esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Sensitive Bands Selection of Forest Fire  
In this study, the huge forest fire that originated in China's Daxinganling Mountains in 1987 was selected as the reference area which obviously reduces the influence of mixed pixel. Twenty random points were generated in the burned area (Figure 1). The DN values (data value range: 0-65,535) of these points in different bands of TM in different seasons are shown in Tables 1-3. During these three months, forest changes can be very pronounced in the event of a fire. Therefore, these three months are the basic months for the occurrence of fires. The total correlation index(r) value of all types between band 5 and band 7 is the lowest; band 5 and band 7 exhibit a small disparity in their spectral responses to different land covers. The total correlation index(r) value of all types between band 2 and band 4 is the largest; band 2 and band 4 exhibit a large disparity in their spectral responses to different land covers (Table 4). Therefore, these four bands are used to derive the Normalized Difference Fire Index (NDFI) in this study [33]. A lower NDFI value indicated a greater possibility of fire distribution.  
Figure 1: Twenty selected random points in the burned area.
# Comparative Analysis of the Methods for Fiber Bragg Structures Spectrum Modeling
## 2 Modeling Methods  
The interference scheme for recording FBGs, as one of the most commonly used schemes for recording fiber Bragg gratings, implements a change in the refractive index along the length of the grating according to the harmonic law [15; 16]:  
\[n(z)=n_{0}+\frac{\Delta n}{2}+\frac{\Delta n}{2}\cdot\sin\left(\frac{2\pi}{ \Lambda}z\right)\,, \tag{1}\]  
where \(\Lambda\) is the FBG period, \(n_{0}\) is the refractive index of the optical fiber core, and \(\Delta n\) is the value of the induced refractive index, which generally can be represented as a function of the grating length, for example, as the Gaussian apodization function [15; 17]:  
\[\Delta n(z)=\Delta n\cdot\exp\left(-\frac{1}{2}\left(\frac{z-z_{\text{M}}}{ \sigma}\right)^{2}\right), \tag{2}\]  
where \(z_{\text{M}}\) is the FBG midpoint and \(\sigma\) is the apodization parameter defining the ratio \(p\) of the induced refractive index at the FBG edges to its value at the midpoint:  
\[\sigma^{2}=-\frac{1}{8}\frac{H^{2}}{\ln p}\,, \tag{3}\]  
where \(H\) is the FBG length. The FBG is formed in the optical fiber; therefore, the refractive index of the core of the optical fiber is the minimum value of the refractive index in the FBG interval, and the maximum will be the sum of the refractive indices of the fiber and the induced refractive index.  
Thus, the initial parameters for the FBG modeling are: the length of the grating \(H\), the refractive index of the optical fiber core \(n_{0}\), the induced refractive index \(\Delta n\), the FBG period \(\Lambda\), and the apodization parameter \(\sigma\).
## 5 Simulations
### Simulation Methodology  
In order to test the SHA and to compare it with the approaches proposed in [19; 20], a discrete event simulation model of the scheduling has been implemented. It is used to simulate scheduling decision for a medical team in the Orthopedic Surgery Department (OSD) at the HCU. One year and the half length (78 weeks) is set for each simulation run. The new patients are assumed to arrive according to a Poisson distribution with an average of six per week. We assume initially a waiting list of 100 patients and every week the new arrived patients are added to the list.  
During the last two years, considering historical data in the OSD, the occurrence probability of each surgery type and their duration have been computed. These values are considered in the simulation for generating the surgery of the arrival patients. Figure 3 shows the occurrence of the most common surgeries performed in the OSD while Table 3 indicates their average and standard deviation. Notice that seven types of surgeries represent around 73% of the total surgeries performed; however, all of them are used in the simulations.  
Figure 2: Scheduling panel. The main interface of the software tool is shown. Particularly, the scheduling panel where once the number of blocks to schedule, the minimum confidence level, and the surgical team are introduced, a summary of the scheduling performed is shown at the bottom part.  
