esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Optimizing the Model
#### 3.1.2 Support Vector Regression (SVR)  
The process of optimizing the SVR model involves adjusting the hyperparameters, cost, epsilon, gamma, and kernel values. The cost parameter determines the degree of error tolerance, while epsilon represents the acceptable range of error between predicted and actual values. In this case, the range for the cost parameter was set as 1, 10, and 100, and epsilon was set to 0.01 and 0.1. These values were commonly used and selected as initial choices for the parameters [77,78]. Additionally, gamma plays a role in adjusting the curvature of the decision boundary, and gamma values were set to 0.01 and 0.1. The kernel was considered with options including linear, polynomial, and Gaussian radial basis function (RBF) kernels.  
During the evaluation process with varying CV values from 2 to 10, consistent results were observed for the test sets in terms of evaluation metrics, as is visually represented in Figure 1. Figure 1 shows the evaluation metrics for each CV value. The R\({}^{2}\) value for the test set was found to be 0.79, with an RMSE of 7.67 MPa and an MAE of 5.25 MPa. Furthermore, the Delta RMSE was 1.6 MPa. Based on these results, the optimal SVR model was obtained with a CV value of 2 and the following hyperparameter combination: cost value of 100, epsilon value of 0.1, gamma value of 0.1, and the Gaussian kernel. The decision to choose a smaller CV value, such as 2, was that a smaller CV value leads to a simpler model and reduced model complexity, which helps avoid overfitting [79]. Table 3 shows the optimal hyperparameter values and CV fold for each model.  
Figure 1: Performance metrics for each cross-validation value of the models, including **(a)**\(\text{R}^{2}\); **(b)** RMSE; **(c)** MAE; and **(d)** Delta RMSE.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Best Hyperparameter** & **CV** \\ \hline \multirow{4}{*}{SVR} & Cost: 100 & \multirow{4}{*}{2} \\  & Epsilon: 0.1 & \\ \cline{1-2}  & Gamma: 0.1 & \\ \cline{1-2}  & Kernel: Gaussian & \\ \hline \multirow{4}{*}{KNN} & Number of neighbors: 5 & \multirow{4}{*}{2} \\  & Weights: distance & \\ \cline{1-2}  & Algorithm: auto & \\ \cline{1-2}  & Power parameter: Euclidean distance & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal hyperparameter values and CV fold.
# CPM-OFDM Performance over Underwater Acoustic Channels
## 5 Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multicolumn{2}{c}{\(h=plq\)} & \multicolumn{6}{c}{**SNR (dB)**} \\ \multicolumn{2}{c}{\(p\)} & \(q\) & **0** & **5** & **10** & **15** & **20** & **25** & **30** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 3: BER values for various modulation indices and SNRs (sorted on values of BER at SNR = 20 dB). bandwidth of 10 kHz. It is also noted that, as would be expected, the BER degrades as the channel bandwidth decreases. However, the BER performance for channel bandwidths of 5 kHz and 2.5 kHz are very similar with the BER offered by 2.5 kHz being slightly better than that offered by a bandwidth of 5 kHz. We also evaluated the PAPR performance of CPM-OFDM for all the 23 values of \(h\). The complementary cumulative distribution function (CCDF) for each value of \(h\) is plotted in Figure 10. For comparison, the CCDF of a typical PSK-OFDM scheme is also plotted. Figure 8: BER performance of CPM-OFDM as a function of transmitter-receiver distances with \(N=512\), \(h=7/16\). Figure 9: BER performance of CPM-OFDM as a function of channel bandwidth with \(N=512\), \(h=7/16\). The best PAPR is observed when \(h=1/2\), which is almost identical to that of PSK-OFDM. The PAPR of other values of \(h\) is marginally inferior to PSK-OFDM and \(h=1/2\).
