esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Improvement Characteristics of a Novel Geotextile with Stitched Transverse Ribs
## 1 Introduction  
Because of being lightweight, having high strength, high flexibility, corrosion resistance, ease of storage, transportation, and construction [1], and due to their cost-effectiveness and environmental friendliness [2], geotextiles have been widely used in road, railway [3], airport, port [4], and other major engineering construction fields since the end of the last century. Geotextiles can play the role of isolation, filtration, reinforcement, etc. In reinforced soil engineering, as one of the early and widely used reinforcement materials, the application of a geotextile can reduce displacements and stress values, and increase the bearing capacity [5, 6]. However, the reinforcement effect of conventional geotextiles is limited in practice since a geotextile is distributed in a two-dimensional plane and the interface friction between geotextile and soil is not enough [7, 8, 6]. Because of the deficiency of geotextiles, geogrids have more applications in engineering, and some scholars have studied geogrid reinforcement [9, 10]. Although geogrids can provide a better interlocking effect on soils, the tensile strength of geogrids is still less than that of geotextiles, as the tensile strength of geotextiles can achieve 1000 kN/m, indicating that geotextiles should provide a very good reinforcement effect. Therefore, a new reinforcement form thus needs to be developed to take full advantage of the tensile strength of geotextiles [11].  
The passive resistance of the transverse ribs, the frictional resistance of the longitudinal ribs, and the embedded locking and occlusion of the mesh all contribute to the reinforcement effect between geosynthetics and soils. Among them, 60-70% of the resistance comes from the transverse ribs [11, 12, 13]. On the basis of existing research results, a novel type of geotextile with stitched transverse ribs is proposed to improve the reinforcement effect between geotextile and soil. The reinforcement effect is determined by the friction characteristics between geotextile and soil, influenced by many properties, such as the types ofgeotextiles and filled soils, the compaction quality of soils, the test boundary conditions, and so on [14; 15; 16]. It is important to obtain an accurate reinforcement effect of geotextiles by carrying out tests [17]. A series of large-scale direct shear tests have investigated the improvement characteristics between conventional geotextiles, geogrids, and the novel geotextile. The reinforcement mechanism of the novel stitched transverse ribs geotextile is also studied. Insights from this study can provide a new understanding of the novel stitched transverse ribs geotextile's reinforcement mechanism in engineering.
## 3 Results and Discussion
### Transmission Electron Microscopy  
The structure of magnetic polystyrene microspheres was characterized by transmission electron microscopy (TEM) with an FEI Tecnai F20. Figure 2 presents the transmission electron micrographs of magnetic polystyrene microspheres. It can be seen from the figure that Fe\({}_{3}\)O\({}_{4}\) nanoparticles are formed by many nanocrystal clusters, with a spherical shape and a rough surface. The particle size is about 200 nm, and the dispersion uniformity is good. After polystyrene is coated on the surface of Fe\({}_{3}\)O\({}_{4}\) particles, the particle size of Fe\({}_{3}\)O\({}_{4}\)@PS increases to 500 nm. Compared with Fe\({}_{3}\)O\({}_{4}\) nanoparticles, a layer of spherical particles is added on the surface, which can effectively prevent the oxidation of Fe\({}_{3}\)O\({}_{4}\) in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Veterinary** & **Precursor ion** & **Product Ion** & **Residence** & **Collision** & **Collision** \\
**Drugs** & **(m/z)** & **(m/z)** & **Time/Msec** & **Voltage/V** & **Energy/eV** \\ \hline Melamine & 127.1 & 85.2/68.4 & 30 & 35 & 17/28 \\ Clenbuterol & 277.0 & 259.0 & 40 & 65 & 15 \\ Clenbuterol & 277.0 & 203.0 * & 40 & 65 & 21 \\ Ractopamine & 302.2 & 284.0 & 40 & 80 & 18 \\  & 302.2 & 164.1 * & 40 & 80 & 23 \\ Salbutamol & 240.2 & 222.3 & 40 & 68 & 14 \\ Clenbuterol-D\({}_{9}\) & 240.2 & 148.1 * & 40 & 68 & 24 \\ Ractopamine-D\({}_{6}\) & 305.1 & 167.2 * & 40 & 70 & 25 \\ Salbutamol-D\({}_{3}\) & 243.1 & 151.1 * & 40 & 63 & 28 \\ \hline \hline \end{tabular} Note. *: Quantitative ions.  
