esa-sceva/satcom-chunk-collection huggingface dataset:
# Forecasting the Number of Road Accidents in Polish Provinces Using Trend Models
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data/Model** & **Annual Data** & **Monthly Data** \\ \hline Exponential & y = 2968.8e\({}^{-0.06\text{x}}\) & y = 5563.1e\({}^{-2\text{E}-05\text{x}}\) \\ \cline{2-3}  & R\({}^{2}\) = 0.9776 & R\({}^{2}\) = 0.0624 \\ \hline Linear & y = \(-\)100.67x + 2672.2 & y = \(-\)0.0491x + 4343.8 \\ \cline{2-3}  & R\({}^{2}\) = 0.9481 & R\({}^{2}\) = 0.0651 \\ \hline Logarithmic & y = \(-\)749.4 ln(x) + 3184.3 & y = \(-\)2093 ln(x) + 24565 \\ \cline{2-3}  & R\({}^{2}\) = 0.9122 & R\({}^{2}\) = 0.068 \\ \hline Polynomial of 2 nd degree & y = 3.7309x\({}^{2}\) \(-\) 182.75x + 2986.9 & y = 4E\(\cdot\)05x\({}^{2}\) \(-\) 3.7996x + 82631 \\ \cline{2-3}  & R\({}^{2}\) = 0.9861 & R\({}^{2}\) = 0.174 \\ \hline Polynomial of 3 rd degree & y = \(0.2451x^{3}\) \(-\) 4.357x\({}^{2}\) \(-\) 109.91x + 2838.1 & y = \(-\)2E\(\cdot\)09x\({}^{3}\) + 0.0003x\({}^{2}\) \(-\) 14.473x + 231109 \\ \cline{2-3}  & R\({}^{2}\) = 0.9906 & R\({}^{2}\) = 0.1745 \\ \hline Polynomial of 4 th degree & y = \(-\)0.0404x\({}^{4}\) + 2.0229x\({}^{3}\) \(-\) 29.91x\({}^{2}\) + 22.025x + 2662.8 & y = \(-\)3E\(\cdot\)11x\({}^{4}\) + 5E\(\cdot\)06x\({}^{3}\) - 0.313x\({}^{2}\) + 8706.8x \(-\) 9E+07 \\ \cline{2-3}  & R\({}^{2}\) = 0.994 & R\({}^{2}\) = 0.3524 \\ \hline Polynomial of 5 th degree & y = \(-\)0.0044x\({}^{5}\) + 0.1989x\({}^{4}\) \(-\) 2.7169x\({}^{3}\) + 10.689x\({}^{2}\) \(-\) & y = 1E\(\cdot\)14x\({}^{5}\) \(-\) 2E\(\cdot\)09x\({}^{4}\) + 0.0002x\({}^{3}\) \(-\) 7.3524x\({}^{2}\) + 155643x \\ \cline{2-3}  & 118.23x + 2799.1 & \(-\) 1E\(\cdot\)49 \\ \hline Polynomial of 6 th degree & R\({}^{2}\) = 0.995 & R\({}^{2}\) = 0.3874 \\ \hline Polynomial of 6 th degree & y = \(-\)0.0001x\({}^{6}\) + 0.0026x\({}^{5}\) + 0.0235x\({}^{4}\) \(-\) 0.5973x\({}^{3}\) \(-\) & y = 1E\(\cdot\)17x\({}^{6}\) \(-\) 3E\(\cdot\)12x\({}^{5}\) + 3E\(\cdot\)07x\({}^{4}\) \(-\) 0.0159x\({}^{3}\) + 494.62x\({}^{2}\) \\ \cline{2-3}  & 1.7467x\({}^{2}\) \(-\) 86.666x + 2774.9 & \(-\) 8E\(\cdot\)06x + 6E+10 \\ \hline \multicolumn{2}{c}{R\({}^{2}\) = 0.995} & R\({}^{2}\) = 0.4732 \\ \hline Potentiometric & y = 3938.4x\({}^{-0.464}\) & y = 4E\(\cdot\)07x\({}^{-0.914}\) \\ \hline \multicolumn{2}{c}{R\({}^{2}\) = 0.8524} & R\({}^{2}\) = 0.0652 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Trend models for the Kuyavian–Pomeranian province.
