esa-sceva/satcom-chunk-collection huggingface dataset:
# Financial Data Quality Evaluation Method Based on Multiple Linear Regression
## 2 Construction of Quality Evaluation Indicator System
### Basic Attributes of Quality Evaluation  
A completeness evaluation encompasses three secondary indicators: record completeness, attribute completeness, and value completeness. The following fields should be assigned:  
1. Required fields: These fields must be filled in accordance with the business rules of each department or according to the specifications outlined in the data dictionary.
2. Key fields: These fields serve as unique primary keys or facilitate associations with related data tables. Examples of such key fields include the identity card number, the unified social credit identifier, and other identification numbers or codes that act as primary keys in related associations.  
Data accuracy encompasses three aspects: logical accuracy, value accuracy, and conceptual accuracy (Table 2). If the identity card number for a piece of data is not accurate or does not correspond to the name, this identity card number is considered to be one piece of erroneous data.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Problem Situation** & **Description and Examples** \\ \hline field empty & Empty values appear in required fields such as gender, nationality, address, contact information, and certificate validity period \\ irregular format & The 18-digit format that does not meet the standard, such as ID number, etc. \\ consistency error & Formats that do not meet the standard, such as the ID number does not match the gender or the date of birth, etc. \\ timeliness error & The validity period of the certificate has “valid until less than the current date”, etc. \\ data record duplication & The same data are repeatedly recorded, and there is a situation of “multiple compilations for one household” \\ \hline \hline \end{tabular}
\end{table}
Table 1: Problem situations and examples of some data.  
The timeliness requirement differs depending on the update frequencies of various information resource tables. As a result, rules can be established differently [19].  
A consistency evaluation measures the level of data association, assessing the logical accuracy and completeness of the same information subject across different datasets [20].  
A uniqueness evaluation gauges the extent of data duplication. For a dataset that consists of four fields, customer number, customer name, customer status, and gender, if N data records have the same values for all four fields, it would produce N-1 duplicate data records. As presented in Table 3, we identified a total of two duplicate data records.  
Compliance ensures that the data types and precision of each data field fall within the specified range. For those fields governed by industry unified standards, adherence to the relevant standards is required. In the absence of unified standards, compliance is based on the data dictionary or relevant regulations provided by each department.
# WaveConv-sLSTM-KET: A Novel Framework for the Multi-Task Analysis of Oil Spill Fluorescence Spectra
## 3 Results and Discussion
### Model Performance Evaluation
#### 3.3.1 Classification Performance
The proposed framework achieves better classification performance than traditional methods and classic deep learning models even without preprocessing. Its robustness to noise makes it well-suited for fast oil spill spectral analysis in complex environments. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Accuracy** & **Recall** & **Precision** & **F1 Score** & **Accuracy** & **Recall** & **Precision** & **F1 Score** \\  & **(Train)** & **(Train)** & **(Train)** & **(Train)** & **(Test)** & **(Test)** & **(Test)** & **(Test)** \\ \hline Hybrid framework & 99.98\% & 99.99\% & 99.98\% & 0.9998 & 99.76\% & 99.76\% & 99.73\% & 0.9975 \\ Transformer & 99.96\% & 99.97\% & 99.97\% & 0.9997 & 99.52\% & 99.53\% & 99.5\% & 0.9951 \\ SVM & 89.83\% & 89.82\% & 89.85\% & 0.8972 & 92.62\% & 92.16\% & 92.42\% & 0.9223 \\ KNN & 87.52\% & 87.46\% & 88.56\% & 0.8733 & 93.81\% & 93.68\% & 94.08\% & 0.9351 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification evaluation index results of the model. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **Raw** & \multicolumn{2}{c}{**SG**} & \multicolumn{2}{c}{**SG + SNV**} & \multicolumn{2}{c}{**Normalization**} \\ \hline \multirow{4}{*}{Hybrid Framework} & Accuracy & 99.76\% & Accuracy & 99.82\% & Accuracy & 99.76\% & Accuracy & 99.82\% \\  & Recall & 99.76\% & Recall & 99.81\% & Recall & 99.75\% & Recall & 99.81\% \\  & Precision & 99.73\% & Precision & 99.79\% & Precision & 99.74\% & Precision & 99.79\% \\  & F1 score & 0.9975 & F1 score & 0.9980 & F1 score & 0.9974 & F1 score &
