esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design of the Wideband GGW Bandpass Filter
### GGW Design  
As shown in Figure 1, a vertically polarized GGW is used [22]. In this subsection, we consider the structure without the central metallic pins (\(t_{xi}\times t_{yi}\ \times\ t_{zi}\), \(i=1,\ldots,6\)), to emulate a standard empty rectangular waveguide. The bottom plate includes two periodic structures of three rows of square metallic pins (\(d\ \times\ a\)) with pitch \(p\), which are separated from each other by a groove width \(W\). Both periodic structures act as artificial perfect magnetic conductor (PMC) sidewalls in a certain frequency band and do not allow wave propagation along the \(x\) and \(y\) transverse directions between the gap \(h\). The upper plate is situated at a height \(b=d+h\) from the bottom part and realizes a perfect electric conductor (PEC) surface. In this way, by adjusting the dimensions of the periodic structure and the width \(W\) of the groove area, it is possible to propagate a quasi-TE\({}_{10}\) mode. Table 2 includes the optimized GGW dimensions that lead to the dispersion diagram of Figure 2, which was obtained with an electromagnetic (EM) simulator (HFSS) to achieve the high-pass characteristic of the GGW with a cut-off frequency \(f_{C,L}=8.3\) GHz at \(|S_{11}|=-3\) dB. As can be seen in Figure 2, the desired TE\({}_{10}\) mode in the GGW propagates along the \(z\)-axis, inside the stopband of the periodic structure, thus avoiding leakage in the transverse plane between 8.3 GHz and 15.5 GHz. The dimensions \(W\) and \(b\) of the GGW given in Table 2 match the WR-75 standard rectangular waveguide size.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** \\ \hline Type & Chebyshev \\ Order, \(n\) & 5 \\ Return loss, \(RL\) (dB) & 20 \\ GGW TE\({}_{10}\) mode cut-off frequency, \(f_{C,L}\) (GHz) & 8.3 \\ Higher cut-off frequency at \(|S_{11}|=-20\) dB, \(f_{C,H}\) (GHz) & 12 \\ Cut-off angle, \(\theta_{e}\) (\({}^{\circ}\)) & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design specifications for the GGW bandpass filter shown in Figure 1.  
Figure 1: Structure of the wideband Chebyshev bandpass filter based on GGW technology (with order \(n=5\)).
## 2 Materials and Methods
### Sample Collection and Analysis  
In this study, surface sediment samples (0-15 cm) from seven sampling sites (Figure 1) were collected from the main channel and river mouth of Fengshan River using a Van Veen dredge in the dry season (December 2015) and the wet season (May 2016). The sampling sites are situated upstream (F1, F2), midstream (F3, F4, F5), and downstream (F6, F7). Among them, site F3 is located nearby the effluent outfall of the Fengshan Water Resources Center. Sites F6 and F7 are situated at the tidal river reach, of which site F7 is located at the river mouth. Sites F1-F6 were sampled at the center of the river channel on bridge, and site F7 was sampled on a boat. According to Taiwan Central Weather Bureau [20], the accumulated precipitation in Kaohsiung City were 94 mm and 359 mm on October-December 2015 (dry season) and March-May 2016 (wet season), respectively.  
After the sediment samples were air-dried, ground, and homogenized, approximately 1 g of dry sediments was digested in a Teflon digestion vessel with nitric acid (HNO3), hydrogen peroxide (H2O2), and hydrochloric acid (HCl) by referring to protocols of the standard method of the Taiwan Environmental Protection Administration (NIEA M353.02C). Firstly, the sediment sample was digested with 15 mL HNO3 at 95 \(\pm\) 5 \({}^{\circ}\)C (not boiling) for 20 min. Then, the sediment sample was digested with 10 mL H2O2 at 95 \(\pm\) 5 \({}^{\circ}\)C (not boiling) for 10 min. Finally, the sediment sample was digested with 10 mL HCl at 95 \(\pm\) 5 \({}^{\circ}\)C (not boiling) for 15 min. After digestion, the digested solution was diluted to 50 mL with deionized water (~18 M\(\Omega\)cm). The concentrations of Cr, Cu, Ni, Pb, Zn, As, and Hg in the digested solution were analyzed by an inductively coupled plasma atomic emission spectrometer (Thermo Scientific iCAP 7400, Waltham, MA, USA) based on the standard method of the Taiwan Environmental Protection Administration (NIEA M104.02C). The standard reference material of sediment (NIST SRM 1646a, Gaithersburg, MD, USA) was analyzed using the same digestion and measurement procedures to validate the results.
