esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 4 Discussion
### Alternative Methods of Coherence Estimation  
The method we present here exploits the difference between a boxcar estimation of coherence and a sibling-based estimation. Here we used the RapidSAR algorithm to calculate this sibling-based estimate; however other methods of identifying sibling pixels have been put forward, e.g., SqueeSAR [43] and NL-InSAR [42]. There may be advantages to other methods of sibling calculation that are worth exploring. For example, NL-InSAR calculates siblings from a single SAR image. This would remove the need for a long time-series of pre-seismic imagery, which may not always be available, and would decrease the number of SAR images to be processed and stored. However, RapidSAR has several attributes that make it particularly well suited to our study. First, it is fully automated and its intended purpose of volcano monitoring means that it was designed to process imagery rapidly, making it suitable for our purpose of emergency response. Second, it allows additional SAR scenes to be incorporated as they are acquired. Third, unlike SqueeSAR, RapidSAR does not require sibling pixels to be interconnected. This allows siblings to be more dispersed through the search window, so that siblings of a landslide pixel are less likely to lie within the landslide.
# QoE Optimization in a Live Cellular Network through RLC Parameter Tuning
## 6 Proof of Concept
### Real Network
#### 6.2.1 Experiment Setup  
The service selected for the tests was a file transfer service. To avoid large values of delay and jitter in the results, the FTP server was installed on a computer with a public IP connected to the UMAHetNet network. To facilitate the analysis of the results, the FTP downloads were conducted with 20 MB files instead of 5 MB files as, in the selected scenario, for FTP downloads of 5 MB files, the transmission of the information was so fast that it did not allow the effects of the RLC layer to be observed efficiently. The considered scenario consisted of two LTE cells (a serving cell and an interfering cell), each one with one user. Only the user connected to the serving cell was analyzed. The user connected to the serving cell was a MONROE node [30], which is Linux-based user equipment (UE) that gathers performance indicators. The user connected to the interfering cell was a smartphone. The main configuration parameters of the UMAHetNet are summarized in Table 2.
# Finding the Least Motion-Blurred Image by Reusing Early Features of Object Detection Network
## 2 Materials and Methods
### Motivation and Method for Finding the Least Motion-Blurred Image  
Photogrammetric object reconstruction workflow starts with selecting an appropriate set of images. Images must uniformly cover the space around the object and be of the highest quality. Image sharpness is the most influential factor in image quality. It is very convenient to collect image samples using a smartphone by filming the object. However, taking a video of the object produces oversampling if the camera is moved slowly, or more frames may be distorted by motion blur if the camera is moved faster in a low-lit environment. Some frames may be more affected by motion blur due to an accident shake of a hand or a sudden move. Such frames must be found and removed. Consecutive frames from the video contain nearly duplicate information, but the motion blur distortions may differ. So it would be useful to pick the single highest quality frame from the set of consecutive frames (see Figure 1).  
Various factors/settings such as resolution, bitrate, compression, codec, sensor and optics quality, camera ISO, shutter speed, and shooting style determines the quality of the images taken with the camera. Most of them are fixed during a single video, except for shooting style, which can introduce motion blur distortions. So the motion blur can be singled out as the distortion that affects image quality. In order to pick the image that is of maximum quality, as an alternative objective, we can aim to find an image that is corrupted  
Figure 1: The need for finding the least motion-blurred (highest quality) image from several images during 3D head reconstruction using a photogrammetry approach. Such a selection process reduces data redundancy and removes distorted images; therefore, the reconstruction process becomes faster and the resulting model is more accurate. In self-recorded videos using smartphones, motion blur is the most affecting image quality factor that can be managed after a video is recorded. So the problem of choosing the highest quality image becomes a motion blur evaluation problem.  
The solution to the problem of finding the highest quality image in the set of images is a method that ranks images according to motion blur distortion level and takes the least distorted image.
## 3 Results and Discussions
### Dynamic Mechanical Analysis
#### 3.3.1 Temperature Dependency  
Figure 7 explains the behavior of asphalt binders in unaged and RTFO aged conditions at low temperatures at fixed frequency of 10 rad/s. It is obvious from the figure that BB has the highest stiffness at lower temperatures compared to oil rejuvenated binders. In the unaged condition, fresh oil addition worked better than waste oil in reducing the stiffness of base binder.  