## 2 Materials and Methods
### Study Design  
This study employed a prospective, within-subject, repeated measures design to investigate the effects of auditory stimuli on postural balance and cognitive tasks in individuals  
Figure 1: Flowchart of participant recruitment and experimental procedure.  
with intellectual disabilities. The study involved four laboratory visits spaced at least one day apart. The initial visit was dedicated to familiarization with the procedures, while the subsequent three visits focused on testing sessions, each conducted under different auditory environments: no sound, nature sounds, and noise. The nature sound condition consisted of birds chirping and water stream sounds, chosen for their association with calming effects on attention, cognition, and motor control. In contrast, the noise condition simulated a typical urban auditory environment, incorporating traffic hum and indistinct crowd sounds, which are commonly linked to interference and distraction. Both auditory conditions were presented at a frequency range of 500-1000 Hz and a moderate volume of 50-65 dB, levels typical of comfortable outdoor environments. A sound-level meter was used to ensure consistency in the auditory stimuli across trials. To validate the stimuli's relevance to the study objectives, participants rated the sounds based on perceived intensity, pleasantness, and their relaxing or disruptive effects. Testing sessions were conducted in a temperature-controlled room (22-24 \({}^{\circ}\)C) free from external distractions, with room humidity and noise levels monitored to maintain a consistent environment. Each trial began with participants assuming a standardized initial posture, with visual cues and verbal instructions provided to ensure proper alignment. These measures are aimed at controlling potential variability in posture and environmental factors. During the experimental sessions, participants performed dual tasks under varying conditions, including two surface types (firm and foam) and two visual settings (open eyes and closed eyes) (Figure 1). The cognitive tasks involved counting (reciting numerical sequences, e.g., 1 to 30) and semantic verbal fluency (generating as many words as possible from predefined categories such as animals, vegetables, or fruits) (Figure 1). Both tasks were performed concurrently with postural balance assessments, measured using center of pressure (CoP) recordings. To prevent potential biases, the order of auditory conditions (nature sounds, urban noise, and silence) was counterbalanced across participants. This ensured that no participant experienced the conditions in the same sequence, minimizing learning, fatigue, and order effects. Furthermore, testing sessions were spaced at least one day apart, and auditory stimuli were rotated across sessions to mitigate cumulative or habituation effects. Participants were also provided sufficient rest between sessions to avoid fatigue or desensitization to specific auditory conditions.
# Risk Assessment of Soil Heavy Metals in the Jiahe River Basin of Yantai City, China
## 3 Results
### Assessment Using Pollution Indices
#### 3.3.2 Ecological Risk Factor (Er)  
The ecological risk factor (Er\({}^{\rm i}\)) is an index used to quantitatively describe the potential ecological risk associated with a specific pollutant [30]; it is defined as follows:  
\[\rm Er^{\rm i}=Tr^{\rm i}\cdot C_{\rm f}^{\rm i} \tag{3}\]  
where C\({}_{\rm f}^{\rm i}\) represents the contamination factor in Equation (2), while Tr\({}^{\rm i}\) is the toxic-response factor for a specific substance. The Tr\({}^{\rm i}\) values for the heavy metals Cd, Hg, As, Pb, Cr, Cu, and Zn are 30, 40, 10, 5, 2, 5, and 1, respectively. Er\({}^{\rm i}\) < 40, 40 \(\leq\) Er\({}^{\rm i}\) < 80, 80 \(\leq\) Er\({}^{\rm i}\) < 160, 160 \(\leq\) Er\({}^{\rm i}\) < 320, and Er\({}^{\rm i}\)\(\geq\) 320 correspond to low, moderate, considerable, high, and very high ecological risk levels, respectively (Table 2). The Er values of seven heavy metals (i.e., Cd, Hg, As, Pb, Cr, Cu, and Zn) in the 213 soil samples were calculated, and their counts on each risk level are listed in Table 2.  
All samples were at the low potential ecological risk level for Pb, Cr, and Zn. For Cu, except for one sample at the considerable risk level, all samples were at the low risk level. For As, most samples were at the low potential ecological risk level, but a few were moderate and a few were at the considerable risk level. Regarding Cd and Hg, most samples were at the low risk level, with some at the moderate or considerable risk level and few at the high or very high levels of potential ecological risk.  
The risk levels of Ni, Co, V, and Mn are not discussed here due to the absence of toxic-response factors.