## 3 Numerical Application
### Application of the Optimization
This amplitude is comparable to the reference image computed with the actual material properties (b) for which the gain is also +17 dB. The correlation coefficient between the reference and optimized images is 0.97, which confirms that the images are almost identical. The material properties used to compute the optimized image are indicated in Figure 5 along with the estimation errors. The actual and estimated slowness curves and the estimation error are also displayed. The slowness estimation error is inferior to 5.0%, with a mean error of 13.4% on the elastic properties. The mean error on the [PERSON]'s parameters is 32.1%. Thereby, the proposed optimization procedure applied to this proof of concept shows promise for improving the quality of TFM images in nuclear welds with experimental data, even though it estimates the material properties with poor accuracy. Figure 4: Images in a 316L stainless steel V-shape weld with simulated signals: isotropic reconstruction model (**a**); actual material properties (**b**); PSO optimization (**c**). The vertical and horizontal echodynamic curves are presented in the side-plots. The optimization is conducted around SDH (1), and each image is normalized by the maximum amplitude in a 10 \(\times\) 10 mm\({}^{2}\) region around this defect (the amplitude of this defect echo is 0 dB for all images). Orange dashed lines indicate the Amax position around defect (1) and black lines indicate the chamfers of the weld.
# Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
## 2 Compensators: Applications and Design
### How to Obtain the Compensator Design Parameters?  
We first consider optimum design to obtain the parameters in compensators (3), (6) and (9) in such a way that a minimum of the maximum passband deviation \(\delta_{o}\) in the passband of interest, defined by the passband edge \(\omega_{p}\), equals  
\[20\times log_{10}\Big{|}G_{c}\Big{(}e^{j\omega}\Big{)}\Big{|}\leq\delta_{opt} \text{ for }0\leq\omega\leq\omega_{p}, \tag{12}\]  
where \(G_{c}\big{(}e^{j\omega}\big{)}\) is given in (2), and the compensators are given in (3), (6) and (9).  
The optimal design is obtained using particle swarm optimization (PSO) in MATLAB [33]. The multiplierless design is obtained by presenting the optimal parameters in a signed power-of-two (SPT) form.  
The detailed design is described in the next section, while the MATLAB programs are explained in the Supplementary Material.
## 3 Materials and Methods
### Case Study Area of the Middle-Mile Route in South Korea  
This study uses a middle-mile route between the Bieungdo port and the Hanjin Jeonju logistics distribution center in South Korea, in Figure 1, as a case study area, where a series of autonomous truck pilot projects has been running. The route is 57 km long with a speed limit of 90 km per hour. It includes both interrupted and continuous flow intersections. It passes through two municipalities, including Gunsan and Jeonju; specifically, it connects Jeonju, a major city in North Jeolla Province, and the Saemangeum National Industrial Complex, Gunsan National Industrial Complex, and domestic commercial vehicle production base in Gunsan.  
We selected this route for the following reasons. First, a case study area in South Korea is an appropriate candidate to explore the impact of ATs on TCO given the readiness of driving automation. Specifically, KPMG International [15] released the autonomous vehicle readiness index in 2020 to evaluate the readiness of AVs by considering policy and regulation, technological advancement, infrastructures, and consumer acceptance. According to this report, the most significant growth was seen in South Korea. This was partly attributable to the massive government-funded AV pilot programs and the accessible infrastructures, including 4G coverage and mobile connection speed. More importantly, significant research efforts have been made on the route for ATs by collaborating with federal and local governments, research institutions, universities, and freight transport operators. Thus, the route in South Korea has the potential to become a focal point of AT operation in the proximate future.  
Second, since the route connects a port to one of the largest distribution centers for logistics in the region, it is a suitable study area for middle-mile travel for the logistics industry. This feature is important in this study, given its emphasis was placed on the use of ATs on highways or freeways for long-distance and regional freight movements rather than on their use for local transportation or transportation over the first and last mile within metropolitan areas. We focused on middle-mile travel because ATs are assumed to be feasible in long-distance settings well before urban areas [7]. Also, the operations of first-mile transport and last-mile transport encounter serious challenges that need to be analyzed separately.  
Third, the proportion of trucks along the route follows a pattern that is comparable to that of South Korea as a whole. Specifically, annual averaged daily traffic of trucks on the route was approximately 2328 (24.5%), while that of freeways in South Korea was roughly 4,846,000 (21.8%). Therefore, the choice of this location as a study area could exclude the possibility of selection bias given the travel patterns of trucks.  
