esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Heat-Treatment Remediation on the Mechanical Behavior of Oil-Contaminated Soil
## 3 Results and Discussion  
### Basic Properties of Soil  
To understand the basic physical properties of the original soil, its particle size distribution curve, specific gravity, and Atterberg limit were obtained. The basic test results reveal that the specific gravity of the soil was 2.85, and the Atterberg limit tests indicated that it was non-plastic. Figure 1 shows the particle size distribution of the soil: D10 (effective particle size) = 0.137 mm, D30 = 0.303 mm, and D60 = 1.403 mm. Based on these data, the uniform coefficient Cu was calculated as 10.2 and the curvature coefficient Cz was 0.47. As a result of the above particle size distribution, gradation conditions, and Atterberg limits, the soil was classified as SP, sandy soil with poor gradation, based on the unified soil classification method. The constant head test yielded a permeability coefficient (k) of \(3.47\times 10^{-6}\) m/sec, consistent with soil with low permeability. The direct shear test revealed a soil friction angle (\(\phi\)) of \(42.5^{\circ}\).  
Figure 1 plots the particle size distribution of the soil after heat treatment. The test conditions are a concentration of gasoline of 1000 ppm and a heat-treatment temperature of 320 \({}^{\circ}\)C. Regardless of the conditions of heat treatment, the particle size distribution curve shifted to the right as a result of heat treatment, indicating that the size of the soil particles was reduced. In many processes that involve heat treatment, numerous factors cause changes in the particle size, including thermal stress such as non-uniform thermal expansion, chemical stress such as a chemical reaction on the surface of particles, and static stress and kinetic stress such as the rapid movement of particles [17; 18; 19]. The thermal shock of the combustion process also reduced the particle size [20]. Accordingly, heat treatment tended to shrink soil particles.
## 2 Materials and Methods
### Relevant Features of Avionics and Test Benches
#### 2.1.5 Description of the Functional Test at Airbus in the HighLift Use Case  
The SFCC is embedded in the test bench as the device-under-test (DuT). Its interfaces are directly connected to the test bench. With the automated test execution, the DuT is initialized and brought into the operational state for the test execution. Depending on the test type, the signals within the SuT are either monitored or monitored and manipulated. These types will be illustrated with an example. In this simplified setup, a sensor transmits the temperature of the hydraulic oil to the control computer via the ARINC 429. The purpose is to test if the control computer performs correctly during the operation.  
Figure 5: Format of an ARINC 429 message–derived from [6] (p. 34-4).  
Additionally, it is tested to see how the control computer handles the sensor values if they are not plausible. The temperature values are additionally manipulated. An abstracted structure of the real-time system is shown in Figure 6.  
The example outlined above describes the receiving of an ARINC 429 signal, verification, manipulation and forwarding. The purely physical signals must be transformed into interpretable information for this purpose. To illustrate the transformation, Figure 7 shows a schematic layer representation of a test bench. It includes an indication of the transformation steps of the physical signals into interpretable information and vice versa. The signal processing is described in the following:  
* Signal acquisition begins with the reception of a complete message. The message is interpreted in the I/O card and passed through the PCIe bus to the main processor and the operating system.
* The driver for the PCIe I/O card is integrated within the operating system. The driver provides an interface that is addressed by the application software.
* The application layer transforms the data into a format that the applications can interpret.
* This means that a 32-bit ARINC 429 message becomes information after decoding, according to the configuration (example: temperature of hydraulic oil: 50\({}^{\circ}\)).  
In addition, a set of meta information is transmitted to each value from the I/O card. This includes: the message number, the repetition frequency of the message, the correctness of the message (parity bit check).  
When the subsystem has achieved the necessary certification at the system level, it leaves the responsible department and is integrated with all the subsystems to a complete system--the iron-bird. This is now tested until the certification bodies approve the aircraft for flight testing. The process described is applied in exactly the same way to the design changes [8].  
