esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Validation of the Models
In the case of the calculated waveform of the horizontal component y of vibrations at the foundation level, for the SM1 model (full fixations), the low dominant frequency was about 7.5 Hz, so it was higher than the measured frequency. This indicates the influence of the susceptibility of the substrate on the measured record of the y component of vibrations. The y component of vibrations also had a wide band of dominant frequencies, ranging from 12.5 to 19 Hz. A higher dominant frequency of about 19 Hz could also be distinguished. This frequency did not show up in the measured vibration record. By comparing the FFT curves calculated based on the representative component in the horizontal y direction at the level of the foundations of the SM2 and SM3 models, one can conclude that there were no significant differences in the dominant frequencies in these models. This may indicate a minor influence of the damping assumed in the subsoil in the form of dashpots. In the calculated vibration curves of the foundations for the SM2 and SM3 models, one can distinguish one lower dominant frequency band of about 5 Hz. Similar ranges of dominant frequencies in the y component could be distinguished for mining tremor No. 2, with the difference that for this component, there was a wider band of higher dominant frequencies, ranging from 12.5 to 19 Hz. In the case of the horizontal x component, the band of dominant higher frequencies was 15 to 20 Hz--see Figure 11. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Measurement** \\ **or Calculation** \\ \end{tabular} } & \multicolumn{3}{c}{**Maximum Value of \(a\) [m/s\({}^{2}\)]**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Direction x**} & \multicolumn{2}{c}{**Direction y**} \\ \cline{2-5}  & **No. 1** & **No. 2** & **No. 1** & **No. 2** \\ \hline measured & 0.22 & 0.65 & 0.31 & 0.54 \\ SM1 & 0.51 & 1.31 & 0.81 & 1.76 \\ SM2 & 0.26 & 0.97 & 0.28 & 0.77 \\ SM3 & 0.21 & 0.82 & 0.26 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Measured and calculated maximum values of foundation vibration (\(a\) [m/s\({}^{2}\)]) in the case of actual masonry building (brick load-bearing walls). Figure 11: _Cont._  
The results of the above analysis led to the conclusion that the accuracy of the proposed 3D FEM models of soil-building systems was successfully verified by full-scale experimental results. Therefore, the validated models could be employed to analyze the transfer of the mine-induced free-field vibration to the building foundation.
## 1 Introduction
Several studies have relied on clinical observations of psychomotor signs of depression [17; 23], including those involving handwriting analysis, which identified slower movements and reduced pressure during handwriting in both adults with depressive disorder (DD) [17] and youth with DD [24]. Handwriting analysis has also been successfully applied in clinical studies of neurodevelopmental disorders such as dysgraphia [25; 26], attention deficit/hyperactivity disorder (ADHD) [27; 28], developmental coordination disorder [29], and autism spectrum disorders [30]. While some studies have used handwriting data to evaluate the diagnostic potential for detecting depressive disorder (DD), most have relied on statistical analyses to identify significant handwriting features [14; 24; 31; 32; 33]. A limited number of studies have employed machine learning approaches to differentiate individuals with DD from control groups [21; 34; 35; 36]. However, to our knowledge, no published research has specifically applied machine learning techniques to handwriting analysis in youth populations with DD. This study aimed to evaluate a machine learning approach for recognizing psychomotor retardation in youth depression. To achieve this, an extensive set of kinematic handwriting features was first extracted from recordings of a simple repetitive task--writing the lowercase cursive letter \"1.\" The feature set was then refined using statistical analysis, followed by a feature reduction algorithm to identify optimal subsets across different feature set sizes. Finally, several machine learning algorithms were applied to classify individuals with depressive disorder (DD) versus a control group:  
The main contributions of this research are as follows:  
* A novel decision-support screening tool based on kinematic handwriting analysis of a simple repetitive handwriting task in youth DD is proposed for the first time. * DD versus control group classification results, using machine learning with reduced feature sets, are systematically compared. * Optimal feature subsets are identified, and the most frequently recurring features are analyzed.
