esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Datasets  
There is no complete agreement between different authors about when a dataset is considered imbalanced. In this paper, we consider that a dataset is imbalanced when the imbalance ratio (IR) is higher than 1.5, since previous experiments [4, 79] have shown that above this threshold, the classification of examples of the minority class(es) is usually significantly lower than the examples of the majority class(es). In addition, many studies in the literature on imbalanced classification start from this IR value to select the datasets [14]. Therefore, this is the lower threshold of the selected datasets. A great variety of datasets are used in this study in order to achieve the objectives described in preceding sections. In order to cover a wide range of imbalance ratio and other data characteristics, the datasets have been selected from different sources:  
* Twenty-two datasets taken from KEEL dataset repository.
* Two datasets were taken from the UCI data repository.
* Fifteen datasets created using the datasets.make_classification function of the python Scikit-learn library (GD datasets).
* One dataset (infection) containing anonymous clinical information from patients hospitalized in an intensive care unit.  
The following characteristics of each dataset are summarized in Table 1: number of instances (#inst), number of attributes (#attrib), imbalance ratio (IR), percentage of borderline examples (BL%) and percentage of overlapping between the majority and the minority class (OVL%). The datasets are sorted in ascending order according to the imbalance ratio (IR). The datasets included in the study have very varied characteristics, allowing an exhaustive analysis of their impact on the effectiveness of the resampling strategies.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ID** & **Name of Dataset** & **\#inst** & **\#attrib** & **IR** & **BL\%** & **OVL\%** & **Source** \\ \hline
1 & Newthyroid & 215 & 5 & 4.84 & 4 & 45 & KEEL \\
2 & Glass6 & 214 & 9 & 6.38 & 7 & 73 & KEEL \\
3 & SUSY & 15,000 & 18 & 7.69 & 30 & 87 & UCI \\
4 & GD2 & 6000 & 6 & 7.74 & 23 & 69 & Scikit learn \\
5 & Yeast3 & 1484 & 8 & 8.10 & 10 & 63 & KEEL \\
6 & Ecoli3 & 336 & 7 & 8.6 & 14 & 45 & KEEL \\
7 & Page-blocks0 & 5472 & 10 & 8.78 & 8 & 19 & KEEL \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about the datasets used in the study.
## 3 Results and Discussion
### Permeability  
#### 3.4.1 Falling Head Permeability Test  
The permeability results of the falling head test are present in Table 5. Soil* arsenic contamination had a substantial effect on the hydraulic behavior of the soil mass. The saturated permeability increases two orders of magnitude as the arsenic concentration increases. The changes in the saturated permeability are due to chemical reactions between the soil elements and the contaminant added, which increases the pore spaces in the soil mass and causes a structural change in the soil after contamination [21].
## 4 Application  
In this section, the proposed MCDM model is applied to a dataset containing LPI information for countries. The flow diagram is presented in Figure 1, and the proposed methodology consists of three main components. In the first stage, the necessity of this study was identified, and the LPI reports published by the World Bank were examined. The number of countries for which the World Bank reports LPI scores varies significantly from year to year. Consequently, a consolidated dataset was developed, encompassing 118 countries for analysis. The data were gathered from the [36], which maintains LPI data from 2007 to 2023. The year 2007 was excluded from the analysis due to a lack of sufficient data. The dataset used for the evaluation process comprises six benefit-oriented criteria (C1: Customs, C2: Infrastructure, C3: International Shipments, C4: Logistics Competence and Quality, C5: Timeliness, C6: Tracking and Tracing) and 118 alternatives. The LPI indicators and their descriptions are presented in Table 1. In the second stage of the proposed methodology, the decision matrix was constructed, criteria weights were determined using the Entropy method, and the performance scores of the alternatives were assessed using four different MCDM methods. The MCDM results were compared with the World Bank LPI rankings. In the third stage, an innovative sensitivity analysis approach was applied to define criteria weights across 105 scenarios. The MCDM methods and World Bank LPI rankings were compared with the defined fixed factors, and the standard deviation value based on the obtained correlations was calculated. Subsequently, the ranking with the lowest sensitivity was determined based on these results.  
