esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Gaps and Challenges
#### 5.2.1 The Gap of Representative Samples  
The growing volume and availability of remote sensing data allows the multi-dimensional analysis to generate LULC classification. However, even with medium-resolution data, performing classification with high accuracy, especially for vegetation analysis, depends on the quality, quantity, and the temporal continuity of the training set [125; 61]. As large-scale mapping approaches rapidly advance, addressing landscape heterogeneities without causing distortions and biases on accuracy poses a challenge. This topic is highly quoted, and discussions have concluded that representative sample datasets make all the difference in the improvement of classifications by different methods [126]. Therefore, the training dataset composition remains an issue, especially in heterogeneous areas with scarce data [127]. Different authors discussed the impact of the low availability of representative samples to perform accurate LULC classification. [PERSON] et al. [128] showed that the LULC classification performance is more dependent on the cropping systems data than on the classification method. Considering that agricultural landscapes with similar crop cover composition have similar phenological development, the sampling strategy is crucial to obtain representative landscape samples. Incorporating more spectral features is important. However, that alone does not help the classifier, so it becomes interesting to stratify training samples according to the different targets [71]. This represents that not only the absence of samples to train the algorithm, but also the absence of representative samples for each class can be detrimental to classification results [129].  
Training samples are usually collected from field surveys that follow statistical and spatial guidelines to determine the data acquisition [3, 33, 83, 130, 131]. Seeing the challenges and the importance of this process in the classification model performance, [PERSON] et al. [132] developed a tool to guide data collection considering a normalized Moran's I index as a useful indicator for choosing the survey route, and the most important fields to survey on that route. However, field surveys involve highly associated costs, in terms of money and time, not to mention that visual interpretation can be difficult when images or photos are not available, which may cause biases [125, 132]. An alternative is the use of unsupervised classification methods [133]. These methods are often vulnerable to outliers, high dimensionality, and noisy features, and require the user to input unknown information about the data. However, their advantage is the ability to create crop type maps where little to no field-level ground data exists [13]. Other effective options include deriving training samples from existing LULC products [134, 135], repeating sampling and data augmentation to expand the training sample [82], clustering methods to assess samples [16], and using expert knowledge through visual interpretation of other products, such as high-resolution images from Google Earth and aerial photographs [17].
# VPNFilter Malware Analysis on Cyber Threat in Smart Home Network
## 1 Introduction  
In the near future, it is calculated that millions of people around the world will live in smart homes; therefore, home security and comfort should be improved using this technology. A smart home is a home based on the integration of electronic devices connected to each other with internet, wi-fi, or Bluetooth, and so on. For example, we can control many things such as light, temperature, or operation of appliances with an integrated system. Technology has radically changed the way people relate to the environment, thanks to the internet. Nowadays, it is possible to get in touch with friends and families instantly from anywhere in the world, know the route to an unknown destination in a snap, and also facilitate the day-to-day operations inside the home thanks to the smart home based on the internet of things (IoT) technology. With the increasing variety and upgrading of technology, criminal practices have evolved. The human being came to use the internet and various technological tools in a malicious way.  
Nowadays, cybercrimes can victimize a large company or an ordinary person. Recently, new malware was implemented in many different routers on the network. Known as VPNFilter malware, it is considered a sophisticated piece of malware variant that mostly targets networking devices from a wide range of manufacturers named Vpnfilter [1]. This malware can collect confidential informationthat passes through an infected router, allowing attackers to gain control of Wi-Fi routers directly to obtain unexpected sensitive personal data. The malware infects routers to manipulate sites visited by users on the same network because the threat acts as the source of internet signal; it need not directly affect the victim's smartphone and computer. The main contributions of this survey paper are as follows: show the known and current VPNfilter malware attacks that can be launched on smart home devices such as the wi-fi router, and demonstrate the maximum security risk introduced by VPNfilter, which can affect the operation of multifunction devices such as smart TV, smart locks, and so on.
