esa-sceva/satcom-chunk-collection huggingface dataset:
# A UAV-Based Visual Inspection Method for Rail Surface Defects
## 2 Methodology  
The inspection method for rail surface defects based on UAV images is proposed in this paper. The UAV-based rail images are captured by UAV equipped with a high-definition camera. The customized image processing methods are applied to analyze and detect these images. The flow diagram of inspection method for rail surface defects based on UAV image in the study is shown in Figure 2. All images used for this paper are captured by a UAV equipped with HD cameras, with the aircraft was flying at an altitude of 30 m above the rail. There are three subjects discussed in this section: (1) the extraction of area of rail tracks; the pseudocode of the tracks extraction is presented in the appendix, (2) UAV-based rail images enhancement based on LWLC algorithm, and (3) the defects segmentation based on the GSME method.
## 5 Experimental Evaluation and Results
### Scalability  
Figure 9 illustrates the scalability of the insertion operation for different hashing schemes with varying numbers of threads. In today's world, with ever-growing amounts of data, the scalability of a system holds paramount importance. Our scheme has demonstrated its scalability across a spectrum of thread counts, consistently outperforming other schemes tailored for the same architecture. As the size of the hashing key increases, ESH exhibits superior scaling compared to the other schemes. Specifically, ESH demonstrates 34% better scalability than CCEH and 12% better scalability than Dash in terms of insertion operations. In the case of search operations, Dash and ESH show near-linear performance, while CCEH lags behind due to the impact of locking and cache-line-level hashing, which is affected by the size of the hash table and requires a larger number of persistent memory writes.  
Although all three schemes employ similar extendible hashing techniques, the differences lie in the design of the lock strips [55], which allows locking to fit into the CPU cache, and the impact of the building blocks of persistent memory on log writing and block flushing [56], which affects write bandwidth. ESH demonstrates higher performance in insertion, search, and delete operations and scales effectively with varying numbers of threads. This is attributed to the modifications made in ESH, such as delaying full-table rehashing by making full use of the allocated hash table. Furthermore, the implementation of optimistic locking at the bucket level allows other threads to access different buckets, reducing the response time for each thread.
## 4 Validation and Discussion
### Construction of the Test Platform
#### 4.1.3 Experimental Design  
In order to verify the accuracy of the DT model and the real-time, stability and parallel operation capability of the DT, the solar car energy consumption as well as the solar panel power supply were tested. The real solar car and the DT have the same operating conditions, and the model parameters are updated by sensors to finally obtain the results of the real solar car operation and the simulation results of the DT. The specific test equipment is shown in Table 3 below, and the physical test platform built is shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Type** & **Range** & **Accuracy** \\ \hline Voltage Sensor & KSD-4U & 0–150 V & \(\pm\)0.1\% \\ Current Sensor & KXK-36 & 0–50 A & \(\pm\)0.1\% \\ Light Intensity Sensor & RS-GZ-V05-2 & 0-65535 Lux & \(\pm\)7\%(25\({}^{\circ}\)C) \\ Temperature Sensor & Pt100 & \(-\)50–200 \({}^{\circ}\)C & \(\pm\)0.35 \({}^{\circ}\)C \\ Tire Pressure Sensor & YB-68 & 0–5.0 Bar & \(\pm\)0.1 Bar \\ Anemometer & PM6252B & 0.40–30.0 m/s & \(\pm\)0.01 m/s \\ Data Acquisition & USB-2000 & \(\pm\)5 V & \(\pm\)0.9 mV \\ Board & WTGAHRS1 & - & \(<\)2 m \\ Accelerometer & WTGAHRS1 & \(\pm\)8 g & \(\pm\)0.01 g \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specifications of test equipment.  
Figure 10: Data transfer test: (**a**) Client: interface for sending data from the upper computer. (**b**) Server: receiving real-time data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Type** & **Range** & **Accuracy** \\ \hline Voltage Sensor & KSD-4U & 0–150 V & \(\pm\)0.1\% \\ Current Sensor & KXK-36 & 0–50 A & \(\pm\)0.1\% \\ Light Intensity Sensor & RS-GZ-V05-2 & 0-65535 Lux & \(\pm\)7\%(25\({}^{\circ}\)C) \\ Temperature Sensor & Pt100 & \(-\)50–200 \({}^{\circ}\)C & \(\pm\)0.35 \({}^{\circ}\)C \\ Tire Pressure Sensor & YB-68 & 0–5.0 Bar & \(\pm\)0.1 Bar \\ Anemometer & PM6252B & 0.40–30.0 m/s & \(\pm\)0.01 m/s \\ Data Acquisition & USB-2000 & \(\pm\)5 V & \(\pm\)0.9 mV \\ Board & WTGAHRS1 & - & \(<\)2 m \\ Greg Sensor & WTGAHRS1 & \(\pm\)8 g & \(\pm\)0.01 g \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specifications of test equipment.  