Figure 6: FTIR spectra comparison of RTFO-aged asphalt binders.  
Figure 7: Effect on low temperature stiffness of asphalt binders at 10 rad/s for (**a**) unaged and (**b**) RTFO aged.  
After RTFO aging, a dramatic rise in stiffness of FR was observed compared to waste oil rejuvenated binders. As FR has more unsaturated bonds to react with oxygen in air compared to FO and UFO, the aging effect is much more severe in fresh oil rejuvenated binder.  
Figures 8 and 9 show the effect of waste oil inclusion on high temperature properties of asphalt binder. As per Superpave criteria, unaged binder should have a minimum 1.0 kPa or above value of G*/sin\(\delta\) to prevent rutting failure. It can be clearly observed that in the unaged condition, there was 1 \({}^{\circ}\)C, 2 \({}^{\circ}\)C and 3 \({}^{\circ}\)C decrease in upper PG grade of UFO, FO and FR binder, respectively. The waste oil in UFO and FO had already been exposed to aging during engine operations which resulted in increased viscosity of rejuvenated binder. FR had the lowest viscosity among all binders due to the unaged fresh oil.  
After RTFO aging, stiffness of base binder was increased and there was a 3 \({}^{\circ}\)C rise in upper grade of BB. After rejuvenating with oil, the stiffening effect of RTFO aging was relaxed by about 2 \({}^{\circ}\)C. There was no considerable difference among the performance FR, FO, UFO binders.
## 3 Results  
The proposed method achieves its highest training and validation accuracy at epoch 20, reaching 99.8% and 99.0%, respectively. The corresponding losses for the training and validation sets are 0.006 and 0.02. The VGG16 architecture demonstrated lower performance in terms of training and validation accuracy at epoch 20, achieving rates of 99.4% and 98.2%, respectively. The corresponding losses were recorded as 0.025 and 0.05. In contrast, the ResNet50 network has a training accuracy of 98.0% and a validation accuracy of 96.5%, with corresponding loss values of 0.04 and 0.15. On the other hand, the proposed method has the advantage of requiring the least amount of time per iteration. Upon assessing the loss curve, it becomes apparent that the loss values of PDL exhibit a more rapid fall and tend towards zero in comparison to other networks. The VGG16 model has a higher iteration time compared to the PDL model, with the former taking around twice as long. On the other hand, the ResNet50 model demonstrates the longest training duration among the three models. All three models eventually converge; however, the PDL and VGG16 models have the fastest convergence rates.  
The PDL model demonstrates a classification accuracy of over 98% and an error rate below 2% after five iterations. Both the ResNet50 and VGG16 models need more than 10 iterations. As a result, adversarial pictures only exhibit a minimal level of resilience. Consequently, the PDL and VGG16 models exhibit notable efficacy and robust convergence in the context of Alzheimer's disease identification. Figure 5 presents the accuracy and loss metrics obtained from trained and validated databases over a span of 20 iterations using mixed data sets. Consequently, the PDL exhibits superior efficiency and accuracy in recognizing Alzheimer's disease. The results prove that among the selected methods, the PDL has a notable capacity for generalization in Alzheimer's disease recognition and is well suited for a broader range of diagnostic situations related to Alzheimer's disease.  
Figure 5: The accuracy and loss curves in the training and validation stages for the 3 models; blue lines represent training, and red lines represent validation. The first raw images, from the top, represent the accuracy and loss of Resnet50; the second raw images represent the accuracy and loss of VGG16; and the third raw images represent the accuracy and loss of PDL.