## 2 Test Methodology
### Sample Preparation  
The physical model of the layered surrounding rock at the bottom of the tunnel is shown in Figure 2. The surrounding rock at the bottom of the tunnel is mainly subjected to the horizontal stress and vertical stress transmitted from both sides of the tunnel. To observe the process of the floor heave phenomenon in the laboratory, intact prismatic samples and samples with prefabricated inverted arches (\(\alpha=0^{\circ}\)) were prepared to test, with an average height, width, and thickness of 150 mm, 50 mm, and 50 mm, respectively. To analyze the influence of weak surfaces between layers of schist and the effect of prefabricated inverts, four types of rock samples were prepared for comparison, namely intact schist, schist with a prefabricated inverted arch, intact sandstone, and sandstone with a prefabricated inverted arch. The geometric shape and dimensions of the invert are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mineral Composition**} & \multicolumn{2}{c}{**Schist**} & \multicolumn{2}{c}{**Sandstone**} \\ \cline{2-5}  & **Grain Size (mm)** & **Content (\%)** & **Grain Size (mm)** & **Content (\%)** \\ \hline Quartz & 0.02–0.15 & 55 & 0.05–0.3 & 55 \\ Muscovite & 0.02–0.4 & 20 & / & / \\ Potassium feldspar & 0.02–0.2 & 12 & 0.05–0.3 & 30 \\ Biotite & 0.2–0.8 & 5 & / & / \\ Calcite & 0.02–0.2 & 4 & / & / \\ Plagioclase & 0.03–0.15 & 3 & 0.05–0.25 & 10 \\ Sericite & / & / & \textless{}0.03 & 2 \\ Calcite & / & / & 0.05–0.3 & 2 \\ Opaque minerals & 0.02–0.2 & 1 & / & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mineral composition and grain size distribution of the tested schist and sandstone.  
Figure 1: Polarized light micrographs: (**a**) schist; (**b**) sandstone. (Qz, Ms, Bt, Kfs, and R stand for quartz, muscovite, biotite, potassium feldspar, and rock debris, respectively).  
The geometric shape of the prefabricated inverted arch is considered to reflect the loading situation in underground circular openings [31]. It has a major effect on the failure pattern and crack propagation type in rock materials [32]. The inverted rise-to-span ratio of the sample was set to 1:20. To ensure uniform force throughout the loading process, the prefabricated inverted arch was cut using a precision steel wire saw, and each surface was polished to be smooth and flat. Increasing the aspect ratio of the sample not only helps to weaken the end effect but also stabilizes the strength index value. In this study, the height-to-width ratio of the rock samples was designed as 3:1 for the tests.
## 3 OTFS-Based Radar Sensing
### GLRT-Based Target Detection
Furthermore, by introducing the term \(\widetilde{h}=h^{*}\phi^{2\pi}\frac{\
u_{1}k_{\
u_{i}}}{M_{S}N_{T}}\), the GLRT takes the following expression:  
\[\Lambda(\mathbf{v})=\frac{e^{-\frac{1}{\sigma^{2}}\min_{\tau,\
u}\|\mathbf{v}- \widetilde{h}\widetilde{\mathbf{X}}\mathbf{w}^{\mathbf{H}}_{(\tau,\
u)} \mathbf{x}^{*}\|^{2}}}{e^{-\frac{1}{\sigma^{2}}\|\mathbf{v}\|^{2}}}\gtrapprox \mathcal{H}_{1}{\mathcal{H}_{0}}\widetilde{\eta}. \tag{21}\]  
When \(\tau\) and \(\
u\) are constant, the value of \(\widetilde{h}\) in Equation (21) is as follows:  
\[\widetilde{h}\approx\frac{\mathbf{x}^{\mathbf{T}}\mathbf{W}\widetilde{ \mathbf{X}}^{\mathbf{H}}\mathbf{v}}{\|\ \mathbf{x}\ \|^{2}}. \tag{22}\]  
This approximation arises from the matrix \(\frac{\widetilde{\mathbf{X}}^{\mathbf{T}}\widetilde{\mathbf{X}}^{\mathbf{x}}} {M_{S}N_{T}}\) exhibiting an approximate identity property. The approximately equal sign converges to an equal sign as \(M_{S}N_{T}\) approaches infinity. For a more comprehensive proof, we refer interested readers to the detailed explanation provided in reference [32]. By adopting the procedure outlined in reference [32] with a phase offset of zero, we establish the approximate identity property of \(\frac{\widetilde{\mathbf{X}}^{\mathbf{T}}\widetilde{\mathbf{X}}^{\mathbf{x}} }{M_{S}N_{T}}\). Considering the parameter \(\widetilde{h}\), we can formulate the GLRT:  
\[\max_{\tau,\
u}\frac{\left|\ \mathbf{x}^{\mathbf{T}}\mathbf{W}(\tau,\
u) \widetilde{\mathbf{X}}^{\mathbf{H}}\mathbf{v}\right|^{2}}{\sigma^{2}\ \|\ \mathbf{x}\ \|^{2}}\gtrapprox \mathcal{H}_{1}{\mathcal{H}_{0}}\widetilde{\eta}. \tag{23}\]  
Within this framework, the cell-averaging constant false alarm rate (CA-CFAR) technique is employed to ascertain an adaptive threshold, \(\widetilde{\eta}=\log(\eta)\). The methodology for calculating \(\widetilde{\eta}(\
u,\tau)\) is elucidated in the reference authored by [PERSON] et al. (2018) [61]. Targets are discerned at locations where the peak magnitudes surpass this specified threshold, with the resulting DD parameters being considered as the attributes linked to these identified targets.