## 3 Tracking Loops Error Characterization
### Tracking Threshold of the PLL  
The tracking threshold of the PLL is the maximum error accepted by the receiver to keep the PLL locked. It is usually obtained from multiple tracking experiments. However, these experiments are complicated and need to be repeated constantly in order to calculate the optimal tracking threshold. Hence, an empirical value (1-sigma) is gained from [23,25,26] for the analysis in this paper. Since the tracking loop error consists of the dynamic stress noise, the thermal noise, the Allan deviation phase noise and the vibration-induced phase noise, the criterion to keep the loop locked is described as follows:  
\[\sigma_{PLL}=\sigma_{T}+\theta_{e}+\sigma_{A}+\sigma_{\phi}\leq 15^{\circ} \tag{10}\]  
In this equation, 15 \({}^{\circ}\)is the 1-sigma empirical threshold.
# Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
## Abbreviations  
The following abbreviations are used in this manuscript:  
AMVR Adaptive Motion Vector Resolution  
AVC Advanced Video Coding  
ACT Adaptive Colour Transform  
AIS Automatic Identification System  
BD-BR Bjontegaard Delta Bit Rate  
BD-PSNR Bjontegaard Delta Peak Signal to Noise Rate  
CTU Coding Tree Unit  
CU Coding Unit  
CNN Convolutional Neural Network  
EPFS Electronic Position Fixing System  
FLIS Frame Level Interval Sampling  
GT Gross Tonnage  
GOP Group of Pictures  
HEVC High Efficiency Video Coding  
ITU-T International Telecommunication Union for Telecommunication Standardization Sector  
ISO International Organization for Standardization  
IBC Intra Block Copy  
IEC International Electro technical Commission  
IMO International Maritime Organization  
JCT-VC Joint Collaborative Team on Video Coding  
MSC Maritime Safety Committee  
NM Nautical Miles  
OOW Officer On Watch  
PSNR Peak Signal to Noise Ratio  
PLT Palette Mode  
QP Quantization Parameter  
RGB Red Green Blue  
RD-cost Rate Distortion Cost  
SCC Screen Content Coding  
Safety of Life at Sea
# Positioning Methods and the Use of Location and Activity Data in Forests
## 2 Types of Positioning Technologies
### RFID and Acoustic Positioning
Hydrophones can be deployed in a similar manner, or using autonomous nodes deployed on the bottom of waterbodies with up-looking hydrophones. Nodes are recovered using a remote-controlled release and are manually downloaded weekly to monthly. PIT antennas are relatively power intensive because the antenna both transmits and receives; larger antennas are frequently on hard AC power and have access to internet uplinks for data transmission. Remote PIT sites are usually powered by solar or propane generators, with manual downloading or data transmission via cellular or satellite uplink (e.g., [PERSON] et al. [142]). Mobile tracking for all three tag technologies can be via hand-held or vehicle-mounted unit, some with integrated GPS. Figure 8: Example of passive integrated telemetry in fisheries management. The antenna array generates an electromagnetic field (white lines). A subcutaneous passive integrated transponder (PIT) tag inside the trout re-emits the array’s signal (black lines) as it passes through the field, providing information on PIT tag number, time, and direction of movement. Arrays may be staged at various locations along the stream reach to characterize, for example, the extent of fish passage. Biotelemetry studies generate large data sets, with hundreds to thousands of fish tagged, 100s to 1,000,000s of records per individual fish, and data from 10 to over 100 individual fixed receiver sites. Substantial data processing prior to the interpretation of movement records is required, such as critical quality assurance/quality control (QA/QC) steps including the estimation of detection efficiency for each receiver [132] and protocols for eliminating \"noise\" records. Monitoring can include project-specific receiver arrays or, increasingly, multi-investigator, multi-agency monitoring networks [96] such as the Columbia Basin PIT Tag Information System (PTAGIS; ptagis.org). PTAGIS operates large scale PIT-detection sites, develops software to collect metadata on fishes tagged by regional organizations, and provides a publicly accessible database of PIT tag detections on PTAGIS and other PIT detection sites across a watershed approximately the size of France via a website, which reports detections in real-time. In 2017 alone, 36 organizations contributed records to PTAGIS for over 2 million PIT tagged fish (and a cumulative total of over 45 million tagged since 1987) and recorded 12.2 million detections at 309 antenna sites for 822,000 individual fish [143]. On the data management side, over 422,000 data files were processed in 2017, and nearly 500 researchers/users executed 342,000 database queries returning over 7 billion rows of data [143].
