esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results and Discussions  
### Verification and Comparisons  
The disturbed soil adjacent to the pile was modeled by \(N\) vertical annular homogeneous zones. To accurately model the gradually varied soil properties, the number of the divided annular zone should be large enough, and the proposed solution should be converged with the increase in \(N\). Figure 2 shows the effect of \(N\) on the pile response. \(\eta\) is the disturbance degree, defined as \(\eta=G_{1}/G_{N+1}\). \(\eta=1\) represents the case where the soil is undisturbed. \(\eta>1\) represents the case where surrounding the soil is strengthened. \(\eta<1\) represents the case where the surrounding soil is weakened. The available parameters used here are shown in Table 1. It can be seen from Figure 2 that the kinematic response factors eventually approach a certain value for a specific case after \(N=15\). That means the results converged and sufficient accuracy guaranteed for \(N\geq 15\).  
The proposed solution can be degenerated to the general case that the pile is embedded in the undisturbed soil, as in [PERSON] et al. [9]. Figure 3 compares the kinematic response of the piles from the present solution by letting \(\eta=1\) and that of Winkler model [18], a finite element model [20], an elastodynamic continuum model [20], and a three-dimensional model [9]. The available parameters used here are the same as that adopted in [PERSON] et al. [9]. The dimensional frequency is defined as \(\omega=\frac{2H\omega}{\pi V_{p}}\), where \(V_{p}\) is the wave velocity of the P wave. From the above comparation, the degenerated solution is in good agreement with that of [PERSON] et al. [9]. Since [PERSON]'s solution was believed to be rigorous, the presented solution was verified to be reliable. Even though there were divergences between the results of different methods, all appropriate methods can predict the kinematic response of piles approximately.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multicolumn{5}{c}{**Pile**} & \multicolumn{5}{c}{**Surrounding Soil**} \\ \hline \(r_{0}\) **(m)** & \(H\) **(m)** & \(\rho_{p}\) **(kg/m\({}^{3}\))** & \(E_{p}\) **(GPa)** & \(\rho_{s,j}\) **(kg/m\({}^{3}\))** & \(\beta_{j}\) & \(v_{s,j}\) & \(G_{N+1}\) **(MPa)** & \(\Delta r\) **(m)** \\ \hline
0.5 & 20 & 2500 & 36 & 1800 & 0.02 & 0.4 & 36 & 0.2 \\ \hline \end{tabular}
\end{table}
Table 1: Available parameters used in the paper.  
Figure 2: The effect of \(N\) on the kinematic response of piles.
# Unsupervised Detection of Multiple Sleep Stages Using a Single FMCW Radar
## 5 Conclusions  
This paper proposes a non-learning-based, three-stage sleep-detection algorithm using FMCW radar sensors. The subject's respiration and movement information was extracted to detect the three stages (wake, _REM_, and Non-_REM_) in the sleep state. Compared to existing research on non-learning-based, two-stage sleep detection, the _REM_ sleep stage was additionally detected, and the subject's motion information was also used to reduce _REM_ sleep misdetection caused by the movement of patients with _REM_-sleep behavior disorder. The performance of the proposed method was evaluated with real clinical data obtained from patients using FMCW radar. Compared with the PSG results, the three-stage detection accuracy was 88.8% for a single subject and an average of 68.91% for the 85 subjects. The results suggest that the proposed sleep-stage detection method can be utilized instead of existing PSG and sleep-stage monitoring methods. However, in the hospital, various sleep stages and states are monitored with various sensors, including brain wave sensors. Neurophysiological information that can be detected using brain waves could not be detected using radar. Therefore, a hybrid sleep monitoring system combing radar and brain waves needs to be considered for future work.  
Conceptualization, Y.-K.Y.; data curation, Y.-K.Y. and C.-W.J.; formal analysis, Y.-K.Y. and C.-W.J.; investigation, Y.-K.Y.; methodology, Y.-K.Y. and C.-W.J.; project administration, H.-C.S.; resources, H.-C.S.; software, Y.-K.Y. and C.-W.J.; supervision, H.-C.S.; validation, H.-C.S.; visualization, Y.-K.Y. and C.-W.J.; writing--original draft, Y.-K.Y.; writing--review and editing, H.-C.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by institute of Information and communications Technology Planning and Evaluation (IIITP) grant funded by the Korean government (MSIT) (No. 2021-0-00305).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Not applicable.  
