esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 4 Results
### Performance Assessment
#### 4.2.1 Statistics
correction is better than the SM correction alone, whereas the performance of the SM-nonlinear and SM-CDF corrections is worse than the SM correction alone. Combining the fitting methods with the SM correction leads to better performance than the fitting methods alone. Of all methods, SMP-Linear performs the best with a median BIAS of 1.69. Figure 4 shows BIAS with all seven correction methods. The BIAS values of GPM are less than 2 mm/day in most areas of Western China, and exceed 4 mm/day in most areas of Eastern China. When SM-based correction is applied, improvements are found in 62% of China including most areas of Southern China, the northeast, and some areas of the northwest. The SM correction effect is the best in southern China, as BIAS of greater than 8 mm/day in most areas is substantially reduced. Applying the linear correction alone to GPM leads to improvement over 72% of the grid cells, which outperforms the SM method alone. However, the GPM-Linear method causes a larger BIAS compared with GPM in southwest China, whereas the SMP-Linear method produces a BIAS similar to GPM in the same region. The improvement from GPM-Linear to SMP-Linear can also be verified based on the spatial extent of improvement (72% versus 74% of grid cells). The nonlinear correction alone does not perform well compared with the SM or linear correction alone, and the correction effect of combining SM with the nonlinear fitting method is better than the nonlinear fitting method alone. The CDF fitting methods (GPM-CDF and SMP-CDF) are the worst, as they degrade the original GPM in most areas. However, SMP-CDF is still better than GPM-CDF, indicating that the SM correction plays an important role in reducing BIAS. Figure 4: BIAS (mean absolute errors) between different products (**a**) GPM, (**b**) SMP, (**c**) GPM-Linear, (**d**) GPM-Nonlinear, (**e**) GPM-CDF, (**f**) SMP-Linear, (**g**) SMP-Nonlinear, (**h**) SMP-CDF and CMPA. The number at the bottom-left corner of each panel denotes the percentage of improved grid cells by the correction method. The analysis period is from May 1 2017 to September 31 2017.
# Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms
## 3 Review of Related Literature
### Discussion  
Many wrapper, filter, and embedded FS were proposed. However, several studies addressed their limitations:  
* The wrapper FS methods are time consuming [21; 22] because the learning steps and the cross-validation are repeated many times.
* The filter FS methods suffer from the multicollinearity; only one feature is randomly selected from a set of highly correlated features. This selection might negatively affect the result, for example, in gene expression [23; 24].
* The embedded FS methods are not well investigated due to their complex structure.
* The hybrid wrapper-filter FS techniques [12; 32] are two-stage approaches and thus require more knowledge in addition to their time-consuming nature.
* The only work presenting the outlier-based FS method [28] used feature ranking and isolation forest. It is a random-based approach that makes the attributes selection and the location of the best split for building the isolation forest unclear [33].
* The combination of wrapper FS with outlier detection [21; 29] is a two-stage approach that works by reducing the features and then removing the outliers. This approach is time consuming due to the limitation of wrapper FS. In addition, the knowledge of wrapper FS and outlier detection are both required.
* Some studies [30; 31; 34] performed the outlier detection using FS methods, which is the opposite of this proposed study.  
To show the significance of this study, the difference between the PPFS methods and the related works is described in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Related Work** & **PPFS** \\ \hline
[28] Outlier-based FS using & Outlier-based FS using PP \\ feature ranking and isolation forest & \\
[28] Outlier-based FS is a random- & based on PSO and GA \\ based approach & optimization methods \\ Filter FS based on specific measures & based on PP that finds interesting \\ (Distance, Correlation,...) & projections by optimizing the PP index \\ Wrapper FS based on AI methods & Based on PP \\ or a combination of methods & \\ Embedded FS based on the Lasso & Based on PP and optimization methods \\ combined with the Linear Regression & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Difference between the existing studies and the proposed approaches.