\end{table}
Table 1: Tandem mass spectrometry detection parameters of melamine and three \(\beta\)-agonists.  
Figure 1: Scanning electron micrographs of Fe\({}_{3}\)O\({}_{4}\)@PS.  
air. In addition, introducing the polystyrene layer enhances the adsorption capacity and biocompatibility of the material.
# Harnessing Biotechnology for the Remediation of Organic Pollutants in Coastal Marine Ecosystems
## 4 Biotechnological Methods for Remediation
### Phytoremediation Approaches
The efficiency of rhizofiltration is influenced by factors such as root surface area, plant growth rate, and the presence of root exudates that can alter the pH and redox conditions of the rhizosphere, facilitating metal precipitation and immobilization [88]. For instance, _Typha angustifolia_ has demonstrated the capacity to accumulate substantial amounts of Cd and Zn, making it an effective candidate for treating industrial wastewater [86].
# Defect Recognition in Ballastless Track Structures Based on Distributed Acoustic Sensors
###### Abstract  
Defect recognition in ballastless track structures, based on distributed acoustic sensors (DASs), was researched in order to improve detection efficiency and ensure the safe operation of trains on high-speed railways. A line in southern China was selected, and equipment was installed and debugged to collect the signals of trains and events along it. Track vibration signals were extracted by identifying a train track, denoising, framing and labeling to build a defect dataset. Time-frequency-domain statistical features, wavelet packet energy spectra and the MFCCs of vibration signals were extracted to form a multi-dimensional vector. An XGBoost model was trained and its accuracy reached 89.34%. A time-domain residual network (ResNet) that would expand the receptive field and test the accuracies obtained from convolution kernels of different sizes was proposed, and its accuracy reached 94.82%. In conclusion, both methods showed a good performance with the built dataset. Additionally, the ResNet delivered more effective detection of DAS signals compared to conventional feature engineering methods.  
DAS; ballastless track; defects recognition; XGBoost; ResNet +
Footnote †: journal: Applied  
radar profiles recorded with it because of the reflection and scattering of targets, the non-uniformity of the medium distribution and the complexity and diversity of geological structures during the underground propagation of electromagnetic waves. In addition, the uncertain factors in the manual interpretation of images will ultimately affect the judgment of the result.  
Distributed acoustic sensors (DAS) based on the phase-sensitive optical-time-domain reflectometer (\(\Phi\)-OTDR) [6] are a new type of sensing technology that uses the interference effect of optical-fiber-back Rayleigh scattering to realize the continuous distributed detection of acoustic signals. DASs not only have the advantages of anti-EMI, non-corrosiveness and no required power supply but can also detect and locate weak vibration signals along optical fibers. They have been widely applied in fields [7; 8; 9; 10; 11; 12] such as oil pipeline security monitoring, perimeter security and rail transit. Collecting the vibration signals of trains and events along a line by using DAS technology combined with signal processing, deep learning and other ways to identify track defects has provided a reliable helper method that has an important application value for the safe and high-quality development of high-speed railways.  
For this paper, we have studied identification technology for ballastless track defects, collected and processed vibration signals and built a defect dataset. According to the design principles of ballastless tracks and the characteristics of the sample data, two pattern recognition methods, based on feature engineering and deep learning, were proposed to verify the feasibility of DAS technology for defect detection in ballastless tracks.
# Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning
## 3 Realization of the Improved Model
### Determination of the Smooth Factor  
Unlike a back propagation neural network, connection weights of a GRNN network are determined when the sample dataset is given. The smooth factor is the only adjustable parameter in a GRNN network, and it is well known that the smooth factor controls the influence sphere of input data, which significantly affects the prediction accuracy. With increasing the value of the smooth factor, the prediction curve is much smoother and neglects details. When the smooth factor is smaller, the approaching performance is better, but at the expense of computing time and the problem of over-fitting. Hence, the smooth factor for a GRNN network is required to carefully determinate.  
