esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Results  
Based on Sentinel-1 data covering 62 scenes in four periods, along with corresponding accurate orbit and DEM data, the PS-InSAR subsidence monitoring was conducted at the Taibao lead-zinc mine. The obtained time-series subsidence information of the vector points was compared with the mine-level monitoring data to validate the feasibility of PS-InSAR monitoring. The Kriging interpolation method was employed to analyze the vector point data, resulting in surface subsidence images before and after implementing different goaf treatment  
Figure 4: Monitoring accuracy verification.  
Figure 5: Mine surface subsidence map.  
methods (Figure 5). During the untreated period from 2015 to 2018, goaves No. 3 and No. 6 exhibited rapid and relatively concentrated trends in surface subsidence rates, reaching a maximum value of approximately 160 mm/year (Figure 5a). Only goal No. 2 demonstrated a comparatively slower rate of subsidence. After implementing various treatment methods on the goaves from 2018 to 2019, significant deceleration in surface subsidence rates along with a gradual reduction in prominent subsidence areas was observed (Figure 5b), with the maximum recorded-subs residence rate reaching around 100 mm/year. Figure 5c,d depicts that surface subsidence gradually ceased following two years of treatment from 2020 to 2021, with a remaining rate below 20 mm/year, effectively controlling it.  
To investigate the behavior of overburdened rock and surface movement in goat with different treatment methods, we selected subsidence curves at various time intervals to analyze pre- and post-treatment characteristics (Figure 6). The subsidence points on these curves were utilized for analyzing surface subsidence patterns before and after implementing diverse treatment methods. Subsequently, multiple PS monitoring points were chosen based on corresponding positions along the surface, and their average values were calculated to obtain time-subsidence curves for surface monitoring points. Finally, temporal trends in surface subsidence were analyzed.  
Figure 6: Surface Settlement Curves before and after goat treatment and time-settlement curves of monitoring points.
# Behind the Door: Practical Parameterization of Propagation Parameters for IEEE 802.11 ad Use Cases
## 3 Experimental Methodology
The STA was placed in seven different locations, as shown in Figure 2. The antennas' height was set at \(1.25\) m.  
The third and final case measures power losses in an indoor propagation scenario: The access point is positioned in a corridor, and the STA is located behind a door (i.e., we move the STA slightly away from the LOS, as in Figure 2, transitioning from LOS to NLOS). We used six new measurement locations to compute these losses, as described in the following section.
## 3 Results and Discussion
### Strain Field and Failure Mode
Figure 6: Strain field nephograms at the peak stress and fractured images of samples under different drying\(-\)wetting cycles and pH solutions: (**a**) \(n=0\), pH = 7; (**b**) \(n=20\), pH = 7; (**c**) \(n=40\), pH = 7; (**d**) \(n=0\), pH = 5; (**e**) \(n=20\), pH = 5; (**f**) \(n=40\), pH = 5; (**g**) \(n=0\), pH = 3; (**h**) \(n=20\), pH = 3; (**i**) \(n=40\), pH = 3. The failure mode of red sandstone subjected to acidic drying\(-\)wetting cycles can be better investigated by combining the strain field evolution and fractured images. For the untreated samples (\(n=0\)), the failure mode was mainly split failure along the loading direction, namely tensile failure. This is the most common mode in the uniaxial compression tests of rocks [40]. When the number of drying\(-\)wetting cycles reached 20, the samples still presented tensile failure with abundant secondary cracks. The failure mode changed from tensile failure to shear failure or tensile-shear composite failure until 40 drying\(-\)wetting cycles. Unlike the action of drying\(-\)wetting cycles, the effect of acid concentration on the failure mode was not evident. When the samples were dominated by tensile failure, the decrease in the solution pH values led to an increase in the secondary cracks and generated a more complex crack system on the sample surface. Moreover, it contributed to the shear behavior of the samples, which caused the sample subjected to 40 drying\(-\)wetting cycles in the acidic solution with pH = 3 to present a pure shear failure instead of tensile-shear composite failure. Hence, the effect of acid concentration on the failure mode was essentially consistent with that of drying\(-\)wetting cycles, while the latter was dominant.