# An Evaluation of Image Slicing and YOLO Architectures for Object Detection in UAV Images
## 3 Materials and Methods
### Dataset  
Deep learning techniques for analyzing UAV images have received considerable attention in recent years due to their wide-ranging applications in agriculture, forestry, urban planning, and disaster response. Despite the potential benefits, several challenges must be addressed to obtain accurate and reliable results. One of the biggest challenges is the variability in lighting conditions, as images can be captured at different times of the day and in various weather conditions. This variability can affect image quality and make detecting and classifying objects difficult. Furthermore, the large data sizes associated with high-resolution images covering large areas require significant computational resources. Another major challenge is the low signal-to-noise ratio caused by atmospheric conditions and sensor limitations, making it challenging to extract meaningful information from images [22]. Furthermore, the quality of UAV images can vary significantly depending on the type of sensor used and the distance to the target, posing challenges in standardizing data for deep learning applications.  
In this study, a dataset consisting of images obtained from UAVs was created for slice size determination. The dataset was created by taking images from UAVs without any processing application. The newly created dataset consists of 40,615 images and four classes: People, Vehicles, Flying Car Parking Area (UAP), and Flying Car Emergency Landing Area (UAI). The dataset was divided into 20,615 images for training and 20,000 images for testing. The characteristics of the dataset are shown in Table 1.  
Sample images of the dataset are shown in Figure 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Characteristic** & **Description** \\ \hline Number of images & 40,615 \\ Number of objects & More than 300 thousand \\ Image Resolutions & \(2720\times 1530\) \\ Object classes & 4 \\ Image source & Captured by various drone-mounted cameras \\ Annotation method & Bounding boxes and class labels \\ \hline \hline \end{tabular}
\end{table}
Table 1: The key characteristics of the dataset.  
Figure 1: General flow diagram of the proposed approach.
## 4 Conclusions  
This research focuses on the design of a multiband antenna capable of achieving a multi-mode response to meet the requirements of modern-day applications. A compact-sized antenna with a physical size of 15 mm \(\times\) 20 mm, corresponding to an electrical size of 0.09 \(\lambda_{\text{f}}\times 0.12\)\(\lambda_{\text{f}}\), was designed using the common PCB material FR4. Initially, a truncated structure was utilized to achieve a wideband response of 3.85-7.37 GHz. Afterward, the various-shaped stubs were loaded to achieve additional low bands of 1.8 GHz, 2.2 GHz, 2.5 GHz, and 3.5 GHz. The frequency reconfigurability was achieved using two PIN diodes, whereas the independent control of the diodes results in the multi-mode operation of wideband, dual-band, and tri-band modes. The dual-band mode covers the lower frequency band of 3.85-7.37 GHz and 3.85-7.37 GHz along with the wideband achieved using the initial design, whereas the tri-band covers the band spectrums of 3.85-7.37 GHz, 3.85-7.37 GHz, and 3.85-7.37 GHz. These frequency band spectrums cover the globally allocated band spectrum for GSM 1800 MHz, LTE (Band 3, Band 7, and Band 40), LTE-4G (Band 1), 5G-sub-6-GHz (lower and upper bands), WiFi (802.11a, 802.11n, 802.11 ac, and 802.11 ax), and WiFi-6E-band applications. Moreover, the comparison with the results of recently reported studies further highlights the potential of the proposed antenna, which is characterized by having multiple advantages, including its compact size, frequency reconfigurability, and multi-mode operation as well as a strong comparison among the numerical and tested results, indicating its suitability for smart electronics and modern-day devices that operate as multiband antennas.  
Conceptualization, W.A.A. and S.M.A.; methodology, software, E.M.A., W.A.A. and A.A.; validation, formal analysis, S.M.A. and H.G.M.; investigation, resources, data curation, W.A.A. and H.G.M.; writing--original draft preparation, E.M.A., W.A.A., A.A. and S.M.A.; writing--review and editing, W.A.A., S.M.A. and H.G.M.; visualization, supervision, W.A.A. and H.G.M.; project administration, S.M.A.; funding acquisition, W.A.A. and A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 2 Related Works  
Travel time prediction (TTP) has gained significant attention in recent years due to its relevance in a range of applications, including departure time suggestions [13], trip planning [14], and traveler information systems [15]. As urban environments become increasingly complex, the need for accurate and reliable travel time predictions has become paramount. Several methodologies have been developed to address this challenge, with varying degrees of success. These methodologies can be categorized based on their approach to path representation, prediction techniques, and the role of spatiotemporal refinement in preprocessing.