Figure 1: Flowchart of the proposed methodology.
## 3 Results  
### Microbial Analysis  
For the three products, the TVC, total LAB, Enterobacteriaceae and _B. thermosphacta_ growth kinetics are shown in Figure 1. Globally, each enumerated flora started to increase quickly after 7 days, which corresponded to the temperature shift from 4 to 8 \({}^{\circ}\)C. The SG initial TVC count was higher than for MAP and VP CSS (4.0 \(\pm\) 0.1, 3.1 \(\pm\) 0.2 and <1.7 (detection threshold) log CFU/g, respectively. Maximum TVC counts were reached after 21 days for SG and 35 days for VP and MAP CSS (approximately 7, 7 and 8 CFU/g, respectively).  
Figure 1: Microbial kinetics in the salmon gravlax (SG) (**A**), cold-smoked salmon packed under vacuum (VP CSS) (**B**) and cold-smoked salmon packed under modified atmosphere (MAP CSS) (**C**) during storage at 4 \({}^{\circ}\)C for 1 week and then at 8 \({}^{\circ}\)C. Bars represent the standard deviation of the mean of 3 replicates.  
In the SG, the initial contamination could not be clearly defined as the TVC was 2-3 log CFU/g higher than the selective counts. LAB, Enterobacteriaceae and _B. thermosphacta_, although in a minority at T0, quickly grew and reached 6.9 \(\pm\) 0.3, 6.5 \(\pm\) 0.5 and 6.1 \(\pm\) 0.5 log CFU/g, respectively, at T21. However, TVC still remained a little higher than LAB (+0.2 log CFU/g on average).  
MAP CSS microbiota was dominated by LAB after 14 days of storage (LAB count equal to TVC and 3-4 log CFU/g higher than the other counts). The Enterobacteriaceaeae that were initially present (2.9 \(\pm\) 0.2 log CFU/g) did not grow in the product. _B. thermosphacta_, which were not detected until 7 days, increased slowly to reach a maximum of 4.6 \(\pm\) 0.2 log CFU/g after 35 days of storage.  
The initial contamination of the VP CSS was lower than in the other products and remained so until 28 days. After 35 days, the maximum count was reached (6.9 \(\pm\) 0.3 CFU/g) and was similar to the SG, but differed in composition. Indeed, LAB, Enterobacteriaceae and _B. thermosphacta_ grew in the product but they never dominated. Their final concentrations were always 2 to 4 log CFU/g lower than the TVC (4.5 \(\pm\) 0.7, 4.1 \(\pm\) 0.2 and 3.2 \(\pm\) 0.9 log CFU/g, respectively, after 35 days).  
For all sampling dates, an important variability in microbial counts was observed independent of the microbial flora considered, in particular, in the case of the VP CSS. Fillets were cut lengthwise and slices were gradually packed, resulting in a plastic blister containing only slices from the head, belly or tail part of the fish. The heterogeneous thickness and fat content may therefore explain the variability within triplicates.
## 4 Results and Discussion
### Simulation and Experimental Results Analysis
#### 4.3.4 Analysis of MIX Number  
The MIX number reflects the degree of mixing between cold and hot water in thermal storage tanks from an energy perspective and provides a more accurate description of their stratification.  
As shown in Figure 16, the elbow-type inlet structure of the heat storage tank, with the change in the time MIX number, tended to first decrease and then increase. The experiment demonstrated that the stratification effect of the water tank varied over time. This shows that the water tank stratification effect changes from poor to good and then to poor with time. At the outset of the experiment, the water temperature hovered around 46 \({}^{\circ}\)C, resulting in a relatively poor stratification effect. As the tank discharged hot water through its outlet and cold water entered the inlet, the stratification gradually improved, resulting in a higher hot water output rate. However, as the quantity of cold water in the tank increased and the amount of hot water decreased, the MIX number reached its minimum value. This indicates that the tank has a better hot water output rate. In the experiment, this occurred at 737 s, where the minimum value was 0.1549; in the simulation, this occurred at 835 s, where the minimum value was 0.1976. The stratification effect was best at this time, and then the MIX number slowly increased. Subsequently, the MIX number began to rise slowly, indicating a gradual decline in the stratification quality.  
