esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
## 3 Materials and Methods
### Experimental Validation
#### 3.3.2 Evaluation Metrics  
To quantitatively evaluate the performance of our RRA-Net, we adopt the peak signal-to-noise ratio (\(PSNR\)) and the structural similarity index (\(SSIM\)) [37] as evaluation metrics.  
The PSNR is the ratio between the maximum possible power of a signal and the power of discrepancy that affects the quality of its representation. It is defined via the Mean Squared Error (\(MSE\)). Given an \(H\times W\) dehazed image \(I_{dehazed}\) and its corresponding haze-free image \(I_{gt}\), the \(MSE\) is defined as  
\[MSE=\frac{1}{HW}\sum_{x}\parallel I_{dehazed}(x)-I_{gt}(x)\parallel_{2}^{2}, \tag{12}\]  
where \(x\) indexes the pixel position of the image. Then, the \(PSNR\) (in dB) is defined as  
\[PSNR=10\cdot\text{log}_{10}(\frac{255^{2}}{MSE}). \tag{13}\]  
On the other hand, the \(SSIM\) is a quality assessment metric based on the degradation of structural information. It extracts three key features from an image, namely the luminance, contrast, and structure. The comparison between \(I_{dehazed}\) and \(I_{gt}\) is performed using three comparison functions on the basis of these three features. Then, the \(SSIM\) is defined as  
\[SSIM=[l(I_{dehazed},I_{gt})]^{\alpha}\cdot[c(I_{dehazed},I_{gt})]^{\beta}\cdot[s (I_{dehazed},I_{gt})]^{\gamma}, \tag{14}\]  
where \(l(I_{dehazed},I_{gt})\), \(c(I_{dehazed},I_{gt})\), and \(s(I_{dehazed},I_{gt})\) are the luminance, contrast, and structure comparison functions, respectively, as defined in [37], and \(\alpha>0\), \(\beta>0\), and \(\gamma>0\) are the parameters used to adjust the relative importance of the three components. We set \(\alpha=\beta=\gamma=1\) in the experiments since this setting is commonly used.
## 2 Network Model and Optimal Cluster Number Calculation
### Network Model  
The network model used in this paper is a WSN model in which \(N\) sensor nodes are evenly arranged in a circular area of diameter \(M\). The BS in the center of the network area has strong computing power. Because the BS energy can be self-replenished, the energy loss of the BS is not considered in this work. On this basis, we can make the following assumptions about the WSN:  
Figure 1: A typical wireless sensor network (WSN) logical hierarchy diagram. BS: base station.  
All sensor nodes are static, nodes transmit data to each other in single or multiple hops, and the node energy cannot be supplemented.
2. The idealized simulation environment does not consider the influence of natural factors such as temperature, humidity, light, and wind on the sensor nodes.
# Forest Fire Risk Forecasting with the Aid of Case-Based Reasoning
## 2 Background
### Realated Works: Using CBR for Forest Fires Forecasting  
The review of the related research revealed two papers most relevant to the present study [44; 45]. The first one [44] proposes a method to overcome the limitations of existing forest fire risk rating assessment methods based on remote sensing data and case-based reasoning principles. The method uses a combination of some dynamic and static indexes to characterize the potential fire environment. The authors built the fire risk rating spatial distribution maps for the DaXingAn Mountains of China using the lightning-caused forest fire risk rating assessment method during 2000-2006. The quantitative estimate of the effectiveness of the method by comparing actual and forecast data presented in the work showed 68.8% effectiveness of the forecast for a dataset with 560 cases.  
The second paper [45] focuses on the development of software, namely the WeVoS-CBR system (ver. 1, [PERSON], University of Salamanca, Salamanca, Spain), which is used to predict the evolution of forest fires. The case-based reasoning methodology combined with a summarization of the SOM ensembles algorithm was implemented. The WeVoS-CBR system successfully predicted the evolution of forest fires in terms of the probability of finding fires in a certain area. However, in [45], the use of CBR allowed one to achieve 58% good predictions for a dataset with 2000 cases and 70% for a dataset with 5000 cases.  