## 2 Laser Processing Method and Control System Overall Scheme for MCR Frequency Modulation
### Performance Requirements of Laser Processing Control System for MCR Frequency Modulation  
Combined with the actual production needs of the laser processing system for frequency modulation, in the laser processing for MCR frequency modulation, the performance requirements are as follows.  
1. Reliability: Reliability mainly refers to the frequency of equipment failure during long-term operation. Taking the average number of failures per week as the reliability evaluation index, the number of failures per week is required to be less than 1.
2. Over-frequency Rate: Over-frequency refers to the frequency exceeding the adjustable range in the process of MCR frequency modulation. According to the engineering requirements, it should be ensured that after the processing of 64 MCRs in each row is completed, the number of over-frequency products is less than or equal to 1, that is, the over-frequency rate is less than or equal to 1.56%.
3. Efficiency: Production efficiency refers to the number of MCRs modulated by the system per unit time. In order to meet the daily production needs, the equipment needs to complete the frequency modulation of each row of MCRs within 320 s.  
According to the above analysis, the performance requirements of laser processing control systems for MCR frequency modulation are listed in Table 1. In order to meet the requirements of over-frequency rate and efficiency, it is necessary to select appropriate control parameters such as power, frequency and machining speed.
## 3 Materials and Methods
### DInSAR Data  
Sentinel 1A SAR (Synthetic Aperture Radar) images were acquired in TopSAR (Terrain Observation with Progressive Scans SAR) Interferometric Wide mode, during ascending orbit over Mt. Etna on December 2 and successively on the next ascending pass on December 14, after the four paroxysms occurred. The differential interferogram (DInSAR) combining the two subsequent passes, encompassing the entire eruptive activity at the central crater, was processed with the GAMMA software, and in order to remove the topographic phase, a Shuttle Radar Topography Mission (SRTM) Version 4 digital elevation model with 3 arc-sec resolution was used, and is shown in Figure 7. This interferogram is very useful to draw the total cumulated ground deformation pattern affecting the volcano after such a violent eruptive phase. The interferogram clearly shows that, from December 2 to 14, the entire upper part of the volcano moved away from the satellite, meaning that it moved downwards and/or eastwards (the satellite views from the west with an incidence angle of about 38\({}^{\circ}\) from the vertical). The downwards and eastwards movement of the ground is more evident on the upper western side of the volcano, while on the eastern side, the downwards movements could be partially compensated by a westward one, coherently with a deflation/contraction pattern.  
Another small area showing local deformation is detected by DInSAR on the middle NE flank of the volcano, related to the uppermost part of the well-known Provenzana-Pernicana fault system [20; 21] that was affected by a seismic swarm on 8 December 2015, following the abrupt deflation produced by the four eruptions. Additionally, in this area, the ground deformation pattern, moving away from the satellite on the southern side of the fault (hangingwall), reveals a downwards/eastwards ground motion, in agreement with the usual oblique normal and left-lateral displacement of this fault system, clearly related to the flank dynamics of the volcano [22; 23; 24; 25].  
Figure 6: Time series of GPS baselines. Black dots are the raw baseline measurements. Red stripes represent the eruptive periods. Green lines are the optimal piecewise linear interpolation of time series for the considered periods. (**A**) Average series of the relative variations (in ppm) of all the considered baselines, that can be read as the measured mean micro-strain. (**B**) Time series of baseline variation between EPLU and EINT GPS stations. (**C**) Time series of baseline variation between ECNE and ECPN GPS stations; the yellow segments represent the linear fit for each independent period.
