esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
However, it is difficult for multiple modules to transmit and receive at the same time due to the interference caused by overlapping signals from different sensors, which are strongly influenced by the measurement distance caused by obstacles in the ultrasonic sensor [23; 24]. Therefore, it is not a suitable technology for use in intelligent wheelchairs because it is difficult for multiple intelligent wheelchairs to transmit and receive signals at the same time. Position estimation techniques such as Wi-Fi/Bluetooth/Zigbee and others use distance-based triangulation to calculate positions using the characteristics of constant attenuation of radio waves in space according to distance [25]. The RSSI (Received Signal Strength Indication) method has a problem of increasing position error due to difficulties in measuring accurate signal strength because of differences in the characteristics between antenna and front-end radio frequency (RF) and has lower accuracy than ToA (Time of Arrival) based on arrival time in an outdoor environment where many people exist due to low permeability [26]. Therefore, it is not a suitable technology as a position estimation system for driving assistance for intelligent wheelchairs. Therefore, this study proposes a position and direction estimation system based on UWB (ultra-wide band) with relatively high permeability and various bandwidths to measure distance based on the arrival time of radio waves in order to solve the problem mentioned above [27]. In the case of UWB sensors, it has the advantage of being able to estimate the position regardless of changes in the topography after being installed at a fixed position, and it is being installed in various mobile devices such as mobile phones. In the future, it is expected to be able to access mobile devices without a separate installation, and because accessibility increases, it is suitable for use as a position estimation device [28]. However, the existing single UWB sensor-based estimation technology has the disadvantage that the position estimation through only UWB sensors is difficult to use as a driving assistance technology due to its low accuracy and difficulty in estimating the direction [29; 30]. Therefore, it is necessary to improve the existing position and direction estimation system for the development of a driving assistance system for the wide use of autonomous driving technology of wheelchairs. In this study, sensors such as encoders and IMU that can implement position estimation are applied to reduce noise and outliers and improve positioning accuracy in the existing UWB sensor-based position estimation method. Through this, this study proposes a position and direction estimation system with a level of accuracy that can assist driving through fusion with existing UWB sensor-based position estimation data based on dead reckoning suitable for wheelchair environments.
## 6 Quantification of Class Overlapping  
Most real-world imbalanced problems exhibit overlapping issues, while the joint effect of imbalanced and overlapping samples severely affects the classification performance [71]. Overlapping issues and the classification of the imbalance nature of data have significantly gained in popularity for their focus on real-world problems, however, a well-defined mathematical explanation of overlapping is still lacking [72], despite different studies in the literature [72; 73] having suggested estimating the class overlapping level. However, a major drawback of these methods is the prior assumption of the normal distribution of data, which is not possible in the majority of real-world datasets. We modified the formula used in [74] in Equation (11) to approximate the overlapping region based on the imbalanced distribution of data, originally designed for binary classification problems with 2D features space. \[Overlapping\;Degree(\%)=\frac{Overlapping\;Region}{Minority\;Class\;Area}*100 \tag{11}\] \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**Accuracy before Hyper-Tuning**} & \multicolumn{6}{c}{**Precision before Hyper-Tuning**} \\ \hline