Below, we use these works to compare and evaluate our results.
## 2 Materials and Methods
### Optimisation of HS-SPME Procedure  
To optimize the extraction of volatile compounds from pineapple by-products by the HS-SPME methodology, a Box-Behnken design (BBD) was used with three factors on three levels, and each independent variable was coded between +1, 0, and \(-\)1, corresponding to a low, medium, and high level, respectively (Table 1). The experiment selected ranges for the independent variables were extraction temperature (30-70 \({}^{\circ}\)C), extraction time (10-60 min), and NaCl concentration (0-4 M). In this work, the proposed BBD consisted of 17 experiments carried out in randomized order, including five central points. The core pineapple sample was selected for the optimization of HS-SPME procedure. The variables were evaluated in a selected ranges on the basis of results obtained in previous analysis and the previous references found [7,8].  
The relationship between the response (Y) and the parameters of independent variables was used to monitor the optimization process. The response (Y) was related to the independent variables (X\({}_{1}\), X\({}_{2}\),..., X\({}_{k}\)) by using a second-order polynomial model, as can be seen in Equation (1) [28].  
\[\mathrm{Y=\beta_{0}+\Sigma\beta_{i}\,X_{i}+\Sigma\beta_{i,i}\,X_{i}^{2}+\Sigma \Sigma\beta_{i,j}\,X_{i}X_{j}} \tag{1}\]  
where Y is the predicted response, X indicates the variables of the process, i and j are design variables, \(\beta_{0}\) is a constant, \(\beta_{i}\) is the linear coefficient, \(\beta_{ii}\) is the quadratic coefficient, and \(\beta_{ij}\) is the interaction coefficient of variables i and j.  
The response obtained from the BBD design was evaluated based on the sum of areas obtained for 11 selected volatile compounds: 4 esters (hexaneoic acid, methyl ester; 3-(methylthio) propanoic acid methyl ester; 1-butanol-3-methyl acetate; ethyl acetate), 3 terpen (limonene; \(\alpha\)-muroulene; \(\alpha\)-copaene), 3 aldehydes (nonanal; decanal; dodecanal) and 1 alcohol (phenylethyl alcohol). All of them have been identified as common and characteristic compounds of the pineapple core in all runs [29].  
The satisfactorily of the fitted model was evaluated by the use of the lack of fit value, the F test, and the coefficient of determination (R\({}_{2}\)) obtained from the analysis of variance [30]. Statistical significance of model parameters was determined at \(\alpha\) = 0.05. Additional confirmation experiments (in triplicate) were conducted under the optimal conditions to verify the model validation.
# Research on the Technologies of the Compressor Remanufacturing Process
## 2 Technical Characteristics of Compressors  
A compressor is a type of pressurisation equipment that converts low-pressure gas into high-pressure gas. It can be divided into volumetric and speed types according to the working principle. Compressors for home appliances are mainly volumetric type. Volumetric compressors can be further divided into reciprocating piston, rolling rotor, scroll and screw types according to the structure form. The compressor for a refrigerator is mainly the reciprocating piston type, while that for the air conditioner is mainly the rolling rotor type.  
The working principle of the reciprocating piston type is to use the reciprocating motion of the piston in the cylinder bore for gas compression. The working principle of the rolling rotor type is to use the blade and rotor for dividing the cylinder bore into suction and exhaust chambers through the crankshaft to rotate the rotor for gas compression. The two types of compressors have evident differences in terms of working principle and product structure. However, the technical features of the products have numerous similarities, as described below:  
1. The compressor can be divided into three parts according to different functions: shell, pump body and motor. The shell is the support and sealing mechanism, the pump body is the booster mechanism, and the motor is the driving mechanism.