## 5 Experimental Results and Analysis
### Experiment 3: POM-PSASIDEA Algorithm Performance Analysis
In Table 5 and Figure 11, it can be seen that the POM-PSAIDEA algorithm has higher optimal fitness values \(f_{\text{max}}\) than the other algorithms in different task size instances in the local and global area scenarios, which indicates that the population size adaptive strategy of the POM-PSAIDEA algorithm is effective in the optimization search process and can trade off exploratorily and exploitatively guiding the algorithm to quickly search for the optimal task planning scheme. Meanwhile, the POM-PSAIDEA algorithm outperforms the other algorithms in terms of the average \(f_{avg}\) and worst fitness values \(f_{\text{min}}\), and the superiority of the average fitness value \(f_{avg}\) indicates that its overall population fitness level is improved. Moreover, the superiority of the worst fitness value \(f_{\text{min}}\) indicates that there are fewer poor solutions in the population, and its adaptive adjustment of the population size effectively avoids the problem of falling into the local optimum. The results of Experiment 3 show that the POM-PSAIDEA algorithm exhibits higher optimal fitness values and average fitness values than the other five algorithms in solving the MSIMPLTS problem, and at the same time, it can effectively avoid falling into the local optimum and maintain a high population diversity. Therefore, the POM-PSAIDEA algorithm is superior for solving the multi-satellite imaging mission planning problem for large-scale target missions.
## 5 Experiments
### Localization Using the Compact Maps  
This section evaluates the performance of the compact maps to solve the localization problem. The objective is to achieve a compactness that presents a balance between computing time and accuracy of localization. To carry out the evaluation, among the mapping results, the spectral clustering algorithm is selected with the _gist_ descriptor (\(k_{3}=32\) and \(n_{masks}=16\)). With this configuration, a map per environment is built, using the training images. After that, the test images are used to solve the localization problem. The previous subsection proved that the best option to build the compressed map was through the use of the _gist_ descriptor. Nevertheless, the three proposed global appearance descriptors are proposed again to solve the localization task (because mapping and localization are two independent processes, and the performance of the descriptors could be different in a localization framework). For each test image, its descriptor is calculated (either by FS, HOG, or _gist_), and then, it is compared with the cluster representatives of the compact map. Afterwards, the most similar cluster is retained. Three distance measures are considered for this comparison: (1) the correlation distance, (2) the cosine distance, and (3) the Euclidean distance. In order to carry out a realistic comparison, despite the real position of the robot being provided by the database, only visual information will be used to estimate the position of the robot. The metric information will be used only as ground truth, for comparison purposes.  
Figure 14: Results of the two clustering methods: average moment of inertia, average silhouette of points, and average silhouette of descriptors vs. number of clusters, when using _gist_ in the Saarbrücken environment.
# Probabilistic Resilience Analysis of the Icelandic Power System under Extreme Weather
## 4 Results & Discussion  
This section presents the application of the developed probabilistic resilience assessment methodology on the Icelandic transmission system, facing the storm event of December 2019. More specifically, our analysis covers the period from 00:00, 09/12/2019 to 23:59, 12/12/2019. We used a 5' temporal resolution along with the forecasts for wind speed and load demand available to the system operator on 08/12/2019.
## 3 Hardware Development
### Mcu  
To perform ADC of sensor signals and corresponding calculations to determine the LPG level in the tank, a Microchip PIC12F683 Microcontroller unit (MCU) is used [28]. Processing speed, memory, and cost were taken into account when selecting it.  
The PIC12F683 is a miniature 8-pin 8-bit PIC MCU, it has a total of \(6\) I/O pins with direction control and four channel 10-bit accuracy ADC. Power features: power-saving sleep mode, wide operating voltage range (\(2.0\) V to \(5.5\) V), deep sleep current: \(50\) nA. Operating current: \(1\)\(\upmu\)A to \(20\)\(\upmu\)A. Program memory comes with \(3.5\) KB memory space, and RAM and EEPROM data memories are \(128\) bytes and \(256\) bytes, respectively.  