Figure 1: The case study area of the middle-mile route in South Korea.
## 6 Conclusions  
In this study, we compared the prediction accuracy of classical and penalized methods across a range of scenarios. Classical methods generally exhibited inferior accuracy, except in scenarios with sufficient information (large sample size, low correlation, and high SNR), where their predictions were comparable to those of penalized methods. Among the penalized approaches, lasso performed best when information was limited (small sample size, high correlation, and low SNR). In contrast, NNG, ALASSO, and RLASSO performed similarly and outperformed lasso in scenarios with sufficient information.  
We also evaluated the effects of initial estimates (OLS, ridge, and lasso) on the prediction accuracy and model complexity of NNG and ALASSO. Ridge initial estimates produced superior predictive performance compared to OLS and lasso initial estimates, suggesting that ridge estimates may be preferable when prediction is the primary goal. Conversely, lasso initial estimates produced simpler models, making them suitable for descriptive purposes, particularly when data contain sufficient information.  
Furthermore, we assessed model performance under different tuning criteria. Models tuned with AIC and CV produced comparable prediction accuracy and generally outperformed those tuned with BIC. However, BIC was most effective in scenarios with sufficient information (excluding scenarios with several small effects) across all approaches, except for lasso, where it consistently underperformed. BIC produced simpler models compared to AIC and CV, which is advantageous when simplicity and practical usability are key considerations.  
Overall, our findings indicate that no single method performs best in all scenarios. The choice of method depends on the amount of information in the data and the criteria used for selecting tuning parameters. Lasso is the preferred choice in settings with limited information due to its superior predictive accuracy. However, when sufficient information is available, classical methods are preferred, as they provide better predictive performance and select simpler models.
## 3 Examples of Chemometrics and Vibrational Spectroscopy Applications in Seafood
### Freshness, Spoilage and Storage
The fish species analysed in this study included horse mackerel (_Trachurus trachurus_), European anchovy (_Engraulis encrasicolus_), red mullet (_Mullus sumuletus_), bluefish (_Pomatanus saltatrix_), Atlantic salmon (_Salmo salar_) and flying gurnard (_Trigla lucerna_) [87]. In this study, the authors split the samples into different freezing and thawing cycles where three batches were defined as fresh, once frozen-thawed and twice frozen-thawed. The Raman data were analysed using PCA models. The authors concluded that the Raman method was able to differentiate and classify the fresh and frozen samples with up to 99.29% confidence [87].
# Analysis on the Spatial-Temporal Distribution Patterns of Major Mine Debris Flows in China
## 4 Analysis of the Spatial Distribution Pattern of Mine Debris Flow  
### Mine Debris Flow Distribution Pattern by Province  
Analyzing the spatial distribution pattern of mine debris flow hazards is the basis for analyzing and assessing their causes and influencing factors, and thus, predicting their development trends. Based on the statistical data from 1954 to 2019, the national percentage  
Figure 1: Distribution of major mine debris flows by month over the years.  
Figure 2: Trends in the number of major debris flow/mine debris flow disasters over a wide span of time.  
of major mine debris flow disasters in different provinces obtained by collation is shown in Figure 3.  
Combined with the major mine debris flows analyzed in the geographic divisions of China (Figure 3a), the number of major mine debris flow occurrences in North China accounted for 3.4% of the national proportion; the overall number of accidents in Northeast China was low, accounting for 0.8% of the country; the number of major mine debris flow occurrences in mines in East China, South China, and Central China were also low, at 6.0, 6.0 and 2.6%, respectively; the number of mine debris flows in Southwest China was significantly higher than other regions, with 51.3% of the national percentage of the first; and Northwest China is second only to southwest China, with a high percentage of major mine debris flows, reaching 29.9%. As can be seen from Figure 3b, in terms of the number of occurrences, the number of major mine debris flows occurring in Sichuan accounted for 28.2% of the total statistics, the highest in the country, accounting for the second, third, and fourth places were Yunnan 16.2%, Xinjiang 12.0%, and Ningxia 5.1%. According to data released by the National Bureau of Statistics of China, the distribution of mine debris flow-prone areas and regional debris flow is basically the same, with debris flow-prone areas providing environmental conditions for the occurrence of mine debris flow, while the development of mineral resources has exacerbated the occurrence and hazards of regional debris flow. For example, the Yanjingou watershed in Mianning County, Sichuan Province, was originally an area prone to flash floods and debris flows, and the accumulation of mining slag in the area of Lugu iron ore mine provided a large number of material sources for the occurrence of debris flow--and mine debris flow occurred many times in history. It is therefore particularly important to select areas with a high incidence of debris flow to carry out mine debris flow investigation and evaluation.