**Datasets** & **GB** & **RF** & **DT** & **KNN** & **R-SVM** & **LR** & **GB** & **RF** & **DT** & **KNN** & **R-SVM** & **LR** \\ \hline IRIS & 90.22 & 92.85 & 90.22 & 87.47 & 89.47 & 82.2 & 87.11 & 89.74 & 87.11 & 84.36 & 86.36 & 79.09 \\ Glass & 90.07 & 90.8 & 86.83 & 81.68 & 84.28 & 79.73 & 86.96 & 87.69 & 83.72 & 78.57 & 81.17 & 76.62 \\ HAR & 83.93 & 82.78 & 81.68 & 76.96 & 78.36 & 75.7 & 80.82 & 79.67 & 78.57 & 73.85 & 75.25 & 72.59 \\ B\_Tissue & 91.19 & 92.05 & 88.34 & 82.22 & 83.93 & 75.93 & 88.08 & 88.94 & 85.23 & 79.11 & 80.82 & 72.82 \\ Car & 81.98 & 78.37 & 76.45 & 71.4 & 74.63 & 65.42 & 78.87 & 75.26 & 73.34 & 68.29 & 71.52 & 62.31 \\ CMC & 82.69 & 80.4 & 77.15 & 75.02 & 76.01 & 70.26 & 79.58 & 77.29 & 74.04 & 71.91 & 72.9 & 67.15 \\ Ecoli & 87.46 & 84.37 & 83.86 & 77.04 & 81.85 & 75.43 & 84.35 & 81.26 & 80.75 & 73.93 & 78.74 & 72.32 \\ Nursery & 78.42 & 79.15 & 77.47 & 74.18 & 76.3 & 66.07 & 75.31 & 76.04 & 74.36 & 71.07 & 73.19 & 62.96 \\ Opt\_Digits & 80.95 & 81.49 & 78.67 & 75.96 & 76.96 & 70.8 & 77.84 & 78.38 & 75.56 & 72.85 & 73.85 & 67.69 \\ Page\_Block & 81.33 & 78.85 & 79.89 & 68.21 & 77.51 & 68.6 & 78.22 & 75.74 & 76.78 & 65.1 & 74.4 & 65.49 \\ Satimage & 81.01 & 79.88 & 81.01 & 70.75 & 78.18 & 67.92 & 77.9 & 76.77 & 77.9 & 67.64 & 75.07 & 64.81 \\ Soyaben & 87.83 & 86.52 & 85.4 & 81.2 & 83.87 & 76.95 & 84.72 & 83.41 & 82.29 & 78.09 & 80.76 & 73.84 \\ Vehicle & 77.98 & 78.98 & 70.84 & 62.15 & 72.52 & 65.86 & 74.87 & 75.87 & 67.73 & 59.04 & 69.41 & 62.75 \\ Volcanoesa & 76.29 & 74.48 & 77.38 & 67.24 & 69.49 & 64.33 & 73.18 & 71.37 & 74.27 & 64.13 & 66.38 & 61.22 \\ Wine & 92.09 & 92.09 & 90.4 & 78.24 & 83.59 & 76.28 & 88.98 & 88.98 & 87.29 & 75.13 & 80.48 & 73.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Recall and F1-Score after Parameters Hyper-Tuning and Synthetic Overlapping.
## 2 Materials and Methods  
### Experimental Sites, Plant Material and Cultivation  
The experiments were conducted during two consecutive cropping seasons (2017 and 2018) at five locations from North to South Europe at NIBIO (Norwegian Institute of Bioeconomy Research, Bergen, Norway) (NO, 60\({}^{\circ}\) N), INHORT (PL, 51\({}^{\circ}\) N), HGU (DE, 49\({}^{\circ}\) N), Sant'Orsola (IT, 46\({}^{\circ}\) N) and Invenio (FR, 44\({}^{\circ}\) N), hereinafter named as Norway, Poland, Germany, Italy and France. As common for the different regions, experiments in Norway, Poland and Germany were carried out in open field, whereas in Italy and France they were performed in polytunnels that were open-sided after anthesis. Details of the respective latitude, altitude, yearly mean temperature, soil type, soil pH and cultivation type, as well as start of flowering, harvest season and day length at start of harvest are given in Table 1. Air temperature (T\({}_{\text{mean}}\), T\({}_{\text{max}}\) and T\({}_{\text{min}}\)) and global radiation were recorded at each location. To describe the environmental conditions of the respective harvest season, weekly mean temperature and global radiation were calculated starting 2 weeks prior to start of harvest of the earliest cultivar 'Clery' (Figure 1).  
Figure 1: Weekly mean temperature and global radiation courses during the harvest season for the five experimental locations in 2017 and 2018, starting two weeks before the first picking of the earliest cultivar ‘Clery’.  
The six short-day strawberry genotypes used in this study were selected for their diverse genetic background and adaptability to different environments: 'Candonga'(E), 'Clery' (IT), 'Florence' (UK), 'Frida' (NO), 'Gariguette' (FR) and 'Sonata' (NL). All cultivars were cultivated in Norway, Poland, Germany and France, while Italy only grew 'Clery', 'Frida', 'Gariguette' and 'Sonata'. Each season, all locations propagated their own plants from mother-plants purchased from the same nursery to avoid epigenetic variation within a cultivar due to different origin. Thus, each year, plug-plants were planted in week 32 in Norway, Poland and Germany for the harvest season the next year. At all sites, three randomized replicated plots, each with 20 plants per genotype, were trialed. In Italy and France, where strawberry are commonly grown in tunnels, cold-stored plants were set in peat bags, as usual, in week 9 in 2017 and 2018 for harvesting in the respective year. Furthermore, in Italy and France, the experimental design consisted of three plots, and of three replications per genotype with 22-24 plants per plot for the harvest in the same year. Plant protection, fertilization and irrigation in open-field sites, and fertigation of the plants in peat bags were performed according to local guidelines.  