2. The pump body requires high machining accuracy and fit clearance. The dimensional accuracy of the pump body drive parts is usually required to be above \(\pm\)4 \(\upmu\)m, and the fit clearance is usually approximately 10-30 \(\upmu\)m.
3. The compressor has strict requirements for contaminant and moisture contents. High contaminant contents accelerate the wear of parts, which causes the compressor to jam or block. High moisture content results in the acidification and degradation of the refrigeration oil, causing carbon accumulation in parts and increased wear or damage to the insulation layer of the motor [13].
4. The failure form of the compressor includes motor and mechanical failures. Motor failure consists mainly of damaged stator winding insulation or a broken circuit. Mechanical failure includes wear of moving parts of the pump body or damage to the seal device.
5. Compressor refrigerants have many types, and they have significant differences in technical requirements, quality requirements and production processes.
6. In the industry, the parts have a low degree of commonality. More than 90% of the parts of different compressor brands are not interchangeable. The motor or crankshaft of different models of the same compressor brand is usually not interchangeable.
## 3 Results and Discussion  
The database queries yielded 487 different results that matched the search terms (see Figure 1). Based on the inclusion and exclusion criteria listed in Table 1, 57 papers were selected for our review. Four additional papers were identified using the reference sections of these initial articles, which resulted in a total of 61 papers for analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Data Item** & **Description** \\ \hline \multirow{6}{*}{Origin of article} & \multirow{2}{*}{Type of publication} & Whether the study was \\  & & published as a journal article, \\  & & a conference paper or in an \\  & & electronic preprint repository. \\  & & Publishing venue, such as the \\  & & name of a journal \\  & & or conference. \\  & & Location of the affiliated \\  & Country of first author & university, institute or \\  & affiliation & research body of the \\  & & first author. \\ \hline \multirow{2}{*}{Study rationale} & \multirow{2}{*}{Study goal} & Application or aim of \\  & & the article. \\ \hline \multirow{6}{*}{Experiment setup} & Species of bee & Exact name of the bee specie. \\  & Number of beehives or bees & Exact number of beehives or \\  & & single bees. \\  & Time of the recordings & Month and hour of the day for \\  & & audio recordings. \\  & Sampling rate of audio files & Sampling rate of audio files \\  & & Resolution and frequency \\  & & response of the microphone. \\  & & Steps applied to the raw data \\  & Audio preprocessing & to prepare it for use by the \\  & & architecture or for \\  & & feature extraction. \\ \hline \multirow{6}{*}{Audio analysis method} & \multirow{2}{*}{Features} & Features extracted from audio \\  & & files for visualization or a \\  & & machine learning framework. \\  & & Name of machine learning \\  & & algorithm if a machine \\  & & learning framework is used \\  & & Metrics used in the study to \\  & & report performance (e.g., \\  & & accuracy, f1-score, etc) if a \\  & & machine learning framework \\  & & is used. \\ \hline \multirow{6}{*}{Reproducibility} & \multirow{2}{*}{Dataset} & Whether the data used for the \\  & & experiment come from private \\ \cline{1-1}  & & recordings or from a publicly \\ \cline{1-1}  & & available dataset. \\ \cline{1-1}  & & Whether the code used for the \\ \cline{1-1}  & & experiment is available online \\ \cline{1-1}  & & or not, and if so, where. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Items extracted from each study.
# Prosody-Based Measures for Automatic Severity Assessment of Dysarthrie Speech
## 4 Materials
### Data Analysis
#### 4.3.5 Data Analysis Summary  
There are significant differences between healthy and severe utterances for articulation rate, speaking duration, CnPVI, CrPVI, Delta-C, Delta-V, and Varco-C in the TORGO dataset. The rhythm metrics CnPVI, CrPVI, Delta-C, and Varco-C significantly differ between mild and severe utterances. Lastly, moderate and severe utterances have significantly different Varco-C measures. The TORGO dataset lacks statistically significant differences for pitch and voice quality measures. Based on the results from mixed ANOVA tests, there are few statistically significant differences in the TORGO dataset compared to the QoLT dataset, but this may be caused by the low number of speakers in TORGO (15 persons) compared to the number of speakers from QoLT (38 persons).  