A simulated comparison of the MIX number between the distributor inlet and elbow inlet is shown in Figure 17, which shows that the minimum value of the MIX number of the water distributor inlet occurred at 773 s, with a minimum value of 0.2048. There is little difference between the MIX numbers of the elbow-type and water distributor inlets, indicating that the layering effect during the exothermic heat release of the elbow-type and water distributor inlets is comparable to that of their inlets. Notably, there was no significant difference in the MIX numbers between the two inlet types.  
Figure 16: Comparison of experimental and simulated MIX numbers for elbow-type inlet.
# New Taipei City Smart Pavement Management Center and Road Maintenance Analysis
## 2 Intelligent Inspection Planning
### Training for Inspection Personnel  
Quarterly training is conducted for inspection personnel, covering road inspection skills, equipment operation, and data management to ensure accurate identification and efficient reporting of road damage.  
Training first focuses on damage identification and assessment. Personnel need a basic understanding of road structure and materials to identify common damage types, such as cracks, potholes, ruts, and subsidence, and must be able to assess the severity to prioritize repairs (Figures 4 and 5).  
Next, training covers inspection techniques and equipment operation. Personnel must be proficient with inspection vehicles and tools, including imaging equipment, radar, LIDAR, and portable devices (e.g., laser distance meters). They also learn GPS and data recording techniques to ensure accurate, traceable inspection data.  
In data management and analysis, personnel are trained to upload, categorize, and store inspection data for subsequent analysis. They also learn how to analyze data and generate reports to assist in maintenance planning and identifying priority repairs [5].  
Figure 4: Pavement Damage Pattern Recognition Diagram—Potholes.  
Figure 3: Operational Flow of the APP Real-Time Reporting System.  
The training should be provided by professional training organizations or experienced instructors to ensure inspection personnel possess the necessary knowledge and skills, thereby enhancing the accuracy and efficiency of inspections and ensuring road safety and maintenance quality [6].
# Optimal Estimation Retrieval of Aerosol Fine-Mode Fraction from Ground-Based Sky Light Measurements
## 2 Model and Methods
### Methodology
#### 2.3.2 Inversion Settings  
For the OE inversion framework, the first task is to determine the observation data and the state vector to be retrieved, as well as the observational uncertainties and a priori estimation error, respectively. These inversion parameter settings are listed in Table 3. The observation vector \(\mathbf{y}\) is composed of the normalized radiance (referred to as \(I\) in Table 3) at 490, 550, 670, 870, and 1610 nm wavelengths. The level of recognized observation error is usually around 3-5% for intensity measurements, and the observation error does not vary with wavelength, observation mode, and observation geometry in this paper. The state vector \(\mathbf{x}\) is composed of the aerosol \(V_{0}\) and \(\mathrm{FMF}_{\mathrm{v}}\). The a priori estimate of \(\mathbf{x}\) comes from the available inversion products of AERONET, and a relative error of 100% is adopted for the state vector during the inversion. The non-state vector \(\mathbf{b}\) is composed of the aerosol model parameters, including effective radius (\(r_{\text{eff}}\)), effective deviation (\(v_{\text{eff}}\)), and refractive indices (\(m_{\text{r}}\), \(m_{\text{i}}\)) as listed in Table 3. The a priori knowledge of \(\mathbf{b}\) is essential to provide certain auxiliary constraints on the rationality of the final solutions. For the fixed observation location of ground-based field tests in our research, the a priori knowledge adopted in the forward simulation and inversion is characterized by the aerosol properties in Table 2. To describe the wavelength-dependence of refractive indices, it is assumed that the parameters following the power-law relationship are adopted [22].
## Results
### Mapping of Burnt Areas with Sentinel-1 and Sentinel-2
Figure 7: Burnt areas mapped with Sentinel-2 and Sentinel-1 images acquired between November 2015 and April 2016 (S1) and December 2015 and May 2016 (S2). Vegetation map of [PERSON] et al., [26] in the background.