# Development of a Non-Invasive On-Chip Interconnect Health Sensing Method Based on Bit Error Rates
## 4 Experimental Verification
The eye width and eye height indicate the manner in which the digital signal passes in horizontal and vertical views, respectively. In this study, eye size was used for fault diagnosis. Figure 12 shows the eye size and DC resistance with their corresponding times to failure during corrosion testing. The DC resistance remained almost constant over most part of the test, and exhibited an abrupt increase near the time to failure; in this case, the time to failure was 1671 min. In contrast, the eye size gradually decreased during the test duration, and the time to failure, i.e., the time at which a 20% decrease was observed from the initial value by following the DC resistance criterion, was noted as 1680 min. After the time to failure, the eye size began to show a steeper decrease until the end of the test. Table 3 shows a summary of sensing results. Relative accuracy is defined to be the difference in the detected time to failure between BER (eye size) and DC resistance, divided by the detected time to failure using DC resistance. In the case of all the tests, the failure detection based on the eye size was close to the time of failure detected based on the DC resistance measurement; in particular, the sensing time difference was less than 0.6% of the total test time. Other BER features during corrosion testing are shown in Figure 13. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multicolumn{4}{c}{**Separation Index**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**SD**} \\ \cline{2-2} \cline{4-6}  & **Test \#1** & **Test \#2** & & **Test \#3** \\ \hline Eye width & 7.31 & 4.28 & 3.64 & 5.08 & 1.96 \\ Eye height & 7.59 & 4.63 & 3.57 & 5.26 & 2.01 \\ Eye size & 8.19 & 5.63 & 3.78 & 5.87 & 2.21 \\ BER average & 8.18 & 3.88 & 3.25 & 5.12 & 2.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Separation analysis results of the BER features. Figure 12: Eye size, DC resistance, and time to failure based on the feature set (Test #1). Figure 13: Eye width, eye height, BER average during solder joint corrosion (Test #1). Figure 14 shows the evolution of the BER matrix from Test #1. While the eye size gradually decreased until around 1684 min, i.e., the time to failure, it began to lose its shape thereafter due to continued chemical stress. These results indicate that the BER matrix reflects SI, and has the potential to serve as an on-chip interconnect health sensing method in real time without requiring additional sensing devices. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Measurement**} & \multicolumn{3}{c}{**TTF (min)**} \\ \cline{2-4}  & **Test \#1** & **Test \#2** & **Test \#3** \\ \hline DC resistance & 1670.7 & 1668.9 & 2043.2 \\ BER (eye size) & 1680.3 & 1676.5 & 2033.8 \\ Relative accuracy & 0.0057 & 0.0046 & \(-\)0.0046 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Time to failure (TTF) based on the measurement features. DC—direct current. Figure 14: _Cont._
## 3 Proposed Model
### The Effects of the New Parameters on the TYM
#### 3.1.2 The Impact of Humidity on Signal Strength  
To measure the different aspects of humidity, we used: Absolute Humidity (AH), which refers to the total mass of steam from water included in a given air volume; Steam Humidity (SH) is the highest amount of steam from water in the air at a given temperature; Relative Humidity (RH) is the amount of AH that exists in the air and thus refers to the highest amount of SH at the same pressure and temperature. The relative humidity of the SH is 100%. Humidity is considered as another variable that puts an impact on the signal strength. It is obvious that the relationship between both AH and RH humidity affects the signal strength within a given time. The relative humidity increases and drops based on RSSI, and this depends on a positive relationship, while the trend of AH, indicates a negative correlation (Table 3) [7].
# Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection
## 3 Material and Characterization  
### Data Collection and Cleaning  
In this study, the fault data were collected from an AC transmission network located in a coastal populous city in Guangdong Province, China. These faults occurred between 2016 and 2019, and the voltage levels varied from 110 to 500 kV. Fault signals were recorded by digital fault recorders (DFRs) installed on substations. The DFR equipment involves PMUs and computer systems to synchronize, store and display analog data for voltage and current signals. These signals can be remotely accessed through a communication network and provide offline data stored in common format for transient data exchange (COMTRADE). The sampling rate is 5 kHz in the dataset. Environmental information and other associated monitoring data were obtained through the inner maintenance system. A patrol report of manual inspection was attached to each fault, describing the inspection result and labeling its cause. The original dataset comprised 551 samples, and 288 of them remained after cleansing. The distribution of fault-cause categories is shown in Figure 1. Lightning, external force and object contact are the three dominant causes. External force refers to collision or damage due to human activity. Object contact is usually caused by floating objects in the air. These are typical causes in a densely populated city, causing more than 90% of known faults.
## 3 Results and Discussion
### Comparison with Literature Values
Interestingly, our results agree well with the literature values from the same group I [24; 25], but differ from values in  
Figure 7: Comparison of our results with data from the literature, y-axis: percentage difference using present study as a reference. Red up-pointing triangle: data from literature measured in low pressure group, Blue down-pointing triangle: data from literature measured in high pressure group. group II [26; 27; 28; 29; 30] beyond experimental uncertainties. More precisely, for O\({}_{2}\) and air-induced broadenings, the percentage differences plotted in Figure 7 are roughly proportional to the highest pressure adopted in the linear regression analysis. A recent study by [PERSON] et al. [18] revealed a so-called super-Lorentzian line shape for HCl diluted in high density argon gas matrices. Such super-Lorentzian effects cause the Lorentzian widths of Ar-broadened HCl transitions to increase with the distance to line center. The authors explained the origin of this non-conventional line shape at the far wings using classical molecular dynamics simulations (CMDS) based on an ab initio intermolecular potential surface of the HCl-Ar system. Their simulated spectra using CDMS resemble high pressure experimental spectra at the far wings of the spectral lines. It is easily understood that without anticipating the proper line shape model suggested by [PERSON] et al. which involves three Lorentzian function with different time constant and amplitude, one would deduce the broadening coefficients that differ from the true value at such high-pressure regimes. The clear discrepancy between the group I and group II results shown in Figure 7 coincide with the theory of [PERSON] et al. Yet, more experimental and theoretical studies (e.g., ab initio intermolecular potential surfaces of the HCl-O\({}_{2}\) and HCl-N\({}_{2}\) systems) are needed to underpin our observations.
## 2 Overview of Artificial Intelligence and Machine Learning in Road Transport Systems
### Advance Approaches for Incident Detectors of Road Traffic Accidents Using AI and ML
#### 2.5.3 Incident Detector through Deep Spatio-Temporal Representation and Stacked Autoencoder  
The prevention of accidents is essential to traffic safety. Numerous users experience the effects of traffic accidents, including delays, gridlock, air pollution, and other negative outcomes. Long short-term memory (LSTM) and gated recurrent units (GRUs), two cutting-edge deep learning approaches, are used in this investigation to identify traffic incidents [63]. Ref. [64], a basic one-hidden-layer neural network with unsupervised learning is described; using the back-propagation technique is a stacked denoising autoencoder (SDAE). By reducing the degree of distortion in reconstructed samples, an SDAE aims to discover latent patterns from partly damaged data by converting them into a compressed representation. To create the deep representation for the spatio-temporal video volumes STVVs from the previously unseen traffic video, a separately stacked denoising autoencoder (SDAE) trained over STVVs from the previously viewed regular traffic video is employed, one for each representation. Based on the reconstruction error and the likelihood of the deep representations, for which the outlier score is produced using the one-class SVM, the probability of an accident is calculated. The ultimate determination of whether to classify an occurrence as an accident is computed using all of these individual ratings (also known as local scores). Two procedures make up the denoising autoencoder:  
Encoding: The encoder creates a hidden representation by using nonlinear mapping from the partially damaged input.  
