esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Evaluation of the Ability of Strain Q3-6 to Decolorize Dyes
#### 3.7.4 Decolorization Study of Congo Red  
The change in the color of the solution before and after the treatment is clearly observed in Figure 9. As shown in Figure 10, the decolorization rate of Congo Red solution at a concentration of 0.1 g/L can reach 91.9% after 3 d. Compared to the results of [PERSON] et al. [80] and [PERSON] et al. [81], strain Q3-6 is more efficient in removing Congo Red.  
Figure 6: The effect of salinity on the decolorization rate of Brilliant blue G-250 (different lowercase letters represent significant differences (\(p<0.05\))).  
Figure 7: The effect of pH on the decolorization rate of Brilliant blue G-250 (different lowercase letters represent significant differences (\(p<0.05\))).  
Brilliant blue G-250 is an anionic triphenylmethane dye, and some studies have shown that biofilms have the ability to remove anionic dyes [83] as well as triphenylmethane dyes [84], which was reflected in the strong dye-removal potential of strain Q3-6 demonstrated in this study. In addition, some studies have found that biofilms are associated with the tolerance [85] of the strain as well as the adsorption of heavy metals [86], which is linked to the previous part of this study regarding the ability of strain Q3-6 to tolerate some antibiotics and heavy metals. Therefore, it can be concluded that strain Q3-6 can be adapted to sewage with complex compositions for the bioremoval of pollutants.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Sea Ice (SI)
#### 2.2.7 Summary and Future Direction  
Despite the significant advances in SI monitoring using RS techniques, there are still several challenges. For instance, due to the fast-changing nature and seasonal changes of SI conditions, RS data with a higher temporal resolution is required for up-to-date information on SI. The launch of recent satellites has an important role in alleviating this issue. Furthermore, the snow cover affects the reflected or backscattered signal from the SI surface and complicates the detection and classification of various SI types, as well as the estimation of their physical parameters [85]. These issues might be resolved using multi-sensor observations. Additionally, the snow cover causes uncertainty in the SI thickness measurements using the altimetry sensors, which could be mitigated by the combined use of laser and microwave altimeters [85]. Moreover, despite the penetration capability of microwave systems into the cloud, the existence of thick clouds, which is common in the Arctic and Antarctic, may affect the microwave signal and results in ambiguous information about SI [29,85]. Another challenge in SI monitoring using RS is the heterogeneity and incompatibility of different measurements. Many SI studies have been carried out by independent teams with varying standards and formats, which is problematic for comparing these measurements and acquiring long-term SI information. In the future, a standardized format for SI measurements could resolve this issue [29].  
The portion of SI studies that have utilized microwave RS, especially SAR data, has been increased in recent years. This is because of the remarkable advantages of this data, recent advances in SAR data processing techniques, and the availability of SAR images. However, multi-source studies are necessary to achieve all-weather, real-time, and large-scale SI monitoring programs. Moreover, using multi-platform measurements (satellites, drones, ships, and ground-based stations) is also important to study different aspects of SI. Therefore, multi-source multi-platform SI monitoring will be of immense importance in future studies.  
Developing more advanced ML models for SI study will be another future direction in this field. Different ML and data processing algorithms have been evaluated for SI studies using various RS datasets. Like many other RS applications, DL methods have proved to be very beneficial for SI studies [77, 78, 79]. However, DL models require a very large number of training data and are computationally expensive [80]. Consequently, it is sometimes more reasonable to utilize other less costly ML algorithms.  
Finally, different oceanographic parameters are not independent, and each parameter affects and gets affected by the other parameters. So far, a few studies have been conducted to relate SI with other oceanographic parameters, Thus, multi-phenomena studies and considering the effects of the other parameters on SI should be investigated further in future studies.
