esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Settlement Caused by Shield Tunnel Excavation
### Stratum Displacement at Different Depths  
To investigate the stratum displacement at different stratum depths affected by tunnel excavation, the settlement curves of the sward midline cross section at the ground surface and 15 m, 30 m and 45 m below the ground surface were calculated by the established model, as shown in Figure 13. It can be concluded that the ground settlement above the tunnel increased with the stratum depth and the maximum stratum settlement occurred above the tunnel axis. For condition 1, the maximum settlement of the ground surface was 2.14 mm and the maximum settlement of the stratum 45 m below the ground surface was 4.30 mm. For these conditions, the settlement curves of the cross section in the shallow stratum were V-type troughs. However, the settlement curves of the stratum near the tunnel top were W-shaped troughs and the maximum settlement occurred above the left and right tunnels.  
With increasing stratum depth, the trends of the settlement curves under the three conditions were similar; that is, the existence of a runway pavement structure had little  
Figure 12: Settlements of characteristic points (**a**) D1 and (**b**) C1.  
Figure 13: Settlement of soil layers at different depths under different conditions (**a**) condition 1, (**b**) condition 2 and (**c**) condition 3.  
influence on the stratum settlement law. Comparing the maximum settlement of stratum at different depths under these conditions, it can be found that the maximum ground surface settlements for conditions 1, 2 and 3 were 2.14 mm, 2.63 mm and 3.13 mm, respectively, and the existence of a runway pavement structure can obviously decrease the ground surface settlement. This is because the settlement of the runway structure with larger rigidity compared with the soil is small and the existence of the runway can greatly limit the deformation of the surrounding soil, thus reducing the ground settlement.  
Comparing the maximum settlement difference of the soil layers at different depths under conditions 1, 2 and 3, the settlement differences between the soil layers at different depths were 0.98, 0.81, 0.60 and 0.39 mm. The maximum settlement difference between conditions gradually decreased with increasing stratum depth, indicating that the influence of the runway layer on the ground settlement gradually decreased with increasing stratum depth. The existence of the runway structure was mainly manifested by the obvious increase in surface stiffness, the settlement of the surface and upper soil was reduced and the settlement difference between different conditions was mainly in the surface and upper soil.
## 2 Materials and Methods
### Assessment of Direct MGO-Trapping Capacity  
The direct MGO-trapping capacity was evaluated using a method adapted from [PERSON] et al. [10]. In this procedure, 100 uL of MGO solution (0.4 mg/mL) was combined with 750 uL of phosphate buffer (0.1 mol/L, pH 7.4), 50 uL of 5-MQ (internal standard, 1 mg/mL), and 100 uL of either buffer (as the blank), carob flour extract solutions (ranging from 1 to 25 mg/mL), or a PM solution (as the positive control with 1 mg/mL). To prepare the sample solutions, each lyophilized extract was dissolved in either water or a 50% methanol solution to obtain final concentrations of 1, 5, 10, and 25 mg/mL. The mixtures were then incubated at 37 \({}^{\circ}\)C for 168 h, with samples collected on intermediate days to monitor trapping capacity over time. After each incubation, a total of 200 uL of OPD (derivatization reagent, 10.8 mg/mL) was added to each sample and control. The mixtures were vortexed for 5 s and allowed to react for 30 min. The remaining MGO concentration was determined by measuring the formation of the derivatized product, 2-methylquinoxaline (2-MQ), in each sample. Quantification was carried out using the previously mentioned Shimadzu HPLC system. Chromatographic separation was performed on a Mediterranean-Sea-ODS2 column (150 mm \(\times\) 3 mm, 5 um; Tecknokroma, Barcelona, Spain). The amount of unreacted MGO was calculated based on the peak area ratio of 2-MQ and 5-MQ. The percentage reduction in MGO was calculated with the following formula:  
\[\begin{split}\text{MGO trapping (\%) = [(MGO in control \text{-- MGO in sample with extract or PM}}\\ \text{solution)/MGO in control] \times 100\%.}\end{split} \tag{1}\]  
Analyses were performed in duplicate. The IC\({}_{50}\) values were calculated from the dose-response curves of each experiment using Microsoft Excel.
