esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Genomic Organization of _hrpG_ and _hrpX_  
_hrpG_ and _hrpX_ are found in all reported pathogenic _Xanthomonas_ spp. with the exception of _X. albilineans_ and _X. saccari_ that lack the _hrp_ T3 SS gene cluster [1], and they are regarded as the master regulators of the T3 SS system in xanthomands [3]. Homologs of these two regulators were found to function in a similar manner in phytopathogenic bacteria such as _Ralstonia solanacarum_, _Burkholderia Pseudomallei_, and _Acidoorax citruli_[19; 43; 44; 45; 46]. In group 2 _Xanthomonas_ spp., _hrpG_ and _hrpX_ are localized in the same genomic region in a reverse orientation of one another spaced with a ~760-840 intragenic region (Figure 1 and Figure S1). While _hrpG_ and _hrpX_ share high DNA sequence similarity between different species, the _hrpG_-_hrpX_ intragenic regions that contain their promoter elements vary between xanthomonads (Figure S1). For instance, the _hrpG_ and _hrpX_ coding regions of _Xeu_ and _Xoo_ share 87% and 91% DNA sequence identity, respectively (Figure S1). On the other hand, the _hrpG_-_hrpX_ intragenic regions of _Xeu_ and _Xoo_ share 67% DNA sequence identity (Figure S1). The variations in the _hrpG_-_hrpX_ intragenic regions suggest that _hrpX_ and _hrpG_ might be subjected to differential regulation in different _Xanthomonas_ species.  
In _Xanthomonas_ spp. of group 1, _hrpG_ and _hrpX_ are located in the _hrp_ T3 SS genomic island [24] (Figure 1). _hrpG_ is localized at the end of the _hrp_2 gene cluster, whereas _hrpX_ is in an operon with _hrcC_ that encodes the structural ring protein of the T3 SS basal body [47]. As of now, besides the knowledge that _hrpG_ is somewhat required for the virulence of group 1 _X. translucens_[47], there's no information regarding the upstream or downstream regulatory networks that control group 1 xanthomonads _hrpG_ and _hrpX_. Therefore, all the information available in this review will only cover the HrpG/HrpX regulatory network of group 2 xanthomonads.  
Figure 2: Schematic representation of the metabolic and genetic regulation of _hrpG_ and _hrpX_ in _Xanthomonas_. Metabolites and environmental cues are represented in rectangles. Arrows and “-|” signs indicate that a protein promotes or inhibits the target, respectively, based on transcriptional or functional analyses. If the analysis was based on data derived from specific _Xanthomonas_ species, it is marked at the bottom of the represented protein names. Data represent information derived from _X. eucsicatoria_, _X. citri_ subsp. _citri_ (_Xcci_), _X. oryzae_ pv. oryzae (_Xoo_), _X. oryzae_ pv. _oryzicola (Xoc_), _X. axomopdis_ pv. _glycines_ and _X. campestris_ pv. _campestris_ (_Xcc_).
# A Weighted Histogram-Based Tone Mapping Algorithm for CT Images
## 5 Discussion
### Edge Enhancement and Auto-Leveling  
As was mentioned in Equation (7), base-detail layer separation is a frequent approach. Edges could be separated from the image, and added back to the compressed image. However, this technique is applicable to all tone mapping approaches. Such edge enhancement was not applied for the reference operators, and, for the sake of fair comparison, the proposed method does not contain such edge enhancement either. Similarly, auto-leveling, a method which slightly stretches image contrast, was not applied in the traditional TMOs or in our proposed method.
# Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET
## 2 Related Studies  
### Shadows in Drone Images  
By definition, a shadow is a dark-colored figure of an object that is produced when the object intercepts a ray of light. There are two major groups of shadows, which are cast shadows and self-cast shadows [5]. Cast shadows can be defined as shadows that are produced by an object onto another object. Meanwhile, self-cast shadows are shadows cast by the object onto itself. Defining the presence of shadow regions in images is a complex problem, as shadows can be further categorized into two other subcategories, umbra and penumbra [6]. Penumbra is the upper region of the shadow produced by a certain object, where only a part of the ray of light is concealed by the object. On the other hand, the shadow is categorized as umbra or the inner shadow region, when the light is fully halted by the object. This shows the characteristics of shadows in an image are uneven or defined by different types of pixel values, which makes it harder to detect the shape of shadows accurately.  
