esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Evaluations on Thoracic Tissue Contour Segmentation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \(J_{\text{PM}}\) & \(J_{\text{RP}}\) & \(J_{\text{BV}}\) & \(J_{\text{NT}}\) & _mIOU_ & _OPA_ & _SMA_ & **Time** \\ \hline FCN-8s [54] & 78.5\% & 75.4\% & **80.6\%** & 82.2\% & 76.8\% & 83.1\% & 82.8\% & 24.4 \\ DeepLab [25] & 64.4\% & 51.5\% & 63.9\% & 61.1\% & 59.5\% & 74.4\% & 57.9\% & 27.2 \\ U-Net [55] & 80.7\% & 72.3\% & 63.7\% & 75.4\% & 73.6\% & 82.0\% & 85.1\% & 26.5 \\ Manual traits [56] & 61.2\% & 46.7\% & 52.3\% & N/A & 49.5\% & 76.7\% & 53.6\% & 29.1 \\ Channel R-CNN & **83.1\%** & **83.6\%** & 73.5\% & **83.9\%** & **79.4\%** & **83.2\%** & **88.4\%** & **23.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of segmenting thoracic histological images as “Electrotome” (EL), “Fibrous tissue” (FT), “Pectoralis major” (PM), “Burnt spots” (BS), “Rib periosteum” (RP), “Fat” (FA), “Blood vessel” (BV), and “Nervous tissue” (NT). Time represents the processing time spent on one frame; the unit is ms. Bold results refer to the best performance. to the background increase the difficulties of segmentation and detection. However, the segmentation accuracy of the proposed channel R-CNN is still close to that of clinicians, which provides a technical basis for further promotion.
## 3 Experiments and Analysis
### Comparison of Fusion Criteria
#### 3.2.2 High Frequency Fusion
Compared with the IDPCNN model with the MSF parameter, most indicator values of the IDPCNN model with the NSML value are larger, which demonstrates that the IDPCNN model with the NSML value can better reflect the energy information of the sonar image, and conform to its own characteristics of the source image.
## 5 Numeric Integration
### Special Case \(\boldsymbol{\beta\to 2}\)  
Figure 3 shows \(Q_{\beta}(\omega)\) for representative values of \(\beta\). In the limit \(\beta=2\), the cosine transform is just a Gaussian,  
\[Q_{2}(\omega)=\frac{\sqrt{\pi}}{2}\exp\left(-\omega^{2}/4\right) \tag{71}\]  
whereas for \(\beta\lesssim 2\) it has a power-law tail  
\[Q_{2-\bar{\beta}}(\omega)\simeq\sin(\bar{\beta}\pi/2)\Gamma(2-\bar{\beta}) \omega^{-3+\bar{\beta}}\;\text{for}\;\omega\gg 1 \tag{72}\]  
This qualitative change is also evident from the high \(\omega\) expansion (21), which becomes useless at \(\beta=2\) where \(\sin(k\beta\pi/2)=0\) for all \(k\). All this is not a problem, but in the \(\omega\) range between the two series expansions, the numeric quadrature fails to reach the required accuracy because of cancellation. This problem can be remediated to a certain degree by transforming not \(f_{\beta}(t)\) but the difference \(f_{\beta}(t)-f_{2}(t)\). The result is then added to the analytic transform \(Q_{2}(\omega)\). In our implementation, this is done for \(1.75<\beta<2\). Even then, for \(\beta>1.9\) the integration fails for some \(\omega\).
