deformation and post-peak failure. Besides, it can be seen that the curves of the pre-holed specimens fluctuate when approaching the peak point. This is caused by the sudden occurrence of cracks, which will be interpreted at length in the next section. Details of the physical dimensions and mechanical parameters of all the samples are shown in Table 3. In which, \(\sigma_{\text{P}}\), \(\varepsilon_{\text{P}}\) and \(E\) denote the uniaxial compressive strength, peak strain and Young's modulus, respectively. Note that the Young's modulus was defined as the slope of the elastic deformation portion of the stress-strain curve, which can be obtained by linear fitting. As seen in Table 3, the peak strength of the three intact specimens was 100.9 MPa, 105.6 MPa and 101.3 MPa, respectively, with the coefficient of variation of 2.49%. Moreover, the corresponding three values of the Young's modulus were also very close. This manifests that the sandstone possessed a relatively high degree of homogeneity. However, compared with the specimens CS-1 and CS-2, the axial strain of the specimen C-3 at the initial loading stage was large (see Figure 4a), which may result from the poor parallelism of the specimen ends or the loose contact between the LVDT and compression platens.
## 3 TBM (Tunnel Boring Machine) Data Processing
### Data Preprocessing  
This study utilized excavation data from TBM No. 2 as the input dataset. During TBM operation, sensors recorded data at a frequency of seconds, generating massive datasets. These datasets included construction parameters such as total thrust, cutterhead torque, and cutterhead rotation speed, as well as auxiliary system data, such as hydraulic oil tank temperature and main drive cooling water temperature.  
Because the sensors operated continuously--even during non-excavation periods such as downtime and maintenance--these datasets contained a significant proportion of non-operational data. These included periods dedicated to surrounding rock support, equipment maintenance, and machine downtime, which were not relevant to the study. Additionally, external disturbances and sensor malfunctions introduced anomalous data points, resulting in datasets that were vast yet sparse in meaningful information.  
To improve the accuracy and reliability of the sensor data, reasonable preprocessing techniques were used, including numerical filtering, boxplot analysis, and low-pass filtering. Therefore, data preprocessing was essential to ensure the quality of the input data and the reliability of the analysis results.
## 4 Result and Discussion
### Comparison with Other Frequency Band Selection Methods  
In order to further illustrate the advantages of the proposed method in the fault severity recognition of bearings, the SAX-LZ of the bearing vibration signals processed by three frequency band selection methods (protrugram, sparsogram, and GA-sparsogram) was employed to compare with the proposed method. The calculated results of the two datasets are shown in Figures 8 and 9.  
As shown in Figures 8 and 9, compared with SAX-LZ, although the monotonicity of SAX-LZ could be improved by the three frequency band selection methods in some cases, the overall monotonicity was not better than that of SAX-LZ without treatment. The reason for this could be that although the above frequency band selection methods could effectively select the frequency band which contained the most fault information, some useful information was also lost. Therefore, the calculated results based on the frequency band selection methods might not be better than those of SAX-LZ without treatment. However, the ACLHC did not need to select the frequency band of the signal in the process of reducing the influence of noise and highlighting the impact components of signal. Therefore, the signals processed by ACLHC cannot only reduce the influence of noise, but also contain all the fault information. The calculated results of the proposed method were better than those of the SAX-LZ of the bearing vibration signals processed by the three frequency band selection methods. Therefore, the signals processed by ACLCH can effectively improve the recognition results of bearing fault severity. The proposed method can be effectively used to recognize the fault severity of bearings under different working conditions.  
Figure 8: The complexity values of Konstruktions-und Antriebstechnik datasets after different treatment.
## 3 The Proposed Method
### The DSP Algorithm
#### 3.2.2 The Attention-Similarity Loss Function  
Based on the differentiable pruning criterion, we can directly prune model parameters with the back propagation algorithm by calculating the gradient according to the loss function. Considering the MHA module is an important module in searched architectures that is responsible for extracting global information, we further develop an attention-similarity loss function for MHA modules.  