# Research on Wind Speed Inversion Method for X-Band Networked SAR Satellite
## 4 Results  
### Accuracy of Wind Speed Inversion  
In order to analyze the applicability of the method to X-band networked SAR data under different conditions, this paper divides the experimental data into two groups. One set of experimental data has a fixed incident angle and different relative wind direction angles; one set of experimental data has a fixed relative wind direction angle and different incident angles. Next, the experimental results of the two sets of data are analyzed separately.
## 2 Patients, Materials, and Methods
### 1H MRS Examinations  
Proton magnetic resonance spectroscopy (1H MRS) was performed using Point-Resolved Spectroscopy Sequence (PRESS) technique (TE = 35 ms, TR = 1500 ms, number of acquisitions: 128, slice thickness = 20 mm). Spectroscopic signal was acquired from Volumes of Interest (VOIs 2 \(\times\) 2 \(\times\) 2 cm) in 7 locations: right and left frontal lobe, right and left basal ganglia, right and left hippocampus, and cerebellar vermis. Quantitative analysis of the spectroscopic data was performed using the dedicated software SAGE 7.0 (Spectroscopy Analysis, GE). The received signal was reconstructed and processed using the Gaussian function with a line broadening of 8 Hz. All the obtained 1H MRS data were good quality; therefore, the phase was not manually corrected. The baseline present in the spectrum was not erased.  
Figure 1: Schemes of measurements of the corpus callosum and brain in children with FASD and children from the control group: (**a**) linear measurements, (**b**) surface area measurements.  
Concentrations of the specific metabolites (N-acetylaspartate--NAA, choline--Cho, myo-inositol--mI, lipids--Lip, lactates--Lac) were calculated in relation to the concentration of creatine (Cr). Total concentration of each metabolite in the brain was calculated as a mean value of the results identified in the seven locations; for each child, concentrations of a given metabolite in all the studied locations were added, and the total value was then divided by seven (number of the locations). Additionally, the mean concentration value was calculated for the symmetrically distributed locations (frontal lobes, basal ganglia, hippocampi); the concentrations in the left and right hemisphere were added, and the total value was divided by two. Consequently, five results were acquired for each metabolite: concentration in the frontal lobes, basal ganglia, hippocampi, and cerebellum, as well as the mean value for all the anatomical locations in the brain.
# Biomass-Based Chemical Looping Gasification: Overview and Recent Developments
## 2 Process of Chemical Looping Gasification of Biomass
### Process Classification
#### 2.2.1 System Configuration
A simple configuration of this type is shown in Figure 6. The system comprises at least two reactors in parallel working alternately to continuously produce syngas. Each reactor works alternately reduction and oxidation cycles and intermittently alternated with short periods of mild fluidization of the bed after each cycle to level off temperature and concentration profiles [12]. The main advantages of this technology include the separation of gas and particles and the ability to work under high pressure, whereas this technology requires high temperature and a high flow gas switching system [11]. [PERSON] et al. [42] used a fixed bed reactor to study the performance of Al\({}_{2}\)O\({}_{3}\)/BaFe\({}_{2}\)O\({}_{4}\) as a synthesized oxygen carrier in BCLG for hydrogen-rich syngas production. [PERSON] et al. [43] conducted a study of chemical looping co-gasification of pinewood and polyethylene in a fixed reactor. The effects of operating conditions during the BCLG process were reported in the investigation. A study of CLG of biomass char using NiO-modified iron ore as an oxygen carrier was carried out by [PERSON] et al. [44]. They reported the reduction of oxygen carrier by biomass char in TGA and a fixed-bed reactor. [PERSON] et al. [45] developed Ca\({}_{2}\)Fe\({}_{2}\)O\({}_{5}\) with Mg/Al/Zn oxides as support materials for BCLG in a fixed bed reaction. This work mainly investigated the effects of Mg/Al/Zn oxides on the reactivity of Ca\({}_{2}\)Fe\({}_{2}\)O\({}_{5}\) and the BCLG performance. [PERSON] et al. [46] presented experimental results in a bubbling fluidized bed reactor for CLG of sawdust pellet with high volatile and low ash content as fuel. It was  
Figure 6: Simple configuration of alternating packed or fluidized bed reactors. found that higher reaction temperatures increased gas production, while the amount of liquid and solid decreased.