Figure 6: Real-time circuit extended by the test bench: schematic representation.  
Figure 7: Test bench in the layer representation with data/signal transformation, according to VDI 2882.
# Performance Optimization Design of Diagonal Flow Fan Based on Ensemble of Surrogates Model
## 5 Numerical Simulation Analysis and Experimental Verification
### Experimental Verification  
We determined the optimization of impeller by proofing to verify the performance of the optimized diagonal flow fan impeller. The impeller before and after optimization is shown in Figure 19. According to the regulations and requirements in 'Fans--Performance testing using standardized airways (ISO 5801:2017)' [13], its air performance was tested.  
Figure 17: Velocity distribution of different flow direction sections before and after optimization.  
Figure 18: Pressure distribution of different streamwise sections before and after optimization.  
After the air performance test, the optimized and original fan performance curves were plotted on the same diagram to visually compare their performance changes, as shown in Figure 20.  
Obviously, the optimized fan performance trends are basically consistent with the original. However, after impeller optimization, the fan had a higher total pressure efficiency when the flow rate was large, which is in line with the purpose of multi-objective optimization. When the optimized blade worked under the design condition, the fan air volume increased by 1.7 m\({}^{3}\)/min to 21.12 m\({}^{3}\)/min, and the total pressure efficiency increased by 3.2% to 38.9%. The test results are basically consistent with the optimized prediction results.
## 2 Related Work
### Wavelet Analysis  
Wavelet analysis is the most powerful tool in the field of signal and information processing, which is widely used in signal filtering, image denoising, image fusion, image edge detection and so on [38]. In image processing, wavelet transform is usually used to divide the image into different frequency bands. The image to be analyzed can be observed from multiple scales. In large-scale space, only the general appearance of the image can be observed; in small-scale space, we can observe the details of the image. The multi-scale observation method can extract some features that are not easily observed in a single scale.  
A function \(\psi(t)\) is called a mother wavelet if it has finite energy and \(\psi(\omega)\) satisfy the condition given by Equation (1):  
\[C_{\psi}=\int_{-\infty}^{+\infty}\frac{|\psi(\omega)|^{2}}{|\omega|}d\omega<\infty \tag{1}\]  
where \(\psi(\omega)\) is the Fourier transform of \(\psi(t)\). Then we generate a wavelet-set \(\psi_{a,b}(t)\) from \(\psi(t)\) by dilating and translating Equation (2):  
\[\psi_{a,b}(t)=\frac{1}{\sqrt{|a|}}\psi\bigg{(}\frac{t-b}{a}\bigg{)}\quad a,b\in R,a\
eq 0 \tag{2}\]  
where is the dilating factor and is the translating factor. For the one-dimensional continuous function \(f(t)\), its wavelet transform is as follows:  
\[W_{f}(a,b)=\frac{1}{\sqrt{|a|}}\int_{-\infty}^{+\infty}f(t)\psi\bigg{(}\frac{t -b}{a}\bigg{)}dt \tag{3}\]  
Image is a two-dimensional discrete signal. To transform it, we need to discretize the dilating factor and translating factor, and then extend the one-dimensional wavelet transform to two-dimensional. The multiresolution decomposition of an image is represented by a series of approximations and details in sub-images. Figure 2 illustrates the results of applying one level of wavelet decomposition, where L, H, V and D represent low-frequency coefficient, horizontal high-frequency coefficient, vertical high-frequency coefficient and diagonal high-frequency coefficient. The decomposition results of the real image are shown in Figure 3.  
Figure 3: The decomposition results of the real image. The right side is the decomposed four different frequency submaps.  
Figure 2: One level of wavelet decomposition. L, H, V, D represent low-frequency coefficient, horizontal high-frequency coefficient, vertical high-frequency coefficient and diagonal high-frequency coefficient.