## 3 ISP-MCTS Algorithm Design
### The Complete Process of ISP-MCTS  
Through the selection policy and the simulation policy, the root node searches from \(s_{1}\) to \(s_{\text{goal}}\). The algorithm is based on the node average score design selection algorithm and takes the maximum score (T\({}_{\text{min}}\)) for the optimization purpose. Therefore, the pseudo code of the ISP-MCTS for solving HFSSP is described as Algorithm 1.  
```
1:Initialize: C, D, T\({}_{1}\), \(s_{1}\), P, N = 0, \(s_{\text{goal}}\), \(s\) = \(s_{1}\).
2:While the halting criterion is not satisfied do
3:Step 1: Selection and expansion
4:ifN() \(\leq\) P then
5:go to step 2
6:else The improved UCT in Section 3.2.1 is applied to select the scheduling policy \(a\)
7: Update state \(s\)\(\rightarrow\)\(s^{\prime}\)
8:if the state \(s^{\prime}\) is a node in the tree then
9:\(s\) = \(s_{1}\), return Step 1
10:else\(s^{\prime}\) is expanded to a leaf node in the tree
11:endif
12:if The new expanded leaf node \(s\) has one optional policy or no one
13:N() = P + 1, return Step 1
14:else go to step 2
15:endif
16:endif
17:Step 2: Simulation
18: The node \(s^{\prime}\) is taken as the simulation start state.
19: Selecting the simulation policy
20: Simulation to \(s_{\text{goal}}\), the completion time T\({}_{n}\) is obtained, go to Step 3
21:Step 3: Update simulation policy
22:ifT\({}_{n}\) < T\({}_{min}\)then
23: The policy set (A\({}_{1\gamma}\), A\({}_{2\beta}\),..., A\({}_{v\delta}\)) from \(s_{1}\)\(\rightarrow\)\(s_{goal}\) is recorded, T\({}_{min}\) = T\({}_{n}\)
24:go to step 4
25:else go to step 4
26:endif
27:Step 4: Backpropagation
28: The information from the leaf node \(s\) to the root node \(s_{1}\) branch path node is updated
29: according to Equation (11)\(\rightarrow\)(14)
30:if The halting criterion is not satisfied
31: Output the optimal policy set, T\({}_{min}\)
32:else \(s\) = \(s_{1}\), return Step 1
33:endif
34:endwhile
```  
**Algorithm 1** The main procedure of ISP-MCTS
## 6 Experiments and Results  
Each model is evaluated on the same test data sampled from the Waymo Open Dataset. To determine the accuracy of acceleration prediction from the trained model, Mean Absolute Error (MAE) is used as the major metric. The MAE value is calculated by taking the average on all MAE errors of the video clips from the test data. The major advantage of using MAE as metric is that, for most frames, the acceleration values are small as the AV is moving steadily on highways or roads, and the absolute values are usually below one. MAE maintains the difference between the predicted and true values and helps us determine which model performs better. Using other metrics, such as MSE, as the metric, however, may further shrink those extremely small values due to the squared sum, leading to insufficient differences in model comparison. VM instance from Google Cloud Platform is used to train all models mentioned and the VM has one single NVIDIA Tesla K80.
## 5 Results Analysis and Discussion
### Analysis of Inconsistency Between MAPE and RMSE Metrics  
We observed an interesting phenomenon: there are significant differences in the relative performance of models across the two evaluation metrics, MAPE and RMSE, specifically the following:  
(1) LSTM-DNN model: achieves optimal MAPE values in multiple scenarios (e.g., 8.63% for 2 min prediction) but shows higher RMSE values compared to shallow learning models in most scenarios.  
(2) Random forest model: demonstrates higher MAPE values across different prediction scales but achieves optimal RMSE performance in multiple prediction scenarios.  