## 3 Materials and Methods
### 3D Object Detection and Pose Estimation Module
#### 3.2.1 Foundation  
In this section, the pose estimation problem is mathematically described, as well as the notation used throughout the work. A camera pinhole model is assumed, where \(K_{\rm C}\in\mathbb{R}^{3\times 3}\) is the intrinsic matrix of the RGB camera, previously obtained in the calibration phase. In addition, we also assume that the depth image is registered to the color one and that the radial distortion has also been corrected. On one hand, let \(\mathbf{x}=[x,y]^{T}\in\Omega\subseteq\mathbb{R}^{2}\) be a pixel of the image captured by the RGB camera, \(I_{\rm rgb}\), where \(\mathbf{y}=I_{rgb}(\mathbf{x})\in\mathbb{R}^{3}\) is the vector that represents the color of the image in the pixel \(\mathbf{x}\). On the other hand, let \(\mathbf{X}_{o}=[X_{o},Y_{o},Z_{o}]^{T}\in\mathbb{R}^{3}\) be one of the vertices of the 3D object in its coordinate system, whose representation in homogeneous coordinates is \(\mathbf{\tilde{X}}_{o}=[X_{o},Y_{o},Z_{o},1]^{T}\in\mathbb{R}^{4}\).  
Mathematically, the problem of estimating the pose of a 3D object consists of determining a matrix \(G\in\mathbb{SE}(3)\) that relates the coordinate system of the object with the coordinate system of the camera, where  
\[\mathbb{SE}(3)=\left\{G=\begin{bmatrix}R&\mathbf{t}\\ \mathbf{0}^{T}&1\end{bmatrix}\in\mathbb{R}^{4\times 4}|R\in\mathbb{SO}(3), \mathbf{t}\in\mathbb{R}^{3}\right\} \tag{1}\]  
Figure 3: Flowchart of AR tracking method.  
is the special Euclidean group, being a collection of rigid transform matrices, and  
\[\mathbb{SO}(3)=\left\{R\in\mathbb{R}^{3\times 3}|RR^{T}=I,det(R)=1\right\} \tag{2}\]  
is the special Orthogonal group. In this way, we have that:  
\[\mathbf{\tilde{X}}_{c}=G\mathbf{\tilde{X}}_{o}, \tag{3}\]  
where \(\mathbf{\tilde{X}}_{c}=[X_{c},Y_{c},Z_{c},1]^{T}\in\mathbb{R}^{4}\) is a point in the camera coordinate system.  
Moreover, any point of a 3D model can be projected onto a 2D image plane using:  
\[\mathbf{x}=\pi(K_{C}(G\mathbf{\tilde{X}}_{o})_{3\times 1}), \tag{4}\]  
where \(\pi(\mathbf{X})=[X/Z,Y/Z]^{T}\).
# System Dynamics Modeling for Estimating the Locations of Road Icing Using GIS
## 3 Results
### Modeling Results for Road Temperature and Moisture  
Road temperature and road moisture are important factors in calculating the amount of road icing generated per hour. Road temperature was trained using the evolution algorithm. The results of the corresponding training set and test set are shown in Figure 8. December 12 th to 18 th exhibited a degree of training of 95% on average. The accuracy of the test set was found to be 91.88%. This was estimated to be a significant value because it far exceeded the target value of 80% set in this study.  
Figure 8: Training and test results of the evolution algorithm for the road temperature.  
Figure 7: Schematic diagram of the road icing simulation.  
Figure 9 shows the results of the road temperature. The average of the estimated values at 18,583 road points was calculated and expressed as a graph along with the actual road temperature values acquired from ASOS in Figure 9a. The reliability between the estimated and actual values was calculated over time and expressed in Figure 9b. As confirmed in Figure 8 above, reliability was calculated at 91.88%.  
Figure 10 shows the results of the road moisture. Average road moisture for 18,583 road points and the precipitation and condensation value that generate it were expressed. Road moisture significantly increased from AM 9:00 at which point it began to rain and a total amount of more than 700 g was maintained until PM 13:00. The average condensation value was low because there were not many waterfront areas adjacent to the highway, but high values were observed in some sections.