Figure 6a shows the schematic connection diagram that was designed with the help of the MCU's datasheet. In Figure 6a, the signals labeled SEN_Z and SEN_A are the signals coming from both Hall-effect sensors, and the SEN_EN signal is their reference.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**\%** & \(A_{norm}\) **[V]** & \(Z_{norm}\) **[V]** & \(\theta\) **[Grads]** & \%\({}_{c}\) & **Error** [Grads]** & **\% in App** \\ \hline \(1\) & \(1.0201\) & \(-0.3505\) & \(-19.3463\) & \(0.6666\) & \(0.333\) & \(1.0\) \\ \(5\) & \(0.9354\) & \(-0.5782\) & \(-31.7205\) & \(4.6666\) & \(0.333\) & \(5.0\) \\ \(10\) & \(0.6021\) & \(-0.8775\) & \(-55.5431\) & \(10.1851\) & \(-0.1851\) & \(10.5\) \\ \(15\) & \(0.1720\) & \(-1.001\) & \(-80.2382\) & \(14.8148\) & \(0.1851\) & \(15.0\) \\ \(20\) & \(-0.2580\) & \(-0.9319\) & \(-105.4774\) & \(19.7727\) & \(0.2272\) & \(20.0\) \\ \(25\) & \(-0.4408\) & \(-0.8503\) & \(-117.4045\) & \(24.5454\) & \(0.4545\) & \(25.0\) \\ \(30\) & \(-0.6236\) & \(-0.7619\) & \(-129.3019\) & \(30.0\) & \(0\) & \(30.0\) \\ \(35\) & \(-0.7956\) & \(-0.5510\) & \(-145.2974\) & \(34.7222\) & \(0.2777\) & \(35.0\) \\ \(40\) & \(-0.9032\) & \(-0.3537\) & \(-158.6125\) & \(40.0\) & \(0\) & \(40.0\) \\ \(45\) & \(-0.9784\) & \(-0.1768\) & \(-169.7539\) & \(45.0\) & \(0\) & \(45.0\) \\ \(50\) & \(-1.001\) & \(0.0476\) & \(177.2736\) & \(49.2857\) & \(0.7142\) & \(49.5\) \\ \(55\) & \(-0.9784\) & \(0.2585\) & \(165.2014\) & \(54.6428\) & \(0.3571\) & \(55.0\) \\ \(60\) & \(-0.9032\) & \(0.4285\) & \(154.6161\) & \(60.4166\) & \(-0.4166\) & \(60.5\) \\ \(65\) & \(-0.7956\) & \(0.5918\) & \(143.3583\) & \(65.0\) & \(0\) & \(65.0\) \\ \(70\) & \(-0.6344\) & \(0.7755\) & \(129.2844\) & \(70.0\) & \(0\) & \(70.0\) \\ \(75\) & \(-0.4193\) & \(0.9047\) & \(114.8676\) & \(75.3571\) & \(-0.3571\) & \(75.5\) \\ \(80\) & \(-0.2150\) & \(0.9659\) & \(102.5508\) & \(79.6428\) & \(0.3571\) & \(80.0\) \\ \(85\) & \(0.0376\) & \(1.0140\) & \(87.8447\) & \(85.0\) & \(0\) & \(85.0\) \\ \(90\) & \(0.4838\) & \(0.8299\) & \(59.7567\) & \(90.0\) & \(0\) & \(90.0\) \\ \(95\) & \(0.8924\) & \(0.4149\) & \(24.9366\) & \(95.6923\) & \(-0.6923\) & \(96.0\) \\ \(99\) & \(0.9892\) & \(0.1837\) & \(10.5183\) & \(98.75\) & \(0.25\) & \(99.0\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Lookup table calibration implementation.
## 4 Discussion
This tool has been proved to be highly accurate in predicting a community's functional capabilities from 16S abundance profiles [26; 40] and has been popular recently [41; 42; 43; 44]. The abundance of genes encoding enzymes, which were relatively higher in +Compost compared with \(-\)Compost, was mostly related to decomposition and plant growth promotion and is well known to be synthesized by Proteobacteria and Firmicutes [45]. Amidases, which increased significantly in +Compost soil, are characteristic of Actinobacteria [18] and are important for the synthesis of indole-3-acetic acid, an important plant-growth-promoting hormone [46]. Likewise, bacterial citrate synthases, which were relatively higher in abundance in +Compost soil, have shown enhanced plant growth under nutrient-limited soils [47; 48]. On the other hand, cellulases, which also increased in +Compost soil, are important for breaking down cellulose into monosaccharides or shorter polysaccharides [49], which improves soil fertility and plant growth through accelerated straw decomposition [50]. The requirement of higher amount of ATP in the decomposition process might have resulted in an increase in the abundance of glucokinase in +Compost soils [51]. On the other hand, compost addition also improved aldehyde dehydrogenase activity, which is known to provide stress tolerance to plants [52]. Furthermore, enzymes known to act on plant development, like acyl-CoA synthetase, which is essential for cuticle development [53], microsporogenesis [54], and pollen development [55], also increased in +Compost soils. Lastly, the increase in the predicted abundance of the fatty acid biosynthesis-specific enzyme 3-oxoacyl reductase, which is known to improve seed yield [56], was also higher in compost-amended soils.