The authors declare no conflict of interest.
# Root Dilaceration Using Deep Learning:
## 2 Materials and Methods  
This study was carried out in accordance with the Helsinki Declaration standards. It was approved by the Ethical Review Board of University (approval number 2023-78). Digital PRs were taken from the same dental panoramic device, Planmeca oy, Helsinki, Finland. This is a retrospective and exploratory study that investigates the role of artificial intelligence in detecting root dilaceration. Figure 1 presents a step-by-step flowchart of the study.  
PRs were randomly chosen from images taken between 2022 and 2023 from patients who were older than 18 years old. The inclusion criteria were the presence of root dilaceration and exclusion criteria were metallic artifacts, position-based distortions and incomplete root formations in the radiographs. Finally, 636 PRs with a total of 983 objects were selected. They were all anonymized to remove identifying information.  
The only class defined was root dilaceration in PRs. After two weeks, all images were examined again by the radiologist to confirm the presence of root dilaceration. Before the labelling process, a calibration session was performed as a pilot on 48 PRs that were not included in this study. An oral and maxillofacial radiologist (B.C.) with more than 6 years of experience in the field labelled the images. The smallest rectangular boxes covering the dilacerated roots were defined as the ground truth. The data were labelled again by the same expert after two weeks, which confirmed consistency with the previous step. LabelMe, the open annotation tool, was used to prepare the annotations. Training, validation, and test folders were randomly created with ratios of 0.8, 0.1, and 0.1, respectively.  
PyTorch and Google CoLab were mainly used to implement deep learning models regarding root dilaceration detection. Figure 2 shows PRs with root dilaceration as an example to show the inputs used for the proposed object detection solution. Before feeding into the deep learning models, data were pre-processed, which included typical operations such as resizing, flipping, normalizing and padding.
## 3 Consistent Path Congestion Model
### Consistent Path Congestion Model and Its Related Algebra
#### 3.1.2 Cumulative Differential Path Congestion Indicator  
Based on the DPCI, the consistency of the relative path congestion situation can be modeled with the cumulative differential path congestion indicator (CDPCI) \(W_{ij}(\theta,T,\Delta t_{n})\). \(W_{ij}(\theta,T,\Delta t_{n})\) is the frequency when the latency of path \(i\)\(L_{i}(\Delta t_{n})\) exceeds path \(j\)\(L_{j}(\Delta t_{n})\) by more than \(\theta\) seconds during the recent period of time \(T\). It describes the fact that \"_during the recent time period \(T\), the situation that the congestion of the current path is worse than the backup path to a certain degree \(\theta\) has happened \(W_{ij}(\theta,T,\Delta t_{n})\) times\". \(W_{ij}(\theta,T,\Delta t_{n})\) is derived from the sum of \(\omega_{ij}(\theta,\Delta t_{n})\) over the period of time \(T\), based on the definition of DPCI \(\omega_{ij}(\theta,\Delta t_{n})\), as shown below:  
\[W_{ij}(\theta,T,\Delta t_{n})=\sum_{n=0}^{T/\Delta t}\omega(\theta,\Delta t_{n}) \tag{5}\]  
where \(T\) is the cumulative window, which is the time period of the history that is taken into consideration. Both \(\theta\) and \(T\) are constant empirical values that are customer-configurable. In our experiment, the sampling intervals were \(\Delta t=5\) s, \(T=100\) s, and \(\theta=20\) ms.
