esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Compression Properties of EMWM  
Figure 21 illustrates the compression experiment of EMWM in the direction of molding pressure, which can be roughly divided into three stages according to the compression curve: linear stage (stage I), soft characteristic stage (stage II), and exponential hardening stage (stage III). In the linear stage, elastic deformation occurs and the specimens exhibit certain elastic properties; this stage is very short compared to the other stages. In the soft characteristic stage, the internal wire structure begins to slip and rearrange itself, and the compression load rises slowly. As the deformation increases, the compression load rises sharply in the exponential hardening stage [1].  
Choosing the loss factor as the evaluation index of compression test is more suitable for practical applications. For the calculation of the loss factor, since the acquired experimental data are discretized with time, the numerical integration of the experimental data can be performed by Matlab 2019b, which can obtain the area of its curve envelope. From Figure 22, it can be seen that the hysteresis curve shows a curved moon shape. The partial loading and unloading curves almost coincide before the soft characteristic deformation stage. This reflects that there is almost no energy dissipation in this stage, which indicates  
Figure 21: Compression experiment of EMWM in the direction of molding pressure.  
Figure 20: Loss factor and stiffness curves with different loading displacements in shear tests.  
that when the deformation reaches a certain range, the specimen undergoes obvious energy dissipation, and the larger the deformation, the larger the dissipation [1].  
Tables 11 and 12 demonstrate the results of the range analysis of the compression orthogonal experiments. Thickness has the greatest influence on the loss factor, followed by filament diameter, and density has a relatively small influence factor. The main reason is that as the pressure increases, sliding friction begins to occur between the wire turns. The greater the thickness of the sample, the greater the number of small curved beams where sliding friction occurs. By comparing the \(K\)-values, it can be concluded that EMWM-3 (molding density 2.0 \(\times\) 10\({}^{3}\) kg/m\({}^{3}\), thickness 6 mm, filament diameter 0.2 mm) has the largest loss factor.
# Shaping 1,2,4-Triazolium Fluorinated Ionic Liquid Crystals
## 2 Materials and Methods
### Synthesis of Triazole Precursors  
Triazole **5** (0.40 g, 0.72 mmol) was dissolved in acetonitrile (5 mL) in a sealed tube. K\({}_{2}\)CO\({}_{3}\) (6 eq., 0.6 g) was then added, and the mixture was stirred at 80 \({}^{\circ}\)C for 2 h. Afterwards, 3 eq. (2.16 mmol) of the corresponding iodo- or bromo-alkane (1-iododecane, 1-iododecane, 1-bromotetadecane, 1-bromohexadecane) were added to the mixture. The obtained mixture was heated at 80 \({}^{\circ}\)C for 1.5 to 6 h in dependence of iodo- or bromo-alkane. The reactions were monitored by TLC (petroleum ether and ethyl acetate 5:1). After cooling, the mixture was evaporated, water was added (50 mL), and the organic compounds were extracted with ethyl acetate (3 \(\times\) 30 mL). The organic phase was dried with Na\({}_{2}\)SO\({}_{4}\), and the neutral triazoles were isolated by chromatography, using petroleum ether/ethyl acetate 20:1 and 5:1 as eluents.  
**5-(3,5-bis(decyloxy)phenyl)-1-methyl-3-(perfluoroheptyl)-1H-1,2,4-triazoleol-Ph-Tri-(10):** white solid. Yieldt = 78% \({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\)) \(\delta\), ppm: 0.88 (t, J = 8 Hz, 6H), 1.28 (m, 24H), 1.45 (q, J = 8 Hz, 4H), 1.79 (q, J = 4 Hz, 4H), 3.98 (t, J = 8 Hz, 4H), 4.07 (s, 3H), 6.60 (t, 1H), 6.76 (d, 2H). ESI-MS analysis for [C\({}_{36}\)H\({}_{48}\)F\({}_{15}\)N\({}_{3}\)O\({}_{2}\) + H\({}^{+}\)]: Calc.: 840.3580 g mol\({}^{-1}\), exp.: 840.3617 g mol\({}^{-1}\).  
**5-(3,5-bis(dodecyloxy)phenyl)-1-methyl-3-(perfluoroheptyl)-1H-1,2,4-triazoleol-Ph-Tri-(12):** white solid. Yieldt = 75% \({}^{1}\)H NMR (300 MHz, CDCl\({}_{3}\)) \(\delta\), ppm: 0.88 (t, J = 6 Hz, 6H), 1.26 (m, 32H), 1.45 (m, 4H), 1.78 (m, 4H), 3.98 (t, J = 6 Hz, 4H), 4.07 (s, 3H), 6.59 (t, 1H), 6.76 (d, 2H). ESI-MS analysis for [C\({}_{40}\)H\({}_{56}\)F\({}_{15}\)N\({}_{3}\)O\({}_{2}\) + H\({}^{+}\)]: Calc.: 896.4206 g mol\({}^{-1}\), exp.: 896.4234 g mol\({}^{-1}\).  