## 3 Results
### Clinical and Radiographic Outcomes Following NSRME and SARME
#### 3.3.8 Buccal Bone Height (BBH) and Dehiscence (DEH)  
In the context of the present review, the BBH and DEH are inverse measurements of one another and were measured similarly. Thus, they were not distinguished but rather discussed interchangeably in the included articles. Seven studies measured the changes in the BBH and DEH after maxillary expansion using CBCT imaging [30; 31; 32; 33; 35; 38; 42]. Four of those studies [30; 31; 32; 33] utilized NSRME procedures while three studies included SARME procedures [35; 38; 42]. The method of measurement varied slightly between studies as some investigators calculated the BBH from the buccal alveolar crest to the buccal cusp tips [30; 33; 38; 42], cemento-enamel junction [31; 32], and root apices [35]. Thus, the high heterogeneity in methodology precluded a direct comparison. Although no clear differences emerged between the effects of SARME and NSRME on the BBH, three studies demonstrated significant decreases in the BBH when tooth-borne devices were used as compared to bone-borne expansion devices [31; 32; 35].
# Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes
## 2 Overview of State-of-the-Art Approaches
### Evaluation Criteria
#### 2.3.4 Dataset  
The evaluation criterion of the dataset will depict how many documents are used to evaluate the approaches from the literature. This will highlight the average number of documents we should pick for our experiments for the evaluation of the proposed approaches.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach** & **No. of Classes** & **Dataset** & **Algorithm/ Methodology** & **Evaluation Parameters** & **Results** \\ \hline
[36] & 11 & 5000 and 10,000 Documents & Binary Relevance, Naive & & \\  & & & Bayes Multi-Nominal, Multi-label kNN & Accuracy & Accuracy (0.88) \\ \hline
[37] & WIPO-alpha (8), Newsgroups (5), OHSUMED (15), ENZYME (236) & Synthetic data, WIPO-alpha, Newsgroups(1000), OHSUMED (54,708), ENZYME (9455) & Hierarchical SVM, Hierarchical Perception & Accuracy, Precision & Accuracy (0.94), Precision (0.89) \\ \hline
[38] & 6 & 45,000 Features & Naive Bayes, Centroid & Accuracy & Accuracy (0.61) \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Approaches** & **Classification Type** & **No. of Classes** & **Dataset** & **Algorithm/ Methodology** & **Evaluation Parameters** & **Results** \\ \hline
[42] & Single-Class & 99 & 2000 Documents & Independent Document Model (IDM) Framework & Precision, Recall, F-Measure & Precision (0.79), F-Measure \\ \hline
[16] & Single-Class & 4 & 400 Documents & Bayesian Network (BN), Naive Bayes (NB), Bayesian Network Learner (BNL) & Accuracy & Accuracy (0.84; NB, 0.83, BNL, 0.76) \\ \hline
[43] & Single-Class & 11 & 30,000 Features & Genetic Programming (GP) & Accuracy & Accuracy (0.61) \\ \hline
[50] & Multi-Class & 11 & J.UCS and ACM & Metadata title, metadata keywords and combined & & Accuracy (0.88) \\ \hline
[58] & Multi-Class & 11 & J.UCS and ACM & Reference section & Accuracy & Accuracy (0.74) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Critical Analysis of Metadata-based Approaches.
## 5 Conclusions  
We have presented a novel feature-to-feature forecasting system for semantic segmentation of future frames. Our system is based on efficient multi-level feature forecasting. We base our work on a feature-to-motion module that forecasts the future flow and uses it to warp past representations into the future. Our method applies this module on the coarsest features to preserve efficiency, and reuses the predicted feature flow to warp the skip connections. The warped skip-connection features are noisy because of the coarse feature flow. To account for that, we train our recognition model with a novel training procedure called MixSkip. MixSkip allows us to train a single ladder-style model that performs comparably to specialized models that train with and without skip connections. Consequently, MixSkip models become more resistant to the noise in skip connections. Our forecasting system achieves competitive accuracy in semantic segmentation forecasting on the Cityscapes dataset. Additionally, we provide extensive validation experiments that reveal the influence of particular skip connections on semantic segmentation accuracy. Experiments also suggest that better skip-connection forecasting may significantly improve the dense semantic forecasting performance. Thus, one direction for future work involves research on efficient methods for skip-connection forecasting. Other possible directions include multi-modal semantic forecasting and applications to different recognition tasks such as instance segmentation or panoptic segmentation.  