More significant differences exist for the QoLT dataset. Measures with significant differences between healthy and severe utterances are as follows: max F0, all speech rate measures (except balance), CrPVI, Delta-C, Delta-V, %V, degree of voice break, and the number of voice breaks. Significant differences exist between healthy and moderate utterances for the following measures: number of pauses, utterance duration, speaking duration, rate of speech, articulation rate, CrPVI, Delta-C, degree of voice break, and number of voice breaks. Between healthy and mild utterances, there are significant differences in speaking rate and all voice quality measures. Measures with significant differences between mild and severe utterances are as follows: all speech rate measures except articulation rate and balance, all rhythm measures except CnPVI and Varco-V, and all voice quality measures. Only voice quality measures have significant differences between mild and moderate utterances. Lastly, significant differences exist between moderate and severe utterances for the following measures: number of syllables, Delta-C, Delta-V, %V, and mean HN.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Corpus** & **Speaker Group** & **Jitter** & **Shimmer** & **HNR** & **\# of VB** & **\% of VB** \\ \hline \multirow{4}{*}{TORGO} & Healthy & 1.85 & 11.46 & 9.59 & 6.00 & 17.13 \\  & Mild & 2.02 & 9.76 & 10.23 & 6.70 & 16.71 \\  & Moderate & 1.80 & 7.98 & 13.75 & 7.80 & 27.10 \\  & Severe & 2.24 & 8.46 & 12.67 & 9.29 & 20.86 \\  & Healthy & 1.68 & 7.54 & 15.12 & 5.71 & 13.15 \\  & Mild & 1.53 & 7.08 & 15.78 & 7.89 & 20.90 \\  & Moderate & 1.62 & 6.94 & 16.14 & 9.30 & 33.39 \\  & Severe & 1.69 & 7.37 & 15.50 & 11.84 & 34.58 \\ \hline \end{tabular}
\end{table}
Table 5: Mean values for voice quality measures from TORGO and QoLT datasets.
## 3 Results and Discussion
### Dose Rate and Dose Assessment and Hazard Indices  
Table 4 reports the calculated values of the radiological hazard indices (\(D\), \(AEDE_{out}\), \(AEDE_{in}\), \(I\)).  
Equation (2) was used to calculate \(D\), giving values that range between 1.3 nGy h\({}^{-1}\) and 66.6 nGy h\({}^{-1}\), with a mean value of 37.7 nGy h\({}^{-1}\). The variability of the absorbed dose rates can be attributable to the different content of natural radionuclides in the mineralogical phases of the analyzed pigments [44].  
Furthermore, Equations (3) and (4) were used in order to evaluate \(AEDE_{out}\) and \(AEDE_{in}\), respectively, giving values that range from 0.002 mSv y\({}^{-1}\) to 0.082 mSv y\({}^{-1}\), with an average value of 0.046 mSv y\({}^{-1}\) for AEDE\({}_{\text{out}}\), and from 0.008 mSv y\({}^{-1}\) to 0.328 mSv y\({}^{-1}\), with an average value of 0.185 mSv y\({}^{-1}\) for AEDE\({}_{\text{in}}\), respectively. All values were found to be lower than 1 mSv y\({}^{-1}\), which was set as the maximum limit by [40].  
Finally, \(I\), assessed using Equation (5), is lower than unity in all cases [40].