To evaluate the impact of latitude-related factors on the anthocyanin synthesis and accumulation, daily temperature (T\({}_{\text{mean}}\), T\({}_{\text{max}}\) and T\({}_{\text{min}}\)) and global radiation were accumulated for 3, 5, 10 and 15 days (3d, 5d, 10d and 15d) before sampling of the berries to be analyzed for each cultivar.
## 3 Methods
### Cycle Mutation
#### 3.1.2 Cycle(\(kmax\))  
In the first version of cycle mutation (Algorithm 4), called Cycle(\(kmax\)), the induced cycle length \(k\) is uniformly random from the interval \([2,kmax]\), such that \(kmax\geq 2\).  
```
1:\(k\leftarrow\text{Rand}(2,kmax)\)
2:indexes \(\leftarrow\text{Sample}(n,k)\)
3:Shuffle(indexes)
4:CreateCycle(\(p,\text{indexes}\))
```  
**Algorithm 4** CycleMutation\((p,kmax)\)  
It first selects the cycle length \(k\) in \(O(1)\) time. Next, \(k\) indexes are sampled uniformly at random without replacement (line 2) with a call to Sample\((n,k)\) of Algorithm 2. Since \(k\leq kmax\), the worst-case runtime of that step is \(O(\min(n,kmax^{2}))\). This array of indexes is randomized (line 3), with a worst-case cost of \(O(kmax)\). Finally, a cycle is induced from the randomized list of \(k\) indexes with a call to the CreateCycle of Algorithm 1 in line 4, which also costs \(O(kmax)\) time in the worst case. Thus, the worst-case runtime is \(O(\min(n,kmax^{2}))\), since the most costly step is sampling the indexes. The average case is also \(O(\min(n,kmax^{2}))\) since the average cycle length \(\bar{k}=\frac{kmax+2}{2}\) is proportional to \(kmax\).  
Cycle(\(kmax\)) limits the induced permutation cycle to a predetermined maximum length, with all cycle lengths up to \(kmax\) equally likely. This enables tuning the size of the local neighborhood, with lower \(kmax\) leading to a smaller neighborhood and higher \(kmax\) creating a larger neighborhood. Thus, increasing \(kmax\) may lead to fewer local optima in the fitness landscape, but would also lead to a more disruptive and less focused search.
# Advancements in Synthetic Generation of Contactless Palmprint Biometrics Using StyleGAN Models
## 3 Datasets  
Palmprint images can be captured using both contact and contactless techniques. To collect palmprints, individuals must place their hands in close contact with a sensor. This ensures that the hands are properly positioned for capture, as shown in Figure 4. On the other hand, contactless capture can be performed with easily accessible commercial sensors, as seen in Figure 4.  
StyleGAN models can be trained with many public palmprint datasets. However, two publicly available datasets were chosen for this specific research due to the following reasons:  
* Each of the two datasets has large amounts of image data with many skin color variations, which translates to better model training.  
Figure 4: Hand position during contact-based (**a**) and contactless (**b**) palmprint capture [26].  
Figure 3: StyleGAN3 generator architecture [20].  
* Images in both datasets are given in very high resolutions, which means more resizing options.
* They have been the most popular choice of datasets in similar past research [17, 19, 22]. Figure 5 shows sample images from both datasets:
* Polytechnic U [27] (DB1) has 1610 images collected in an indoor environment with circular fluorescent illumination around the camera lens. The dataset contains both the left and right hands of 230 subjects. Approximately seven images of each hand were captured for the age group of 12-57 years. The image resolution was 800 \(\times\) 600 pixels.
* Touchless palm image dataset [28] (DB2) consists of a total of 1344 images from 168 different people (8 images from each hand) taken with a digital camera at an image resolution of 1600 \(\times\) 1200 pixels.
## 4 Test Results and Analysis
### Energy Dissipation  
To determine the energy dissipation capacity of the structure, the equivalent viscous damping coefficient \(\xi\) and the energy dissipation coefficient E were used in this study. Taking Figure 19 as an example, the equivalent damping factor \(\xi\) expression for the hysteresis loop is determined as follows:  
\[\xi=\frac{1}{2\pi}\frac{S_{ABC}+S_{CDA}}{S_{OBE}+S_{ODF}}, \tag{5}\]  
The energy dissipation capacity indexes of specimens SF-1 and SF-2 under ultimate load and damage conditions are shown in Table 6. The energy dissipation coefficient E for each level of cyclic loading is shown in Figure 20.  
Table 6 shows that the total energy dissipation, which is equivalent to the viscous damping coefficient, and the energy dissipation coefficient of the frame using NEDC are greater than those of the frame using standard hook bolt connectors.  