Conceptualization, S.S.; methodology, I.S., J.S. and S.S.; software, I.S.; validation, I.S.; investigation, I.S. and J.S.; writing--original draft preparation, I.S.; writing--review and editing, J.S. and S.S.; visualization, J.S. and I.S.; supervision, S.S.; project administration, S.S.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by Rimac Technologies, Croatian Science Foundation under grant IP-2020-02-5851 ADEPT, and the European Regional Development Fund under grant KK.01.1.1.01.0009 DATACROSS.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 4: Two qualitative examples of short-term and two of mid-term forecasting on Cityscapes val. The rows show: the last observed frame, future frame (unobserved), ground truth, and our forecast.
## 3 Case Study
### Synthetic Sentinel-2 Image Production by Image Fusion  
Sentinel-2 and the MODIS images had six similar spectral bands (bandwidths) but different spatial resolutions, as is listed in Table 1. Before the image blending exercise, all the image bands were resampled to a unified spatial resolution of 20 m and the MODIS images were re-projected to map projection of the Sentinel-2 images. Then, the clear-sky image pairs of the Sentinel-2 and MCD43A4 images were prepared for model calibration and the MODIS images at all other Sentinel-2 acquisition dates were used for prediction of synthetic Sentinel-2 images by PSRFM.
## Materials and Methods  
### Study Sites  
Ground-measured growth and yield and remote sensing data of crop growth were obtained for three separate rice paddy sites: one (field area of \(\sim\)0.4 km\({}^{2}\)) site was located at the experimental fields of Chonnam National University (CNU), Gwangju and the other two sites were commercial farm fields (each field area of \(\sim\)1 km\({}^{2}\) at Buan and Gimje), Chonbuk Province, South Korea (Figure 1). The mean annual air temperature and the annual precipitation averaged over the past three decades (1981-2010) are 13.8 \({}^{\circ}\)C and 1391 mm year\({}^{-1}\) in Gwangju, 13.3 \({}^{\circ}\)C and 1313 mm year\({}^{-1}\) in Gimje, and 12.6 \({}^{\circ}\)C and 1250 mm year\({}^{-1}\) in Buan, respectively (Korea Meteorological Administration) [29]. The East Asian monsoon climate is dominant from June to October in these regions, with more than half of the annual precipitation falling during this period.  
Figure 1: A shape map of South Korea and three study sites at Buan (top left circle) and Gimje (top right circle), Chonbuk and at Chonnam National University (CNU) in Gwangju (bottom circle), respectively (**a**) and a pseudo-coloured RapidEye satellite image (**b**) and a subset imagery (**c**) taken on 13 September 2014. The map (**a**) was produced using ArcGIS (ESRI, Inc., Redlands, CA, USA) and the RapidEye images were reproduced using ENVI (Harris Geospatial Solutions, Inc., Broomfield, CO, USA).