Figure 9: Socket communication model.  
In order to ensure the smooth conducting of the test, the test site is required to be in good road condition, capable of being driven on for a long time, and to not affect the normal vehicle driving; hence, the 400 m standard athletic track on campus was chosen as the test site. The test was conducted at 40 km/h cruising speed, with a cruising time of 30 min. The conditions on the test day are shown in Table 4, and the formula for calculating energy consumption is shown in Equation (15):  
\[W=\int_{0}^{t}P\cdot dt=\int_{0}^{t}(U\cdot I)\cdot dt \tag{15}\]  
where \(W\) denotes the energy consumed in t time in kWh; \(P\) denotes the solar car output power in \(W\); \(U\) denotes the bus voltage in V; \(I\) denotes the bus current in A; \(t\) denotes the time in hours.
## Material and Methods
### Assessment of Nitrification and Denitrification Performances  
F19 cells were precultured in NM, then incubated at 30 \({}^{\circ}\)C with shaking at 200 rpm for 16 h, and next used as the seed cultures. To evaluate the simultaneous nitrification and denitrification capabilities, 100 mL of the modified medium with ammonium chloride as the sole nitrogen source was inoculated with 1 mL of F19 seed culture and then cultured at 20 \({}^{\circ}\)C with shaking at 200 rpm.  
The influencing factors, including C/N ratios, pH, temperature, and carbon sources, on nitrogen removal from F19 were analyzed with separate experiments. In C/N ratio experiments, the C/N ratios were set as 1, 2, 5, 6, and 10, respectively. To determine the optimal pH, nitrogen removal from F19 was tested in media ranging in pH from 5.0 to 11.0. For the temperature experiments, the culture temperatures were 5, 10, 15, 20, 25, 30, and 35 \({}^{\circ}\)C. Five carbon sources, including sodium acetate, glucose, sodium bicarbonate, trisodium citrate, and ethanol, were used to investigate the nitrogen removal effects.  
For nitrification analyses, 500-mL flasks containing 100 mL of the modified medium, with an initial concentration of 40 mg NH\({}_{4}\)+-N/L used as the sole nitrogen source, were inoculated with 1 mL of F19 seed culture and cultured at 20 \({}^{\circ}\)C with shaking at 200 rpm. For denitrification analyses of strain F19, potassium nitrate (40 mg/L), sodium nitrite (10 mg/L), and hydroxylamine (40 mg/L) were used as the sole nitrogen sources, respectively.  
For the effects of concentration of ammonium, the capability of high-strength ammonium removal by strain F19 was evaluated using the initial ammonium concentrations of 30, 50, 100, 400, 800, 1000, and 2000 mg/L as the sole nitrogen source.  
All of the above experiments were conducted with inoculation of 1 mL of F19 seed culture. Unless otherwise stated, all the experiments were conducted at an initial ammonium concentration of 40 mg/L, C/N of 2, initial pH of 7.0, culturing temperature of 20 \({}^{\circ}\)C, and with the carbon source as glucose. The optical density at 600 nm (OD\({}_{600}\)) was used to monitor cell growth. The concentration of ammonium, nitrite, and nitrate were measured periodically as previously described [24]. All the assays were performed in triplicate.
# Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
## 2 Materials and Methods
### Dimensionality Reduction  
The wavelet scattering method generates a large number of features, so it could be convenient to reduce the number of features because smaller datasets are easier to explore and make analyzing data much easier and faster for machine learning algorithms. In this work, Principal Component Analysis (PCA) was used to reduce the dimensionality by creating linear combinations of the original features and selecting subsets that capture as much of the information as possible [42]. The PCA option of the MATLAB Classification Learner App that allows selecting the number of desired new features was used. The performance of several classifiers with different numbers of PCA obtained features was later assessed.