Since there is no a priori information, the smooth factor is determined by a search method in a special region. A series of values from 0.10 to 0.50 with steps of 0.02 is employed to check the prediction performance. Figure 7 indicates that the smooth factor should be satellite-specific determined, and the final results are given in Table 2. Taking C14 as an example, the smooth factor increases from 0.10 to 0.50, the clock differences between true and prediction firstly decrease and then increase with an optimal value of 0.20.  
Figure 6: Clock differences between true (GFZ precise clock products) and prediction, using different sampling interval of input data.  
Figure 7: _Cont._
# Ontology-Based Context Event Representation, Reasoning, and Enhancing in Academic Environments
## 3 Event-Driven Approach Based on an Ontology Model
### Event Reasoning  
Ontology reasoning consists of executing a program to query logical consequences from a set of asserted facts or axioms. In order to realize the intelligent context reasoning, the final set of context modules described above were imported and integrated for modeling a customized academic environment. This ontology is composed of class hierarchies, data properties, object properties, and instances, which are useful to answer any question regarding the events that have occurred in the academic environment. The questions \"what happened?\", \"when?\", \"who was involved?\", and \"at what time did the event occur?\" can be executed in the proposed model. The context reasoning using queries over the context modules is presented as a question-answering approach, which is implemented by using SQWRL [43] over events, location, time, and person contexts. Reasoning about the location context considering the query \"Where was the temperature increased?\" is transformed into SQWRL and is shown as follows.  
TemperatureIncrease (?event) \(\wedge\)  
happensIn (?event,?space) \(\rightarrow\)  
sqwr:select (?space)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Object Property** & **Domain Class** & **Range Class** \\ \hline happensIn & AcademicEvent & PhysicalSpace \\ \hline hasPersonInvolved & AcademicEvent & Person \\ \hline hasTemporalEntity & AcademicEvent & TemporalEntity \\ \hline isDetectedBy & AcademicEvent & Node \\ \hline nodelsLocatedIn & Node & PhysicalSpace \\ \hline hasDeviceAssigned & Person & Device \\ \hline isAssignedTo & Person & PhysicalSpace \\ \hline hasPersonDetected & PhysicalSpace & Person \\ \hline \hline \end{tabular}
\end{table}
Table 3: Object properties between context modules.  
Concerning time context reasoning, we propose \"What time did Professor [PERSON] enter cubicle 222?\" and the corresponding SQWRL query about the presence events as follows.  
\begin{tabular}{l} PersonEntersSpace (?event) \(\land\) \\ hasPersonInvolved (?event,?person) \(\land\) \\ hasName (?person, \"[PERSON]\") \(\land\) \\ happensIn (?event,?space) \(\land\) \\ Cubicle (?space) \(\land\) \\ hasNamePhysicalSpace (?space, \"222\") \(\land\) \\ hasTemporalEntity (?event,?time) \(\rightarrow\) \\ sqwrl:select (?time) \\ \end{tabular}  
Finally, the person context is reasoned by queries such as \"Who has given academic advice in cubicle 222?\", where the SQWRL query about academic advising events and actors involved are presented as follows.  
\begin{tabular}{l} AcademicAdvising (?event) \(\land\) \\ happensIn (?event,?space) \(\land\) \\ Cubicle (?space) \(\land\) \\ hasNamePhysicalSpace (?space, \"222\") \(\land\) \\ hasPersonInvolved (?event,?person) \(\land\) \\ Professor (?person) \(\rightarrow\) \\ sqwrl:select (?person) \\ \end{tabular}  
Event reasoning using context information is a useful process for developing a query system based on time, actor, and location information about academic events.
# Improving the Energy Conversion Efficiency for Hydrokinetic Turbines Using MPPT Controller
## 2 Design of the MPPT Control Algorithm
\begin{array}{c}-\frac{1-d}{L}\\ 0\end{array} \right]U_{fd} \tag{23}\]  
Figure 4: Boost converter model. Figure 5: Buck converter model. The final step to complete the simulation model, and the most important one, is to introduce the MPPT algorithm that controls the boost converter. Because of its simplicity and overall good performance, we implemented a simple perturb and observe (P&O)-type method. The power versus rotational speed curve of the hydrokinetic turbine (Figure 1) shows that the power increases up to a certain speed, after which it decreases. Because the output voltage is directly proportional to the rotational speed for a PMSG, by making small changes to the voltage output (\"perturb\" and then measure, \"observe\", the power output), one can determine where the operating point is on the curve. The general description of the algorithm is given in the following flowchart representation from Figure 6.  