## 5 Experimental Results
### Algorithm Effectiveness Analysis
The influence of Pareto ranking method on the multi-objective solution is analyzed by comparing it with the single-objective solution. Taking the MK03 problem as an example, ten experiments were run to extract one solution from the multi-objective Pareto optimal solution group each time. The results were compared with the previous single-objective solution. The results are shown in Table 4. Target 1 represents the makespan. Target 2 represents the total number of machine shutdowns. Target 3 represents the total number of handling batches. The experimental results show that in the single-objective optimization, the makespan reaches the minimum, but the total number of machine shutdowns and the total number of handling batches are relatively large, which can result in machine abrasion and energy consumption. In the multi-objective optimization, the solution is more in line with the \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Single**} & \multicolumn{8}{c}{**Pareto**} \\ \cline{2-11}  & & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Target 1 & 204 & 219 & 234 & 209 & 221 & 237 & 213 & 206 & 217 & 204 & 220 \\ Target 2 & 36 & 23 & 20 & 21 & 21 & 17 & 27 & 25 & 24 & 39 & 24 \\ Target 3 & 114 & 106 & 97 & 107 & 102 & 98 & 86 & 89 & 91 & 90 & 86 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results with Pareto ranking method. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Problem**} & \multirow{2}{*}{\(\alpha\times\beta\)} & \multirow{2}{*}{_Size_} & \multirow{2}{*}{_LB_} & \multicolumn{4}{c}{**SFLA-uGSA**} & \multicolumn{4}{c}{**SFLA**} \\ \cline{4-9}  & & & & _Best_ & _Average_ & _SD_ & _Best_ & _Average_ & _SD_ \\ \hline LA01 & \(10\times 5\) & 50 & 570 & 575 & 577.9 & 1.88 & 575 & 579.2 & 2.61 \\ LA02 & \(1  
multi-objective co-optimization situation by reducing the number of machine shutdowns and handling batches with a small amount of sacrifice to the goal of the makespan.
# A Long Short-Term Memory Network-Based Radio Resource Management for 5G Network
## 4 Result and Discussion
### Performance Comparison between LSTM-RRM and DRRM
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Number of UEs**} & \multicolumn{2}{c}{**90\% UE Throughput (Mbps)**} \\ \cline{2-3}  & **DRRM**[20] & **LSTM-RRM** \\ \hline
5 & 215 & 235 \\
10 & 180 & 220 \\
15 & 150 & 210 \\
20 & 110 & 180 \\
25 & 90 & 160 \\
30 & 80 & 140 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Analysis of 90% UE throughput for LSTM-RRM and DRRM. Figure 7: Comparison of 90% UE throughput. The comparison of outage percentage between the LSTM-RRM method and DRRM [20] is shown in Figure 8 and Table 6, determined by varying the UE from the 5 to 30. This outage analysis shows that the LSTM-RRM method achieves higher outage than the existing DRRM [20]. For example, the outage percentage of LSTM-RRM varies from 87% to 98%, whereas the outage percentage of DRRM [20] varies from 83% to 96%. The outage of the DRRM [20] is less than the LSTM-RRM method due to its complexity. However, the optimal resource allocation of the LSTM-RRM method leads to an increase in outage through the 5G system. Figure 9 and Table 7 show the dual connectivity comparison between the LSTM-RRM method and DRRM [20]. Dual Connectivity (DC) specifies the capacity of connecting the different BSs in the same radio access technology. Specifically, Figure 9 and Table 7 illustrate the percentage of connected UE in dual connectivity with respect to the amount of UEs in the system. The LSTM-RRM method has higher dual connectivity than the DRRM [20] because of its effective RRM between the UEs. The dual connectivity of LSTM-RRM for 30 UE is improved up to 12% when compared to the DRRM [20]. The optimal power and bandwidth allocation using LSTM is used to improve the con \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Number of UEs**} & \multicolumn{3}{c}{**Outage Percentage (\%)**} \\ \cline{2-3}  & **DRRM [20]** & **LSTM-RRM** \\ \hline
5 & 83 & 87 \\
10 & 85 & 91 \\
15 & 90 & 94 \\
20 & 97 & 98 \\
25 & 97 & 98 \\
30 & 96 & 98 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Analysis of outage percentage for LSTM-RRM and DRRM. Figure 8: Comparison of outage percentage.
# Efficient Control of a Non-Linear System Using a Modified Sliding Mode Control
## 1 Introduction
SMCSPO is a robust control scheme for trajectory tracking and perturbation estimation, but its characteristics can be further improved by reducing the reaching time, decreasing the error from the system output, and making the system more insensitive from the incoming perturbation signal. In this research, efficient control of a hydraulic non-linear robot manipulator with modified sliding mode control named proportional derivative sliding mode control with sliding perturbation observer (PDSMCSPO) is proposed to reduce more chattering from the output of conventional SMC. Chattering can be reduced by decreasing the value of the switching gain K for it to become more insensitive to high-frequency perturbation as compared with SMC. From the proposed controller with SPO, a more robust controller has been achieved, which reaches the desired state faster and has a better performance at reducing error as compared with SMCSPO. This proposed PDSMCSPO has reduced the convergence time, decreased the error from the output, and made the system more insensitive to the perturbation signal as compared with previous SMCSPO [24]. The manuscript is organized in this way: Section 2 of this paper presents the control theory and details the previous one, and our proposed scheme and their characteristics are discussed. Section 3 describes the simulation and experimental results of both schemes and their comparison are mentioned. Section 4 entails the concluding remarks to this study.