```
Input:\(\mathcal{B},Pd\) Output:\(\Omega\)
1\(\Omega:=0\)forall\(b_{i}\in\mathcal{B}\)do
2\(f_{i}:=(1,\left\lfloor Pd\cdot(i-2)\right\rfloor)\); \(l_{i}:=\left\lceil Pd\cdot(i+1)\right\rceil\); forall patient (\(w_{j}\)) scheduled in block \(b_{i}\)do
3 ifthe preference order (\(j\)) \(\
otin[f_{i},l_{i}]\)then
4\(\Omega:=\Omega+\min(|j-f_{i}|,|j-l_{i}|)\)
```  
**Algorithm 1** Calculation of \(\Omega\) parameter in a scheduling of \(|\mathcal{B}|\) time blocks
# Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links
## 4 The Rainfall Rate Estimation Model
### Equivalent Rain Height Model  
Stratiform and convective rain events have a different impact on satellite links, not only in terms of specific attenuation, but mainly as for the equivalent rain height, which can change on an event basis. In particular, the proposed model includes two distinct formulations for \(h_{R}\):  
* In the case of stratiform events, the presence of the melting layer, which is also known as bright band, induces extra attenuation on electromagnetic waves propagating through the troposphere: as a matter of fact, in this case, the rain rate is constant approximately up to the freezing level, below which frozen precipitation particles start to melt during their fall (melting layer). According to the SC EXCELL model [23], the additional attenuation due to the melting layer is taken into account by means of an equivalent rain height \(h_{BB}\), to be added to \(h_{R}\), with the latter being usually set equal to the zero-degree isotherm height \(h_{0}\). In mathematical terms, the stratiform rain height is given by [23]: \[h_{R,str}=h_{0}+h_{BB}(f)\ [\text{km}],\] (8) \[h_{BB}(f)=4.58\cdot e^{-0.0675f}+0.51\ [\text{km}],\] (9) where \(f\) is the frequency in GHz.
* In the case of convective events, the corresponding rain height is higher than the zero-degree isotherm height because of the strong updrafts and downdrafts, which push water particles up beyond \(h_{0}\) and also prevent the formation of a definite melting layer. Consequently, the convective rain height is an increased value of \(h_{0}\): \[h_{R,env}=\tau(m)h_{0}\ [\text{km}],\] (10)  
In (10), the _rain height enhancement factor_\(\tau(m)\) is higher than 1 and depends on \(m\), namely the month number of the calendar year.
# Robust and Accurate Recognition of Carriage Linear Array Images for Train Fault Detection
## 3 Methodology
### Alignment Quality Assessment Method  
In an ideal alignment between scene and target point sets, their Gaussian mixtures across horizontal coordinates should align closely, as depicted in Figure 5a. However, even after optimization, notable disparities can remain between point sets of different categories, as highlighted in Figure 5b. Furthermore, when two carriage images undergo vertical mirroring, their Gaussian mixtures might appear similar despite a mismatch in point set categories, as demonstrated in Figure 5c. Clearly, relying purely on Gaussian mixture similarity for point set matching is not sufficient.  
To tackle these issues, this study proposes an intuitive yet effective method to evaluate the quality of point set alignments, providing a reliable metric to gauge the success of template matching. In detail, following the point set alignment, the scene point set, denoted as \(S\), undergoes a transformation into the coordinate space of the target point set \(M\). For each point \(s\) in \(S\), its closest counterpart in \(M\) is determined, expressed as follows:  
\[NN(s)=\underset{m\in M}{arg\,min}\,distance(T(s),t) \tag{8}\]  
Here, \(NN(s)\) signifies the closest point to s within \(M\), while \(distance(a,b)\) quantifies the spatial separation between points \(a\) and \(b\). Given \(\tau\) as a set distance threshold, if \(distance(T(s),t)<\tau\), it is inferred that point s has correctly matched. The fraction of accurate matches, denoted as \(match\_ratio\), is derived using a subsequent formula. When \(match\_ratio\) exceeds a threshold \(\gamma\), the template matching process is judged successful. The \(match\_ratio\) serves as the score for match quality. Finally, among all templates, the category corresponding to the highest-scoring template is recognized as the image's identification result.  
\[match\_ratio=\frac{number\,of\,correctly\,matched\,points\,in\,S}{total\, number\,of\,points\,in\,S} \tag{9}\]
## 6 Open Issues and Future Research Directions
### Various Quality of Service (QoS) Requirements  
Various kinds of services, such as streaming media and real-time communications, have their own set of QoS requirements, such as jitter and end-to-end delay, as well as high bandwidth. Voice over Internet Protocol (VoIP) and video streaming and services, for example, necessitate a constrained low end-to-end delay and jitter and delay [112]. On the other hand, data transfer applications require high levels of reliability and packet delivery ratio [113].  