# Hierarchical Guided-Image-Filtering for Efficient Stereo Matching
###### Abstract  
Stereo matching is complicated by the uneven distribution of textures on the image pairs. We address this problem by applying the edge-preserving guided-Image-filtering (GIF) at different resolutions. In contrast to most multi-scale stereo matching algorithms, parameters of the proposed hierarchical GIF model are in an innovative weighted-combination scheme to generate an improved matching cost volume. Our method draws its strength from exploiting texture in various resolution levels and performing an effective mixture of the derived parameters. This novel approach advances our recently proposed algorithm, the pervasive guided-image-filtering scheme, by equipping it with hierarchical filtering modules, leading to disparity images with more details. The approach ensures as many different-scale patterns as possible to be involved in the cost aggregation and hence improves matching accuracy. The experimental results show that the proposed scheme achieves the best matching accuracy when compared with six well-recognized cutting-edge algorithms using version 3 of the Middlebury stereo evaluation data sets.  
stereo matching cost aggregation; image filtering; binocular stereo vision Article
## 4 Discussion
### Limitations of the Study  
A wide variety of PAPE protocols were used in the selected studies, which complicates results comparison and determination of the best approach. Differences in intensity, duration, and type of activation exercises can significantly influence outcomes. Few studies investigate athletes' perception of effort and fatigue after PAPE application, which is crucial for understanding the impact of these exercises on athletes' overall feelings and well-being. Some of the included studies may have methodological limitations, such as small sample sizes, lack of control groups, or inconsistency in outcome assessment, affecting the credibility of conclusions.
# Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
## 4 Results
### Variation in Precipitation in the GRB  
The spatial distribution of average annual precipitation in the GRB from 2002 to 2018 is shown in Figure 3a. The mean annual precipitation of the GRB during the study period ranges from 1500 mm to 1800 mm, with an average value of 1644 mm. The areas with large precipitation are mainly concentrated in the central and northern regions. The precipitation in the central and northern parts of the GRB is more than 1700 mm, while the average annual precipitation in other regions varies from 1500 mm to 1600 mm. Generally speaking, the average annual precipitation is quite different in spatial distribution.  
Figure 3: Spatial distributions in multi-year average (2002–2018) **(a)** precipitation, **(b)** ET, and **(c)** runoff in the Ganjiang River basin.  
Figure 2: Results of small-scale basin division in the Ganjiang River Basin **(a)** the hydrological stations control layer, **(b)** the area control layer.  
We also calculated the annual and monthly mean precipitation of 172 sub-basins from 2002 to 2018, as shown in Figure 4a,b. Before 2013, the annual precipitation change trend of each sub-basin is relatively consistent, but after 2013, the changing trend of each sub-basin is different. The average standard deviation (STD) values of annual precipitation in 172 sub-basins from 2002 to 2013 is 184.63 mm, and after 2013, it is 240.33 mm. The higher precipitations in most basins occurred in 2002, 2010, 2015, and 2016, and the lower precipitations occurred in 2003, 2007, and 2011. The precipitation in the sub-basins has a large inter-annual variation, and the maximum annual precipitation in the same sub-basin is 2 to 2.5 times that of the minimum annual precipitation.  
For the monthly mean precipitation variation of 172 sub-basins in the GRB (Figure 4b), the higher precipitation in most sub-basins occurs in May and June, and the lower precipitation is in October. In May and June with high precipitation, the difference in precipitation in different sub-basins is more than 100 mm. In August, the difference in precipitation between different sub-basins increases further and the difference can reach more than 150 mm. The monthly mean precipitation of different sub-basins varies greatly in the same month. However, the changes in monthly precipitation in the 172 sub-basins of the GRB show a relatively consistent trend during the study period.  
Figure 4: Annual and monthly mean (**a,b**) precipitation, (**c,d**) ET, (**e,f**) runoff, and (**g.h**) TWSC variation in the 172 sub-basins from 2002 to 2018 (the blue line represents the average of the 172 sub-basins). The left panel shows annual results and the right panel shows monthly mean results.