_Appl. Sci._**2013**, \(3\), 267-277; doi:10.3390/app3010267  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
**Spectral Shifts of Nonadiabatic High-Order Harmonic Generation**  
**[PERSON] and [PERSON] * Departement de Chimie, Universite de Sherbrooke, Quebec J1K 2R1, Canada;**  
**E-Mail: [EMAIL_ADDRESS]**  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-819-821-7098; Fax: +1-819-821-8017.  
_Received: 21 November 2012; in revised form: 25 February 2013 / Accepted: 4 March 2013 / Published: 13 March 2013_  
**Abstract: High-order harmonic generation (HHG) is a nonlinear nonperturbative process in ultrashort intense laser-matter interaction. It is the main source of coherent attosecond (1 as = \(10^{-18}\) s) laser pulses to investigate ultrafast electron dynamics. HHG has become an important table-top source covering a spectral range from infrared to extreme ultraviolet (XUV). One way to extend the cutoff energy of HHG is to increase the intensity of the laser pulses. A consequence of HHG in such intense short laser fields is the characteristic nonadiabatic red and blue shifts of the spectrum, which are reviewed in the present work. An example of this nonperturbative light-matter interaction is presented for the one-electron nonsymmetric molecular ion HeH\({}^{2^{+}}\), as molecular systems allow for the study of the laser-molecule orientation dependence of such new effects including a four-step model of MHOHG (Molecular High-order Harmonic Generation).**  
**Keywords: high-order harmonic generation; blue shift; red shift; attosecond pulses**
# Voting-Based Contour-Aware Framework for Medical Image Segmentation
## 3 Experiment
### Implementation Details  
First, we crop the training data to their non-zero region. After the cropping, each patient scan is subjected to z-score normalization based on the mean and standard deviation of the intensities. We adopt data augmentation including random rotations, scaling, Gaussian blur, brightness, contrast, and mirroring. The five-fold cross-validation method is used to verify the robustness of the model while avoiding selection bias.  
We implemented all models in Pytorch (Torch 1.7.1, torchvision 0.8.2, Meta Platforms, Menlo Park, CA, USA) and trained models on NVIDIA Tesla A100 40 GB GPUs (Nvidia, St. Clara, CA, USA) and NVIDIA Tesla V100 32 GB GPUs. We slightly modified our contour enhancement modules to adopt them on the different backbone networks. For Segnet, we used batchnorm and a rectified linear unit (ReLU) in each contour enhancement module. For nn-UNet, we adopted instance normalization and leakyReLU in each contour enhancement module. We used the grid search method to choose the suitable parameters \(\lambda\) = 20 and \(\gamma\) = 1 in Equation (2). We used a batch size of 8, 1000 epochs, the Stochastic gradient descent optimization algorithm with Nesterov momentum (\(\mu\) = 0.99), and the initial learning rate (\(\alpha_{0}\) = 0.01), decreasing according to poly learning rate policy:  
\[\alpha\ =\alpha_{0}\times(1-\text{epoch}/\text{epoch}_{\text{max}})^{0.9} \tag{10}\]
# A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry
###### Abstract  
This paper presents a history of the development of channel codes in deep-space telemetry and aeronautical mobile telemetry. The history emphasizes \"firsts\" and other remarkable achievements. Because coding was used first in deep-space telemetry, the history begins with the codes used for _Mariner_ and _Pioneer_. History continues with the international standard for concatenated coding developed for the _Voyager_ program and the remarkable role channel coding played in rescuing the nearly-doomed _Galileo_ mission. The history culminates with the adoption of turbo codes and LDPC codes and the programs that relied on them. The history of coding in aeronautical mobile telemetry is characterized by a number of \"near misses\" as channel codes were explored, sometimes tested, and rarely adopted. Aeronautical mobile telemetry is characterized by bandwidth constraints that make use of low-rate codes and their accompanying bandwidth expansion, an unattractive option. The emergence of a family of high-rate LDPC codes coupled with a bandwidth-efficient modulation has nudged the aeronautical mobile telemetry community to adopt the codes in their standards.  
block codes; convolutional codes; turbo codes; LDPC codes; telemetry +
Footnote †: journal: _Entropy_  
1
Footnote 1: email: [EMAIL_ADDRESS]
# Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning
## 5 Case Study
### Sensitivity Analysis  
Table 5 provides a comparison of the total delay time for different adjustment factors (\(p\)) in the two delay scenarios, along with the traditional Q-learning algorithm's reordering of train schedules. Additionally, the average convergence iteration over multiple experiments is considered. It reveals that when the adjustment factor (\(p\)) is set to 0.002, the algorithm effectively identifies the optimal train schedule. As the value of \(p\) increases, the convergence speed of the algorithm also increases. However, it is important to note that excessively large values of \(p\) may cause the algorithm to quickly enter the later stages of greedy selection, potentially resulting in convergence to a local optimum. Consequently, it is crucial to select the adjustment factor appropriately based on the specific circumstances to accelerate the convergence speed of the algorithm without compromising accuracy.  