## 2 Theories and Methods
### Coherence Analysis of EEG and SEMG  
Coherence reflects the similarity of the two signal changes, which is defined as the result of normalizing the signals simultaneously recorded by different independent leads in each frequency [21]. It measures the similarity and phase-locked activity between two signals in a certain frequency domain. Coherence analysis can help us understand how the brain controls muscle movement [22]. Coherence is studied to explore whether there is synergy or interference between the brain and muscles in joint motion. [PERSON] et al. demonstrated that the cohesion of hand muscles and contralateral EEG was highest in the vicinity of the C3, C4, P3, and P4 channels of the brain [23], and that human muscles were controlled by the contralateral brain. The coherence coefficient formula is calculated as follows:  
\[R^{2}{}_{\text{sEMG,EEG}}(f)=\frac{|S_{\text{sEMG,EEG}}(f)|^{2}}{|S_{\text{ sEMG}}(f)|\times|S_{\text{EEG}}(f)|} \tag{10}\]  
where \(R^{2}{}_{\text{sEMG,EEG}}(f)\) is the coherence coefficient, and its value is between 0 and 1. A larger value indicates a higher coherence of the two signals. \(S_{\text{sEMG}}(f)\) and \(S_{\text{EEG}}(f)\) are the power spectra of the two signals at frequency \(f\), and \(S_{\text{sEMG,EEG}}(f)\) is the cross power spectrum. The confidence level was set to 95%.  
In the paper, under the condition of three different factors, the data of two leads (C3, P3) were recorded during the whole movement of the elbow joint. Coherence analysis was performed with the sEMG signals of the two major elbow flexion/extension muscles, respectively. First, the line frequency was removed from the EEG data using 96 Hz low pass filtering and 50 Hz notch filtering. Then, surface Laplacian filtering (SLD) spatial filtering was used for the EEG raw data, removing the common noise of all electrodes to improve the signal-to-noise ratio of the EEG data. Through preliminary research, we determined the coherence analysis of the \(\beta\) (13-30 Hz) and low \(\gamma\) (30-45 Hz) rhythms of EEG signals.
## 7 Multi-Objective Optimization in Wireless Sensor Networks: Trends
### Focus of Research with Respect to Optimization Objectives  
Optimization problems relating to wireless sensor networks can be broadly categorized as design optimization, deployment optimization, optimal operation, optimal planning, optimal layout, optimal management and optimal placement. Figure 7 shows distribution of articles corresponding to the aforementioned optimization objectives. Research community is predominantly inclined towards tackling the issues of design, deployment and operation related optimization problems. For example, optimal design of data forwarding protocol has been proposed in [137] to minimize energy consumption, uniform battery power depletion and minimize delay. Sensor network deployment problem has been considered in [206] with the objectives of coverage maximization, satisfaction of detection threshold and energy minimization. A coverage control strategy has been proposed in [140] for solving the conflicting problems of energy consumption, equilibrium energy and network coverage in wireless sensor networks. Less frequently tackled problems are related to planning, layout, management and placement.
## 2 Materials and Methods
### Experimental Storage Design  
For all cultivars analysed, two fruit treatments were taken into consideration: whole fruits (WF) and oven-dried fruits (DF). Regarding the fruit treatments, for the WF, we collected fresh ripe fruit and directly put them into a normal \(-20\)\({}^{\circ}\)C domestic refrigerator (Zoppas, Vittorio Veneto, Italy) and a laboratory \(-80\)\({}^{\circ}\)C refrigerator (HFU B series, Thermo Fisher Scientific, Milan, Italy). For the DF, after the collection of fruit from the field, we put them in a laboratory oven (Pbi international, Milan, Italy) at 65 \({}^{\circ}\)C for 1 week. The loss of water and the complete dryness of fruits was monitored by checking the fruit weight at intervals of 24 h, until no further weight reductions were detected. Then, dried fruits were placed at \(-20\)\({}^{\circ}\)C and \(-80\)\({}^{\circ}\)C, as indicated before for WF.  
Fruits were stored for five different storage times: 0, 1, 3, 5, and 7 months after the harvest day. During each storage time (except at 0, when the fruits were immediately analyzed), fruits of three cultivars were stored at \(-20\)\({}^{\circ}\)C and \(-80\)\({}^{\circ}\)C temperature conditions. For each combination of cultivar/storage time/treatment/storage temperature, three repetitions of 5 fruits were made. A summary of the experimental storage design is described in Table 2.  
For the analyses of fruit nutritional quality, the WF and DF were extracted and analyzed after 1, 3, 5, and 7 months of refrigerated storage; for the \"time 0\", WF were immediately extracted and analyzed after collection, while DF were immediately extracted and analyzed after oven-drying. The analyzed nutritional parameters were anthocyanins, phenolic acids, vitamin C, and folate. All the parameters were analyzed by HPLC-UV-FD.