# The RND Efflux Pump Gene Expression in the Biofilm Formation of _Acinetobacter baumannii_
## 3 Discussion
The global regulator _adeN_ of the _adelJK_ does not only regulate the _adelJK_ expression but also the expression of many other factors involved in biofilm formation [38]. In addition, the levels of guanosine tetraphosphate (ppGpp) could control the expression of _adeABC_ and _adelJK_[39; 40]. Another controller for the expression of the _adelJK_ and _adeABC_ efflux pump genes is the BaeSR (two component system), which is involved in controlling the expression of other efflux pumps that favor biofilm formation (such as MacAB-TolC) through cell detoxification and maintenance responses [39; 41]. In previous studies, different _A. baumannii_ strains exhibited different responses to RND efflux pump gene deletion and the role of these pumps in biofilm formation may differ between the strains [6; 16; 33]. _A. baumannii_ was shown to require certain expression profiles of efflux pumps to initiate and maintain biofilm formation [33]. Furthermore, the overexpression profile of the RND pumps could be specific for each country [8]. In our study, it was observed that the _adeF_ gene relative expression was remarkably higher than the two other RND efflux pump genes in tested isolates. Similarly, in French and Korean studies, overexpression of the _adel_ gene was detected as compared to the expression of other RND pumps [6; 29]. Overexpression of _adeG_ was reported in isolates from Canada [42]. [PERSON] and colleagues reported that isolates that are genotypically characterized according to ERIC typing showed similar biofilm formation potential [28]. Similarly, we observed that isolates that belong to the same ERIC clone showed similar biofilm forming potential, except in clusters B and G. Notwithstanding, isolates that belong to the same ERIC clusters (G:28-36 and H:4-23) or closely related clusters (A:25 and B:7) showed similar efflux pump relative expression patterns in biofilm form compared to the planktonic form despite the difference in the biofilm phenotype. In conclusion, overexpression of the RND efflux pumps AdeABC, AdeFGH, and AdelJK was not correlated with the biofilm phenotype in any of the tested isolates. Therefore, their roles in biofilm formation do not appear to be pivotal and their expression differs according to the genetic background of each strain. Consequently, efflux pumps may be a part of more complicated mechanisms that contribute to biofilm formation.
# ErbB4 Is a Potential Key Regulator of the Pathways Activated by NTRK-Fusions in Thyroid Cancer
## 2 Materials and Methods
### Experimental Validation  
To confirm the observed difference in \(ERBB4\) expression between samples containing and not containing NTRK-fusions, we screened 146 \(BRAF\) V600E-negative \(ETV6\)-\(NTRK3\)-positive papillary thyroid cancer FFPE blocks obtained from Altai Regional Oncological Dispensary. RNA was isolated from three 5 \(\upmu\)m thick sections using the RNeasy High Pure FFPET RNA Isolation kit (Roche Life Science, Penzberg, Germany). NTRK-fusions were detected with ETV6-NTRK3-specific primers and probes, while \(ERBB4\) and \(TBP\) expression were analyzed via quantitative RT-PCR with specific primers and probes (Table 1). The successive reverse transcription and PCR reactions were performed in 25 \(\upmu\)L containing TaqMan Path 1-Step Multiplex Master Mix (Thermo Fisher Scientific, Waltham, MA, USA), 450 nM of primers, and 150 nM of probes. To construct the calibration curve, sample RNA analysis was accompanied by the amplification of standard synthetic plasmids involving target sequences in five concentrations in duplicate. For normalization, the TBP expression was used.
# A Sensor for Characterisation of Liquid Materials with High Permittivity and High Dielectric Loss
## 3 Sensitivity Analysis  
The sensor structure and some dimensions are shown in Figure 1. The substrate is 20 mm wide and 28 mm long. Port 1 and Port 2 are connected to coaxial lines. The quartz glass tube has a length of 75 mm, an outer diameter of 1.5 mm, and an inner diameter of 0.9 mm. The volume ratio between liquid sample and glass tube is 0.36. Simulations of liquid samples of different concentrations are undertaken. During the measurement, the samples are injected into the glass tube using a syringe. Each glass tube is used for only one concentration to avoid contamination.