Compared to the traditional \(\varepsilon\)-greedy strategy of the Q-learning algorithm, the proposed method demonstrates favorable outcomes. The traditional algorithm requires a larger \(\varepsilon\) value during the exploration process, which leads to a slower convergence speed. In contrast, the proposed method achieves good results by selecting a more suitable adjustment factor, enabling a faster convergence while maintaining accuracy.
###### Abstract  
Integration of metamaterial and nonvolatile memory devices with tunable characteristics is an enthusing area of research. Designing a unique nanoscale prototype to achieve a metasurface with reliable resistive switching properties is an elusive goal. We demonstrate a method to exploit the advantages of a phase-change material (PCM) as a metamaterial light absorber and a nanoscale data storage device. We designed and simulated a metamaterial perfect absorber (MPA) that can be reconfigured by adjusting the visible light properties of a chalcogenide-based PCM. The suggested perfect absorber is based on a Ge\({}_{2}\)Sb\({}_{2}\)Te\({}_{5}\) (GST) film, and is tuned between two distinct states by heat treatment. Furthermore, we fabricated and characterized a resistive switching memory (ReRAM) device with the same features. The MPA/ReRAM device with a conventional metal/dielectric/metal structure (Ag/GST/Al\({}_{2}\)O\({}_{3}\)/Pt) consisted of arrays of Ag squares patterned on a GST thin film and an alumina-coated Pt mirror on a glass substrate. Based on the numerical data, amorphous GST showed perfect absorbance in the visible spectrum, whereas, crystalline GST showed broadband perfect absorbance. The fabricated ReRAM device exhibited uniform, bidirectional, and programmable memory characteristics with a high ON/OFF ratio for nonvolatile memory applications. The elucidated origin of the bipolar resistive switching behavior is assigned to the formation and rupture of conductive filaments.  
metamaterial; phase-change material; memory; tunable; perfect absorber; Ge\({}_{2}\)Sb\({}_{2}\)Te\({}_{5}\)
# A Resilient Large-Scale Trajectory Index for Cloud-Based Moving Object Applications
## 5 Experimental Study
### Construction of Indexes  
Table 2 shows the average construction times for indexes on the SF, RioBuses, and UK + IE datasets. We only show the _LPs_ that have been reported during the empirical study, as the other _LPs_ are irrelevant. In other words, RMOI is unlikely to use the unreported _LPs_ on the given datasets and resources. Furthermore, we exclude the construction time of the local index as all of the participant indexes have similar structures.  
Partitioning and distributing the trajectories dominate the construction time. DTR only depends on one dimension in distributing the data, while LSpark uses a hierarchical spatial tree. The partitioning phase in RMOI consists of space-based (space-splitting) and object-based (hashing) partitioning based on the predicted _LP_. With \(\alpha=1\), RMOI only depends on object-based partitioning; when \(\beta=1\), it depends on spatial-splitting partitioning. On SF and RioBuses datasets, RMOI's construction times are decreased with increasing \(\beta\), as space-splitting is more expensive. However, hashing partitioning is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Resource Availability**} & \multicolumn{2}{c}{**LSpark**} & \multicolumn{2}{c}{**DTR**} & \multicolumn{2}{c}{**RMOI**} \\ \cline{3-6}  & & **(Cons. Time)** & **(Cons. Time)** & **Cons. Time** & **Locality Pivot** \\ \hline \multirow{3}{*}{SF} & \multirow{3}{*}{High} & \multirow{3}{*}{67.08} & \multirow{3}{*}{49.05} & 53.29 & \(LP_{3}\) \\  & & & & 52.33 & \(LP_{2}\) \\  & & & & 47.09 & \(LP_{1}\) \\ \cline{2-6}  & Low & 100.17 & 78.09 & 74.60 & \(LP_{1}\) \\ \hline \multirow{3}{*}{RioBuses} & \multirow{3}{*}{High} & \multirow{3}{*}{68.78} & \multirow{3}{*}{40.51} & 53.11 & \(LP_{4}\) \\  & & & & 35.85 & \(LP_{1}\) \\ \cline{2-6}  & Low & 112.11 & 61.35 & 63.59 & \(LP_{2}\) \\  & & & & 58.91 & \(LP_{1}\) \\ \hline \multirow{3}{*}{UK + IE} & \multirow{3}{*}{High} & \multirow{3}{*}{60.92} & \multirow{3}{*}{62.61} & 68.30 & \(LP_{5}\) \\  & & & & 60.92 & \(LP_{3}\) \\ \cline{1-1}  & & & & 56.91 & \(LP_{2}\) \\ \cline{1-1}  & & & & 55.96 & \(LP_{1}\) \\ \cline{1-1} \cline{2-6}  & Low & 106.77 & 101.84 & 101.20 & \(LP_{6}\) \\ \cline{1-1} \cline{2-6}  & & & & 108.44 & \(LP_{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The construction time (in seconds) for LSpark, DTR, and RMOI, where \(LP_{x}\) means \(LP=x\).  