Shadow regions often cause drawbacks in many image processing tasks. This problem gives researchers not choice but to include shadow removal as a preprocessing step before going into the main part of the processing such as object detection. The presence of shadow in aerial images is always mentioned to cause errors, as it mirrors the properties of objects. For example, in an attempt to detect drones, an improvement is needed specifically in removing shadows, as it causes mirror images of the drone that bring confusion to the detector [1]. The same problem also occurred in road traffic detection, such that duplicate images detected from the shadow have become the limitation of the tested method [7]. Another example is weed detection in agriculture fields; the shadows of weeds have caused a high number of errors [8]. Other than removing shadows to enhance the visual representation of images, shadow detection also helps to define shadow properties, which benefits some tasks such as building height estimation [2]. The need for shadow detection has become more significant as drone usage has been popularized. Many industries have used drones to facilitate their operations in different kinds of surroundings, including indoor, industrial, cities, forestry, and farming areas. Drone images commonly contain complicated shadows compared to ground-based images, as they are captured across various heights and times, which result in shadows with different sizes and levels of brightness. Thus, a method used for shadow detection in ground-based images may not be suitable to detect shadows in aerial images. This further proves that it is crucial to explore a method that is flexible enough for all types of images, which is also a big challenge in this problem.
# EmuCD: An Emulator for Content Dissemination Protocols in Vehicular Networks
## 2 Related Work
Vehicles in Network Simulation (Veins) [15] is an hybrid simulation framework composed of the general purpose simulator OMNeT++ and the road traffic simulator SUMO. It grants the network simulation capabilities to directly control the road traffic, and thus to simulate the influence of VANET communications on road traffic. Similarly, the road traffic simulation provides information to the network simulation. [PERSON] et al. (2016) developed a simulation framework based on NS-3 and SUMO, where they support the use of 802.11p Wireless Access in Vehicular Environments (WAVE), Long Term Evolution (LTE) and Cellular vehicle-to-everything (C-V2X) as communication technologies and allow for applications/protocols to use Cooperative Awareness Message (CAM) and Decentralized Environmental Notification Message (DENM) messages. Custom applications can be implemented using C++. Loop (loop over orderly phases) ([PERSON] and [PERSON], 2017) is a trace-based simulator for VANETs which takes vehicles' geographical locations and radio reception events, and creates a synthetic environment to simulate communication protocols on the target VANET. It is an interesting solution developed specifically for VANETs, but its focus is security and it does not easily simulate other types of protocols. Finally, there is mOVERS ([PERSON] and [PERSON], 2017), an emulator that is able to recreate scalable vehicular scenarios of data gathering and content dissemination by replicating the same software of the OBUs and integrating with real vehicular mobility and connectivity. This emulator has been previously used to test different routing and content dissemination strategies ([PERSON] and [PERSON], 2017; [PERSON], 2018), but its main drawback, as most simulators/emulators, is the need for a custom implementation of the protocol within the code (rendering the testing of a closed-source protocol impossible). The existing solutions require the protocol to be integrated with the simulator/emulator, according to their rules and APIs, in a predefined programming language, usually C++. This results in the need for the developer to first become familiar with the simulator/emulator, and only then start working on the development of the protocol. Besides, the integration of real traces with the most common simulators is also a task with limitations and challenges, caused by different approaches (simulation versus emulation) and their influence on the software's architecture ([PERSON] and [PERSON], 2017). Considering these aspects, we designed an emulator where each node of the network is built in a container where we install a simple engine (daemon) that consumes a data trace with the mobility and connectivity of the node. Our daemon works by configuring the communication interfaces of the container's operating system, making our solution completely independent of the protocol being developed. This gives freedom to the protocol's developer(s), without API or programming language restrictions, and allows portability on any hardware, as long as it has a compatible operating system.
# Formation Mechanism of Metro Rail Corrugation Based on Wheel-Rail Stick-Slip Behaviors
## 1 Introduction
Meantime, abnormal rail corrugation with a short recurrence period also occurs in some large radius curves and straight sections [31; 32; 33]. Based on this, from the perspective of microscopic wheel-rail stick-slip properties, this paper analyzes the generation mechanism of rail corrugation phenomenon commonly occurring on the metro line, including curve and straight track sections. Firstly, according to the field investigation, the occurrence situation of rail corrugation on Tianjin Metro Line 6 was analyzed. Then, by analyzing the curve passing form of a single wheelset, the cause of rail corrugation was explained from the wheel-rail contact stick-slip theory. Finally, using the method of multibody dynamics, the vehicle-track space rigid-flexible coupling model was constructed to study the relationship between wheel-rail stick-slip characteristics and rail corrugation, and the proposed wheel-rail contact stick-slip theory was verified. Compared with the existing literature, the difference of this paper is that the cause of rail corrugation on metro lines is explored from the microscopic level of wheel-rail stick-slip behaviors, and the developed theory can well explain the rail corrugation phenomenon on curves and straight lines.