# Within and Across-Language Comparison of Vocal Emotions in Mandarin and English
## 3 Results
### Within-Language Comparison of Vocal Emotions
#### 3.1.1 Mandarin  
A significant effect of emotion on all prosodic cues for Mandarin existed (Mean pitch: X\({}^{2}\) = 565.29, \(df\) = 4, \(p\) < 0.001; Pitch range: X\({}^{2}\) = 205.71, \(df\) = 4, \(p\) < 0.001; Mean absolute slope: X\({}^{2}\) = 320.36, \(df\) = 4, \(p\) < 0.001; Rise: X\({}^{2}\) = 93.82, \(df\) = 4, \(p\) < 0.001; Fall: X\({}^{2}\) = 171.16, \(df\) = 4, \(p\) < 0.001; Rise slope: X\({}^{2}\) = 106.39, \(df\) = 4, \(p\) < 0.001; Fall slope: X\({}^{2}\) = 152.80, \(df\) = 4, \(p\) < 0.001; Speech rate: X\({}^{2}\) = 161.24, \(df\) = 4, \(p\) < 0.001; Intensity: X\({}^{2}\) = 806.23, \(df\) = 4, \(p\) < 0.001). For spectrum cues, the effect of emotion was significant as well (CPP: X\({}^{2}\) = 259.72, \(df\) = 4, \(p\) < 0.001; H1: X\({}^{2}\) = 209.87, \(df\) = 4, \(p\) < 0.001; H2: X\({}^{2}\) = 262.13, \(df\) = 4, \(p\) < 0.001; H4: X\({}^{2}\) = 273.75, \(df\) = 4, \(p\) < 0.001; H1-H2: X\({}^{2}\) = 193.82, \(df\) = 4, \(p\) < 0.001; H1-A1: X\({}^{2}\) = 73.816, \(df\) = 4, \(p\) < 0.001; H1-A2: X\({}^{2}\) = 89.116, \(df\) = 4, \(p\) < 0.001; H1-A3: X\({}^{2}\) = 90.89, \(df\) = 4, \(p\) < 0.001). Therefore, these vocal emotions demonstrated significantly different acoustic patterns in terms of prosodic cues and spectral cues. To understand the physiological mechanism of phonation for different emotional expressions, we looked at the EGG results of five emotions in Mandarin. Figure 1 illustrates the pitch-normalized EGG waveform of five emotions. The nature of the EGG waveform differed across emotions. Neutral
# Securing Construction Workers' Data Security and Privacy with Blockchain Technology
## 2 Literature Review
### Evolution of Blockchain Technology: From Bitcoin to Blockchain 3.0  
Blockchain technology is recognized as a critical enabler of progress and innovation within several domains, including the construction industry. In 2008, [PERSON] introduced a white paper that unveiled the concept of blockchain as a technology for validating cryptocurrency transactions [61]. This groundbreaking publication brought the term \"blockchain\" into mainstream recognition and paved the way for its subsequent evolution. The evolution of blockchain can be categorized into different phases: Blockchain 1.0, which primarily revolves around cryptocurrencies; Blockchain 2.0, which incorporates \"smart contracts\"; and Blockchain 3.0, the current and final stage that focuses on diverse applications and encompasses widespread adoption of blockchain technology in global institutions and entrepreneurial ventures [62].  
Since its inception, the concept of blockchain has been shaped and defined by researchers and professionals in various ways. For instance, [PERSON] et al. [63] described blockchain as a distributed append-only data structure with timestamps, while [PERSON] et al. [64] emphasized its decentralized peer-to-peer network for transparently sharing real-time updates. [PERSON] et al. [65] defined blockchain as a type of distributed ledger technology that allows data to be shared across multiple locations without intermediaries. [PERSON] [66] highlighted the direct peer-to-peer interactions enabled by blockchain, which are facilitated by a consensus mechanism and cryptographic security. In the construction industry, stakeholders join a blockchain network where governance is based on consensus and consent [62]. The integration of smart contracts into blockchain technology has opened possibilities for diverse industries, including the construction sector [67]. Smart contracts stored on the blockchain are self-executing digital contracts that automatically fulfill predetermined conditions [65]. By eliminating the need for traditional intermediaries, such as banks, smart contracts enable efficient and cost-effective transactions on the blockchain [68].
## 2 Materials and Methods
### Analysis of Pesticides  
Treated carrot samples were homogenized using a Waring blender in an ammonium acetate-acetic acid solution (20 mM) prepared in methanol-water (95:5) followed by sonication for 5 min and centrifugation at 3500 rpm for 10 min to extract pesticides from the samples. The supernatant of extracts and the aliquot from the aqueous solution in the lab-scale reactor were taken for HPLC analysis performed on an Agilent 1260 Infinity HPLC system (Agilent Technologies, Santa Clara, CA, USA) with a Diode Array Detector. After injecting 10 uL of sample, the separation was performed in a ODS Hypersil C18 250 x 4.6 mm, 5.0 um, column (Thermo Fisher Scientific, Waltham, MA, USA). A mobile phase system of (A) water and (B) acetonitrile was used. A multi-step gradient of 20% A and 80% B for 0 min; 80% A and 20% B for 40 min; and, 20% A and 80% B for 50 min was used. The flow rate was 1.0 mL/min, the temperature was 30 \({}^{\circ}\)C, and the wavelength was set at 270 nm.