## 3 Results and Discussion
### Antimicrobial Activity against Spoilage and Foodborne Pathogenic Bacteria  
_A. citrodota_ is one of the well-known medicinal plants with therapeutic properties [11]. In our study, the antimicrobial activity of _A. citrodota_ aqueous and ethanolic extracts against five common foodborne and spoilage pathogenic bacteria was evaluated, and the minimum inhibitory concentration (MIC) and the minimum bactericidal concentration (MBC) are shown in Table 5. In order touse plant extracts in the food industry, it is very critical to determine precisely the MIC and MBC values for each extract and regulate the optimum amount of the antimicrobial agent to secure microbial safety. Gram-positive bacteria were more sensitive than Gram-negative bacteria. The strongest antimicrobial activity was demonstrated by the ethanolic extract, whereas the aqueous extract had no effect on _E.  
Figure 9: Effect of aqueous _A. citrodota_ extract (mg (dry matter)/mL) as growth stimulator of (**a**) _Lacticaseibucillus rhamnus_ GG and (**b**) _Lacticaseibucillus rhamnus_ OXLAL-1 at different alcohol concentrations. Significant difference (\(p<0.05\)) compared to the control (MRS broth, 0% alcohol concentration) is indicated with *.  
coli_. However, MIC and MBC values were significantly (\(p<0.05\)) higher compared to gentamycin, which was used as a positive control [24].  
_A. citrodota_ ethanolic extract was rich in acteoside compared to the aqueous extract.  
Acteoside is a glycoside, which, according to [PERSON] et al. [49], has the ability to interact with ionized phosphocholine or phosphoglycering phosphate groups in phospholipid model membranes, an interaction that probably enhances the antimicrobial properties of acteoside [49]. Similar to our results, [PERSON] et al. [50] showed that _S. aureus_ was sensitive (MIC 4 mg/mL) to the methanolic extract of _A. citrodota_, while _E. coli_ was resistant, and [PERSON] et al. [51] determined the MIC of the hydroethanonic extract at \(9\pm 1.5\) mg/mL against \(S\). Enteritidis [51].  
Importantly, the spoilage and pathogenic microbes tested were more sensitive to the extracts than the _L. rhamnus_ strains, as MIC and MBC values were significantly (\(p<0.05\)) lower as shown in Table 6, in agreement with the literature [52; 53].
## 3 Key Techniques of near Real-Time Remote Sensing
### Dynamic Network Scheduling and Task Planning Technology  
The operational and managerial challenges of remote sensing service systems are exacerbated by the diverse needs of users, the randomness of those needs, and the intricate nature of satellite resources. There exists the case of multiple users accessing the satellite resources. Real-time remote sensing services (NRS), based on satellite Internet technology, effectively address these challenges through comprehensive scheduling and control of measurement and operational resources. This includes managing random access between space and Earth, planning information transmission paths, and utilizing the ICRS application system (refer to Figure 1). Moreover, remote sensing tasks take into account the priority of the mission and the capacity of the system resources to call for satellite resources. The result is a system that achieves rapid responsiveness to user needs. Two key technologies need to be addressed:  
Firstly, the scheduling management of highly dynamic satellite networks involves crucial elements such as inter-satellite link state perception, network time synchronization, topology planning and control, and routing scheduling. This encompasses routing control and service transmission based on inputs such as inter-satellite and satellite ground network topology, business modes, and routing strategies.  
Secondly, task planning under complex constraint conditions necessitates the resolution of challenging issues like complex constraint modeling, intricate problem solving, and integrated planning for both satellite ground and inter-satellite collaboration.
# Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification
## 2 Materials and Methods  
### Datasets  
The ECG signals used in this work were obtained from the online repository of the MIT-BIH Arrhythmia Database ([[http://www.physionet.org/physiobank/database/mitdb](http://www.physionet.org/physiobank/database/mitdb)]([http://www.physionet.org/physiobank/database/mitdb](http://www.physionet.org/physiobank/database/mitdb))). The database included forty-eight 30 min ECG recordings sampled at 360 Hz and collected from two leads. Here, only signals from the modified limb lead II (MLII) are used. Signals were extracted from the original files using the native Python waveform-database (WFDB) package ([[https://github.com/MIT-LCP/wfdb-python](https://github.com/MIT-LCP/wfdb-python)]([https://github.com/MIT-LCP/wfdb-python](https://github.com/MIT-LCP/wfdb-python))) and resampled at 150 Hz, which is a sampling frequency commonly found in wearables and screening devices. The 109,494 heartbeats present in the MIT-BIH Arrhythmia Database were relabeled from the original 15 heartbeat types into the five types defined by the AAMI (NB, SVEB, VEB, FB, and UB) as described by [PERSON] et al. [4].  