For robotic motion tracking using neuromorphic vision sensor, algorithms have been developed to distinguish the background and the moving object, to be used in combination with Hexapod robot [20] or iCub humanoid robot [21]. Deep neuronal networks applied in low-latency conventional vision-based robotics require high computational power and are dependent on a powerful, graphics processing unit (GPU)-driven PC. For a camera with say 50 frames per second, the response latency is not less than 20 ms, which is far from enough to support a fast-reacting robot. If the frame rate is increased to 5 kHz, the corresponding data rate will be 234.375 MB/s for a resolution of \(128\times 128\) pixels. An alternative is to use an AER type camera device [22]. A robotic goalkeeper based on the DVS128, designed by [PERSON] and [PERSON] [23], achieves 3 ms reaction time. However, this system is using a PC to process data, which is reducing its mobility and power efficiency, and it cannot capture rich non-linear structures of visual input since neuronal networks are not applied in the system. In order to utilise SNN for motion processing and implement the interface between SpiNNaker and external devices, a neuromorphic robotic platform consisting of a DVS, SpiNNaker and a servo motor has been designed. It is a ready-to-use neuromorphic platform for running any SNN to process motion in real-time, for example in the field of neuroprosthetics such as motor neuroprothetics, which allows people to bypass the spine and directly control the prosthesis, which is vital for patients with spinal cord injury. In order to evaluate the performance of its closed-loop execution and also demonstrate the benefit of using SNN in such applications, the platform is implemented on a robot goalkeeper. A similar problem of trajectory prediction for robotics, which utilises an event-based camera and a neural network trained to predict the ball's position has been also investigated by [PERSON] et al. [24]. In this paper, the architecture of the system and the principle of hardware communication are described and demonstrated. An evaluation based on efficiency and accuracy is also presented and discussed. The interface developed in this paper uses a single microcontroller to establish low-latency communication between DVS, SpiNNaker and a servo motor. It is the first neuromorphic interface based on SpiNNaker that supports a servo to precisely control the position of the robotic arm. It does not require a second chip to convert the communication protocol between microcontroller and SpiNNaker, which reduces power consumption. Furthermore, an Arduino microcontroller is used. It has a number of built-in libraries for external devices, which means more sensors and actuators supported by the Arduino platform can be added easily into the platform. The components that comprise the system are off-the-shelf, and the software implemented only uses the standard libraries. This neuromorphic system offers a platform for many other applications at low-cost and simple for further development.
# A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion
## 3 The Proposed Method for Fault Diagnosis
### Fault Model Modeling  
In this section, we give the method to determine the fault model. Due to the change of time and environment of the sensor measurements, the measured data have a certain degree of fuzziness. The membership function can be used to represent the fault features extracted from the observed data.  
Suppose \(X\) is a sample space of a type of attribute feature variable extracted from the sensor--for example, the amplitude of fundamental harmonic in the frequency spectrum of sensor signal, the membership function of fault model can be modeled as:  
\[\mu_{F}\left(x\right):X\rightarrow\left[0,1\right],x\in X, \tag{14}\]  
where \(\mu_{F}\) represents the membership function of fault \(F\) under the corresponding attribute.  
Two main factors are considered in the determination of membership function: the working performance of the sensor itself such as the linearity of sensor measurements and various kinds of interferences in the working process of the sensor such as the interference of mechanical noise and electromagnetic waves. If only the second factor is considered, the probability density function of the measured value of the same physical quantity is generally regarded as a form of Gaussian distribution. Gaussian distribution possesses the following advantages [72]: first, the Gaussian distribution is manageable, that is, a large number of data that is related to Gaussian distribution can be put through it to obtain a exact form. Second, if the error can be seen as superposition of many independent random variables. According to the central limit theorem, the error is supposed to have the form of Gaussian distribution. Third, Gaussian distribution has an extremely wide range of practical background, and a lot of the probability distribution of random variables in production and scientific experiments can be approximately described by Gaussian distribution. We take the Gaussian type of membership function as a fault model based on the above several points, and the details of membership function determination are presented as follows:  
Figure 1: The procedure of fault diagnosis with the proposed method.  
1. Assuming \(n\) groups of data are observed, each group consists of \(m\) consecutive observations in the same time interval \(\Delta t\). In order to make the generated fault model representative, \(n\) should be more than 3 and 30, respectively, and \(n\) groups of data should be observed at different times.
2. Calculate the mean value \(\mu\) and standard deviation \(\sigma\) of the \(n\) groups' observations. Here, observation \(i\) in group \(j\) is denoted as \(x_{i,j}\). The mean value \(\mu\) and standard deviation \(\sigma\) can be obtained as: \[\bar{X}=\frac{\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}x_{i,j}}{n*m},\] (15) \[\sigma=\sqrt{\frac{\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}\left(x_{i,j}- \bar{X}\right)^{2}}{n*m-1}}.\] (16)
3. The Gaussian type of membership function is obtained as: \[\mu_{F}\left(x\right)=\exp\left(-\frac{\left(x-\bar{X}\right)^{2}}{2\sigma^{ 2}}\right).\] (17)
## 3 Results and Discussion
### Effect of pH on Adsorption  
Figure 3 illustrates the effect of solution pH on PTSA adsorption onto resins. The maximum PTSA uptake of each resin was observed when the pH value was about 2.3, which could be owing to the electrostatic interaction between the negatively charged PTSA and protonated amine groups on resins. With the solution pH value further increasing, even though the anionic species of PTSA increased (p\(K_{a}\) = 1.7 [34]), the degree of protonation of amino groups on resins decreased, which was not conducive to electrostatic attraction. On the contrary, when the pH value was <2.3, with the pH value decreasing, protonation of amino groups on resins increased, but the neutral molecule form of PTSA increased, which could hinder adsorption though electrostatic interaction. Additionally, lower adsorption capacity in strong acidic solutions might also contribute to the introduced sulfate anion from pH adjustment. Sulfate anion could also be loaded onto resins by electrostatic interaction and result in the competitive adsorption with the PTSA anion [20].  