**5-(3,5-bis(tetradecyloxy)phenyl)-1-methyl-3-(perfluoroheptyl)-1H-1,2,4-triazoleole-Ph-Tri-(14)2:** white solid. Yield = 82% \({}^{1}\)H NMR (300 MHz, CDCl\({}_{3}\)) 8, ppm: 0.87 (t, J = 6 Hz, 6H), 1.26 (m, 40H), 1.45 (m, 4H), 1.78 (m, 4H), 3,98 (t, J = 6 Hz, 4H), 4.07 (s, 3H), 6.59 (t, 1H), 6.76 (d, 2H). ESI-MS analysis for [C\({}_{44}\)H\({}_{64}\)F\({}_{15}\)N\({}_{3}\)O\({}_{2}\) + H\({}^{+}\)]: Calc.: 952.4832 g mol\({}^{-1}\), exp.: 952.4947 g mol\({}^{-1}\).  
**5-(3,5-bis(hexadecyloxy)phenyl)-1-methyl-3-(perfluoroheptyl)-1H-1,2,4-triazoleole-Ph-Tri-(16)2:** white solid. Yield = 82% \({}^{1}\)H NMR (300 MHz, CDCl\({}_{3}\)) 8, ppm: 0.87 (t, J = 6 Hz, 6H), 1.25 (m, 48H), 1.45 (m, 4H), 1.78 (m, 4H), 3,98 (t, J = 6 Hz, 4H), 4.07 (s, 3H), 6.60 (t, 1H), 6.76 (d, 2H). ESI-MS analysis for [C\({}_{48}\)H\({}_{72}\)F\({}_{15}\)N\({}_{3}\)O\({}_{2}\) + H\({}^{+}\)]: Calc.: 1008.5458 g mol\({}^{-1}\), exp.: 1008.5497 g mol\({}^{-1}\).
# Cross-Scene Sign Language Gesture Recognition Based on Frequency-Modulated Continuous Wave Radar
## 4 Experimental Setup and Result Analysis
### The Experimental Analysis
#### 4.2.3 Recognition Accuracy of Different Frequency Bands  
In this part of the experiment, three different experimental scenarios are designed to make comparisons. Through the experiment, the implementation of the algorithm is analyzed. In the crowded experimental environment full of obstacles, the reflected echoes of static objects are mixed with those of target objects. The frequency wireless signal determines its penetration ability and propagation path loss. The classification results are a visual summary through the classification of sample data and the recognition of three experimental scenes of the learning model algorithm. To verify that different environments significantly impact cognitive results, we collected the same data. The transmission frequencies of millimeter wave signals in three experimental scenarios are 24 GHz and 77 GHz. In the three environments, all motion data are the same. The experimenter is collected in the frequency bands of 24 GHz and 77 GHz, and the experimental data is put into the learning network model for recognition. The complete recognition results are shown in Figure 14. The experimental results show that the accuracy of gesture recognition is a 77 GHz bandwidth signal, which is higher than that of a 24 GHz bandwidth signal. Three experimental scenarios show that the 77 GHz band is more suitable for small human behavior perception. The unified use of the 77 GHz band signal makes the experimental results reach the best due to the test signal.  
Figure 12: Influence of height on recognition rate.  
Figure 13: Influence of packet rate on recognition rate.
# Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera
## 3 System Configuration
### Thermal Image Processing  
The proposed system uses the thermal image processing algorithm that was proposed in our previous study [18]. The overall block diagram for thermal image processing is visualized in Figure 8. This step is responsible for detecting faulty PV modules using image processing. Let \(I_{RGB}\) and \(I_{th}\) denote the images captured by the RGB and the thermal camera, respectively. Each step of the image processing algorithm is summarized below:
## 5 Durability Tests (Wetting and Drying)
### Standard Durability Test [42]  
For durability tests, silty sand soil samples stabilized with 10% CACW and 2% cement were created. The mold was 4 in. (101.6 mm) in diameter and 4.6 in. (116.8 mm) in height for the soil samples. Each specimen underwent six stages of compression to achieve its modified Proctor's maximum dry density. To attain the modified Proctor's maximum dry density, fewer blows were required. 39 strikes were determined to be the appropriate number after numerous tests were conducted for each layer. All samples were extruded from the molds after compaction. For each blend, four samples were created. These samples were chosen to indicate weight loss in two cases and volume change in two additional cases. The volume change samples' height and diameter were noted. At 23 \({}^{\circ}\)C and 100% relative humidity in the lab, all samples were cured for seven days. The samples were moved to an oven and held there for 42 h at 71 \({}^{\circ}\)C after spending five hours at room temperature in a water tank. For stabilized soils, this procedure equals one cycle of wetting and drying. At the end of this cycle, the specimens designated for volume change were weighed and dimensioned using a vernier caliper. The other two specimens were brushed with a standard brush with two strokes and a force of about 3 lb (1.36 kg). To apply the 3 lb. (1.36 kg) force, each sample was balanced on a balance, and it was brushed while being observed on the balance scale. Weighing the samples both before and after brushing was performed. The remaining 11 cycles were measured similarly, subjecting each specimen to 12 cycles in accordance with [42]. At the end of each cycle, the respective specimens' weight reduction and volume change were noted. After 12 cycles, the samples were dried at 110 \({}^{\circ}\)C to a constant weight. The two equations below [42] were used to compute the volume decrease and weight reduction as a result:  
(1) Change of Volume (\(V_{c}\)):  
\[Vc\ (\%)=\left[\frac{Vi-Vf}{Vi}\right]\times 100 \tag{8}\]  
(2) Weight loss (\(W_{l}\)):  
\[WI(\%)=\left[\frac{Wi-Wf}{Wi}\right]\times 100 \tag{9}\]  
In which, \(Vc\) is the change in sample volume after \(n\) cycles; \(Vi\) = the first volume of the sample (cm\({}^{3}\)); \(Vf\) = the last volume of the sample (cm\({}^{3}\)), whereas \(W_{l}\) = the loss of weight of the sample after \(n\) cycles; \(Wi\) = initially computed oven-dry weight and \(W\!f\) = last adjusted oven-dry weight.  
It is also significant to note that the oven-dry weight was altered, and this weight may now be determined using the following equation [42]:  
\[\text{Oven-dry weight adjusted = C/D \times 100}\]  
In which C is the weight after drying in an oven at 230 \({}^{\circ}\)F (110 \({}^{\circ}\)C), D is the percent of water left on the sample plus 100.
# A Methodology for Exploiting Smart Prosumers' Flexibility in a Bottom-Up Aggregation Process
## 4 Case Studies  
The algorithm for the day-ahead management phase was applied to two different case studies. Both cases refer to a residential prosumer with the same controllable devices, with the difference that in the second case, a photovoltaic (PV) with storage system was present. According to the Italian Standard CE1 0-21 [26], the first case is referred to as \"passive end-user\" because the prosumer has neither a generator nor a storage system, while the second case is referred to as \"active end-user\". The power profile of the prosumer (excluded the PV contribution) is forecasted applying a Monte Carlo approach [27; 28] and is shown in Figure 5. The flexible devices are:  
* An electric storage water heater (boiler, maximum power 1.5 kW);
* A washing machine (WM, maximum power 2 kW);
* A dishwasher (DW, maximum power 2 kW);
* A fridge (maximum power 0.3 kW).  
Figure 5: Residential user’s power profile.  
The contribution of each controllable device to the forecasted daily load profile in Figure 5 is shown in Figure 6. The figure gives a clear representation of the time interval where the flexible devices are present and can be controlled.
## 6 Conclusions  
In this study, we proposed a machine learning-based method for MODIS ET downscaling with Landsat 8 data. Eleven indicators, including albedo, LST, and VIs such as NDVI, EVI, SAVI, MSAVI, NDMI, D1609, NDIIb7 and TVDI, derived from Landsat 8 data, were used as predictor variables and MODIS 8-day 1 km ET product (MOD16A2) as a response variable to build machine learning models. Among the three machine learning algorithms including SVR, Cubist, and RF examined, RF and Cubist resulted in higher model accuracies. rRMSEs from RF models were less than 20% for the four study sites in the US and South Korea, which were within the error range of the MODIS ET product (rRMSE around 25%). The variable importance analysis showed that vegetation growth-related indicators such as NDVI, EVI, and SAVI were most important in 8-day ET modeling by RF, while vegetation moisture-based indicators such as NDIIb7 and NDWI had slightly lower importance. LST-related indicators representing temperature at the time of image acquisition were less important than either vegetation greenness or wetness indicators. The predicted Landsat downscaled ET had an overall good agreement with MODIS ET, as the average rRMSE was around 22%. The downscaled ET product showed a similar temporal trend as MODIS ET. In addition, the product demonstrated higher spatial details; especially in crop areas, the downscaled product could represent ET variation at patch scale. Neither MODIS ET nor Landsat downscaled ET product yielded a very good agreement with _in situ_ ET probably because of land cover heterogeneity around flux tower sites, however, the downscaled ET estimates were closer to _in situ_ observations (R\({}^{2}\) = 0.76 and 0.77, RMSE = 11.8 and 12.5 mm/8 days for Site 1 and Site 2, respectively). The results showed the potential of using machine learning approaches for ET downscaling considering their effectiveness and ease of implementation. Our next study will explore spatial-temporal fusion methods that combine the advantages of high spatial resolution of Landsat data and high temporal resolution of MODIS ET in order to generate 8-day ET at 30 m resolution.  