## Appendix A Proof of Invariance  
The output for every layer in our network,  
\[f_{i}^{\prime}=\max_{j:(i,j)\in E}h_{\theta}(x_{i},x_{j}) \tag{1}\]  
is permutation invariant. In other words, given a set of points \(p_{1},p_{2},\cdots,p_{n}\), after rearranging it into \(p_{1}^{\prime},p_{2}^{\prime},\cdots,p_{n}^{\prime}\), the result would still be the same.  
Permutation invariance is achieved because the max function is symmetric.  
Suppose the points are shifted by adding a constant \(c\) for translation invariance. Then, we compute the edge feature between \(x_{i}\) and \(x_{j}\) with both terms replaced with \(x_{i}+c\) and \(x_{j}+c\), respectively:  
\[e_{n}ij^{\prime} =\text{ReLU}(\theta_{1n}\cdot(x_{j}+c)-(x_{i}+c)+\theta_{2n} \cdot(x_{i}+c)) \tag{2}\] \[=\text{ReLU}(\theta_{1n}\cdot(x_{j}-x_{i}))+\theta_{2n}\cdot(x_{ i}+c) \tag{3}\]  
Therefore, the first term in the equation above has translation invariance with the ReLU function, while the second term does not. Therefore, our model has partial translation invariance. If the model achieves full translation invariance, the trainable function \(\theta_{2n}\) above is zero. However, this means that our model would depend only on the relativepositions of the points represented by the term \(x_{j}-x_{i}\) and ignore the true position of \(x_{i}\). As a result, the model would only process the input of an unordered set of parts of the object, and not its orientation and positions. Since the parameters for \(x_{i}+c\) are small enough, they would not propagate much after layers of computation.
# Line Scan Spatial Speckle Contrast Imaging and Its Application in Blood Flow Imaging
## Materials and Methods
### Data Processing  
As mentioned in Section 2.2, the temporal speckle contrast \(K\) can be calculated as [12].  
\[K_{t}=\frac{\sigma_{t}}{\langle I\rangle}=\frac{\sqrt{\frac{\sum_{i=1}^{N} \left(l_{i}-\langle I\rangle\right)^{2}}{N-1}}}{\langle I\rangle}=\frac{\sqrt {\frac{N\sum_{i=1}^{N}I_{i}^{2}-\left(\sum_{i=1}^{N}I_{i}\right)^{2}}{N(N-1)} }}{\frac{\sum_{i=1}^{N}I_{i}}{N}} \tag{8}\]  
Note that \(i\) refers to the index of raw laser speckle image in the sequence. \(N\) is the total number of speckle images. \(I_{i}\) is the intensity of the captured \(i\)-th speckle image. In this paper, 15 consecutive frames are captured by the camera and can be calculated to the temporal speckle contrast using Equation (8).  
The spatial speckle contrast \(K\) can be computed as [20].  
\[\begin{array}{l}K_{i,j}=\frac{\sigma_{s}}{\langle I\rangle_{s}},\\ \langle I\rangle_{s}=\frac{\sum\limits_{x=i-\left(m-1\right)/2y=j-\left(m-1 \right)/2}\frac{x+\left(m-1\right)/2}{m^{2}}}{\frac{m^{2}}{m^{2}}}\\ \sigma_{s}=\sqrt{\frac{\sum\limits_{x=i-\left(m-1\right)/2y=j-\left(m-1 \right)/2}^{i+\left(m-1\right)/2}\frac{(I_{x,y}-\left(I\right))^{2}}{m^{2}}} }\end{array} \tag{9}\]  
Note that \(K_{i,j}\) is assigned to the central pixel in the coordinates \((i,j)\) and a sliding \(m\times m\) pixel window is used to compute the spatial standard deviation (\(\sigma_{s}\)) and the mean intensity <I>. Moving the \(K_{i,j}\) element along the \(x\) and \(y\) direction can calculate a spatial speckle contrast 2-D map. In this paper, LS-SSCI was computed using a typical square sliding window of \(7\times 7\) pixels followed by averaging 15 time points to compare the LS-TSCI.