# Extending Appearance Based Gait Recognition with Depth Data
## 5 Experimental Validation
### Experimental Setup
#### 5.1.3 Height Distributions  
The recognition accuracy of classification methods that use the height feature varies depending on the population size (number of subjects under test) and the height distribution of that population. To study the effect of the height feature on classification, five different height distributions were simulated. The goal was to consider height distributions that might be of interest in typical application scenarios.  
The considered height distributions are presented in Figure 10.  
Figure 10: Height distributions used in the experiments.  
Figure 9: Examples of obtained GEI **(a)** and BGEI **(b)** images are shown. For the same person, the top row is for the _normal_ variation, the middle for _backpack_, and the the bottom for the _sloses_ variation. In the second column, the _time normal_, _time backpack_, and _time shoes_ variations are shown.  
The _Real_ (Figure 10a) height distribution corresponds to the actual height distribution of the subjects in the TUM GAID dataset. The _Gauss_ (Figure 10b) height distribution is pure Gaussian distribution included for easy testing and comparability. In the experiments, the selected average height and the corresponding standard deviation were 173.2 cm and 9.5 cm. This distribution is also highly representative of the height distribution of same sex adults of a particular demographic. The _Adult_ (Figure 10c) height distribution is representative of a population of adult subjects of a particular demographic. The _Age_ (Figure 10d) height distribution is representative of a population with individuals of all ages (generic population including adults and children) of a particular demographic. The _Similar_ (Figure 10e) height population is representative of individuals with similar heights as is sometimes the case in specific applications such as people recognition in sports, military, schools, etc.  
The height estimation error was simulated in accordance with the results reported in [22]. To the estimated height feature of each sample, a random error \(\pm\)0.5% of its value was added across all height distributions.
# A Fast, Decentralized, Self-Aligned Carrier Method for Multicellular Converters +
## 4 Simulation Results  
### Simulation Models  
To verify the validity of the proposed algorithm, the models of the proposed DSA-PSC and DSA-LSC methods are built using MATLAB/Simulink software. Without loss of generality in number of phases or levels, Figure 10 demonstrates a four-phase DC/DC converter with DSA-PSC method, and Figure 11 presents a five-level inverter with DSA-LSC method. The parameters of these power converters are presented in Tables 3 and 4.  
Figure 10: _Cont._  
Figure 9: Proposed rule for the carrier level update with DSA-LSC method.  
Figure 11: Simulation model of the multilevel DC/AC converter (five levels), (**a**) topology; (**b**) connection between each cell controller.  
Figure 10: Simulation model of the multiphase DC/DC converter (four phases), (**a**) topology; (**b**) connection between each cell controller.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Unit** & **Value** \\ \hline \multirow{2}{*}{Output inductor} & L\({}_{1}\)... L\({}_{d}\), & H & 0.06 \\  & R\({}_{1}\)... R\({}_{d}\), & \(\Omega\) & 2.5 \\ \hline Load resistor & R & \(\Omega\) & 20 \\ \hline Voltage source & E & V & 100 \\ \hline Switching frequency & f\({}_{sw}\) & kHz & 10 \\ \hline Sampling time & T\({}_{s}\) & s & \(1\times 10^{-7}\) \\ \hline Duty\_cycle & D & & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of four-phase DC/DC converter.
## 3 Methodology and Model Parameters
### Numerical Simulation Model  
The numerical simulation model is designed as a 3-m cube featuring a vertical wellbore at its center to replicate an actual wellbore, as illustrated in Figure 9a. In this setup, the four  
Figure 8: Numerical simulation flow chart.  
lateral sides of the model are fixed horizontally, while vertically, they are subject to free boundary conditions. Normal stress is applied to the model's outer boundaries to maintain a balance between the internal and external forces. Given that the stresses in all three directions exceed 120 MPa, the gravitational effect on the cube is considered negligible. The mesh layout of the model is displayed in Figure 9b. A more accurate simulation result can be obtained when a denser mesh is set compared to when a sparse mesh is set for numerical simulation; thus, a denser mesh is generated around the wellbore area. After establishing the numerical simulation model, as shown in Figure 9, the rock, cracks, and solution cavities are generated by changing the material parameters of the designated area in the numerical simulation model according to the numerical simulation scheme, as introduced in Section 3.1.