(3) LASSO regression: exhibits higher MAPE values compared to other models but achieves competitive RMSE performance relative to other linear models.  
This inconsistency primarily stems from the different calculation methods of the two metrics:  
MAPE: calculates the relative error between predicted and actual values, giving equal weight to all data points.  
RMSE: calculates the square root of the mean squared absolute errors between predicted and actual values, assigning higher weights to larger errors.  
In urban road travel time data, there are numerous low values (such as 25-32 s) and a few extremely high values (such as 10 times the normal values). For low-value data points, even small absolute errors can result in large relative errors (affecting MAPE), while for high-value data points, even when the relative error is not large, the square of the absolute error can be substantial (affecting RMSE).  
This explains why random forest performs well on RMSE--it may be more accurate in predicting extreme values, even though it has larger relative errors on regular values; while LSTM-DNN excels on MAPE--it has smaller relative errors in predicting regular values, even though it may have larger absolute errors when predicting extreme values.  
This inconsistency reminds us that when evaluating travel time prediction models, we should consider multiple evaluation metrics simultaneously and select the most appropriate model based on practical application requirements. For example, if the application scenario is more concerned with relative errors (such as providing travel time predictions to users), models with better MAPE performance should be prioritized; if absolute errors are more important (such as traffic management system decisions), models with better RMSE performance should be prioritized.
# Investigating Wearable Fitness Applications: Data Privacy and Digital Forensics Analysis on Android
###### Abstract  
Wearable devices are becoming more and more prevalent in our daily lives as people become more curious about how well they are doing in monitoring, improving, or maintaining their health and fitness. Fitness trackers and smartwatches have become almost ubiquitous, so these devices have begun to play a critical role in forensic investigations. In this paper, the authors conducted a forensic analysis of the controlling applications for three popular fitness bands and smartwatches (i.e., Amazon Halo, Garmin Connect, and Mobvoi) on an Android smartphone device to (1) provide forensic investigators with a road-map of forensically relevant data that are stored within these applications and (2) highlight any privacy concerns that the stored data within these applications may present to the applications' users. Our findings indicate that the three fitness applications store a wealth of user data. In particular, the Amazon Halo app stores daily, weekly, and monthly activity-related data for at least the last 13 days. The user's Tone Analysis results were also recovered. The Garmin Connect application also records detailed user activity information, as it was possible to recover the last 15 days worth of user activity data. The Garmin Connect user's general location was also determined via the application's weather notification feature. Lastly, the Mobvoi application records all data points from the time the device is first used until the last time the device is used. These data points may include heart rates taken every 5 min and step counts. Our findings highlight the possibility of collecting personally identifiable information about users of these devices and apps, including their profile information, habits, location, and state of mind. These findings would be pertinent to forensic investigators in the event that these or similar applications are part of an investigation.  
data privacy; data security; fitness trackers; forensic analysis; IoT forensics; mobile forensics; user privacy; wearable devices +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Data Analysis  
The pre-processing of different measurements was introduced in the previous sections, and data analyses were then performed to address the scientific questions raised previously. For all different measurement data, the SIF data were dominant, and only the time steps of the meteorological data and PAR, SM, and vegetation parameters that corresponded to the SIF data were selected in order to ensure direct comparability. The ratio of F760/PAR (referred to as AF760 toc and AF760 tot hereafter) was also calculated to remove the influence of solar irradiance.  
The temporal resolutions of the measurements are displayed in Table 2. The hourly and daily mean values (from 8:00 a.m. to 5:00 p.m.) of the different parameters were calculated. For the hourly scale, the hourly mean F760 and F760/PAR as well as the meteorological data were calculated along with the PAR. The fPAR derived from the linear regression and  
Figure 2: Linear regressions of the fPARchl on the different VIs computed from SCOPE 2.0 simulations.  
APAR was also averaged hourly to calculate F760 tot. For the daily scale, the paired daily data mentioned above were also calculated depending on the average hourly mean values.  