# An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks
## 5 Performance Evaluation  
In this section, we present the performance evaluation of the proposed IWLC scheme. First, the simulation settings are presented in detail and IWLC is analyzed by different weight settings. Then, the performance of the proposed IWLC is evaluated and compared with the benchmark methods, namely, WSCA and ACO. The metrics used in the simulation are PDR, number of clusters, network lifetime, cluster head changing ratio, and energy consumption.
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 3 Vehicle Motion Estimation Using an Upward-Facing Camera
### Feature Detection, Description and Matching
#### 3.2.2 Binary Robust Independent Elementary Features Descriptor  
The BRIEF algorithm described in [50] is not designed to be rotationally invariant and tolerates small amounts of rotation only. In other words, if two images of the same viewpoint are taken with a large camera rotation (\(\gtrsim\)10\({}^{\circ}\)), the feature description using BRIEF will fail. Indeed, given \(n\) (\(n=128,256\) or 512) predefined pixel pairs within an image patch, BRIEF uses local binary tests to produce \(n\)-bit strings. A so obtained vector representation is very sensitive to rotation variation. Thus, ORB uses a rotation-invariant version of BRIEF (rBRIEF (rotation-aware BRIEF)) presented in [48].  
After applying the FAST and BRIEF algorithms, the Hamming distance [51] is used to match the obtained strings of descriptors. However, false matches might happen between certain very similar features in the viewpoints. These types of mismatches can cause severe ego-motion estimation error. Thus, the outliers need to be removed.
## 5 Conclusions  
(1) The spatial distribution of ET derived from MODIS and GF-4 showed that the texture of ET derived from GF-4 was more obvious than that from MODIS, and GF-4 was able to express the variability of ET spatial distribution. The correlation between ETs derived from two sensors showed significant linear correlation; and the ET values derived from GF-4 were higher than that of MODIS.  
(2) Even in the same study area, trends in the maximum value, the average value, and the standard error of ET at different times were not the same. This validates that even if in two MODIS remote sensing images scope, due to different surface types and meteorological conditions, the daily variation of ET in the remote sensing pixel scale does not follow the constant linear rule.  
(3) Since the temporal resolution of GF-4 is 3 minutes in our paper, the land surface ET every three-minutes could be obtained by using all available meteorological data and other remote sensing data. At this scale, the extrapolation of instantaneous ET from a single image was avoided, and the spatial diversity at each imaging time was attained.  
(4) This paper has validated ET derived from GF-4 and MODIS, and the verification results also showed that the error was within the normal range, but more observational methods to validate ET estimation results of GF-4 are needed, which should be a major goal for future studies.
## 3 Results and Discussion
### Measurements
#### 3.2.2 I-V Curve Measurements of Studied Modules  
Preparing the implementation of the on-site measurements is shown in Figure 13. The position of the modules was adjusted to the actual slope of the halls where the modules were located. The shadow created by the stands was used to adjust the position of the module. A photograph in Figure 13 shows the measurement procedure, in which the I-V curve of the PV modules was measured, followed by thermographic analysis of the modules in the short-circuit state.  
PV modules with no visible damage to the glass cover were subjected to I-V curve measurements to evaluate the influence of the hailstorm on their performance. The PV modules' I-V curve measurements were performed using an IEC (EN) 62446 standard compliant PV tester, the Metrel MI 3108 EurotestPV. This measurement procedure estimated a PV module's I-V curve under standard test conditions (STCs) based on the PV module technical characteristics and the current environmental conditions (solar irradiance and PV module temperature), which was easily comparable to the manufacturer's I-V curve under the STCs. This enabled an assessment of hailstorm damage on PV module performance. The technical characteristics of the PV tester I-V curve measurement module, along with the PV solar irradiance sensor (temperature compensated monocrystalline PV cell) and cell temperature sensor, are given in Table 8. The PV module I-V curve measurement procedure is given in Figure 14. The procedure for measuring the I-V curve with the PV tester is given in Figure 14.  
Figure 13: Photo documentation of the measurement procedure.  