One drawback of this method is that it requires some tuning of the perturbation increment, which is different for each application. If the perturbation increment is too small, the system requires a longer time to reach the maximum power point; if the increment is too large, the system has significant oscillations around the maximum power point. Other methods are available [24; 25; 26], such as the incremental conductance method, where instead of measuring the power, conductance (current over voltage) is measured along the power curve. This has the advantage of no oscillations at the maximum power point but it requires a more sophisticated algorithm implementation. Finally, the buck converter is controlled according to the application requirements. For the considered case, the load being resistive, the duty cycle of the converter can be directly imposed. The specific parameters for different components are presented in Table 1. For the simulated MPPT algorithm, the parameters of the electric generator of the hydrokinetic turbine previously tested in real operating conditions were implemented. Moreover, the parameters of the same turbine rotor were considered. The initial values of the boost and buck converters were calculated by using standard equations; because of the instabilities of the control algorithm, these parameters along with a suitable load were adjusted until the desired response was obtained. Figure 6: Flowchart of the MPPT algorithm [23].
# Performance Analysis of a 3D Wireless Massively Parallel Computer
###### Abstract  
In previous work, the authors presented a 3D hexagonal wireless direct-interconnect network for a massively parallel computer, with a focus on analysing processor utilisation. In this study, we consider the characteristics of such an architecture in terms of link utilisation and power consumption. We have applied a store-and-forward packet-switching algorithm to both our proposed architecture and a traditional wired 5D direct network (the same as IBM's Blue Gene). Simulations show that for small and medium-size networks the link utility of the proposed architecture is comparable with (and in some cases even better than) traditional 5D networks. This work demonstrates that there is a potential for wireless processing array concepts to address High-Performance Computing (HPC) challenges whilst alleviating some significant physical construction drawbacks of traditional systems.  
wireless networks; packet-switching; buffer management; on-chip radio communication; parallel computing; interconnect network; link utility; power consumption Article
# Aircraft Mode S Transponder Fingerprinting for Intrusion Detection
## 5 Discussion  
The proposed method (together with the new aircraft signature) is capable of detecting a change in the signature of an aircraft, reaching a low probability of false alarm and a high detection probability. It is also able to detect attacks in which the spoofer does not flood the channel with a big number of messages, but injects only small quantities of messages representing one (or a small number of) aircraft.  
Moreover, the proposed method is fully adaptive and does not need any kind of knowledge about the intruder signature and about the legitimate aircraft signature distribution. It is also capable of working in the case of multiple signatures for the same aircraft and in the case of legitimate aircraft and intruders that are inside the coverage area of the sensor at the same time, if the historical data are enough.  
Concerning the method's weaknesses, the fact that it uses the historical data to estimate the signature of a legitimate aircraft and that it uses the incoming signal to improve the knowledge about the aircraft, its performance is reduced in detecting very slow changes in the aircraft signature.  
The method detects abrupt changes in the signatures, and an attack can be seen when it pop-up or for some following time instants; when a new cluster representing the intruder is formed, the ability to detect it goes to zero. The sensitivity to this phenomenon depends on various parameters such as the size of the sliding window (\(M\)), the size of the sample to extract the signatures (\(N\)), and also the selected value for \(K\). In general, this dependency from historical and recent data is useful to take into account the aging of the devices and cannot be discarded, but in this particular case, it could be also exploited from a smart attacker to design a specific attack that slowly changes the signatures of a given aircraft and then starts to send fake data.  
However, in the author's opinion, this weakness can be drastically reduced with different approaches. For example, sharing the aircraft database between different stations or increasing the size of the sample used to compute the signature that, in general, increases the algorithm's performance and also the robustness to slowly changing attacks.  
Moreover, a mechanism to inhibit the insertion of fake information inside the aircraft database can help. This can be done with any kind of antimasking algorithm that, if an intruder is detected, stops updating the signature database. This method is commonly used in Radar applications [37].  
Finally, in the author's opinion, the solution is the use of \"qualified\" messages for database updating. For example, using only the messages confirmed by a secondary radar interrogation/reply process to update the data-base of signatures totally solves this problem.