## 6 Conclusions  
The limitations and the potential accuracy of SHSS methods were illustrated under different meshing techniques for the better evaluation of fatigue-related problems. In comparison to other SHSS approaches, the IIW QSE method gave better results for the solid parabolic single elements, and the ASME TTSL method stood out for solid or shell parabolic elements in non-linear stress state. The LSE 515 method performed better for nominal stress calculation with shell parabolic elements. The Haibach and Xiao and Yamada methods had scattered results. The anomalies in SHSS results were not encouraging the use of these methods. The nodal averaging had insignificant effect on SHSS results except for the ASME TTSL method. The parabolic solid or the shell elements were not affected by nodal averaging, whereas linear solid or shell elements showed decrease in SHSS results. In general, shell elements had more consistent SHSS results as compared to solid elements for the both stress states. To conclude, there is no single solution available for every fatigue-related problem. We must analyse the given condition and accordingly make some rules or guidelines for the better evaluation of SHSS results to predict the fatigue life of the structure with more confidence and clarity.  
Conceptualization, N.I.; methodology, N.I. and H.F.; software, M.K.; formal analysis, A.N.; writing--original draft preparation, N.I.; writing--review and editing, H.F. and H.D.B.; supervision, H.D.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Higher Education Commission (HEC), Pakistan and the APC was also funded by Higher Education Commission (HEC), Pakistan under the grant of UESTP-HRDI initiative.  
The corresponding author gratefully acknowledges financial support from Higher Education Commission (HEC), Pakistan, during studying in the Belgium.  
The authors declare no conflict of interest.  
Figure 10: Guidelines for the evaluation of SHSS considering different mesh techniques used in this study.
# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 4 Pending Issues
### Mutual Interference  
To close this section, let us consider a world full of self-driving cars each with its lidar imaging system emitting pulses or waves at high frequency. Imagine them in a traffic jam, or in an area with large vehicle density. The uniqueness of the signal emitted by each lidar needs to be ensured, so the source of one vehicle does not trigger the detection in other vehicles in their surroundings. Although some measurement principles may have advantages over the others, the implementation of discrimination patterns among each individual vehicle may be challenging [145]. For instance, FMCW detection appears to be better than direct pulse detection as the modulation frequency and amplitude, combined with the coherent detection implemented, may help to add personalized signatures to each lidar. However, the implementation of this concept at the mass scale needs to be carefully considered, and possibly supported by improved data processing to filter out false detections while preserving the reliability of the unit.
# Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions
## 5 Theory and the Application of Different Types of Graphs
### Directed Acyclic Graph (DAG)  
The Directed Acyclic graph (DAG) is the most important type of directed graph in the field of modeling the manufacturing systems. DAG can also explain some foundations for other types of directed graphs used in shop scheduling problems. Thus, we elaborated on some details of DAG and its mathematical background.  
DAG is usually represented by the notation of _G_(_N_,_E_) where \(N\) is the set of nodes and \(E\) is the set of directed edges in \(G\). Either nodes or edges can have weights. However, following the majority of references in our dataset, we considered weights on nodes in this paper.  
Each node \(n\) has a set of predecessor and successor nodes which are connected to \(n\) through incoming edges and outgoing edges, respectively. A _first node_ does not have any predecessors and a _last node_ does not have any successors. Note that there might be multiple first and last nodes in a DAG. The _length of the path_ to node \(n\) is calculated by the maximum of the summation of weights of nodes on the path from all first nodes to \(n\)[123]. The maximum of lengths of all paths from a first node to a last node is the _length of the longest path_, or the _critical path_ which is an important measure in DAG calculation. The length of the longest path generally implies a critical performance measure in the corresponding system modeled by a DAG. It should be noted that the longest path can be defined only in directed graphs.  
To find the length of the longest path, first the DAG should be topologically sorted, then, the length of the longest path to each node should be calculated using dynamic programing [34]. In the following subsection these two steps will be explained.