Given Q, K, V as the inputs, each head of the MHA module will calculate an attention weight based on Q and K, and apply the attention weight on V to get a weighted sum as the output. Therefore, the attention weight directly influences the ability to extract global information, and it can be employed to evaluate the quality of the MHA head. In general, a smaller attention weight difference from the well-trained attention head indicates a closer performance of the pruned MHA head. On top of this point, we propose to calculate the attention-similarity loss function as follows:  
\[\mathcal{L}_{attn}=Cos\_Sim(Attn,Attn_{prund}) \tag{8}\]  
where \(Cos\_Sim\) refers to the cosine similarity function. \(Attn\) refers to the well-trained attention weight and, \(Attn_{prund}\) refers to the pruned attention weight.  
Figure 3: The differentiable structured pruning criterion. With a Gumbel-Sigmoid activation, DSP employs a soften one-hot vector to guide the pruning. 1 corresponds to remained parameters and 0 corresponds to removed parameters.  
When pruning CNN and FFN modules, DSP adopts the standard loss function (e.g., CTC or CE). When pruning MHA modules, DSP will replace the standard loss function with the proposed attention-similarity loss function \(\mathcal{L}_{attn}\). In this way, a more accurate gradient can be calculated to optimize \(\beta\).
# An Image-Processing Method for Extracting Kinematic Characteristics of Droplets during Pulsed GMAW
## 3 Extraction of Droplet Region by Image Processing
### The Period with an Undetached Droplet
#### 3.1.3 Droplet Cutting and Recognition  
After the above image process, the position of the minimum diameter of droplet neck was found using the coordinates \((X_{l},\,Y_{l})\) and \((X_{l},\,Y_{l})\). The target droplet is cut off and recognized from the wire in this section.  
Taking Figure 12 as the operation object, \((X_{l},\,Y_{l})\) and \((X_{l},\,Y_{l})\) were connected by a straight line using white pixels to seal the target droplet first. Then, pixels around the droplet connection line were set to black in order to cut off the connection between the target droplet and the wire. The processed effect and partial enlarged image are shown in Figure 17.  
The target droplet was cut off and had a complete and continuous contour, which meant it has the necessary conditions to become hollow. Thus, the following step was to fill the hollow target droplet with a hole-filling algorithm. As shown in Figure 18, the hollow target droplet was filled with white pixels, whose area was the largest compared to the other white regions of the background. Therefore, the target droplet could be recognized by extracting the largest white connected domain (see Figure 19).  
Figure 17: The processed effect and partial enlarged image of cutting: (a) full image; (b) partial enlarged image.  
Figure 18: The effect of the hole-filling algorithm.  
To sum up, for droplet cutting and recognition, the target droplet was sealed and detached by connecting (\(X_{I}\), \(Y_{I}\)) and (\(X_{J}\), \(Y_{J}\)), which were endpoint coordinates of the minimum distance at the neck. Above all, droplet cutting and recognition during the period with an undetached droplet can be summarized as follows:  
Step 1: Sealing the target droplet;  
Step 2: Detaching the target droplet;  
Step 3: Filling hollow areas;  
Step 4: Recognition of the target droplet.
# Entropy Weighted TOPSIS Based Cluster Head Selection in Wireless Sensor Networks under Uncertainty
## 4 Cluster Heads Formation Method for WSN
### Node Selection Criteria:  
In our experiment, we have calculated reliability, residual energy, the number of neighbor nodes, the distance from the sink (BS), the average distance of cluster nodes, and the distance ratio (see Table 2). Following the first simulation round, the best CHs were chosen based on six criteria: residual energy, number of neighbors, distance from the sink (BS), average distance of cluster nodes, distance ratio, and reliability. Based on our hypothesis, we divided the network into 14 clusters for this study, with a cluster head in each cluster. We have utilized the NSG2.1 simulator and tool command language for this simulation. Here, we have generated 100 nodes within \(100\times 100\,\mathrm{m}^{2}\) and run the simulation on NS2 for the first round. For the subsequent round, the selection of CHs has been made by using our proposed algorithm (see Section 4.2). We have plotted the node, and it has been depicted in Figure 1.