## 2 Methodology
### SE Based Modulus Selection Method  
When using the VMD algorithm, the decomposition modulus can be set in advance. By setting reasonable convergence conditions, the computational complexity of the model can be effectively reduced [41]. A new method for determining the modulus K using the SE values of the VMFs is proposed. The concept of SE was introduced by [PERSON] et al. in 2000 [48], and it is used to evaluate the complexity of time series. The higher self-similarity of the time series means the smaller SE value, and vice versa [49]. After the decomposition of the DD time series, a number of subsequences and their corresponding SE values can be obtained. If there are two or more subsequences with similar SE  
Figure 1: Specific procedures of variational mode decomposition (VMD).  
values, it is assumed that over-decomposition occurs, which leads to modal confusion; if the SE values of the VMFs are quite different from each other, the maximum K value that allows this state should be selected as the final decomposition modulus to avoid under-decomposition of the VMD. The specific process of SE is illustrated in Figure 2.  
In Figure 2, Equations (7)-(9) are as follows  
\[B_{i}^{m}(r)=\frac{number\ of\ X(j)\ such\ that\ d[X(i),X(j)]\leq r}{N-m},\ i\
eq j \tag{7}\]  
\[B^{m}(r)=(N-m+1)^{-1}\sum_{i=1}^{N-m+1}B_{i}^{m}(r) \tag{8}\]  
\[SE=-\ln\Bigl{[}A^{k}(r)/B^{m}(r)\Bigr{]} \tag{9}\]  
where \(m\) is an integer that represents the length of the comparison vector, \(r\) is a real number indicating the measure of similarity, \(X(i)=[u(i),u(i+1),\cdots,u(i+m-1)]\), \(d[X,X*]\) is defined as \(d[X,X*]=max\bigl{|}u(a)-u*(a)\bigr{|},\ X\
eq X*\), \(u(a)\) is the element of vector \(X\), \(d\) represents the distance between vectors \(X(i)\) and \(X(j)\), and the value of \(j\) is in the range \([1,N-m+1]\).  
As shown in Figure 2, the values of \(m\) and \(r\) need to be determined before calculating the SE. Typically, values of the embedding dimension \(m\) are taken as 1 or 2. The selection of a similar tolerance \(r\) depends largely on the practical application scenario, usually \(=0.1*std\sim 0.25 std\), where std is the standard deviation of the original data. In the paper, set \(m=2\) and \(r=0.2*std\).
# Shallow Subsurface Wavefield Data Interpolation Method Based on Transfer Learning
## 3 Results
### Training and Results
#### 3.4.2 Comparative Network Effectiveness Experiments  
In Figure 13, to evaluate our designed network, we compared it with the U-net residual (U-resnet) [28], Convolutional Auto Encode (CAE) [29], and MWCNN [30]. We trained these models on the source domain dataset and, after training, tested them under identical conditions using integrated data from forward simulations at different source locations, comparing their results. In the network training, in order to achieve the best effect, the super-parameter was finally set as follows: the initial learning rate is 0.001, the weight attenuation coefficient is 0.1, the batch size is 128, and iterations for 100 epochs. In the target domain, the data volume was limited, making it challenging to achieve optimal metrics. However, overall, the interpolation results are satisfactory.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **SSIM** & **PSNR** & **RMSE** \\ \hline Our Target Method & **0.9269** & **75.5380** & **0.0426** \\ Method 1 & 0.8634 & 74.4304 & 0.0484 \\ Method 2 & 0.8979 & 72.2162 & 0.0594 \\ Method 3 & 0.8336 & 72.1527 & 0.0629 \\ Method 4 & 0.6964 & 69.0959 & 0.0895 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of ablation experiment indicators (bold indicates the best effect).  
Figure 11: The _f-k_ spectrum of the experimental results: **(a)** complete data; **(b–f)** represent the above five training modalities in turn.  
Figure 12: Comparison of extracted signals (the blue curve represents the original signal, and the red curve represents the interpolated signal): **(a)** transfer learning; **(b)** Method 2: transfer learning without multi-scale convolutional modules.  
In Figure 14, we compared the respective _f-k_ spectra, revealing that the interpolation results from our network most closely resemble the _f-k_ spectrum of the original data. Finally, we compared the three networks using the three evaluation metrics, as shown in Table 3, which also highlights the advancement of our designed network.  
Figure 14: The _f-k_ spectrum of the experimental results (the red ellipse indicates areas different from the original image): (**a**) complete data; (**b–e**) represent the above five training modalities in turn.  
Figure 13: Experimental results: (**a**) complete data; (**b**) missing 60% of data; (**c**) results from our network; (**d**) results from U-resnet; (**e**) results from CAE; (**f**) results from MWCNN.