First, the utility of F760 tot for drought monitoring in comparison with F760 toc, F760/PAR, and VIs (NDVI, NIRv) was evaluated during most of the growing stages. Secondly, to reveal the effect of different parameters on F760 tot under a long-running drought, correlation analyses of F760 tot with PAR and other vegetation parameters such as LAI and Chl were conducted for the four plots. Third, the goal of this part is to discuss the detailed differences in the relationship of the root zone soil moisture (20 cm SM) with F760 tot, AF760, and the VIs. Furthermore, the lag responses of 20 cm SM to F760 tot were also explored with the development of wheat. All correlation analyses were performed based on the Pearson correlation coefficient, and the statistical significance of the Pearson correlation coefficient was evaluated with a two-sided t-test at a confidence level of 95%.
## 4 Influence of Substrate on Scattering Resonances  
In fact, nanostructures can not be applied separately from substrate. For example, many optical elements can be efficiently applied when the electromagnetic field in nanoparticles are strongly coupled to substrate, hoping to absorb as much light as possible, such as photodiodes, solar cells, and so on. So, we place the nanoparticles on substrate to study the scattering characteristics. This interaction with a substrate must affect the electrical resonance, the magnetic resonance of the particles, and the interaction between them.  
Figure 12a shows the scattering spectra of a single nanocylinder with and without substrate. \"N\"-curve indicates scattering without substrate, and \"Y\"-curve indicates scattering with a semi-infinite substrate of silica. It can be seen that the resonant frequency of ED and MD is slightly changed. The bandwidth of total scattering increases due to the presence of substrate. In addition, the electromagnetic field distributions are shown in Figure 12. The electric field in resonant mode is slightly extended into substrate, which result in enhancement of scattering bandwidth.  
Figure 11: Scattering spectrum of three-different-nanocylinders-combination with decreasing spacing from 100 nm in air. The size of two different kinds of cylinders are: 1  
Furthermore, the effect of substrate on scattering resonances to double silicon nanocylinders and trimer nanocylinders is shown in Figure 13. A clear performance is that the bandwidth of scattering resonances increases when the silicon nanostructure is located on the silica semi-infinite substrate. This indicates that the effect of resonant modes can be extended into the substrate. The substrate provides an additional height to the nanoparticles, so that a part of the electromagnetic resonance mode extends into the substrate, and the resonant modes are obviously widened.  
Figure 12: (**a**) Scattering cross-section of a single silicon nanocylinder with a height of 100 nm and a diameter of 100 nm on a semi-infinite substrate of silica. The N-curve describes the scattering spectrum when there is no substrate, and the Y-curve describes the scattering spectrum of the structure on the substrate; (**b–e**) The electromagnetic field of the single-silicon-nanocylinder on a semi-infinite substrate of silica at \(\lambda=438\) nm and \(\lambda=494\) nm; (**b,d**) The electric field distribution of the longitudinal section along the XOZ plane through the center of the nanocylinders; (**c,e**) The magnetic field distribution of the longitudinal section along the XOZ plane through the center of the nanocylinders.  
Figure 13: _Cont._
# Noise Reduction in CWRU Data Using DAE and Classification with ViT
## 1 Introduction
### Research Objective  
There exist several models to classify mechanical equipment failures and several methods to remove noise from data. This study analyzed how a noise removal method based on the denoising autoencoder (DAE) affects the classification of frequency-based data. To identify the frequency component in the data after noise removal, time series data were converted to frequency-based image data via short-time Fourier transform (STFT). While CNNs typically perform the best in image data classification, the vision transformer (ViT) was selected in this study, which has recently been achieving the best performance in image classification. It is anticipated that the Transformer model, originally designed for natural language processing, when utilized within the Vision Transformer (ViT) framework, will demonstrate high performance in processing vibration time series data collected over time. This prediction is based on the model's ability to effectively capture temporal dependencies and complex patterns within sequential data.  