The following conclusions can be obtained from the variations in the energy dissipation coefficient E in Figure 20. When the horizontal displacement is between 0 and 25 mm and the horizontal displacement exceeds 60 mm, the energy dissipation coefficient E increases with displacement. When horizontal displacement ranges from 25 to 60 mm, the energy dissipation coefficient E decreases with an increase in displacement. The energy dissipation coefficients E of specimens SF-1 and SF-2 exhibit the same curve trend with increasing displacement. The increase in the isoenergetic dissipation coefficient of SF-2 is more significant than that of SF-1.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Specimen** & **Displacement** & \begin{tabular}{c} **Total Energy** \\ **Consumption** \\ \end{tabular} & \
\begin{tabular}{c} **Energy Consumption** \\ **Factor** \\ \end{tabular} &
\begin{tabular}{c} **Equivalent** \\ **Damping Factor** \\ \end{tabular} \\ \hline SF-1 & 120 & 79,879.92 & 0.628133 & 0.09997 \\ \hline SF-2 & 150 & 160,672.392 & 0.718837 & 0.114406 \\ \hline \end{tabular}
\end{table}
Table 6: Factors associated with energy consumption for specimens SF-1 and SF-2.  
Figure 19: Dissipation capacity area.  
The energy consumption statistics of both specimens for each loading cycle are shown in Figure 21. The energy dissipated in a single cycle of SF-1 is slightly greater than that of SF-2, up to a horizontal displacement of 75 mm. After the horizontal displacement exceeds 75 mm, the energy consumption capacity of SF-2 for a single cycle becomes significantly higher than that of SF-1. This indicates that hook bolts exhibit a slightly better energy dissipation performance under minor earthquake action than that obtained with the NEDC. However, the energy consumption of NEDC is significantly higher than that of hook bolt connectors under solid vibrations.
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 2 Materials and Methods
### Machine Learning Models
#### 2.3.2 Artificial Neural Networks (ANN) [36; 37; 38]  
Artificial Neural Networks (ANNs) are a subset of machine learning models that are inspired by the structure and function of the human brain. ANNs consist of interconnected layers of nodes or \"neurons\" that process information using dynamic-state responses to external inputs.  
In this study, we implemented a feed-forward neural network, one of the simplest types of ANN. This type of network processes information in a forward direction, from the input layer through hidden layers and finally to the output layer. Each neuron in a layer is connected to every neuron in the subsequent layer, and these connections have associated weights that are adjusted during the learning process.  
ANNs excel at capturing non-linear relationships in data and are highly flexible and adaptable. They can approximate virtually any function given enough data and neurons in the hidden layers. However, their black-box nature means they lack the interpretability of simpler models like MLR [9; 39].
## 4 Signal Processing
### Independent Component Analysis  
#### 4.4.1 ICA Compliance  
ICA is a powerful method to blindly separate potential components and source signals from the mixed observation signals, and it has been successfully and widely used in many fields, such as separating brain activity from artefact, finding hidden factors in stock data and denoising for natural images [26]. In the problem of vital signal detection using a UAV-mounted radar, the source signals are respiration and the platform motion.  
To apply ICA, the following three assumptions are given: the components are independent of each other; the independent components must be in non-Gaussian distribution; and the number of mixed observed signals is greater than or equal to the number of independent components [27]. In this section, the proof that the problem of respiratory signal extraction in this study satisfies the three assumptions to apply ICA is given.  
The corresponding continuous-time signal of the interferenced respiratory signal component in Equation (9) is  
\[r(t)=\frac{4\pi}{\lambda}X(t)+\frac{4\pi}{\lambda}d_{UAV}(t) \tag{15}\]  
The corresponding continuous-time signal of the ground echo signal component in Equation (9) is  
\[g(t)=\frac{4\pi}{\lambda}d_{UAV}(t) \tag{16}\]  
From Equations (15) and (16), it can be concluded that the observed signals are a linear combination of the respiration \(X(t)\) and UAV platform motion \(d_{UAV}(t)\). The information on the value of \(X(t)\) does not give any information on the value of \(d_{UAV}(t)\), and vice versa, the two variables are said to be independent [28]. Moreover, the respiratory signal is non-Gaussian, since in the signal model, the distribution of respiration depends on a sinusoidal signal. The motion of the UAV platform depends on many random factors, such as the quality of satellite communications and the wind external environment, so it is usually a non-Gaussian signal. In fact, if only one of the independent components is Gaussian, ICA is also achievable [28]. Finally, according to the range sampler described in Section 4.2, the number of the observed signals is greater than or equal to the number of independent components. Therefore, the problem in this study satisfies the three conditions to apply ICA.