## 3 Application and Dataset
### The Empty-Dish Recycling Robot  
The working process of empty-dish recycling robot is shown in Figure 2. In addition to the external robotic arm for grasping, the robot is equipped with an inner camera system for taking photographs and an object detection system. It also has components of a recycling-station and body as Figure 2a shows. The object detection system is based on an object detection network that has been trained to recognize different kinds of dishes. When the model has been loaded and the camera is going to take a photo, the state of the robot preparing to grasp the dish is shown in Figure 2a. As the photo is successfully taken, the system performs object detection based on the photo, detects the positions of different dishes, and then passes the position information to the grasping system. Figure 2b is the process of the robot grasping the dish according to the position information. When the grasping process is finished, the robot arm puts the dish into the recycling platform as shown in Figure 2c. After repeating the above process several times, the dishes in the photo are sorted and recycled.
## 3 Modified and Simplified T-Type Structure Designed for Low Attenuation Units
### Design of 1 dB and 2 dB Attenuation Units with Low IL  
Since the proposed simplified T-type structure avoids the introduction of transistors in the on-state path, there is theoretically no resistor \(R_{on}\) to cause any loss to the signal. Therefore, the modified structure was inherently allowed to maintain extremely low insertion loss performance across a wide bandwidth. In addition, the same technique was employed to form 1 dB and 2 dB attenuation cells in symmetrical arrangement. The corresponding schematics are presented in Figure 6a,b, respectively, where inductors \(L_{1}\), \(L_{2}\) were utilized to optimize the port matching.  
Figure 4: Schematic of the switched T-type attenuator cell: (**a**) modified structure with a series tail capacitor \(C_{T}\); (**b**) modified structure with a parallel bypass capacitor \(C_{B}\).  
Figure 5: Simulated results: (**a**) \(S_{11}\); (**b**) phase error according to transistor on/off.  
Figure 6: Schematics of (**a**) a 1 dB attenuator with modified simplified T-structure; (**b**) 2 dB attenuator with modified simplified simplified T-structure; and (**c**) conventional bridge T-type 2 dB attenuator for comparison.  
Based on the RF performance simulation results in Figure 7, it can be shown that the 0.5 dB and 1 dB as well as the 2 dB unit all achieved accurate amplitude and phase control within 10-20 GHz, with maximum phase errors of 0.12\({}^{\circ}\), 0.24\({}^{\circ}\), and 0.34\({}^{\circ}\), respectively. However, it is essential to note that this proposed approach of multiple parallel branches is not applicable to the large attenuation case. According to Figure 7b, the standing wave performance deteriorates significantly with increasing attenuation range, which will severely aggravate the amplitude/phase characteristics after cascading (generally, the \(S_{11}\) of each unit needs to be controlled below \(-15\) dB). The above issue is due to the fact that parallel branches create a reduction in impedance that cannot be avoided. In addition, too many branches will consume more area and thus nullify the compactness advantage.  
Figure 8 demonstrates the performance comparison between the proposed structure and the conventional bridge T-type structure (as shown in Figure 7c) for 2 dB attenuation. It can be seen that the two architectures had quite close phase shift errors. Although the \(S_{11}\) of the proposed method was inferior to that of the conventional method, it was still maintained at \(-17\) dB, which has less impact on performance after cascading. Additionally, based on the results in Figure 8a, the maximum IL of the proposed topology was only 0.32 dB, which is considerably lower than that of the conventional method (i.e., 0.9 dB), validating the effectiveness of the improved method. In conclusion, the proposed structure exhibited merits in the attenuation range below 2 dB, while other forms of topology were required for large attenuation cells.
## 2 Innovative System Configuration, Dynamics Equations, and the Simulator
### System Dynamics Equations  
#### 2.2.1 Basic Thermal Dynamics of a Control Volume  
To construct the control-oriented simulator, the lumped-parameter and low-ordered dynamics of the thermal system were established. The dynamics equations were separated into two types: the coolant pipe and the heat source body. First of all, in the thermal dynamics field, a control volume is defined to be the surface of a volume that allows the mass and energy input and output. For the control volume of a coolant pipe as shown in Figure 2a, based on energy conservation and [PERSON]'s law of cooling, a first-ordered dynamic equation was derived as:  
\[\dot{T_{i}}=\frac{\dot{m}_{in}C_{v,w}(\Delta T)-\dot{m}_{out}C_{v,w}(\Delta T )+h_{b,i}A_{b,i}(\Delta T)}{\rho_{i}V_{i}C_{v,w}} \tag{1}\]  
where \(T\) was the lumped temperature; \(\dot{m}\), \(C_{v}\), \(h\), \(A\), \(\rho\), \(V\) were the mass flow rate, specific heat at constant volume, heat transfer coefficient, surface area, density, and control volume, respectively. The subscripts \(i\), in, out, amb, and \(w\) represent the \(i\)th control volume, input, output, ambient and water, respectively. For a control volume of a heat source body, the heat transfer is shown in Figure 2b. Similarly, based on the energy conservation and [PERSON]'s law of cooling, a first-order dynamics equation was formulated:  
\[\dot{T}_{b,j}=\frac{\dot{Q}_{j}-h_{w,j}A_{w,j}(\Delta T)-h_{b,j}A_{b,amb}( \Delta T)}{\rho_{j}V_{j}C_{v,j}} \tag{2}\]  
where \(\dot{Q}\) was the generated waste heat power; subscripts \(b,j\) were body and the \(j\)th heat source. With the above two governing equations, the control-oriented model of the IHTMS can be constructed.  