## 4 Discussion
### Expression of Protein Markers in Ascitic Fluid-Derived Ovarian Cancer Cells  
It has been recognised that metastatic lesions of advanced ovarian cancer begin with the deposition of small clusters of cancerous cells, carried around within the peritoneal cavity by the movement of ascitic fluid. The proteomic profiles of the primary tumour compared to the metastatic sites have differential patterns to some extent [10; 22; 23]. In addition, the proteomic signatures, associated with epithelial to mesenchymal phenotypic transition and vice versa, in ascitic fluid-derived ovarian cancer cells, have been described [24; 25; 26]. Cells derived from the ascitic fluid of ovarian cancer patients are known to express various protein markers associated with epithelial and mesenchymal phenotypes, including CD44, AC133, EGFR, integrin beta-1, cytokeratin-18 and E-cadherin [27]. Ascitic-derived cells cultured in cell monolayers and floating conditions express distinct protein markers. Adherent cells possess mesenchymal protein profiles, but floating cells display more epithelial markers [25]. Ascitic fluid cells expressing CA125 and EpCAM have been characterised in a large cohort of ovarian cancer patients [28]. Cells that have been cultured from a primary tumour and ascitic fluid display both epithelial and mesenchymal (E/M) markers. These E/M hybrid transition cells produce epithelial ovarian cancer tumours in vivo in a xenograft model [29].  
In agreement with previous studies, we observe in our study that ascitic fluid-derived cells show immunostaining for CA-125, cytoketatin-18, E-cadherin, N-cadherin and vimentin. The morphological appearance of these cells, derived from ascitic fluid and cultured as a 2D cell monolayer, displays both compact colony and loose/motile characteristics. However, when these cells are cultured in a 3D cell suspension, they form three different main morphologies. The compact spheroid phenotype is displayed by the majority of ascitic cells and followed by small clusters. Evidence suggests that ovarian cancer cells have the ability to form compact spheroids, and these cells possess a myofibblast-like phenotype, increasing invasive capacity [30]. This is consistent with our findings that the majority of ascitic-derived cells demonstrate a high expression of vimentin, a mesenchymal protein that is linked to fibrobl phenotypes. A recently published study has demonstrated that vimentin positive cancer cells in high-grade serous tumour tissues are possibly the cells that migrate and invade the peritoneal membrane [31]. However, we apply caution to the interpretation of the protein markers that were used in our study. Even though cells in ascitic fluid were positive for epithelial cell origin, the ability of these cells to develop ovarian tumours in an animal model has not been tested. We also think that heterogeneous cells presented in ascitic fluid, including malignant and non-malignant cells, may play an important role in responsiveness to anticancer drugs.
# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
## 2 Related Work
The use of physics and software metrics such as SNR, RSSI or PRR allows calculating the instantaneous LQE parameter without considering its historical trend. In a recent research, [PERSON] and [PERSON] [32] proposed an LQE deep forest algorithm that estimates the PRR from the input of the mean of LQI, RSSI, and SNR metrics and stability parameters CV\({}_{\text{LQI}}\), CV\({}_{\text{RSSI}}\), and CV\({}_{\text{SNR}}\) that the authors calculated as the standard deviation divided by the mean. [PERSON] et al. [33] predicted the LQE using long short-term memory (LSTM) and gated recurrent units (GRUs). However, the implementation of these algorithms has the disadvantage of needing large amounts of historical data for networks that are already in operation.
## 2 Materials and Methods
### Smote-Based Data Equalization  
In the case of class imbalance, where the number of samples from the minority class is significantly smaller than the majority class, there is a risk of classifier bias towards the majority class, leading to performance issues [31]. To address this, minority class oversampling techniques such as SMOTE (Synthetic Minority Over-sampling Technique) can balance the class distribution and improve classifier performance.  