# Review of Substrate Integrated Waveguide Circuits for Beam-Forming Networks Working in X-Band
## 5 New Trends (Empty SIW)
This means that the substrate used for the device can be a common FR4, which is low cost, rigid, and easy to machine. The performances of the waveguide built by this process were very promising, since the losses were lower than SIWs using very expensive substrates, such as Rogers RT/duroid@5880 or Rogers RO3003(tm) (Figure 23). In this comparison, the dielectric loaded SIWs were designed so that the TE\({}_{10}\) cut-off frequency was the same for all samples. Moreover, the conductor losses were neglected in  
Figure 21: Stack of layers for the integrated empty substrate. FR4 substrates (green), Prepreg (dark red), and copper (orange). Figure 22: Section of the empty SIW. FR4 substrates (green), Prepreg (dark red), and copper (orange). order to compare only the dielectric losses. The dielectric-absent SIW was simulated using a low-cost material (ITEQ IT-158) with the corresponding prepreg (Tuc TU-84P NF). It has to be stressed that the use of low-cost substrates compensates for the complexity of the process, thus, the cost of the device is very low. Using this technology, a resonant slotted array was also built (Figure 24). The antenna was composed of 20 slots, each 12.82 mm in length that corresponded to a resonant frequency of 11.75 GHz. The spacing of the slots was 25.64 mm, nearly \(\lambda_{\mathrm{g}}/2\) in the dielectric-absent SIW, and the antenna was fed by an SMA connector placed in the middle of the array. The experimental results were in very good agreement with simulations (Figure 25). The 15 dB bandwidth around 11.75 GHz was 100 MHz (0.85%), and the half power beam width was nearly \(8^{\circ}\). These results showed that the process can be used for the fabrication of microwave components. Figure 24: Antenna prototype built with the empty SIW [82]. Figure 23: Comparison of the losses due to the dielectric in SIW structures with different substrates and the proposed empty SIW [82]. Conductor losses are neglected. Figure 25: Comparison between simulated and experimental results for the dielectric-absent antenna [82]. (**a**) reflection coefficient; (**b**) radiation pattern.
## 4 Construction of 3D Interpreted Model Network
### Information Extraction and Knowledge Fusion  
The mainstream CAD software mainly uses AP242 edition1 version to realize the storage of 3D annotation in STEP file by means of tessellated data, which cannot retain the semantics of technical information. Therefore, this paper proposes the corresponding information extraction and knowledge fusion algorithm to extract geometric and technical information from this kind of STEP file. The proposed algorithm forms the geometric information relational network, the geometric and technical information relational network in turn. Furthermore, through the processing of XML knowledge index file, the construction of 3D interpreted model network is realized. The specific process is shown in Figure 9.  
Figure 8: The structure of knowledge index file.  
Figure 9: The algorithm of information extraction and knowledge fusion.  
Step 1: extract geometric information relation. Traverse all ADVANCED_FACE in the STEP file as the child node of CLOSED_SHELL. Based on each ADVANCED_FACE, search FACE_OUTER_BOUND, EDGE_LOOP, ORIENTED_EDGE and EDGE_CRUVE in turn to establish the relationship between ADVANCED_FACE and EDGE_CRUVE. Merge the same EDGE_CRUVE nodes to generate the geometric information relational network;  
Step 2: extract the relationship between geometric and technical information. Traverse GEOMETRIC_ITEM_SPECIFIC_USAGE to associate SHAPE_ASPECT and GEOMETRIC_MODEL_ITEM (ADVANCED_FACE or EDGE_CRUVE). Traverse the DRAUGHTING_MODEL_ITEM_ASSOCIATION to associate SHAPE_ASPECT and DRAUGHTING_CALLOUT. Search TESSELLATED_ANNOTATION_OCCURRENCE based on DRAUGHTING_CALLOUT, and finally associate GEOMETRIC_MODEL_ITEM and TESSELLATED_ANNOTATION_OCCURRENCE. Integrate this type of relationship into the geometric information relational network to form the geometric and technical information relational network;  
Step 3: fuse product information and design knowledge. Parse the XML knowledge index file. Based on ANNOTATED_MODEL_ITEM, integrate the design knowledge into the geometric and technical information relational network to form the 3D interpreted model network.
## 4 Experiments
### Comparing WEAT and SEAT with Proposed Sentiment-Based Method  
As introduced in Section 2.2, WEAT [12] and SEAT [13] are a joint effect of explicit and implicit bias. The factors of our method are more naturally connected with those of SRA and IAT, and it can distinguish explicit and implicit bias. Experimental results support the effectiveness of this connection. In the case that explicit and implicit bias might be quite different, and the difference is seriously concerned with the task, our method can be a better choice for bias identification. Furthermore, due to the requirement of massive data in high-quality training of word or sentence embedding, WEAT and SEAT do not work well on small data (e.g., the language of an individual). However, our method derives bias cases from every single statement and is not as sensitive to the data size as WEAT does.