## 1 Introduction
### Recent Lava Flow-Forming Eruptions at Etna Volcano  
Mt Etna is one of the most active volcanoes in the world, showing a quasi-continuous activity at the summit craters and frequent events along fissures on its flanks [55; 56]. Both summit and flank eruptions are likely to produce lava flows, and these present a major hazard to communities living and working in close proximity to the volcano [1]. Among the several lava flows produced by Mt Etna since the year 2000, the most damaging ones occurred in 2001 and 2002-2003. Lava flows extensively devastated tourist facilities, which are located at high altitudes on the southern and northern flanks of the volcano, with serious damage to the local economy [57]. The following flank eruptions occurred in 2004, 2006 and 2008-2009 from fissures opened at the eastern base of the Southeast Crater, one of the summit craters of Etna volcano. Fortunately, all these lava flow fields developed in the uninhabited Valle del Bove, a wide depression cutting the eastern flank of Mount Etna. It is worth to note that the 2008-2009 eruption was the longest flank eruption after the large 1991-1993 event and the second longest since the seventeenth century [58].  
After the 2008-2009 flank eruption [59], the volcanic activity of Etna has shown a more explosive behavior, with an increasing number of paroxysmal events at the summit craters [55; 60]. Indeed, 57 eruptive episodes occurred from 2011 to 2018, most of them characterized by the emission of lava fountains, pyroclastic material, and short-lasting lava flows [61]. A 2013 analysis of lava flow hazards and their distribution around the Etna volcano showed them to be far more dangerous than previously expected [62]. Here, we investigate the three most recent eruptive episodes occurring at Etna in February-April 2017, August 2018 and December 2018, which were characterized by lava emission forming extensive lava flow fields (Figure 1).  
Figure 1: Google Earth 3D view (from the South-East) of Etna’s lava flows extracted from PlanetScope images used to test our classifier: February–April 2017 (green), 23 and 27 August 2018 (turquoise and yellow, respectively), and 24 December 2018 (red). “Valle del Leone” is the northeastern part of “Valle del Bove”, a large horseshoe-shaped depression located on the eastern side of the volcano. The active summit craters are also indicated: North-East Crater (NEC), Voragine (VOR), Bocca Nuova (BN), South-East Crater (SEC), and New South-East Crater (NSEC).
## 3 Results and Discussion
### Effect of Fine Fraction Content on Cracking Resistance
#### 3.1.2 Toughness Index (TI)  
The toughness index gives a measure of the ability of the asphalt concrete to resist cracking fracture after reaching maximum resistance, (see Figure 6 below).  
Figure 6: Toughness index versus temperature.  
The toughness index gives a notion of the post-peak behavior of the mixture. At 20 \({}^{\circ}\)C differences in asphalt binder type and fine fraction content are more pronounced than at lower temperatures. Two trends are observed based on the nature of the asphalt binder; PMB 45/80-65 mixtures present higher values due to the greater deformation ability provided by this type of asphalt binder.  
However in terms of gradation, significant variations are also shown. Asphalt concrete manufactured with the lower content of fine aggregates present more ductile breaking behavior.  
The multi-linear relationship between the toughness index and the bitumen penetration at 25 \({}^{\circ}\)C (BG), test temperature (T) and the fine fraction content (F) can be obtained from Equation (3).  
\[TI=-468.297+22.828\times BG+18.829\times T-9.457\times F\] \[R^{2}=0.614 \tag{3}\]  
The statistical results are consistent with the graphical interpretation and a correlation could be established with a goodness of fit (R\({}^{2}\)) of 0.614. As the fine fraction content increases the mixture presents a more brittle cracking response. This is consistent with the tensile stiffness index results.
# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 4 Data Fusion by Platforms
### Space-Borne Platforms
The major limitation of these methods is the availability of poor spectral variability and characterization. To address this problem, the fusion of images is conducted to improve their class variability (Table 2). However, intraclass variability is removed in this process. Another solution is the utilization of soft clustering, which delivers landcover in proportion to every pixel according to the Landsat resolution, resulting in high-quality pixels [162].