## 2 Theoretical Background  
### Acceleration Mechanisms of Compressible Flow Inside the Nozzle  
Figure 1 is a schematic diagram of the acceleration mechanism of a compressible flow inside the nozzles with different longitudinal sections. Generally, the typical longitudinal section has three main types, namely, the convergent section, the divergent section and the convergent-divergent section. The corresponding nozzles are called the convergent nozzle, the divergent nozzle, and the Laval nozzle, as shown in the Figure 1.  
Along the flow direction, the cross-sectional area of the convergent nozzle and the divergent nozzle is getting smaller and larger, respectively; while that of the Laval nozzle reduces first and then increases. Most importantly, the acceleration of compressible flow inside a nozzle is determined by the flow speed and the type of longitudinal section of the nozzle [26; 27; 28; 29]. To give a more detailed description, the characteristic equation derived from the conservation equations and the adiabatic equation [26; 27], is introduced to make a theoretical analysis of the flow characteristics inside the nozzles with different longitudinal sections, as shown in Equation (1).  
\[\frac{dA}{A}=({M_{a}}^{2}-1)\frac{dv}{v} \tag{1}\]  
where \(A\) is cross-sectional area of the nozzle flow path (m\({}^{3}\)); \(M_{a}\) is Mach number; and \(v\) is flow velocity (m/s).  
It can be deduced from Equation (1) that the velocity change of flow, \(dv\), in the nozzle depends on the change in the cross-sectional area of the nozzle, \(dA\), and the Mach number, \(M_{a}\). To be more specific, when the velocity of flow is subsonic, that is, \(M_{a}<1\), the fluid can be accelerated by the convergent nozzle, namely, \(dA<0\). This is because the \(dA<0\) and the \(({M_{a}}^{2}-1)<0\) can make the \(dv>0\), which means the flow is accelerated. It should be noted that the maximum speed that can be obtained by the subsonic flow inside the convergent nozzle is the speed of sound, which is called the choking [26; 29]. On the other hand, when the velocity of flow is supersonic, that is, \(M_{a}>1\), to further increase the velocity of the fluid, namely, \(dv>0\), the change in cross-sectional area should meet the condition \(dA>0\), that is, the flow can be accelerated in the divergent nozzle. Most importantly, to accelerate the fluid from subsonic to supersonic, the fluid should first be accelerated from subsonic to the speed of  
Figure 1: Schematic diagram of different types of nozzles: (**a**) Convergent nozzle; (**b**) Divergent nozzle; (**c**) Laval nozzle [26].  
sound by a convergent longitudinal section, and then the sonic flow can be continuously accelerated to supersonic by a divergent longitudinal section, as shown in Figure 1c. Therefore, the fluid whose velocity is originally subsonic can be largely accelerated to a supersonic state by a Laval nozzle.
## 2 Materials and Methods
### Data Sources and Preprocessing
#### 2.2.6 Land Use Datasets  
In this study, the random forest supervised classification method [46] was employed on the Google Earth Engine (GEE) cloud platform to classify land cover. The classification system referenced was developed by the Ministry of Ecology and Environment for national ecological status investigation and assessment, which was adapted to the specific land use characteristics of the mining area. Consequently, land use types were categorized into farmland, forest land, grassland, water bodies, unused land, and construction land. In GEE, we loaded Landsat 5 images from 1989 to 1993 and Landsat 8 images from 2018 to 2022 for the training of land use classification models. First, GEE's built-in functions were used to remove clouds and shadows. Sample points were then selected through manual visual interpretation and historical land use data of the mining area and subsequently divided into an 80% training set and a 20% validation set. A variety of features, including spectral bands, spectral indices, and texture features, were extracted from the remote sensing images to enhance classification accuracy. Next, a random forest classifier was constructed in GEE  
Figure 4: Spatial distribution of topographic data: (**a**) DEM, (**b**) slope, and (**c**) topographic position index (TPI).  
Figure 5: Spatial distribution of mining thickness.  
and trained using the feature variables. The resulting classification model based on Landsat 5 data achieved an overall accuracy of 91.29% and a Kappa coefficient of 0.8954, while the model based on Landsat 8 data achieved an overall accuracy of 91.87% and a Kappa coefficient of 0.9076, indicating consistently high classification performance. Finally, the two classification models were applied to generate land use classification maps for 1994 (Figure 6a) and 2023 (Figure 6b).