## 3 Wireless Localization Technology
### Range-Free Techniques
#### 3.2.3 Fingerprinting  
Fingerprinting technology is a technique that determines a device's location based on the strength of the received signal and the reference location in a radio map with the most similar signal strength as shown in Figure 8. There is a pre-condition that a radio map must be pre-made. During actual localization, only the process of comparing the received signal and the radio map is performed, thus the location can be estimated relatively quickly. The radio map is a kind of set of on-grid values created in advance by measuring the strength of the signal received from the anchor at several reference locations with known positions. The advantage of this technology, if utilized based on Wi-Fi, is that localization is possible without additional infrastructure or equipment, using only existing commercial devices.
# Research on Slope Stability Prediction Based on MC-BKA-MLP Mixed Model
###### Abstract  
Quantifying slope mechanical parameters as comprehensive indicators is crucial for predicting slope stability. The Mohr-Coulomb (M-C) criterion, a classical method for determining the relevant parameters of rock mass mechanics, effectively reflects the failure characteristics of rock masses in most types of slopes. Based on this, effective stress and shear strength from the M-C criterion are selected as key indicators, and a characteristic dataset is constructed by integrating these with other influencing factors of slope stability. The safety factor, calculated using the Bishop method within the framework of limit equilibrium analysis, serves as the output variable. Subsequently, a novel Black Kite Algorithm (BKA) was developed to enhance the prediction model of a multilevel perceptron neural network. The results demonstrate that the mean square error (RMSE) of the BKA-MLP model is merely 2.41%, significantly lower than that of alternative models. Additionally, the R\({}^{2}\) value reaches approximately 95%, indicating a high level of interpretability. The SHAP-based interpretability analysis of the trained model highlights effective stress, shear strength, and slope angle as the three most sensitive features. Based on these findings, targeted landslide prevention measures were proposed, providing a new approach for slope stability analysis and disaster prevention.  
Keywords:slope stability; M-C criterion; MLP; BKA algorithm; SHAP +
Footnote †: journal: Applied Sciences
# A Simple and Efficient Method for RSS-AOA-Based Localization with Heterogeneous Anchor Nodes
###### Abstract  
Accurate and reliable localization is crucial for various wireless communication applications. A multitude of studies have presented accurate localization methods using hybrid received signal strength (RSS) and angle of arrival (AOA) measurements. However, these studies typically assume identical measurement noise distributions for different anchor nodes, which may not accurately reflect real-world scenarios with varying noise distributions. In this paper, we propose a simple and efficient localization method based on hybrid RSS-AOA measurements that accounts for the varying measurement noises of different anchor nodes. We develop a closed-form estimator for the target location employing the linear-weighted least squares (LWLS) algorithm, where the weight of each LWLS equation is the inverse of its residual variance. Due to the unknown variances of LWLS equation residuals, we employ a two-stage LWLS method for estimation. The proposed method is computationally efficient, adaptable to different types of wireless communication systems and environments, and provides more accurate and reliable localization results compared to existing RSS-AOA localization techniques. Additionally, we derive the Cramer-Rao lower bound (CRLB) for the RSS-AOA signal sequences used in the proposed method. Simulation results demonstrate the superiority of the proposed method.  