# Cyber Ranges and TestBeds for Education, Training, and Research
## 5 Challenges and Future Directions  
CR research teams should be focusing on improving various aspects of their testbeds. In addition, modern CRs should be enriched with novel features, such as various telecommunication capabilities, emulated Banking systems, hospitals [81], simulated smart grids, automated vehicles [82], Virtual Cyber Centres of Operation, wireless sensor networks, real time Intrusion Detection Systems [83], honeypots [84], novel authentication mechanisms [85], mobile security scenarios, and several privacy mechanisms. By adding these features, new attack scenarios can be easily deployed on a testbed, revealing vulnerabilities of the various systems and thus giving the researchers the opportunity of developing innovative defence mechanisms. Moreover, any novel CR should be built in a way that could be easily used for research purposes inside EU projects. This could be accomplished if the CRs are capable of being connected to various real-world devices to the network, making it that way ideal for launching attacks and testing the defence mechanisms of various systems. One other important aspect that should be taken into account is the capability of modern CRs to create measurable data in a semi automated way with limited human intervention.  
Modern CRs should include a portable version for demonstration purposes and for easy deployment as a modern teaching instrument in various cyber security events that take place around Europe. Moreover, research teams should also be working towards the capability of their CRs to provide remote access to researchers. Via such a federated model, researchers all around the world will be given the opportunity to implement various protocols and study their behaviour in custom tailor-made environments. Finally, the need for moving from traditional cyber ranges to digital twins is a trend that is going to become dominant in the near future, especially for replicating critical infrastructures.
# High Linearity DC-38 GHz CMOS SPDT Switch
## 2 Circuit Design
For V\({}_{\rm DD}\)/V\({}_{\rm SS}\) of 2.5 V /\(-\)2.5 V, the SPDT switch achieves S\({}_{31}\) better than 25.5 dB for 0-28 GHz and 25.26 dB for 0-38 GHz. The result is slightly better than the simulated one (isolation better than 23.9 dB for 0-28 GHz and 23.66 dB for 0-38 GHz) for V\({}_{\rm DD}\)/V\({}_{\rm SS}\) of 1.8 V /\(-\)1.8 V because transistors M\({}_{4}\) and M\({}_{5}\)/M\({}_{6}\) are closer to being open and short, respectively. Figure 3c shows the simulated input reflection coefficient at port-3 (S\({}_{33}\)) and S\({}_{32}\) of the SPDT switch. For 0-38 GHz, the SPDT switch achieves S\({}_{33}\) of \(-\)1.66\(\leftarrow-\)3.83 dB for V\({}_{\rm DD}\)/V\({}_{\rm SS}\) of 2.5 V /\(-\)2.5 V, and \(-\)1.94\(\leftarrow-\)4.22 dB for V\({}_{\rm DD}\)/V\({}_{\rm SS}\) of 1.8 V /\(-\)1.8 V, close to the ideal value of 0 dB (i.e., port 3 is short). Compared with V\({}_{\rm DD}\)/V\({}_{\rm SS}\) of 1.8 V /\(-\)1.8 V, better S\({}_{33}\) is achieved for V\({}_{\rm DD}\)/V\({}_{\rm SS}\) of 2.5 V /\(-\)2.5 V because transistors M\({}_{4}\) and M\({}_{5}\)/M\({}_{6}\) are closer to being open and short, respectively. Moreover, the magnitude of S\({}_{32}\) is the isolation between the transmitter and the receiver (TX-to-RX isolation). For V\({}_{\rm DD}\)/V\({}_{\rm SS}\) of 2.5 V /\(-\)2.5 V, the SPDT switch achieves TX-to-RX isolation better than 25.9 dB for 0-28 GHz and 22.5 dB for  
0-38 GHz. The result is slightly better than the simulated one (TX-to-RX isolation better than 23.8 dB for 0-28 GHz and 20.6 dB for 0-38 GHz) for \(\rm V_{DD}/V_{SS}\) of 1.8 V\(/-\)1.8 V because transistors M\({}_{4}\) and M\({}_{5}\)/M\({}_{6}\) are closer to being open and short, respectively. Figure 3d shows the simulated power gain (i.e., \(\rm P_{out}(dBm)\)-\(\rm P_{in}\) (dBm)) against \(\rm P_{in}\) characteristics at 28 GHz in various \(\rm V_{G2}\) and \(\rm V_{B}\) conditions. As can be seen, positive/negative gate-bias is helpful for linearity enhancement (15.1 dBm, from 16 dBm (for the case with \(\rm V_{G1}/V_{G2}/V_{B}\) of 2.5 V\(/0/0\)) to 31.1 dBm (for the case with \(\rm V_{G1}/V_{G2}/V_{B}\) of 2.5 V\(/-\)2.5 V\(/0\))) because larger \(\rm P_{in}\) (i.e., AC signal with larger negative \(\rm V_{in}\)) is required to conduct the off-state series switch transistors M\({}_{4}\). Negative-body bias is helpful for insertion-loss reduction due to M\({}_{4}\) being closer to being open.