**Acknowledgments:** This research was supported by the Space Technology Development Program and Technology Development Program to Solve Climate Changes through the National Foundation of Korea (NRF) funded by the Ministry of Science, ICT, & Future Planning of Korea (Grant: NRF-2013M1A3A3A02042391, NRF-2014M1A3A3A03034799, and NRF-2012M1A2A2671851). The research was also supported by National Natural Science Foundation of China (Grant: 41401493; 41130744) and 2015 Beijing Nova Program (ox2015B060).  
**Author Contributions:** [PERSON] led manuscript writing and contributed to the data analysis and research design. [PERSON] supervised this study, contributed to the research design and manuscript writing and served as the corresponding author. [PERSON] contributed to the data analysis and discussion of the results. [PERSON] contributed to the discussion of the results and manuscript writing.  
**Conflicts of Interest:** The authors declare that there are no conflicts of interest.
# EpiInfer: A Non-Markovian Method and System to Forecast Infection Rates in Epidemics
## 6 Experiments with BBC Data on the Flu  
The dataset that we used for this experiment is the BBC Pandemic dataset [29], where people from various locations and age groups in the United Kingdom reported their daily number of contacts. The separation into groups was done in the same way as in the experiment on CoMix/JHU data: people were separated into the following groups based on their daily contacts: 1-5 contacts, 6-10 contacts, 11-15 contacts, 16-20 contacts, 21-50 contacts, 51-100 contacts, and 100+ contacts.  
The time series information for people infected by the flu in the UK was taken from the Our World in Data website ([[https://ourworldindata.org/influenza](https://ourworldindata.org/influenza)]([https://ourworldindata.org/influenza](https://ourworldindata.org/influenza)), accessed on 30 June 2025) which provides weekly infection data. For this experiment, we used 11 data points, representing the weekly number of infected individuals.  
Because EpiInfer works with daily data, we approximated the daily data by dividing the weekly infection values by 7. (A better approach might be to use splines, but we decided to keep the approach as simple as possible.) At that point, we could predict from week \(w\) to week \(w+1\) by taking this generated daily data up to week \(w\) and then estimating the daily infection values seven days after \(w\). Predicting the number of infections for weeks \(w+2\) (14 days out) and \(w+3\) (21 days out) was carried out using the same daily prediction method.  
Table 3 shows the comparison of all the models on this data for predictions 7, 14, and 21 days into the future. EpiInfer performs the best, followed by ARIMA, though with a high \(p\)-value for 7 days.
# A New Fault Diagnosis Method for Unbalanced Data Based on 1 DCNN and L2-SVM
## 4 Experimental Verification
### Case Study 1: Application in CWRU Datasets
#### 4.1.4 Comparison Experiments
The accuracy of BPNN decreases from 90.2% to 86.2%, while SVM decreases from 89.8% to 85.8%, R-CNN from 93.4% to 89.8%, S-CNN from 92.4% to 90.1%, SMOTE-CNN from 94.1% to 92.1%, and CSCNN from 97.5% to 95.2%. In contrast, the accuracy of 1 DCNN-L2 SVM is 97.9%, which is only a 0.6% decrease. Therefore, the proposed 1 DCNN-L2 SVM outperforms several other methods in unbalanced fault classification.
## 6 Evaluation
### Test Environment  
#### 6.1.1 Testbed Specification  
We evaluated our framework on a testbed with two nodes: a source and a sink. The nodes were connected by an InfiniBand interface. Each node was equipped with Intel Xeon E5-2650 v4 CPU with 32 cores and 16 GB of DRAM. The operating system used was Linux kernel v. 3.10.0-1160. Both nodes used the Lustre file system v. 2.13.0 [41] with a single object storage server (OSS) and four object storage targets (OSTs) mounted over a 1 TB drive. The Lustre file system at both endpoints was configured with a stripe count of 4 and a stripe size of 1 MB [42].