Training and testing sets are constructed by following a clinically realistic inter-patient paradigm, where the data used to develop and evaluate the classifier come from different patients. The inter-patient approach implemented here corresponds to the division proposed by [PERSON] et al. [29] and adopted by others [4] where the four recordings with paced beats are excluded and the remaining 44 recordings are divided into two datasets each containing 22 recordings. The first set, called DS1, is used for training and is composed of all heartbeats of records: 101, 106, 108, 109, 112, 114, 115, 116, 118, 119, 122, 124, 201, 203, 205, 207, 208, 209, 215, 220, 223, and 230. The second set, called DS2, is used for test and is composed of all heartbeats of records: 100, 103, 105, 11, 113, 117, 121, 123, 200, 202, 210, 212, 213, 214, 219, 221, 222, 228, 231, 232, 233, and 234. The total number of beats on each class using this division is reported in Table 1. Due to the low number of FB present in the database as well as the very few UB that remain after excluding paced heartbeats, both types of heartbeats are not considered for classification but are nevertheless included in the evaluation.  
The computer code used for dataset preparation as well as feature extraction and selection, and model training and validation is available online (see Supplementary Materials).
## 3 Results & Discussion
### Discussions
These two features, individually, have as much importance as the optical images, and together they represent 47% of the model's predictions. The rest of the feature importance is represented by the sum of eigenvalues, standard deviation of height values, planarity, entropy, anisotropy, and absolute height features, each of which has importance less than 10%. Scattering, radius of k nearest neighbours, omnivariance, and changes in curvature features did not contribute to the model's performance at all. Additionally, 1D and 2D geometric features computed with the eigenvalues, such as linearity and planarity,were found to be effective, while the 3D geometric feature, scattering (or sphericity), was found insignificant. The change in absolute heights, such as height difference and standard deviation of absolute heights, were found to be dominant predictors compared to absolute heights which indicates the physical properties of road geometry are represented better by the statistical relations of physical height values, rather than the height values. Figure 5: Example of individual prediction samples. In order to highlight the contribution of highly important features to the model's performance, model training was repeated with the same hyper-parameters and backbone (ResNet152) using only the selected feature set (change in absolute heights, optical image, and linearity). The statistical results of the model performance trained with the three most important features are closer to the model trained with the whole feature set (see Table 2). However, this model is inferior compared to the best model achieved, as the other features, whose relative importance is smaller, also contribute to the model's performance at a total of 30%. The use of the three most important features showed better performance compared to the model trained with satellite images only. Even though the feature selection reduced the computational burden by employing a smaller feature set, the contribution of less impacting features cannot be disregarded, as they provide useful information regarding the geometric properties. Figure 6: Example of predictions in woodland areas. Figure 8: Importance of optical image and LiDAR features. Figure 7: Example of predictions over shadow areas.
## 3 Results  
### Effects of Curcumin and PEA on LPS-Induced TG2 Expression Levels in BV2 Cells  
#### 3.1.1 Effects of Curcumin and PEA on LPS-Induced TG2 RNA Expression Levels in BV2 Cells  
Neuroinflammation has an important role in the progression of many neurodegenerative diseases. In this study, we initially focused the attention on the anti-inflammatory effects of PEA and curcumin, nutraceuticals tested under in vitro neuroinflammatory conditions induced by challenge with LPS. To study the anti-inflammatory effects on a BV2 cell model, this cell type was treated with LPS 1 \(\upmu\)g/mL in the presence or absence of the nutraceuticals tested at a concentration of 10 \(\upmu\)M for PEA and curcumin. The exacerbated activation of microglia, however, induces neuronal death, a histological characteristic of neurodegenerative diseases such as PD and AD (2018, 2019). In order to confirm the pharmacological effects of the two neuronutraceuticals under investigation in our study (Cu and PEA), we firstly evaluated the RNA expression levels of some already known pro-inflammatory or anti-inflammatory markers, together with the RNA expression levels of TG2. We found that the molecules tested reduced the pro-inflammatory markers Interleukin 1B (IL-1B) and Interleukin 6 (IL-6), and inducible Nitric oxide synthase (iNOS) mRNA levels were increased by the LPS presence (Figure 1A-C); conversely, they increased the RNA level synthesis of the neuroprotective or anti-inflammatory markers' triggering receptor expressed on myeloid cells 2 (TREM-2) and Arginase-1 (ARG-1) (Figure 2A,B) in LPS-stimulated BV2 cells. Intriguingly, we noted that TG2 expression shows a similar profile of pro-inflammatory markers. In fact, TG2 RNA levels increased under LPS-derived pro-inflammatory stimulus, and they decreased when the tested nutraceuticals were added (Figure 1D).