# Measurement of Large-Sized-Pipe Diameter Based on Stereo Vision
## 3 Results and Discussion
### Validation Experiment  
In the validation experiment, a seamless steel pipe is used as the test object, and ten cross sections of the pipe are taken for diameter measurement. The single measurement value of the CMM is used as the reference. In this experimental system, the average of five measurements is taken as the final measurement. The validation-experiment results are shown in Table 1.  
By analyzing the experimental results, we find that in the measurements of ten cross sections, the maximum absolute error and the average absolute error are \(-1.71\) mm and \(0.847\) mm, respectively. The relative error is within \(\pm 0.570\%\), which is close to the result of typical manual measurement with a tape.  
To analyze the repeatability in measurement, we use common seamless steel pipes with outer diameters of 285 mm, 299 mm, and 325 mm for replicated tests. For each steel pipe, a cross section is selected and measured 15 times. The final measurement results are shown in Figure 16.  
It is seen from the results that the maximum absolute error, i.e., the largest difference between a measurement and an average, is 0.865 mm, 0.724 mm, and 0.715 mm, respectively, for the three types of steel pipes. Based on calculation with the Bessel formula, the diameter-measurement repeatability standard deviations of the three types of pipes measured by this experimental system are 0.551 mm, 0.356 mm, and 0.428 mm, respectively. In general, the system exhibits good measurement repeatability. The maximum repeatability standard deviation is 0.551 mm for pipes in the diameter range from 285 mm to 325 mm, thus meeting practical needs.  
To analyze the measurement efficiency of the experimental system, a total of 90 measurements were made on the three types of pipes mentioned above, and the time consumption of the measurements was recorded. The result is shown in Figure 17, and it is found that the average measurement times for the three types of pipes are 1.262 s, 1.305 s, and 1.391 s, and the maximum measurement time is 1.495 s.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Cross Section Number** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline CMM (mm) & 299.514 & 299.501 & 299.494 & 299.493 & 299.493 & 299.500 & 299.545 & 299.167 & 299.512 & 299.514 \\ This article (mm) & 298.454 & 298.565 & 298.790 & 299.255 & 300.586 & 299.355 & 297.837 & 298.561 & 298.185 & 300.171 \\ Absolute error (mm) & \(-1.06\) & \(-0.936\) & \(-0.704\) & \(-0.238\) & 1.09 & \(-0.145\) & \(-1.71\) & \(-0.606\) & \(-1.33\) & 0.657 \\ Relative error (\%) & \(-0.354\) & \(-0.313\) & \(-0.235\) & \(-0.0790\) & 0.365 & \(-0.0480\) & \(-0.570\) & \(-0.203\) & \(-0.443\) & 0.219 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Validation-experiment results.  
Figure 16: Replicate test results of pipe diameter.
# Solanesol Biosynthesis in Plants
## 1 Introduction  
Solanesol is a non-cyclic terpene alcohol composed of nine isoprene units, and serves as a key intermediate in the synthesis of ubiquinone-based drugs and supplements such as coenzyme Q10, vitamin K2, and the anti-cancer agent synergizer \(N\)-solanesyl-\(N\),\(N^{\prime}\)-bis(3,4-dimethoxyyl) ethylenediamine (SDB) [1; 2; 3]. Solanesol was first isolated from tobacco (_Nicotiana tubacum_), and has been subsequently reported in other solanaceous plants, including potato (_Solanum tubacum_), tomato (_Solanum lycopersicum_), eggplant (_Solanum melongen_), and pepper (_Capsicum annuum_) [3; 4]. Tobacco has the highest solanesol content amongst all solanaceous plants. De novo synthesis of solanesol is extremely challenging because of its long carbon chains [5]; hence, solanesol production remains reliant on extraction from tobacco leaves [3; 6]. The content of solanesol in plants is jointly determined by genetic and environmental factors; pathogen infection, moderately high temperature, moderate drought, moderate shade, long-wavelength/extended irradiation, rare-earth elements, and tobacco topping all increase the solanesol content of tobacco [1; 3; 7]. Therefore, deeper studies on the biological function of solanesol in plants, the medicinal value of solanesol and its derivatives, and the biosynthetic pathway and its key enzymatic genes are of significant medicinal importance.