Figure 3: Effect of five resins on the pH adsorption of \(p\)-toluene sulfonic acid (PTSA).  
Figure 2: Fourier transform infrared spectroscopy (FT-IR) spectra of chloromethylated polystyrene (CMPS) and synthetic resins.
# LaRaCog: A Protocol for Cognitive Radio-Based LoRa Network
## 2 Related Works
The work explains LoRaWAN inconvenience when transmitting on a channel before receiving the parameters from the GW. This drawback increases the rejected packets rate within the LoRaWAN network and decreases the radio coverage of the GW. LoRa+ is based on the LoRaWAN protocol with modifications at the RX2 slot, which will be opened just before the uplink transmission (Tx frame). The results show a significant decrease in the collision and decrease the number of GWs by 80% within a high-density area of ED. On the other side, IEEE 802.22 is a CR-based standard for a wireless regional network using white spaces in the television (VHF/UHF TV broadcast bands) frequency spectrum between 54 and 862 MHz, potentially at the 1300 to 1750 MHz and 2700 to 3700 MHz where regulations allow [21]. This development is aimed at using CR techniques to allow the sharing of a geographically unused spectrum allocated for TV (white space) without causing interference. The standard specifies the air interface, including the cognitive medium access control layer (MAC) and physical layer (PHY). Sub-GHz such as the UHF bands are attractive for the LoRa implementation thanks to the ability to cover wide areas with a low power. Furthermore, 5G will open new frequency bands in the sub-GHz spectrum [22]. These bands might be accessed by the LoRa devices acting as secondary users when CR is applied. The presented state-of-the-art works analyzed scenarios in which LoRa performance is generally well studied and detailed when cognitive concept is applied directly or indirectly. The most relevant work is what have seen in [9; 13], but our work in LoRaCog generates a fully operational protocol that allows the LoRa network to operate on both licensed and unlicensed bands, and it can be used as a complementary one for LoRaWAN when needed. We detail in this work the CR tasks assigned to the LoRa networks elements, i.e., spectrum sensing, spectrum decision, and the resulting action. By exploiting the geolocation of the GWs and their spatial distribution, they are responsible for performing a cooperative spectrum sensing and reporting the results to the NS. The latter is responsible for making the final decision on the channel availability based on the received sensing from the GWs and the historical data it stored on the cognitive operating channels. Once the decision on a given channel is taken, the EDs will be informed accordingly to operate on these channels. In addition, the proposed protocol is adaptable to a heterogeneous use of licensed/unlicensed bands by exploiting the diversity of the SFs. This feature facilitates the adoption of LoRaCog to operate simultaneously in parallel with LoRaWAN.
# A Weighted Histogram-Based Tone Mapping Algorithm for CT Images
## 4 Materials and Methods
### Implementation  
Our algorithm is implemented in Python3.6 [40] using Numpy 1.13.3 [41], Scipy 1.0.1 [42], and Numba 0.38.0 [43]. For color space transformation, scikits-image 0.14.0 [44] was used. The source code follows PEP8 [45] recommendations and is part of the supplementary material. Luminance HDR 2.5.1 [46] was used to generate the reference TMO images, using the Ubuntu 18.04 operating system. The TMQI score is calculated using the original source code from the authors [39], using Octave 4.2.2 [47]. All of the custom calculation codes, as well as the example images, are available as a supplement to the paper.  
The calculations were performed on a Dell Latitude E7440 notebook (Round Rock, TX, USA) which was equipped with an Intel(R) Core(TM) i7-4600U CPU @ 2.10 GHz (Santa Clara, CA, USA), 8 GB of DDR3 RAM at 1600 MT/s transfer speed. The data was stored on a 240 GB SAMSUNG SSD PM85 drive (San Jose, CA, USA), using btrfs filesystem. The operating system was Ubuntu 18.04 64 bit. The computation time is dominated by the FFT calculation for which we used the Scipy implementation, but this part could be replaced with other numerical libraries, or could be executed on GPUs. However, some constant overhead is inevitable due to disk reading, data conversion, initializations, etc.  
Figure 8: The effect of the \(1/r^{a}\) weighting function and clipping. Rows from top to bottom have \(a=0.7\), \(1.0\), \(1.5\), \(2.0\), respectively, and the clip limits in the columns from left to right are \(1\), \(5\), \(10\) and \(20\), using \(1/N\) units where \(N\) is the number of histogram bins.