localization; received signal strength; angle of arrival; linear-weighted least squares +
Footnote †: journal:  
0000-0001-5880-7880][PERSON]. [PERSON]  
0000-0001-5880-7880][PERSON]  
0000-0001-5880-7880][PERSON]
# Research on a Dynamic Calibration Method for Photogrammetry Based on Rotary Motion
## 4 Experiment and Analysis
### Test Procedure
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Rotate** & & & **Fitting Radius** & **Z to the Deviation** & **Angular Variation** & **Linear Speed** & **Angular Speed** \\
**Speed** & & & **(mm)** & **(mm)** & **(\({}^{\circ}\))** & **(mm/s)** & **(\({}^{\circ}\)/s)** \\ \hline \multirow{4}{*}{0.5} & \multirow{2}{*}{Target point 1} & mean value & 167.970 & 0.000 & 45.837 & 524.420 & 178.883 \\ \cline{2-7}  & & standard deviation & 0.018 & 0.053 & 6.377 & 24.805 & 8.462 \\ \cline{2-7}  & \multirow{2}{*}{Target point 2} & mean value & 73.745 & 0.000 & 45.837 & 230.238 & 178.883 \\ \cline{2-7}  & & standard deviation & 0.017 & 0.038 & 6.378 & 10.881 & 8.455 \\ \hline \multirow{4}{*}{1.0} & \multirow{2}{*}{Target point 1} & mean value & 167.973 & 0.000 & 90.918 & 1053.511 & 359.355 \\ \cline{2-7}  & & standard deviation & 0.019 & 0.056 & 11.766 & 51.128 & 17.438 \\ \cline{2-7}  & \multirow{2}{*}{Target point 2} & mean value & 73.751 & 0.000 & 90.918 & 462.558 & 359.354 \\ \cline{2-7}  & & standard deviation & 0.019 & 0.042 & 11.766 & 22.439 & 17.432 \\ \hline \multirow{4}{*}{2.0} & \multirow{2}{*}{Target point 1} & mean value & 167.978 & 0.000 & 205.670 & 2112.440 & 720.536 \\ \cline{2-7}  & & standard deviation & 0.016 & 0.066 & 29.469 & 127.005 & 43.322 \\ \cline{2-7}  & \multirow{2}{*}{Target point 2} & mean value & 73.749 & 0.000 & 203.754 & 924.852 & 718.518 \\ \cline{2-7}  & & standard deviation & 0.018 & 0.040 & 33.717 & 72.341 & 56.197 \\ \hline \multirow{4}{*}{3.0} & \multirow{2}{*}{Target point 1} & mean value & 167.979 & 0.002 & 299.341 & 3167.066 & 1080.255 \\ \cline{2-7}  & & standard deviation & 0.017 & 0.075 & 49.143 & 163.237 & 55.691 \\ \cline{2-7}  & \multirow{2}{*}{Target point 2} & mean value & 73.749 & 0.001 & 299.341 & 1390.462 & 1080.255 \\ \cline{2-7}  & & standard deviation & 0.017 & 0.047 & 49.142 & 71.676 & 55.691 \\ \hline \multirow{4}{*}{4.0} & \multirow{2}{*}{Target point 1} & mean value & 167.981 & 0.000 & 313.244 & 3990.063 & 1360.949 \\ \cline{2-7}  & & standard deviation & 0.015 & 0.089 & 235.571 & 720.142 & 245.634 \\ \cline{2-7}  & \multirow{2}{*}{Target point 2} & mean value & 73.747 & 0.000 & 308.932 & 1751.876 & 1361.078 \\ \cline{2-7}  & & standard deviation & 0.016 & 0.053 & 241.094 & 403.235 & 313.297 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calculation results at different speeds. is within 8%. However, when the rotational speed is greater than 4.0 r/s, the real-time measurement frame rate of the photogrammetry system is about 4 frames/s, and there is a certain deviation in the interval time between two adjacent frames. Therefore, when the rotational angle of a target and the image acquisition time correspond to each other, errors are prone to occur, resulting in an error in the speed calculation, and the resulting error value is large. It shows that when the real-time measurement frame rate of the photogrammetry system is 4 frames/s, the maximum rotational speed of a target that can be accurately measured cannot exceed 4.0 r/s.
## 2 Integrating Virtual Reality and Augmented Reality into Sensory Food Research
Moreover, combining VR/AR with biometric measurements, such as eye tracking or facial recognition, provides deeper insights into unconscious consumer responses [27]. Integrating VR and AR into sensory food research, particularly wine tasting and analysis, offers innovative opportunities. A key example is the \"E-Flavor\" project, which aimed to enhance sensory flavor analysis using immersive technology. In this project, researchers developed virtual tasting environments using Blender for 3D modeling and Unity to ensure compatibility with the VR headset. These virtual environments were explicitly optimized to simulate real-world sensory evaluations, providing an interactive experience for both consumers and evaluators. User testing confirmed the effectiveness of the VR setup, ensuring smooth interaction with controllers when manipulating tasting elements. The findings highlighted several benefits:  
* Educational and Scientific Value: VR enhances learning by creating engaging, interactive experiences. * Cost Reduction: Virtual environments minimize the need for physical laboratory setups, reducing research costs. * Expanded Research Opportunities: Researchers can conduct sensory studies in controlled virtual settings, improving data collection and analysis [28].