# Experimental Investigation of the BIPV System under Sanluurfa Meteorological Conditions
## 1 Introduction
### Literature Review
[35] produced 63.8 kWh/m\({}^{2}\) of electrical energy in the BIPV model with a bifacial panel, achieving an average efficiency of 6.3% and a performance ratio of 0.7. [PERSON] et al. [36] investigated the nonhomogeneous rear reflection results of different colors in the bifacial panel in their mini module BIPV experiment. They found that the cell with a low reflection ratio heated up, resulting in power loss in the modules. A similar study was conducted by [PERSON] et al. [37] on transparent PV panels in a BIPV system. According to the study, an increase in efficiency was achieved with various background colors (14% blue, 25% red, and 35% white). [PERSON] et al. [38] concluded that the highest production of bifacial panels integrated into the outer shell of a building was on the roof. Additionally, the improvement in electricity production was increased between 4.7% and 18.8% through simulation. Many studies have presented simulation models to calculate the energy efficiencies of BIPV facades [39]. However, in the literature, there has been no comparison between monofacial and bifacial panels under the same conditions. In addition, a reflective surface has not been used to significantly increase the contribution of the rear side of the bifacial panel applied to BIPV. However, in our study, this situation was different for the bifacial and monofacial panels located in the southern part. The connection between the exterior environment and the rear side of the panel was severed using a transparent material between the wall and the panel frame. In the study, a comparison of three facades with panels having the same production potentials both experimentally and numerically under Sanlurfa conditions has also been made.
## Materials and Methods
### Spectral Feature Selection Algorithms
#### 2.3.3 Variable Combination Population Analysis  
Variable combination population analysis (VCPA) is a feature wavelength selection algorithm and its main idea derives from model cluster analysis [32]. VCPA has been applied in spectral data analysis. Firstly, the binary matrix sampling method (BMS) is used to resample the sample space, and further combined with the exponential decay function (EDF) to eliminate variables unrelated to sample characteristics. The VCPA is usually combined with the partial least squares regression (PLSR) algorithm, which uses BMS to sample several sets of variable subsets from the sample variable space and calculates the probability of each wavelength point variable appearing in this iteration. By using the EDF to remove wavelength points with lower frequencies, the variable set space is reduced. The retained variables are repeated with BMS sampling and EDF removal. This process is repeated N times, and the remaining variables with higher frequencies are combined to form the final selected feature wavelength. In this study, the sampling frequency is 1000 and the iteration number is 50; no more than 15 candidate variables are selected during each iteration process, and the first few variables with higher frequencies are ultimately selected as the characteristic wavelength variables.
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 3 System Composition  
Figure 5 shows the main composition of the real-time tunnel deformation monitoring system, and Figure 6 shows the details. The working principle is briefly described as follows.  
Install the system in the tunnel and turn it on. After calibration, adjust the direction of laser to the image acquisition module. The command to measure tunnel deformation is first generated at the industrial computer and sent to the wireless adaptor by a USB port. The communication between the adapter and the image acquisition module is wireless. The wireless adaptor is shown in Figure 7. The image acquisition module receives the command and begins to capture laser spot images and sends them to the industrial computer through the corresponding wireless adaptor in real time. The software installed in the industrial computer receives the images and processes them, obtaining the tunnel accumulative deformation and instantaneous deformation velocity. If the deformation values exceed the safe threshold, another command is generated and sent to the sound and light alarm module and the command center through a wireless adaptor, giving an early warning to workers and managers.  
Figure 4: Sketch map. 1. reference monitoring image acquisition module A; 2. reference monitoring image acquisition module B; 3. laser transmitter module; 4. image acquisition module.
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 3 Background
### Comparison of LoRa and Other LPWAN Technologies
#### 3.3.4 Summary of the Advantages of LoRa  
From the comparisons performed in the previous sections, it is evident that LoRa has many advantages over other LPWN networks. The most significant advantages of LoRa with respect to its deployment in future infrastructure networks are [16]  
* Orthogonal packet transmission using different SFs (from 6 to 12) concurrently, without data collisions or degradation in performance, reducing TOA.
* Long range communication where outdoor coverage can reach several kilometres, while indoor coverage reaches hundreds of meters and coverage in multi-hop scenario approaches tens of kilometres,
* Low cost of network deployment as nodes can reach the sink directly without intermediary routes,
* Adaptive data rates and multichannel modem transceivers for receiving messages from a large number of network nodes,
* Very low energy consumption for a long battery lifetime,
* High penetrating ability of radio signals; and,
* Operation in the unlicensed range.