Figure 1: System configuration of the innovative IHTMS designs.
# Detection of Malicious Websites Using Symbolic Classifier
## 2 Materials and Methods
### Evaluation Metrics and Methodology
#### 2.8.2 Evaluation Methodology  
The evaluation methodology in this paper is to show not only the values of evaluation metrics in the test dataset but in the entire dataset. In the classic train/test procedure with GPSC and random hyperparameter search method, each evaluation metric was used on the train and test datasets, and then the mean and standard deviation of these metrics were calculated. To terminate the search for a GPSC hyperparameters combination all values of used evaluation metrics had to be greater than 0.97. If the value of any metric was below 0.97 then the process would be repeated, i.e., the GPSC hyperparameters would be randomly chosen and the process of training GPSC would start all over again. In the case of GPSC with random hyperparameters search and 5-fold cross-validation the procedure of obtaining values of evaluation metric consists of the following steps:  
* First step--obtain and calculate the mean values of evaluation metric after performing 5-fold cross-validation and if the mean values of all evaluation metrics used was above 0.97 then perform second step (final train/test), otherwise perform 5-fold cross-validation with new randomly chosen hyperparameters; and
* Second step--perform final train/test with same hyperparameters used in 5-fold cross-validation. Obtain the values of each evaluation metric on the train and test dataset and calculate their mean and standard deviation values. If the mean value of each evaluation metric was above 0.97 then terminate the process. Otherwise, the algorithm starts from the beginning with GPSC with 5-fold cross-validation with randomly selected hyperparameters.  
It should be noted that the standard deviation of each evaluation metric value is a potential indication of overfitting the GPSC (symbolic expression). The large standard deviation indicates for example that symbolic expression could detect malicious websites with high classification accuracy in train datasets. However, when a symbolic expression is applied to the test dataset the classification accuracy is significantly lower. The standard deviation will be shown in the results section in graphical form (error bars in bar plots) and numeric form (tables).
## 4 Experiments and Results
### Reconstruction of Synthetic 1D NMR Spectra  
The synthetic 1D NMR spectrum is generated according to Equation (1), and its specific parameter settings are shown in Table 3. The total number of data points is 512, and 31 complex data points are acquired (means 6% NUS data). Then, simulated Gaussian noise with a mean of zero and a standard deviation of 0.005 is added to the FID. Here, the maximum value of the noise is lower than that of the lowest spectral peak.  
The reconstruction results are shown in Figure 2. Under very limited sampled data, the first and second reconstructed spectral peaks are severely damaged in LRHM low-fidelity reconstructions, while the SLS and the proposed method reconstruct the spectral peaks with high fidelity (see the supplementary materials Table S1 for the correlation of each peak in Figure 2). In Table 4, it is clearly shown that the robustness of the proposed method is consistent with SLS, and the Pearson correlation coefficient is much higher than LRHM at low intensity spectral peaks. The proposed method can accelerate the reconstruction of the best method, SLS, without compromising the spectra quality. Thus, it is correct and with the expectation that no significant difference between Figure 2f, the reconstructed spectra using SLSMF, and Figure 2e, the reconstructed spectra using SLS. Accordingly, LRHMF only accelerates the algorithm of LRHM, thus Figure 2c,d should be the same. In addition, in the supplementary materials, we displayed more reconstruction results of traditional methods with moderate-fidelity (Figure S2) and high-fidelity (Figure S3) from 100 simulation data trails and the correlation of their corresponding spectral peaks. Since the 1D reconstruction is fast (several seconds) for all compared methods, there is no need to compare the computation time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline Parameters & Peaks ID & **1** & **2** & **3** & **4** & **5** \\ \hline Amplitude (\(A_{j}\)) & 0.3 & 0.4 & 0.5 & 1 & 1 \\ \hline Damping factor (\(\tau_{j}\)) & 0.01 & 0.02 & 0.03 & 0.04 & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter settings of the synthetic 1D NMR spectrum.