## 3 Results and Discussion
### Sensory Evaluation Results
In general, the addition of legume seed flour primarily resulted in a reduction in the degree of rising of the loaf, which was particularly noticeable at the highest level of addition (25%). However, the evaluators had no major concerns regarding the loaves' shapes and the appearance of the crust surface. Therefore, all bread samples received ratings above 5 points on a 9-point hedonic scale for overall appearance, indicating consumer acceptability. The bread was also highly rated in terms of aroma and taste. The control bread received the highest ratings in these aspects (8.6 and 8.7 points, respectively), as it was exceptionally aromatic and had a delicate taste with a slightly perceptible sour note. According to the evaluators, the bread with a 10% addition of GP, YL, or NL (GPB10, YLB10, and NLB10) as well as a 15% addition of GP or NL (GPB15 and NLB15) had comparable aromas to the control sample. Similarly, bread with a 10% addition of GP, YL, or NL (GPB10, YLB10, and NLB10) as well as a 15% addition of GP or NL (GPB15 and NLB15) received scores comparable to the control sample in terms of taste. With a higher proportion of legume seed flour, a characteristic aroma and an aftertaste described as \"bean-like\" or \"pea-like,\" were noticeable. According to some panelists, the bread with the highest proportion of legume seed flour (25%) had a slightly bitter aftertaste. Based on the scores given for the taste of the bread, the consumer acceptability threshold was set at a level of 15% inclusion of GP and NL and 20% YL. [PERSON] et al. [71] obtained higher scores for the taste and aroma of bread with the addition of lupine sourdough at levels of 3% and 6% compared to the control sample. According to the panelists, this bread was also characterized by a more pronounced acidic taste. In terms of texture, the control bread received the highest rating (8.7 points) and was characterized by a uniform fine-pored structure of the crumb (Figure 2). The bread with a 10% addition of GP, YL, or NL (GPB10, YLB10, and NLB10) as well as a 15% addition of GP or YL (GPB15 and YLB15) obtained comparable scores for this characteristic. As the level of legume seed flour addition increased, the crumb became increasingly compact, and larger pores were also visible in the cross section. Therefore, the breads with a 20% proportion of GP or NL (GPB20 and NLB20) and a 25% proportion of YL (YLB25) were rated below the consumer acceptability threshold in terms of texture. The inclusion of legume seed flours in the recipe affected the color of both the crust and the crumb of the bread. The control bread had a light brown golden crust and a beige-colored crumb. As the proportion of legume seed flours increased, both the color of the crust and the crumb gradually darkened, with the crumb becoming more yellow in color. The consumer acceptability threshold for color was set at 15% for GP and YL and 20% for NL. A similar darkening of the crumb color was
# Innovative Data-Driven Machine Learning Approaches for Predicting Sandstone True Triaxial Strength
## 1 Introduction
The traditional strength criterion analysis theories, such as meridian and envelope, were innovatively introduced into the comparative analysis of models, and an HHO-MLP model with excellent prediction accuracy and aligned with the theoretical trend was successfully selected from four HHO-MLP models developed by different activation functions. Comparative analyses with five existing strength criteria revealed that the performance of the HHO-MLP model surpassed that of existing strength criterion models on both the training and testing sets. Further investigation in the principal stress space demonstrated that the failure envelope presented by the HHO-MLP model exhibited good continuity and smoothness, coinciding with different strength criteria in different stress phases. Additionally, the relationship between the failure envelope and experimental data suggested that the HHO-MLP model provided conservative estimates of sandstone true triaxial strength, but they were not as conservative as the Hoek-Brown criterion. Overall, the HHO-MLP model demonstrated excellent performance in predicting the dataset and presented a failure envelope consistent with theoretical expectations. Furthermore, the model could generate an explicit expression that is continuous and differentiable, ensuring its potential for further extended applications.
# Preliminary Field Results of a Dedicated 5G Positioning Network for Enhanced Hybrid Positioning +
## 3 Experimental Methodology  
### Experimental Procedure  
The HOP-5G testbed was deployed in the heliport at the Airbus premises, and it was used to assess the precision of the real-time downlink ranging measurements at a static UE from a flying 5G base station. The experimental procedure was based on hovering the UAV at several line-of-sight (LoS) points. As it is shown in Figure 2, each of those sequential points are classified based on the distance \(d\) to UE and the UAV height \(h\) from ground.  