## 2 Physical Layer-Based Full-Duplex Underwater Acoustic Communication Systems
### Challenges Faced by FD Physical Layered UWAC Systems
Furthermore, the increased depth's multipath effect has an influence on the UW communication channel's vertical and horizontal link configuration. Vertical links have little time dispersion, while horizontal links have a lot of multipath spreading [37, 50]. Doppler Spread/Doppler Shift: The Doppler change in frequency is caused by the continuous movement of physical layered dependent UWAC sensors at the receiver and source sides [48, 51]. The effect of Doppler shift can be easily reduced by preserving subcarrier spacing, but this method degrades spectral efficiency as well as data rate, and this area needs to be explored further [48, 52].
# Efficient Task Scheduling Using Constraints Programming for Enhanced Planning and Reliability
## 3 Methodology
### Justification for Selecting Constraints Programming
#### 3.4.2 Deterministic vs. Stochastic Environments  
Many of the alternative approaches, particularly those based on machine learning or reinforcement learning (RL), are designed to handle dynamic or stochastic environments where task requirements may change in real time. For instance, reinforcement learning has shown potential in adapting to unpredictable changes in task availability or resource constraints. However, these methods require extensive training data and computational resources, which may not always be available or practical in MRO settings.  
In contrast, the MRO scheduling problem in this study is relatively deterministic, with the task requirements and constraints known in advance. While unforeseen maintenance events (e.g., unscheduled repairs) can occur, the focus of this paper is on optimizing the scheduling of regular inspections and fault repairs, which follow predictable patterns. Constraint programming excels in such deterministic environments, where it can efficiently model the problem using predefined constraints and deliver optimal schedules without the need for large training datasets or dynamic updates. This deterministic nature makes CP a more appropriate choice for this specific problem.
# Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models
## 3 Statistical Models for Forecasting Traffic  
In this section, we provide a description and formulas for two models that we will use for forecasting mobile network traffic: the SARIMA and Holt-Winters models. Table 7 includes the notations used for the parameters of the SARIMA and Holt-Winters models.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Group** & **Applications** \\ \hline
1. Time series is similar to the total traffic profile & Others, Streaming Applications, Web Applications \\
2. Time series is not similar to the total traffic & DB Transactions, File Systems, File Transfer, Games, Mail, Music Streaming, P2P Applications, Security, Terminals \\
3. Time series is not similar to the total traffic & Instant Messaging Applications, Legacy Protocols, VolP, Network Operation \\ \hline \hline \end{tabular}
\end{table}
Table 6: Groups of applications.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Description** \\ \hline \multicolumn{3}{c}{Time series parameters} \\ \hline \(x_{t}\) & Time series of data \\ \(y_{t}\) & Forecast value of \(x_{t}\) \\ \hline \multicolumn{3}{c}{SARIMA model parameters} \\ \hline \(p\) & Order of the non-seasonal AR part \\ \(d\) & Degree of differencing for the non-seasonal part \\ \(q\) & Order of the non-seasonal MA part \\ \(p\) & Order of the seasonal AR part \\ \(D\) & Degree of differencing for the seasonal part \\ \(Q\) & Order of the seasonal MA part \\ \(m\) & Number of periods in each season \\ \(Lx_{t}=x_{t-1},\;\;L^{i}x_{t}=x_{t-i}\) & Lag operator \\ \(e_{t}\) & Error terms \\ \(\phi_{i}\) & Parameters of the non-seasonal AR part \\ \(\theta_{i}\) & Parameters of the non-seasonal MA part \\ \(\Phi_{i}\) & Parameters of the seasonal AR part \\ \(\Theta_{i}\) & Parameters of the seasonal MA part \\ \(\Theta_{i}\) & Parameters of the seasonal MA part \\ \hline \multicolumn{3}{c}{Holt-Winters model parameters} \\ \hline \(s_{t}\) & Smoothed value of \(x_{t}\) \\ \(b_{t}\) & Estimate of the trend \\ \(c_{t}\) & Seasonal change factor \\ \(a\) & Data smoothing factor \\ \(\beta\) & Trend smoothing factor \\ \(\gamma\) & Seasonal change smoothing factor \\ \hline \hline \end{tabular}
\end{table}
Table 7: Main notation.  
Figure 3: “VoIP” traffic (group of applications No. 3).