PV module I-V curve measurements are given for 2 out of 13 PV modules with no visible damage to the glass surface. The environmental conditions at the moment of I-V curve measurement for both PV modules are given in Table 9.  
The numerical and graphical results of the I-V curve measurements of the first (washed) PV module are given in Table 10 and Figure 15; Figure 15 shows both I-V and P-V curves. The results are given for three scenarios--measured values, calculated STC values (measured data estimated for STC values based on manufacturer data), and nominal STC values representing data from the manufacturer's datasheet.  
The results indicate that both PV modules degrade significantly in terms of electrical performance in comparison to the manufacturer's data even though the modules have been operating for only one year and their degradation should not be as high as presented levels. The most significant deviation is observed in the output power, which decreases by more than 20 % for both PV modules.
## 4 Hardware Implementation and Result Discussion  
### Verification of Pipeline Conflict Reduction for Double Update Queues  
From the illustration shown in Section 3.4, it can be seen that double update queues can reduce the pipeline conflicts and increase the percentage of updated LLRs during decoding. To demonstrate the effect of double update queues in making more LLRs updated, we choose the PCM in 5G NR (code rate = 22/27). Figure 5 shows the percentage of updated LLRs per iteration during decoding depending on the number of pipeline stages. With the increase in pipeline stages, the percentage of update LLRs per iteration is gradually getting worse. Compared with optimized results in [15], the double update queues increase the percentage of updated LLRs by 4-31%. Compared with the single update queue, the improvement is between 12% and 63%.
## 3 Results and Discussion
### Links with Maximum of Heat Release  
Figure 8 presents the relationship between the maximum of heat release with the maximum value of the first derivative of heat release rate (graph a), the centre of combustion (graph b) and the combustion duration (graph c).  
The direct relationship between RoHR\({}_{\text{MAX}}\) and (dRoHR/d\(\theta\))\({}_{\text{MAX}}\) is related to the development of the premixed phase of the main combustion. In tests on HP EGR circuit control (Set 1), this link was not observed, as the increase of EGR rate led to a longer ignition delay and to a longer combustion duration if compared to the reference condition without recirculation [1]. When applying EGR from the low-pressure loop and VNT control (Set 2), the baseline condition was obtained with the standard HP EGR valve opening derived from the ECU map according to the engine manufacturer calibration. Referring to this working point, the development of the main combustion when activating LP EGR circuit outlined was different, as RoHR\({}_{\text{MAX}}\) and (dRoHR/d\(\theta\))\({}_{\text{MAX}}\) presented a slight increase. This behaviour may be justified considering the better mixing between air and recirculated gases occurring in the longer low-pressure EGR circuit, as also discussed in Reference [1] referring to pressure diagrams and heat release curves. As a consequence, the results for Set 2 are reported in Figure 8a for the three tested operating conditions.  
The rail pressure increase in Sets 3 and 4 led to the enhancement of premixed phase of the main combustion, in which the rise of both the presented quantities was obtained through the better spray development and penetration and the faster mixture formation already outlined.  
One further consequence of the enhanced mixture formation and combustion is the shift of crank angle corresponding to 50% of heat release (\(\theta_{50}\)) towards the TDC position, observed in Figure 8b for Sets 3 and 4, when the rail pressure increased and higher values of maximum heat release were  
Figure 8: Relationship between RoHR\({}_{\text{MAX}}\) and (dRoHR/d\(\theta\))\({}_{\text{MAX}}\) (a), \(\theta_{50}\) (b), (\(\theta_{90}\)–\(\theta_{10}\)) (c) in different test sets.  
observed. In Set 1, this outcome was obtained when lower EGR rates were actuated, while in Set 2 when a prevailing share was due to the LP EGR loop, as previously discussed.  
The same conclusions can be applied to the combustion duration (\(\theta_{90}\)-\(\theta_{10}\)) shown in Figure 8c. Changes in the premixed phase of the main combustion affected the whole combustion process, modifying the overall duration.