Figure 1: Example equipment of the HOP-5G testbed for UAV-mounted and ground deployments: (**a**) UAV-mounted BS or UE with the key building blocks of its payload; (**b**) Ground-fixed BS or static UE with its key building blocks.  
Figure 2: UAV hovering positions for the corresponding ranging measurements at the UE.  
The position solution of the GNSS receiver within the UAV payload is logged and disseminated in real-time to the UE. The time of those UAV GNSS solutions are used to associate the 5G real-time measurements with the corresponding UAV hovering position. For each of those points, the 5G measurements are collected over 30s. As the 5G PRS transmissions are scheduled once every 10 ms, the 5G OAI UE produces a maximum of 5000 measurement messages over those 50 s for each UAV hovering point.  
The downlink 5G PRS are transmitted over a bandwidth of approximately 80 MHz at 3.75 GHz and a subcarrier spacing of 30 kHz. This 80 MHz bandwidth is defined as result of the supported sampling rate of the USRP X300, which is set to 92.16 MSps. Each BS transmits its PRS in a different slot allocation to fully avoid inter-cell interference.  
In addition to the flying BS, a ground-fixed BS is also deployed close to the UE, in order to fully stabilize the fine synchronization of the UE received radio frames. Thus, the measurement conditions are considered optimal.
## 2 Methodology
### Tunnel Axis Segmentation
#### 2.2.2 Smoothing Central Axis of a Tunnel  
To obtain more accurate axis segments, the central axis acquired in Section 2.2.1 needs to be further smoothed to depress the noises. Fortunately, we find that the SG smoothing filtering is an ideal tool to strike a balance between maintenance of axis details and depression of noise. Essentially, this algorithm performs a local least squares polynomial regression of degree \(k\) over at least \(k+1\) points.  
Through SG smoothing, the linear and nonlinear patterns/characteristics of the central axis become more obvious and it can be easily recognized in the subsequent segmentation algorithm. As shown in Figure 4, we compare the distribution of points along the central axis in the tangent space, i.e., (\(d\), \(\theta\)) plane. Parameter \(d\) represents the sum of the lengths of any two connected points along a central axis limited from the beginning point to the current processing point, and \(\theta\) represents the angle between the tangent to the current points and the horizontal axis defined in the tangent plane. Obviously, a linear segment of a tunnel is expected to be transformed into a horizontal linear segments in the tangent space. However, a circular segment with a fixed curvature is anticipated to be transformed into a sloping linear segment because the angle between tangents at endpoints of the circular arc is proportional to the length of the arc. In practice, even though we perform the axis segmentation in tangent plane, the axis points contain too many details and \"zigzag\" noises because irregularly spaced axis point measurements can blur the horizontal and the sloping linear characteristics in tangent space.  
We use two types of the geometric tunnel axis based on synthetic points to prove the applicability of the SG algorithm. We adopt the synthetic data rather than the real tunnel point clouds because the Nanjing tunnel dataset used in our paper has a small curvature, which directly results in the curvature of the extracted tunnel axis is not prominent in the projected _xy_-plane. Because of this, the real tunnel point clouds cannot fully test the applicability of the SG filtering algorithm and its influences for the follow-up tunnel axis segmentation algorithm. As shown in Figure 4a, two kinds of geometric tunnels axis points without noise are transformed into tangent plane, where the linear features are prominent. However, after adding a certain degree of noise, the linear distribution patterns in (\(d\), \(\theta\)) space are unrecognizable as proved in Figure 4b. After further performing the SG smoothing, the linear distribution of axis points in Figure 4c becomes notable again although it is not as prominent as the situation in Figure 4a.  
Figure 4: Comparisons of linear distributions of axis points in tangent space before and after SG (Savitzky-Golay) smoothing. (**a**) the ideal tunnel axis points without noise in their data and tangent spaces; (**b**) some degree of Gaussian noises have been added into the axis point clouds; (**c**) the distribution of axis points in data and tangent spaces after SG smoothing.
# Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set
###### Abstract  
Recent years have witnessed an ever-mounting interest in the research of sparse representation. The framework, Sparse Representation-based Classification (SRC), has been widely applied as a classifier in numerous domains, among which Synthetic Aperture Radar (SAR) target recognition is really challenging because it still is an open problem to interpreting the SAR image. In this paper, SRC is utilized to classify a 10-class moving and stationary target acquisition and recognition (MSTAR) target, which is a standard SAR data set. Before the classification, the sizes of the images need to be normalized to maintain the useful information, target and shadow, and to suppress the speckle noise. Specifically, a preprocessing method is recommended to extract the feature vectors of the image, and the feature vectors of the test samples can be represented by the sparse linear combination of basis vectors generated by the feature vectors of the training samples. Then the sparse representation is solved by \(l_{1}\)-norm minimization. Finally, the identities of the test samples are inferred by the reconstructive errors calculated through the sparse coefficient. Experimental results demonstrate the good performance of SRC. Additionally, the average recognition rate under different feature spaces and the recognition rate of each target are discussed.  
synthetic aperture radar; classification; sparse representation +
Footnote †: journal: Applied Sciences
## 5 Discussion
### Managerial Insights  
The results from this study provide actionable insights for warehouse managers looking to optimize operations in dynamic environments. Key takeaways include the following:  
* Buffer areas should be viewed as a flexible resource for managing demand fluctuations, but they should be used thoughtfully. Overusing them can cause inefficiencies, while underusing them can lead to congestion in forward areas. Managers should focus on clearing out excess inventory in buffer areas first, as shown in the model, to keep operations smooth and efficient.
* The variability in Simulation PD outcomes highlights the critical need for robust and accurate demand forecasting systems. Investments in machine learning or advanced analytics can close the gap between predictive and perfect knowledge scenarios.
* The significant cost savings and efficiency gains observed for larger part groups indicate that the proposed model is particularly beneficial for high-volume warehouses.
* Managers overseeing complex operations should prioritize strategic replenishment planning, which reduces direct picking and minimizes labor costs.
* While Simulation KD demonstrates the lowest costs, achieving perfect demand knowledge is often unrealistic. Managers should aim to combine predictive tools with adaptive strategies to mitigate forecasting errors and maintain flexibility.  
These insights serve as a roadmap for implementing advanced replenishment strategies that align with real-world constraints and opportunities.  
Furthermore, managers should be aware of the model's sensitivity to cost parameters. The optimal policies for replenishment and buffer use will shift based on the relative costs of different operations. For example, if the cost of direct picking were to decrease significantly, the model would naturally favor more direct fulfillment from the reserve area and rely less on maintaining high stock levels in the forward and buffer areas. Conversely, an increase in inventory holding costs would incentivize the model to maintain leaner inventories and use the buffer more sparingly. A sensitivity analysis of these cost parameters could provide valuable insights for tailoring the strategy to a specific operational environment.
## 4 Evolutionary Computation Technique and Parent Selection Methods
### Tournament vs. Lexicase Parent Selection  
The parent selection method plays a critical role in the evolutionary process. In TS [58], a tournament is conducted among \(n\) individuals. The one with the best overall performance across all training cases, i.e., the highest aggregated fitness value, is selected. In this study, the value of \(n\) is set to 2 for all experiments to save computational time and cost. While this smaller tournament size increases the chances of low-fitness candidates being selected as parents and high-fitness candidates being missed [58], this effect is mitigated in our case due to the significant population sizes used. Larger populations increase diversity and the likelihood of high-fitness candidates being retained, ensuring robust evolutionary progress despite lower selection pressure.  
In contrast, LS [59] does not rely on an aggregated fitness value. Instead, it selects specialists who excel at particular training cases. The process begins by considering the entire population, with individuals filtered out iteratively based on their performance on random training cases. The individual who performs best on the selected training case is chosen as a parent. One is randomly selected if two or more individuals perform equally well on a training case. LS has demonstrated exceptional problem-solving abilities in various fields, including regression [60] and evolutionary robotics [61]. Notably, it has shown outstanding results in the evolution of both combinational [20; 37; 38] and sequential [44] digital circuits in recent years.