more expensive on UK + IE, because the dataset is sparse and contains more segments with respect to the number of trajectories, as shown in Table 1. Even though RMOI is slightly better than other indexes in some cases, there are no significant differences. However, given the overhead of both the features' computations and the prediction models, RMOI's construction complexity is at a competitive level.
## 1 Introduction
### Tree-Based Ensembles and Other Methods
(2018) optimized a random forest using two novel algorithms (light spectrum optimizer and puma optimizer) and evaluated it on two separate quarry datasets (one from Malaysia and one from Istanbul). Both optimized RF models performed very well (test R\({}^{2}\)\(\approx\) 0.96-0.97 and RMSE on the order of 6-16 m) and significantly outperformed an unoptimized RF. More importantly, when the model trained on one site was validated on a completely different site (an \"engineering case\" not seen in training), it still achieved a respectable R\({}^{2}\) \(\sim\)0.88. This indicates strong generalization capacity, likely due to the robustness conferred by optimization and the use of diverse training data. Moreover, by using SHAP (Shapley Additive Explanations) analysis, [PERSON] et al. (2018) identified the critical factors for each site's model--for instance, hole diameter and maximum charge per delay were most important in the Malaysian quarry, while specific charge and the spacing-to-burden ratio mattered most in the Istanbul site. Such insights are valuable for practitioners to understand regional differences and to focus on controlling the right parameters in each operation.
## 4 Experiments and Results
Based on Stokes-Einstein theory, the diameter of a fluorescent nanoparticle can be estimated using the diffusion constant \(D\), and the closer to the adopted standard diameter of fluorescent nanoparticle the estimated diameter is, the more correct the obtained diffusion constant \(D\) (the acquired MSD result) is [28]; the smaller the offset \(\sigma\) (the MSD error) is, the more correct the acquired MSD result is [24]. Figure 11: Relationships between each case of mean square displacement (MSD) and its corresponding diffusion constant \(D\), for (**a**) M1, (**b**) M2, and (**c**) M3. Figure 10: Slopes of coplanar displacement measurement error of the X-axis (**a**) and Y-axis (**b**) for M1, X-axis (**c**) and Y-axis (**d**) for M2, and X-axis (**e**) and Y-axis (**f**) for M3, respectively. The accuracy of the diffusion constant \(D\) and the size of the offset \(\sigma\) both present how correct the acquired MSD result is. The correctness of the acquired MSD result can be confirmed by considering the overall performance of the diffusion constant \(D\) (accuracy of the estimated diameter) and the offset \(\sigma\), where the influence of the diffusion constant \(D\) has a larger weight than the offset \(\sigma\) for judging the correctness of the acquired MSD result. The correctness of the acquired MSD result testifies to the measurement accuracy of the proposed imaging system. Table 1 lists the \(\sigma\) of each magnification of each of the three diameters of fluorescent nanoparticle calculated using the respective diffusion constant. Evidently, the MSD error and calculated diameter corresponding to M2 were relatively small and exhibited the best agreement with the adopted fluorescent nanoparticles (particularly, for the case of measuring a 50 nm fluorescent nanoparticle). The selection of a magnification not corresponding to the adopted fluorescent nanoparticle will cause an inaccurate measurement; using a suitable magnification is important for fluorescent nanoparticle 3D tracking. The experiment proved the availability of the 3D zoom tracking of the adopted fluorescent nanoparticle with the proposed micro-imaging system.