# Land Suitability Mapping Using Geochemical and Spatial Analysis Methods
## 3 Materials and Methods
### Soil Sampling and Laboratory Procedures  
Topsoil samples (0-30 cm depth) were gathered from 112 sites from the Ioannina basin (Figure 4). A sample grid map covered the study area with equal distant spacing between the intersection points. The distance between the intersection areas is related to the size of the grid cell. The spacing distances as short as 1 km have been used in this study. At each sampling site, its surface was cleared of vegetation and debris, and a bulk soil sample was collected using a plastic spade to avoid metal contamination, following the soil sampling procedure of [PERSON] et al. [26]. Each 2 kg bulk soil sample comprises four 0.5 kg subsamples taken from a square frame (2 m \(\times\) 2 m).  
Preparation and chemical analysis of soil samples was performed using the procedures to soil treatment of [PERSON] et al. [26]. The soil samples were digested with an aliquot of the aqua-regia acid solution (HCI:HNO\({}_{3}\), 3:1 \(v/v\)). The supernatants were used for the measurement of Cd and Co by atomic absorption spectroscopy (AAS) under the following conditions: (a) for Cd: wavelength 228.8 nm and lamp current 8 mA; and (b) for Co: wavelength 240.7 nm and lamp current 10 mA.  
Certified Cd and Co atomic absorption standards (1 mg/mL) were obtained from Fisher Scientific Co. For this study, intermediate working standard solutions, generally at concentrations of 1-5 mg/mL, were produced by diluting the 1 mg/mL standard solution with ultrapure water. Working standards were produced daily from the intermediate standard. Analytical quality was monitored using reference samples, in-house reference materials and method blanks.  
Figure 4: Map showing soil sampling sites in the Ioannina plain.
## 1 Background
This method relies on same measurements and instrumentations than classical efficiency method, the only difference exists in continuous modification of one parameter: the angle of the guide vanes or the speed of the runner. It presents the advantage of obtaining continuous efficiency curves over the entire operating range while reducing significantly the total time required performing the tests [PERSON] et al. (2018); [PERSON] et al. (2018). This approach could be also used for fast detection of the different operating regions where hydraulic instabilities arise in the machine, such as part-load and full-load cavitating vortex ropes, harmful rotor-stator pressure fluctuations and vibrations, or even flow rotating-stalls [PERSON] et al. (2018). Indeed, [PERSON] (2018) shown that a rotating-stall developing in a reduced scale model of a pump-turbine can be successfully detected by pressure fluctuation measurements during a transient test over the S-region of its characteristic in turbine mode. The alternative dynamic method tested in this study allows us to:  
1. reduce significantly the time necessary to draw the complete efficiency characteristics of a hydraulic machine;
2. detect hydrodynamic instabilities within the operating range of the machine;
3. establish an alternative/complementary faster procedure for hydraulic efficiency measurements. The current work comes with two examples of implementation and validation of dynamic efficiency measurements on hydraulic turbomachinery model testing: a double-regulated axial counter-rotating variable speed microturbine and a centrifugal multi-stage variable speed pump-as-turbine (PAT). The experimental methodology to obtain the hydraulic efficiency hill-chart from measurements using the static and the dynamic methods is presented. Then, the experimental setup, including the employed hydraulic test rig and the two case studies are introduced. The instrumentation and the testing protocol are also provided. The results focus first on the selection and validation of the optimal acceleration/deceleration ramps of the runners to ensure a steady-state condition for the dynamic measurements. Finally, the resulting hydraulic hill-charts obtained by the two methods are compared. A discussion and concluding remarks come to complete the work.
# Bending Behavior of Corroded H-Shaped Steel Beam in Underground Environment
## 3 Analysis of Results
### Bending Test of Corroded Beam
#### 3.2.4 Ductility Analysis  
The ductility index is defined as the ratio of \(\Delta_{\text{u}}\) to \(\Delta_{\text{y}}\) and is denoted by \(\varphi\); \(\Delta_{\text{u}}\) is the displacement corresponding to the ultimate load and \(\Delta_{\text{y}}\) is the displacement corresponding to the yield load. The \(\Delta_{\text{u}}\) and \(\Delta_{\text{y}}\) values are listed in Table 6. The energy dissipation index is defined as the area enclosed by the load-displacement curve and the x-axis when the load becomes maximum, and is denoted by \(\Psi\). Table 6 summarizes the ductility index and energy dissipation index of each sample. As can be seen, the ductility index of the uncorroded H-shaped steel beam is larger than that of all corroded beams. Additionally, a higher loading level resulted in a lower ductility index when the corrosion period was not changed. Secondly, the energy dissipation index of the uncorroded steel beam is also larger than that of the corroded beams, and a higher loading level resulted in a lower energy consumption index when the corrosion period was not changed.  