Decoding: The decoder is used to turn the hidden representation into a reconstructed representation; in addition, the three categories developed for incident detection are the modeling of patterns of traffic movement, vehicle activity analysis, and automobile interaction modeling.  
Deep learning techniques often succeed brilliantly, which accounts for their exceptional effectiveness in expressing visual data. To identify accidents during this stage, we employ deep learning. We demonstrate an unsupervised deep learning system that can automatically identify distinguishing elements in security footage for accident detection [65]. Ref. [66], it is shown that the stack denoising convolutional autoencoder differs from stack denoising autoencoder in that it uses convolutional connections rather than completely linked connections between layers. In general, SDCAE's prediction impact is unquestionably superior to SDAE's. The primary reason is that the convolutional autoencoder, as opposed to the autoencoder itself, is better able to manage the spatial dependence on traffic flows.
# Calibration of Mean Wind Profiles Using Wind Lidar Measurements
## 4 Results and Discussion  
The results presented in Section 3 highlight that, using a commercial Lidar anemometer with 12 measurement points rather uniformly distributed between 30 m and 200 m (Setup #0 in Table 2), a very large scatter is observed among the pairs of parameters \(z_{0}\) and \(z_{d}\) identified from different measured profiles. Moreover, in cases of sites with a large distance between the elevation \(z_{0}+z_{d}\) and the lowest measurement point, an accurate estimation of the profile parameters cannot be achieved.  
To improve the accuracy in the estimation of \(z_{0}\) and \(z_{d}\), two alternative strategies are investigated. The first is based on a the analysis of different choices of the elevations of the measurement points; if successful, this strategy would enable the use of available Lidars without the need for additional instruments. The second relies on the availability of a further point of measure at a lower elevation than the lowest Lidar point of measure; this would require installation of a sonic or a cup anemometer together with the Lidar.
# Improving Symbolic System-Level Synthesis by Solver Coordination and Domain-Specific Heuristics
## 8 Experimental Results
### Test Instances
The resulting topology of the application graph \(g_{i}^{A}\) is again a TTSP graph since it could also be created by the TTSP definition shown in Section 4.2. Note that the application graph in Figure 4 is one example which could result from the introduced two-step procedure. For the parameters of each randomized application \(A_{i}\), we chose a random period \(P_{i}\) from a set of non-harmonic periods \(P_{i}\in\{1\text{ ms},2\text{ ms},5\text{ ms},10\text{ ms},20\text{ ms},50\text{ ms},100\text{ ms}\}\) common in the automotive domain [31; 39]. The deadline \(D_{i}\) of each application \(A_{i}\) was constrained to its deadline, i.e., \(D_{i}=P_{i}\). The WCET of a computational task \(t\in\mathbf{T}^{t}\) was implicitly defined by randomly selecting a utilization of a computational task \(U_{t}=C_{t}/P_{i}\in\{10\%,15\%,20\%,25\%,\ldots,80\%\}\). Regarding the messages exchanges between tasks, based on the task communication of real-world automotive applications [39], we chose for each message \(m\in\mathbb{T}^{m}\) a random number of packets in \(|\mathbb{P}^{m}|=\{5,6,7,\ldots,15\}\). Since packets are the entities that are scheduled on the resource of the platforms NoC, the number of packets relating to a routed message influence considerably the number of constraints in the time-triggered scheduling problem in system synthesis (cf. Section 6). Table 1 summarizes the average values of key characteristics of each of our testcase instance sets. We label the sets of instances with \(N\times N\)-\(U_{fact}\) using the dimension \(N\) of the 2D mesh of the NoC and the total computational utilization factor \(U_{fact}\). Each set of instances consists of 25 randomly generated instances that where generated as described above. Table 1 reports the average total number of applications, the average total number of computational tasks, average total number of messages and average total number of packets of a test instance in the associated instance set along with the standard deviation. With increasing \(N\) and \(U_{fact}\), we assume that the problem of synthesis becomes harder to solve as more tasks have to be bound, more messages to be routed on a larger NoC and overall more entities have to be scheduled. Note that for all the instances there exists at least one feasible synthesis. This has been checked independent of the results presented in the next section.