SMOTE sampling operates by interpolating between samples from the minority class to generate new samples. The underlying principle of SMOTE is as follows: For each sample in the minority class, its K nearest neighbors (typically K = 5) are identified using a distance metric such as Euclidean distance [32]. A new sample is then created by randomly selecting a point along the line connecting the minority class sample and one of its K nearest neighbors. The principle is shown in Figure 2. The formula used for generating the new sample is as follows:  
\[\begin{split}& X_{new}=X+rand(0,1)\times(X_{near}-X)\\ &=[1-rand(0,1)]\times X+rand(0,1)\times X_{near}\end{split} \tag{6}\]  
where X\({}_{new}\) denotes a final synthetic sample, \(X\) denotes a minority class sample of the input, \(X_{near}\) denotes a nearest neighbour sample of the selected \(X\), rand(0,1) is a random number between 0 and 1.  
In SMOTE sampling, the selection of the number of nearest neighbor samples is crucial. If the number is too small, it may generate overly similar synthetic samples, thereby impacting the classifier's generalization ability. On the other hand, selecting too many nearest neighbor samples may lead to the generation of complex synthetic samples, increasing the computational complexity of the classifier. Hence, in practical applications, choosing an appropriate number of nearest neighbor samples is necessary based on specific circumstances.  
This paper applies the SMOTE algorithm to the three datasets used in the experiments to achieve balanced calculation. Consequently, the proportions of each category in the datasets are altered, as depicted in Figures 3-5.  
Figure 2: Schematic diagram of SMOTE algorithm.
# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 6 Existing Public Data Set  
At present, the data sets of tool wear samples mainly include the NASA data set and PHM data set [127].  
National Aeronautics and Space Administration (NASA) introduced a variety of commonly used scientific data sets. The milling data set is packaged into a mat file format, collecting the experimental data of the tool wear on the milling machine at different feed speeds and cutting depths. The data set is provided by the BEST laboratory at the University of California, Berkeley. The New York Society of Forecasting and Health Management (PHM) released the data from the 2010 high-speed CNC machine tool health prediction competition. This data set recorded the relevant information of the high-speed CNC milling machine through the dynamometer, accelerometer and acoustic emission meter. These two data sets were widely used to verify the tool wear monitoring methods based on deep CNN.
## 4 Aerodynamic Design Method Investigation
### Influence of Key Section Airfoils on the Aerodynamic Performance of the Configuration
#### 4.3.5. Symmetrical-Plane Airfoil Design  
While the wing design can increase the nose-up moment of the aircraft, it also influences the lift-to-drag ratio. Owing to the long chord length of the fuselage, the pressure distribution across the airfoil of the symmetrical-plane section has a significant impact on the pitching moment. However, modifications to the upper surface of the fuselage are geometrically limited because of the engine bumps and the modified inlet. Consequently, only the inner side of the engine bumps and the lower surface of the fuselage, which is relatively flat, can be modified.  
From the previous investigation on the fore-loading/aft-reflexing design of the kink and tip airfoils, it can be inferred that the aft-reflexing design is an effective method to increase the longitudinal moment of the aircraft. Therefore, the aft-reflexing design is adopted at the symmetrical-plane airfoil of the fuselage. Some additional considerations are also necessary: (1) the airfoil area must remain constant when modifying the fuselage to satisfy the loading requirements; and (2) as the thickness of the airfoil section of the fuselage is considerable, the symmetrical-plane airfoil should be appropriately aft-reflexed to prevent the appearance of strong shock waves, which can deteriorate the aerodynamic efficiency of the aircraft. This is a contradictory problem, which must be considered in the airfoil design.  
As the upper surface has a considerable influence on aerodynamic performance, the upper surface of the symmetrical-plane airfoil is only changed slightly, and the increment in camber is primarily distributed along the lower surface. Figure 28a shows two designed airfoils--Sym-L&R1 and Sym-L&R2--with the same area as the initial airfoil. Figure 28b shows the camber increments of each configuration.  
Figure 29 illustrates the aerodynamic characteristics of the initial configuration and the configurations with the modified symmetrical-plane airfoils. Table 8 depicts the comparison of the aerodynamic characteristics of the initial and modified configurations. In the modified configurations, the cruise angles vary slightly. The maximum moment increment of Sym-L&R2 during cruise is 51.12%, while the lift-to-drag ratio is reduced slightly. Thus, symmetrical-plane airfoil design is an effective method to not only generate a favorable nose-up moment, but also scarcely influence the lift-to-drag ratio of the aircraft.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & \(\kappa\) \\ \hline fore-loading & 0.02 \\ aft-reflexing & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The efficiency factor \(\kappa\) of fore-loading and aft-reflexing the tip airfoil.  
Figure 28: The fore-loading and aft-reflexing modification of symmetrical plane airfoil. **(a)** Airfoils comparison. **(b)** Camber increments distributions.