Figure 13 shows the relationship between the value of the ductility index and the energy dissipation index and corrosion rate, respectively. As can be seen, the two indices decreased as the corrosion rate increased. Moreover, the ductility of the corroded steel beams exhibits a similar pattern when it is represented by the ductility index and energy dissipation index, respectively. In this study, the ductility index and energy consumption index of GC-180-6 were only 0.46 and 0.48 times equal to those of the uncorroded H-shaped beam, respectively. The reduced ductility is attributed to two factors. On one hand, the corrosion reduced the ductility of steel, which, in turn, lowered the ductility of the corroded H-shaped steel beams. On the other hand, the sustained load caused unrecoverable deformation to the beams, which resulted in the inferior ductility of the corroded H-shaped steel beams.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimens** & \(\Delta_{\text{y}}\)**/mm** & \(\Delta_{\text{u}}\)**/mm** & \(\varphi\) & \(\Psi\) \\ \hline G-0-0 & 2.95 & 44.49 & 15.08 & 11,290 \\ GA-60-0 & 3.08 & 42.89 & 13.93 & 10,118 \\ GA-60-3 & 3.10 & 40.85 & 13.18 & 9335 \\ GA-60-6 & 3.33 & 30.29 & 9.10 & 6372 \\ GB-120-0 & 3.14 & 40.01 & 12.74 & 8774 \\ GB-120-3 & 3.22 & 38.17 & 11.85 & 8294 \\ GB-120-6 & 3.32 & 32.45 & 9.77 & 6532 \\ GC-180-0 & 3.48 & 38.6 & 11.09 & 8047 \\ GC-180-3 & 3.75 & 35.84 & 9.56 & 7195 \\ GC-180-6 & 4.27 & 29.69 & 6.95 & 5454 \\ \hline \hline \end{tabular}  
* Notes: \(\Delta_{\text{y}}\) = the displacement corresponding to yield load; \(\Delta_{\text{u}}\) = the displacement corresponding to ultimate load; \(\varphi\) = the ductility index; \(\Psi\) = the energy dissipation index.  
\end{table}
Table 6: Ductility index.
# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 3 Digital Twin Architecture Proposal for Interurban Intelligent Transportation Systems
### Pollution Use Case  
The purpose of this second use case is to utilize the digital twin to recommend strategies to drivers that can help mitigate pollution episodes.  
Aimsun Next [28] includes the emission model COPERT [31], providing estimations of emissions caused by vehicles in each simulation. The pollutants available are carbon monoxide (CO), nitrogen oxides (NO\({}_{\text{x}}\)), and particulate matter (PM), specifically particles with a maximum diameter of 2.5 \(\upmu\)m (PM\({}_{2.5}\)).  
For this use case, the strategy used to achieve a reduction in emission levels is variable speed limits. Maintaining a moderate speed and a constant pace helps reduce pollution, as vehicles consume less fuel when traveling at steady speeds. Speeds between 70 and 90 km/h are generally considered the most effective for minimizing emissions and maximizing fuel efficiency. However, this optimal speed range can vary depending on factors such as vehicle type, engine design, and driving conditions. Additionally, the most convenient speed limit for mitigating emissions may change in response to real-time traffic conditions. In this way, a study conducted in Barcelona [32] indicates that the reduction of the speed limit to 80 km/h, implemented in 2008 in certain areas, caused an increase in NOx and PM\({}_{10}\) levels, while variable speed policies helped reduce these emission levels. This is why variable speed limits play a crucial role, offering a more responsive, adaptive, and efficient approach to managing traffic-related pollution by adjusting to current conditions and ensuring vehicles operate within their most efficient speed range.  
Speed limits, as in the first use case, are modified not only on the incident section, but also on some of the previous sections of the highway.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 3 Materials and Methods
### Test Setup
**TOT_Power_IN:** A floating-point value representing the total input power of the amplifier, in dBm. 9. **TOT_Power_OUT:** A 2D array (one floating-point value per target gain and tilt pair) representing the total output power of the amplifier, in dBm. 10. **OSA_PARAMS:** A structured dataset containing additional parameters, including device vendor information, fiber connector type (flat or angled), RBW in GHz.