# GPU-Enabled Serverless Workflows for Efficient Multimedia Processing
## 3 Architecture of the Serverless Processing Platform
### Functions Definition Language for Serverless Workflows  
To facilitate the creation of data-driven serverless workflows from configuration files, a Functions Definition Language (FDL) has been defined that, in contrast to previous versions of SCAR, supports the definition of multiple functions from a single YAML file. The processing of these files is possible due to the implementation of a completely redesigned parser in the SCAR client.  
This language focuses on the definition of the resources for each function (i.e., container image, script to be executed, memory, CPUs, GPU access, etc.) and allows to set them to use the aforementioned execution modes. This way, a performance preprofiling of the multiple stages of a scientific workflow determines whether a certain function should be executed (i) exclusively in AWS Lambda, because it complies with its computing limitations, (ii) exclusively in AWS Batch, because the application may require additional memory/execution time beyond the maximum available in AWS Lambda or, finally, (iii) using the _lambda-batch_ execution mode to easily accommodate disparate computing requirements.  
In contrast to the classic FaaS platforms, in the FDL a user script has to be defined for each function, containing the commands to process the file that triggers the event. Hence, previously developed multimedia applications are supported without the need to adapt them to the Functions as a Service model.  
The different functions are linked together through Amazon S3 buckets, which can be defined within the input and output variables. This allows data-workflows to be easily created, being the output bucket of one function the input of another, which will result in a new event that will trigger it. As an enhancement, the FaaS Supervisor component has also been improved to handle the new FDL, as well as to filter the output files according to their names and/or extensions in a postprocessing stage. This stage allows the upload of several files to different output buckets, thus allowing to split workflows into different branches, as shown in the use case described in Section 4.  
A detailed example of the FDL shown in Figure 2, together with a test video and deployment manual are available in GitHub (_av-workflow_ example: [[https://github.com/grycap/scar/tree/master/examples/av-workflow](https://github.com/grycap/scar/tree/master/examples/av-workflow)]([https://github.com/grycap/scar/tree/master/examples/av-workflow](https://github.com/grycap/scar/tree/master/examples/av-workflow)) (accessed on 26 November 2020)). This corresponds to the serverless workflow used as a case of study in the following section, for the sake of reproducibility of the results. As can be seen in the _scar-av-workflow-yolov3_ function, enabling GPU-accelerated computing is as simple as setting the enable_gpu variable to true and choosing an instance type that has at least one graphics processing unit. Of course, the application must support the execution on a GPU.
# Cross-Layer Methods for Ad Hoc Networks--Review and Classification
## 3 Cross-Layer Method Publications Review
### One Layer
#### 3.1.2 Channel Layer
When the helper node re-sends a data packet to a recipient, it cannot send its own packets. To solve this problem, the authors suggested using network packet coding. The helper node creates a modified packet based on its own packet and the packet intended for the recipient node. The receiving node, based on a corrupted packet from the sending node and a modified packet from the helper node, recovers both the packet from the sender and the packet from the helper node. As a result, network bandwidth is increased, and latency is reduced.
## 1 Introduction
Afterward, they infer future demand based on the nonlinear pattern recognition and correlation establishment between the predictor variables and the outcomes. This way of learning has attracted many system managers in the field of aviation to harness the uncertainties and to solve future spare parts demand. On the other hand, the limitation to the use of ANN remains, as it is difficult to explore the best forecasting model because of the sensitivity analysis requirement on learning rate, momentum coefficient, and the number of hidden neurons. This work develops an ANN-based network that forecasts the demand for aviation service parts with lumpy patterns. This work is motivated by the fact that ANNs do not require any parametric assumptions about the data. Particularly, it has been demonstrated that the feedforward multilayer perceptrons utilized here are universal approximators; therefore, they should be able to capture the data-generating process of intermittent demand time series. ANNs are adaptive models that do not require human professionals to provide them with predetermined architecture. The proposed ANN technique enables interaction between the quantity of demand and the inter-demand intervals of demand events, or their lags if such interactions can be discovered from the data without requiring expert input. Their versatility makes them naturally beneficial for accommodating the irregular nature of demand. We demonstrate that the proposed method is superior to others in terms of forecasting and inventory performance based on a comprehensive comparison to the other techniques when measured using the MASE metric. The performance evaluation based on the MAE metric is comparable to other approaches. The remaining sections are organized as follows: Section 2 of this paper is a review of the relevant literature. The proposed methodology is comprehensively described in Section 3. Section 4 presents the findings and comparative analysis of the various existing approaches. Section 5 provides a summary of the paper based on our analysis of the results and discusses possible future research directions.