# Semi-Coprime Array with Staggered Beam-Steering of Sub-Arrays
## 5 Conclusions and Future Work  
In this paper, we presented a novel idea of staggered beam-steering of SAs for SAAs to control HPBW. For the proof of concept, the proposed technique was applied to an SCA with Dolph-Chebyshev weights. we also shed that by selecting the values of \(\Delta\) and SLA appropriately, we can maintain the HPBW, PSLR, and consequently directivity of the array within reasonable limits. We also showed that the proposed array offers the narrowest main lobe among its competitors and maintains a PSLR irrespective of the steering angle. Moreover, the parametric analysis presented here identifies the appropriate range in which the parameters \(\Delta\) and SLA can be adjusted to achieve a significant improvement in the performance metrics. As mentioned earlier, the appropriate values of \(\Delta\) and SLA depend on the widths of the SA1 and SA2 main lobes as well as the angular separation between their grating lobes, which are further dependent on the number of elements in the SAs and their inter-element spacing. Therefore, in future, we are determined to devise a mechanism to find the appropriate values of \(\Delta\) and SLA for a given set of SAs, to achieve given values of the performance metrics. It may be noted that the proposed strategy could be employed for arrays of RF and audio sensors for spatial filtering of RF or audio signals, respectively. The proposed scheme is a good choice for the situations where a pencil beam is required along with the restriction of small system size/weight, e.g., satellite-borne communication systems.  
All authors equally contributed to the conceptualization, formal analysis, investigation, methodology, validation, visualization, and manuscript draft. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 8: Performance metrics of the SCASS-C for variable \(L\), fixed SLA = 21 dB and \(\Delta\) = 0.15\({}^{\circ}\).
# An Improved Shoulder Line Extraction Method Fusing Edge Detection and Regional Growing Algorithm
## 5 Conclusions  
Aiming to improve the poor continuity and inaccuracy of extracted shoulder lines, this study proposed an extraction method that fused edge detection and the regional growing algorithm. The experimental results showed that the CPA and PA values of the edge detection method were in the ranges of 77.6-83.7% and 79.1-80.1%, respectively; the CPA and PA values of the regional growing algorithm were in the ranges of 81.1-83.1% and 78.1-84.5%, respectively; the CPA and PA values of the method proposed in this study were in the ranges of 86.7-89.7% and 85.8-91.7%, respectively. Moreover, the Dice and IOU values of the method studied in this paper were closer to 1 than those of the edge detection method and the regional growing algorithm. This method could guarantee shoulder line continuity and integrity. Meanwhile, burr removal reduced errors when the grid shoulder line was transformed into a vector.  
Shoulder lines have obvious turning points above and below the line, and the terrain factors (slope, curvature, etc.) also change accordingly. The geometric mechanisms of the P-N terrain above and below the line are significantly different. The positive terrain above the line basically maintains the original slope state after loess accumulation, and slope erosion is mainly surface erosion. The negative terrain below the line is dominated by gully erosion and gravity erosion, and various gravity landforms are widely developed. In summary, shoulder lines can be used as an important topographic index for regional soil erosion intensity and landform division. The accurate extraction of shoulder lines can provide a  
Figure 16: Examples of landforms in loess ridge and hill regions. (**a–d**) show the distribution of shoulder lines in loess ridge and hill regions, respectivelynew perspective for the quantitative study of loess landforms and is very important for the study of Loess Plateau landforms, soil erosion characteristics, and ecological environments.  
Conceptualization, F.L. and H.J.; methodology, H.J.; validation, H.J., H.W. and W.L.; writing--original draft preparation, H.J., W.L. and H.W.; writing--review and editing, H.J., H.W. and W.L.; supervision, F.L.; funding acquisition, F.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (grant number: 42271421).  
Not applicable.  
Data Availability Statement: Data will be made available on reasonable request.  
The authors declare no conflict of interest.
# FollowMe: One Social Importance-Based Collaborative Scheme in MONs
###### Abstract  
The performance of mobile opportunistic networks mainly relies on collaboration among nodes. Thus far, researchers have ignored the influence of node sociality on the incentive process, leading to poor network performance. Considering the fact that followers always imitate the behavior of superstars, this paper proposes FollowMe, which integrates the social importance of nodes with evolutionary game theory to improve the collaborative behavior of nodes. First, we use the prisoner's dilemma model to establish the matrix of game gains between nodes. Second, we introduce the signal reference as a game rule between nodes. The number of nodes choosing different strategies in a game round is used to calculate the cumulative income of the node in combination with the probability formula. Finally, the Fermi function is used to determine whether the node updates the strategy. The simulation results show that, compared with the random update rule, the proposed strategy is more capable of promoting cooperative behavior between nodes to improve the delivery rate of data packets.  
mobile opportunistic networks; cooperative forwarding; evolutionary game theory; social importance +
Footnote †: journal:  
_Future Internet_