## 2 Experimental Program
### Material Properties and Mix Proportions  
The ordinary Portland cement (OPC) conforming to ASTM type 1 (ASTM C150) was used for production of reference concrete mixture for specimens of first group 'CC' [42]. For the second group 'GC', industrial wastes, i.e., low calcium FA from local power plants and SG from local steel industries, were used to produce GPC mixture. For the third group 'GCD', QRD was obtained from the local quarries and incorporated along with the FA and SG to produce the GPC mixtures. The raw QRD was first processed by heating in an oven at 200 \({}^{\circ}\)C for 24 h, then it was mechanically grinded to achieve the mean particle size of 90 \(\upmu\)m [9]. The mixture proportions of GPC, as shown in Table 1, were based on the previous studies [43]. All the concrete mixtures were proportioned to achieve a target compressive strength of 40 MPa at 28 days. It was made sure that industrial wastes, FA, SG, and QRD, for all the mixes were obtained from the same source (power plant, steel industry, and rock quarries, respectively) to avoid error in results due to variation in source materials. The chemical analysis of FA, SG, and QRD was carried out by performing X-ray fluorescence (XRF). The chemical composition and physical properties of QRD, FA, and SG are shown in Table 3.  
In GPC, the geopolymerization process starts when source materials react with the alkaline activators. The sodium hydroxide (SH) and sodium silicate (SS) were mixed in predetermined ratios to make alkaline activator solution (AAS). The SH solution of 12 M molarity was prepared by mixing a predetermined quantity of SH pellets of 2.10 specific gravity in water. It was prepared 24 h before mixing with SS solution due to heat evolution resulting from exothermic reaction. The SS solution, with a ratio of SiO\({}_{2}\) to Na\({}_{2}\)O = 2.0, was obtained from a local supplier. It was composed of Na\({}_{2}\)O, SiO\({}_{2}\), and H\({}_{2}\)O in 15.0%, 29.95%, and 54.99% by mass, respectively. The properties of SS solution are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Sc. **}} & \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Specimen**} & \multicolumn{3}{c}{**Mix Proportions (\%)**} & \multicolumn{3}{c}{**Mix Quantities (kg/m\({}^{3}\))**} & \multicolumn{3}{c}{**Mix Quantities (kg/m\({}^{3}\))**} \\
**No.
## 4 Experimental Outcomes
### Performance Module
#### 4.1.1 Classification Results  
In the case of _No Aug_ configuration, it can be observed from Table 2 that DenseNet-161 is the architecture with the highest mean AUC of 96.3%. The ResNeXt and ResNet-50 networks are slightly behind, with AUC of 96.2% and 96.1%, respectively. The MobileNet-v2, ResNet-34, and VGG-16 collectively form a third cluster with AUC of around 95%. Conversely, the SqueezeNet is the worst-performing model in our experimental setup, managing to achieve merely 72.4% AUC.  
In the case of the _Basic Aug_ configuration, all architectures performed considerably better than the previous _No Aug_ configuration. The results reveal that ResNeXt obtained the highest AUC of 97.9%, beating all other architectures. The DenseNet-161 and ResNet-50 achieved similar performances with the AUC of 97.7% and 97.6%, respectively. Once again, the performance of the SqueezeNet failed to present significant outcomes, thus abiding by the _No Aug_ configuration.  
The second augmentation setup, called _Adv Aug_, emerged to be even better than both previously conceived _No Aug_ and _Basic Aug_ setups. The DenseNet-161 reached the top AUC of 98.2%. The ResNet-50 appeared to be the second best model, with a slightly lower AUC of 98.0%.  
Finally, as noted during the _No Aug_ and _Basic Aug_ configurations, the SqueezeNet is the model which offers least reliability with the largest inter-fold variability; however, it improved the AUC from the previous setups.  