## 3 Wood-Based Composite Boards
### Wood-Based Boards Containing a Binder
#### 3.1.1 Different Types of Industrial Wood-Based Boards  
Fiberboards are made from wood fibers using a thermomechanical process. Fiberboards are produced by adding a synthetic binder to fibers, followed by a thermocompression process involving heat and pressure. There are different types of fiberboards, depending not only on the process used but also on the final density of the board, ranging from a few hundred kg m\({}^{-3}\) to over 800 kg m\({}^{-3}\)[40]. Among fiberboards, the following are particularly worthy of mention medium density fiberboard (MDF), hard density fiberboard (HDF), or hardboard (HB), most of which are used in furniture.  
The particleboard group includes panels made from wood ground into a variety of grain sizes and geometries. Particles can range from a few millimeters (particleboard in particular) to tens of centimeters (for the oriented strand board or OSB) [40]. Like fiberboards, particleboards are manufactured by thermocompression, with the addition of a petrochemical binder. These panels are used in furniture and construction.  
Finally, the last group of wood-based panels is called veneer panels. These boards have the distinctive feature of being manufactured from solid wood, which can be either thin layers of wood or planks. Wood can be glued in a variety of ways, depending on the intended application and therefore on mechanical requirements (in case of a use in construction) or aesthetics (for furnishings). Plywood panels, glued laminated panels, or laminated veneer lumber boards (LVL) are used in construction, as their particular structure (gluing sheets together) gives them high mechanical strength at lower cost than solid wood. Finger-jointed panels, on the other hand, as their name implies, are glued at the edge of the panel. The aesthetics of solid wood are conserved, and these boards are used for furnishings.  
Panels made from reconstituted wood (MDF or particleboard) and intended for furniture use can then be laminated (i.e., covered with a wood veneer to give them the appearance of solid wood) or laminated (covered with a layer of plastic of a certain color) for aesthetic purposes. These boards account for the majority of wooden furniture on the market today.  
All these boards are bonded with an adhesive system. Numerous binders have been developed to respond to the diverse characteristics expected of the panels, but also for economic reasons. Numerous synthetic petrochemical binders are used in panel manufacturing today, classified into two categories: thermosetting binders and thermoplastic binders (Table 1).
# Deep Learning-Based Automated Background Removal for Structural Exterior Image Stitching
## 2 Structural Exterior Image Stitching through Automated Background Removal
### Optimal Image Selection for Cost-Effective Digital Image Stitching  
To construct the structural exterior map, a number of spatially continuous digital images, which is often expressed by video frames, should be acquired, because FOV is often much smaller than the entire ROI of a large-scale structure. Moreover, image resolution should be large enough to inspect micro-scale damage on the target structure. For these reasons, structural exterior map construction using entire scanned digital images typically require tremendous computational costs. Furthermore, image stitching errors are often inversely increased when the entire scanned digital images are excessively used. In order to address these technical issues, optimal image or frame selection is necessary. In this step, the optimal image selection algorithm using image feature matching-based overlap ratio calculation was proposed.  
Figure 3 describes the flow chart of the overlap ratio-based optimal image selection algorithm. First, image features such as point, corner or edges were extracted from every ROI image (\(I_{1}\), \(I_{2}\), \(I_{3}\)... \(I_{n}\)) using a scale-invariant feature transform (SIFT) [20]. Since SIFT is invariant to image translation, scaling, rotation and partially invariant to illumination changes, it is advantageous for UAV's close-up scanning data processing. Next, the image features were initially matched between the adjacent images, which are defined as the matching vectors. Then, the false matching vectors were removed by using a random sample consensus (RANSAC) because similar image features on the repeated target structure's texture are often mismatched. After RANSAC, the correct matching vectors, called the inlier vectors, were obtained which physically imply how much \(I_{q}\) was translated from \(I_{p}\) along the \(x\) and \(y\) directions. Based on the assumption that there is no working distance change between the target structure and the digital camera mounted  
Figure 2: Automated background removal using deep learning network based on a U-Net architecture.  
on UAV, the overlap ratio \(R\) between \(I_{p}\) and \(I_{q}\) was calculated using the inlier vectors' averaged magnitude along the \(x\) and \(y\) directions, which is given by:  
\[R\ =\ \frac{100*(Height-\mu)*(Width-\
u)}{Height*Width} \tag{2}\]  
where \(Height\) and \(Width\) are the height and width of \(I_{p}\). \(\mu\) and \(\
u\) are the inlier vectors' averaged magnitude along the \(x\) and \(y\) directions as shown in Figure 3. Once \(R\) was calculated, the optimal image that satisfies the predefined threshold of \(R\) (\(R_{th}\)) was determined. The above procedure was repeated by the iteration as shown in Figure 3, until the entire optimal images were obtained.