In the preliminary research conducted prior to this study, vibration data from electric motors were utilized to investigate fault diagnosis methodologies. A Denoising Autoencoder (DAE) was applied to the vibration data to remove noise, and failures were diagnosed using a Machine Learning-based One-Class Support Vector Machine (OC-SVM). During the fault diagnosis process, statistical feature extraction techniques were applied to the vibration time-series data. Notably, high diagnostic accuracy exceeding 90% was achieved for features such as the mean and root mean square (RMS), with the exception of certain other features [21].  
This study was conducted in the order shown in Figure 2. To enhance the accuracy of failure classification, the noise in the data from the Case Western Reserve University (CWRU) vibration dataset was removed using the deep learning-based DAE method, and the failure types were classified using the ViT. Then, the images were transformed using the STFT technique, and the ViT models were trained using raw data and preprocessed data for classification. The time series data classification performance of the ViT was evaluated by comparing the classification results with those reported in other studies that used the same dataset. The remainder of this paper is organized as follows. Section 2 describes the techniques used according to the research objectives. Section 3 details the application cases, describes the utilized dataset, presents the results of applying each technique, and compares the final application results with those from other studies. Finally, Section 4 presents the conclusions from this study and the directions for future research.  
Figure 2: Research flowchart.
# Towards the Internet of Smart Trains:
## 6 IoT-Enabled Services: From More Efficient Operations to New Business Models
### Smart Infrastructure  
Infrastructure monitoring (bridges, viaducts, tunnels, rail gaps, frozen soil, leaky feeders) can provide significant benefits in different aspects like efficiency or safety.  
Furthermore, the lack of safety and security monitoring of railway infrastructure increase the risk of train collision, derailment, terrorism and failures in the wagons. For instance, with 35% of train delays still caused by infrastructure or rolling stock failures, this is one obvious area where IIoT could offer vast improvements in performance.
# AR Displays: Next-Generation Technologies to Solve the Vergence-Accommodation Conflict
## 4 Overview of Proposed Next-Generation 3D Display Solutions
### Multifocal Displays  
In contrast to varifocal displays, multifocal displays do not mandatorily require eye tracking and they provide the viewer with true monocular focus cues. In this case multiple focal planes are presentedsimultaneously from the perspective of human perception, thus allowing for the viewer to refocus between available focal planes. In practice, the most widespread approach is to use time-multiplexing for representing series of focal planes, which, in contrast to varifocal designs, considerably decreases the maximum image brightness, as the single focal plane is shown only for a 1/n fraction of the time, where n is the number of focal planes. Though this concept has been one of the most widely studied subjects [13; 14; 15; 16; 17; 18; 19] as means for mitigating VAC in stereoscopic systems, in practice it is quite a challenge to derive a competitive design. One of the most interesting recently-proposed approaches is the utilization of an oscillating varifocal lens with a very-high image refresh rate-capable spatial light modulator. A proof of concept for such a design has been provided by [PERSON], where an ultra-fast digital micromirror device is used as an image source and a varifocal lens is driven continuously to yield 40 focal planes at a 40 Hz refresh rate [19]. One of the key parts of this solution is utilization of separate infrared beam-based focal length tracking, allowing very rapid real-time synchronization between given focal length and activation of the image source. In practice, considering current technological capabilities, to be implemented in a competitive product, this solution most likely would sacrifice most of the focal planes for increased image brightness, full-color capability, and improved overall image refresh rate. Alternatively, the company Avegant is also working on a multifocal approach, which is based on time-multiplexed generation of multiple focal distances. In this case the variation in focal distance is achieved by stacking multiple proprietary electrically-controllable optical path extension elements [20; 21]. By operating them in various combinations, multiple focal lengths can be created.  
The scope of proposed multifocal near-eye display solutions is considerably larger than reflected here; nevertheless, we want to highlight our (LightSpace Technology) multi-focal approach based on volumetric technology, which is described in greater detail in Section 5.