Therefore, it can be summed up that the ResNeXt and DenseNet-161 remain the top-performing models, and the augmentation configurations considerably improved the performance of all CNN architectures. However, the SqueezeNet failed to produce convincing results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multicolumn{3}{c}{**Area under the Curve (AUC)**} \\ \cline{2-4}  & **No Aug** & **Basic Aug** & **Adv Aug** \\  & **(None, Ori,** & **(None, Ori,** & **(None, Ori,** \\  & **Oro, Ost)** & **Oro, Ost)** & **Oro, Ost)** \\ \hline \multirow{4}{*}{ResNet-34} & 92.0 \(\pm\) 0.8 & 94.5 \(\pm\) 1.0 & 95.4 \(\pm\) 0.6 \\  & 96.2 \(\pm\) 0.8 & 98.6 \(\pm\) 0.5 & 98.9 \(\pm\) 0.5
## 4 Results and Discussion  
Six different dermatological images shown in Figure 5 were analysed using the proposed methodology. Five different subjects were selected for manual counting of infectious spots on the images.  
Figure 4: Process algorithm to determine the optimal Sfactor.  
Figure 3: Study of the first case corresponding to Figure 1. (**A**) Representation of W as a function of Sfactor values. (**B**) Evolution of the model fit as a function of SfactorR.  
Figure 6: Representation of W as a function of S\({}_{\rm factor}\) values for the 6 patients.  
The number of points detected for each optimal \(S\) found has been compared with the number of points identified manually (obtained as an average of the manual count performed by five different evaluators under the same lighting conditions), the results of which are shown in Table 4.  
The \(SEN\) factor has been obtained, which indicates the sensitivity of the methodology for the determination of \(S\) with the proposed methodology  
\[SEN=1-Abs\Big{|}1-\frac{NP\ for\ selected\ S}{NP\ manual} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Trial** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \multirow{4}{*}{NP manual} & 804 & 263 & 379 & 166 & 442 & 405 \\  & 797 & 260 & 385 & 162 & 439 & 420 \\  & 802 & 268 & 389 & 169 & 440 & 417 \\  & 800 & 261 & 389 & 173 & 437 & 425 \\  & 808 & 259 & 392 & 163 & 447 & 431 \\ \hline \(\overline{NP_{m}}\) & 802 & 262 & 387 & 167 & 441 & 420 \\ \hline \(\mathrm{S_{factor}}\) & 0.89 & 0.85 & 0.84 & 0.86 & 0.89 & 0.89 \\ \hline NP for selected S & 809 & 256 & 396 & 167 & 443 & 423 \\ \hline Sensitivity & 0.992 & 0.976 & 0.976 & 0.998 & 0.995 & 0.992 \\ \hline \end{tabular}
\end{table}
Table 4: Results for Sensitivity.  
Figure 7: Evolution of model fit as a function of \(\mathrm{S_{factor}}\) for the 6 patients.
## 4 Discussion
### BSi Storage in Primary Producer Biomass and Surficial Sediment and BSi, N, and P Stoichiometry
element translocation from leaves to roots and rhizomes. Both processes usually occur at the end of the vegetative growth phase. The BSi stock was high at VS and less high at RS, but values were comparable with those of other studies [19; 22; 55; 56; 57; 71]. In our analysis, the BSi bulk was on average 24-33 g Si m\({}^{-2}\) in the SAV meadow, of which 6-12% was accounted for by MPB and by SAV fresh detritus, while in bare sediments at BS, BSi was 20-25 g Si m\({}^{-2}\), of which 11-20% was from MPB (Table S1). In contrast, the BSi stock at RS was one order of magnitude lower than VS and BS sites and comprised between 2-4 g Si m\({}^{-2}\), of which 60-80% was from epilithic macro- and microalgae. Accumulated debris at the rocky site contributed only 0.47-0.75 g Si m\({}^{-2}\). The nutrients stored in the biomass were relevant compared to the nutrient content in the overlying water. At RS and BS sites, BSi in primary producers resulted one order of magnitude higher than DSi in overlying water, while at the VS site, it was four times higher than DSi in overlying water (Figure 6). In this study, we did not quantify the amount of Si retained in the biomass of epiphytes growing on SAV leaves and filamentous macroalgae. Previous studies reported that epiphytes biomass on macrophytes varies greatly, from a negligible amount up to the same quantity as the host biomass, and depends on plant morphology, light, nutrient availability, and allopathic effects [80; 81]. The BSi stocks in SAV and EA communities in this study should, therefore, be considered as a conservative estimate, since the microalgal epilithic community is commonly depicted as being very efficient in storing and retaining nutrients [16]. Additionally, diatoms are an important component of the epiphytic communities [71; 82; 83] and accumulate a large quantity of BSi. For example, epiphytic diatoms on _Cladophora glomerata_ can store up to \(\sim\)15% BSi, a larger amount than the BSi content stored by _C. glomerata_ itself [71].