## 4 Experiments and Evaluations
### Evaluation Parameters  
The most commonly used metrics in the literature--F1 score, accuracy, recall, and precision--were employed to evaluate the classification models' performance. These metrics serve as standard benchmarks for assessing and comparing the effectiveness of different classification algorithms and are widely utilized in various machine learning and deep learning applications [45,46].  
These evaluation parameters are derived from the confusion matrix, which consists of four key components:  
* True Positive (TP): The number of correctly classified attack instances.
* True Negative (TN): The number of correctly classified normal instances.
* False Positive (FP): Normal instances incorrectly classified as attacks.
* False Negative (FN): Attack instances mistakenly classified as normal.  
The evaluation metrics are computed as follows.  
Accuracy measures the proportion of correctly classified instances (_TP_ + _TN_) to the total number of instances (_TP_ + _TN_ + _FP_ + _FN_).  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
Precision quantifies the proportion of correctly classified attack instances (_TP_) to the total predicted attack instances (_TP_ + _FP_).  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{2}\]  
Recall (sensitivity) measures the proportion of correctly classified attack instances (_TP_) relative to all actual attack instances (_TP_ + _FN_).  
\[\text{Recall}=\frac{TP}{FN+FP} \tag{3}\]  
F1 score represents the harmonic mean of precision and recall, providing a balanced measure that accounts for both false positives and false negatives.  
\[\text{F1 Score}=\frac{2 TP}{2 TP+FP+FN} \tag{4}\]  
While precision emphasizes the accuracy of positive classifications, recall provides insight into the model's ability to identify actual attack instances correctly. The F1 score serves as a comprehensive measure that balances both precision and recall, making it one of the most widely adopted performance indicators in classification tasks.
## 3 Test and Result Analysis of Three-Dimensional Bending Test
#### 3.1.2 Curve Reconstruction Algorithm Based on Curvature  
In this paper, the tangent angle recursive algorithm is used to reconstruct the curve according to the curvature information. The diagram of tangent recursion is shown in Figure 7. The curve is divided into several small segments; each small segment can be seen as an arc, \(O_{n},O_{n+1}\) is the two endpoints of the small segment, the corresponding curvature is \(k_{n},k_{n+1}\). \(l_{n}\) is the corresponding arc chord length, its length is approximately equal to the arc length \(\Delta s_{n}\). The angle between the tangent of the \(O_{n}\) point and the x-axis is \(\theta_{n}\), and the angle between the tangent of the \(O_{n+1}\) point and the x-axis is \(\theta_{n+1}\).  
Figure 7: Diagram of tangent angle recursion algorithm.  
Derived from the geometric relationship:  
\[\left\{\begin{array}{l}\Delta\theta_{n}=\theta_{n+1}-\theta_{n}\\ l_{n}=\frac{2\sin(\Delta\theta_{n}/2)}{k_{n}},k_{n}\
eq 0\\ l_{n}=\Delta s_{n},k=0\\ \Delta x_{n}=l_{n}\cos(\theta_{n}-\frac{\Delta\theta_{n}}{2})\\ \Delta y_{n}=l_{n}\sin(\theta_{n}-\frac{\Delta\theta_{n}}{2})\\ x_{n+1}=x_{n}+\Delta x_{n}\\ y_{n+1}=y_{n}+\Delta y_{n}\end{array}\right. \tag{20}\]  
By the definition of curvature \(k(s)=\underset{\Delta\theta\to 0}{\lim}\Big{|}\frac{\Delta\theta}{ \Delta s}\Big{|}\):  
\[\theta(s)=\int k(s)ds \tag{21}\]  
According to the linear interpolation method described in Section 3.1.1, the curve is divided into \(n\) segments, and the relationship between the angle and the arc length in each segment is obtained by combining Equations (15) and (16) as follows:  
\[\theta(s)=\frac{1}{2}M{s}^{2}+N{s}+c \tag{22}\]  
The value of \(c\) is related to the boundary condition of each segment. Therefore, it can be solved by using the curvature value of each segment boundary as the boundary condition.  
When the cubic spline interpolation method is used, the relationship between curvature and arc length is obtained, as shown in Equation (17). Then, after a series of conditions, each undetermined coefficient is solved and combined with Equation (15) to obtain:  
\[\theta(s)=c+a_{i}(s-s_{i})+\frac{1}{2}b_{i}(s-s_{i})^{2}+\frac{1}{3}c_{i}(s-s_ {i})^{3}+\frac{1}{4}d_{i}(s-s_{i})^{4} \tag{23}\]  
After the interpolation function of each curve segment is obtained, the curve can be reconstructed by substituting it into Equation (20)