## 4 Discussion  
For the comparison between the layout lines methods, a trial-and-error method was implemented in the field in order to analyze the different geometries obtained. Because some of the methods can be considered old-fashioned with low efficiency, the precision and time were compared regarding the stake-out of four construction lines (axes), respectively eight markings on batter boards. Each method yielded feasible results, resulting in collinear markings on the batter boards, with very few and small deviations (as shown in the last picture of Figure 14). The second and third methods both produce the same coordinate for stake-out and the topographic nail representing this method is the middle one; the interior one was marked using the classical method and the exterior one was determined using reference line function. In terms of time efficiency, the methods were timed, achieving routinely predictable results: the batter boards survey with manual calculation of coordinates  
Figure 13: Reference line method applied on field (**left**) and general concept (**right**).  
took longest, with roughly 2 h of work; the batter boards survey with CAD implementation as well as the classical theodolite method took approximately 1 h; the reference line method was the most efficient and easy to put into practice, with 15 min of implementation.  
The desideratum was to mark reliable points on each batter board, in order for the builders to further position construction layout points without instrumentation and expertise. As shown in Figure 14, this is obtained using construction twine by stretching it between the points (nails) on the wood planks, and a mechanical projection with a plummet on the ground from the twine intersection. Although not as precise as a layout with a total station, this established method is viable for smaller to medium-scale projects, and can ensure a required point standard deviation of \(\mu_{p}=\pm\)(1-2) cm. These layout points are used by the builders for guidance regarding the excavation area, foundation and structural elements positioning, and assures the verticality of the erected building.  
In future, building information modelling (BIM) will also have a significant impact on the work of surveyor engineers, as it is currently regarded as a major paradigm shift in the construction industry, especially for civil engineers and architects [31]. Traditional building design models depend to a large extent on two-dimensional technical drawings, and a BIM platform expands the three primary spatial dimensions (width, height and depth), integrating multiple advantages due to their economic benefits in design and construction phases [32; 33]. Future perspectives include the establishment and integration of the site layout plan with the BIM design in order to achieve sustainable and productive practice regarding the construction industry.
# Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
## 5 Experiment and Analysis  
In this section, we test the algorithm on a simulated image and on a raw infrared image, and give quantitative comparison and qualitative analysis. First, we compare three excellent stripe non-uniformity correction algorithms, namely, midway histogram equalization algorithm (MHE) [9], total variation algorithm (TV) [21] and CNN [23]. All free parameters for these comparison methods are set to default values with the corresponding references. In addition, in the raw infrared image test, we added the conventional filtering method (guided filtering [18] and Gaussian filter) for comparison. The destriping ability of this algorithm is further illustrated.
# Stitching-Based Resolution Enhancement in Wavefront Phase Measurement of Silicon Wafer Surfaces
## 3 System Calibration
### Distortion
For a triangle with vertices \((x_{i},y_{i})\), \((x_{j},y_{j})\), \((x_{k},y_{k})\), the interpolating polynomial is typically expressed in the form  
\[P(x,y)=ax^{3}+by^{3}+cxy^{2}+dxy+ex+fy+g, \tag{16}\]  
where the coefficients are determined through interpolation constraints. To achieve a smooth representation across the entire domain, the interpolation within each triangle is represented using a Bezier polynomial defined in barycentric coordinates. The Bezier representation takes the form  
\[B(u,v,w)=\sum_{i+j+k=3}C_{ijk}u^{i}v^{j}w^{k}, \tag{17}\]  
with the control points determined by the interpolation process. To ensure continuity of both the function and its first derivatives across the triangle boundaries, the Clough-Tocher scheme is employed. This method subdivides each triangle into three smaller sub-triangles, enforcing interpolation constraints that maintain smoothness across the entire domain. The resulting interpolant provides a continuous and differentiable approximation of the data, effectively capturing the discrepancies between real and theoretical circle positions [39]. The interpolant is guaranteed to be continuously differentiable. The gradients of the interpolant are chosen so that the curvature of the interpolating surface is approximately minimized. The gradients for this are estimated using the global algorithm described in [40; 41]. Figure 10 shows the difference between two overlapped images with and without distortion correction. It is worth noting that after distortion correction, the circles match better than without correction, as in the latter case only the inner circles are matched because the system aberration is larger at the edges of the field of view.