Fault tolerance is necessary for certain applications for higher QoS, which may be implemented using topology-based routing protocols. As a result, meeting QoS requirements in UAV routing is yet another unsolved problem. The modeling findings of UAVs in high-speed movements reveal increased latency, which is one of the major disadvantages. As a result, the delay threshold is regarded as a difficult problem. Furthermore, a significant issue in routing that supports mobility is that the protocol must be prepared to accommodate the overhead when nodes are mobile and the network topology changes often. The majority of routing protocols are constrained by delays and costs. Nevertheless, other criteria such as route mobility, QoS metrics, stability, connection quality, and security technology and access control may be considered while designing an efficient routing protocol. Further study might be done to construct adaptive FANETs to improve QoS using various solutions such as the K-means clustering method [114].
## 6 Conclusions  
In this paper, we have realized a number of spectrum sharing techniques for in-building small cells, namely dedicated access, CoPSA, LSA, and LAA by exploiting the high external wall penetration loss of 3D buildings. The ABS based eICIC technique has been used to avoid or minimize co-channel interference signal generated due to sharing the same spectrum in space simultaneously. Small cells are considered to be enabled with multiple bands to realize spectrum sharing techniques for them. For each technique, system-level average capacity, spectral efficiency, and energy efficiency metrics by defining an optimal number of ABSs have been derived. Irrespective of the type of technique, the spectral efficiency varies linearly with \(L\) and the energy efficiency shows a negative exponential decays with \(L\).  
It has been found that LAA provides the best spectral efficiency and energy efficiency performances due to the good indoor channel conditions and no need for imposing ABSs to LAA because of operating the transceiver 2 at a different spectrum. Moreover, the spectrum bandwidth available in the 60 GHz is huge enough to achieve a high data rate. An optimal value of \(L\) that trade-offs both the spectrum and energy efficiencies has been defined and it has been shown that the spectrum sharing techniques can meet the spectral efficiency and energy efficiency requirements for 5G mobile systems. Furthermore, LAA has been found to be more cost effective since it requires the lowest density of small cells (i.e., \(L\)) of all other techniques to satisfy the spectral efficiency and energy efficiency requirements for the 5G mobile networks.  
This research received no external funding.  
This paper is partly submitted to IEEE GLOBECOM 2019 Workshop on Advancement in Spectrum Sharing, Waikoloa, HI, USA, 9-13 December 2019 [21].  
The author declares no conflict of interest.
## 3 Results
### The Analysis of the Spectral Band Regions  
In this set of control experiments, we aimed at exploring the influence of the spectral band regions on SIF inversion. Thus, we first set the number of PCs to three when using the PCA and the polynomial as third order to ensure a normal SIF value range.  
Figure 3 shows the data-driven algorithm inversion results of groups A and B. From a qualitative point of view, the SIF value distributions were both consistent with the surface radiance images (shown in Figure 1), and the SIF value results all showed obvious directional effects: the northern side of the picture showed significantly higher SIF values than the southern side. To illustrate this, the high vegetation area in the north determined the more significant SIF signals, whereas the texture of the lower SIF area in the middle and east revealed that these areas were covered by buildings or roads, where there was no vegetation emitting fluorescence at all. One main difference between Figure 3a,b is the square building structure in southwestern area, showing obtrusively high and relatively moderate values using R-NIR and sole NIR regions, respectively. Due to the rationality of SIF, all human-made structures do not emit chlorophyll fluorescence, which indicates that the values in Figure 3a are invalid, while the NIR bands are enough to retrieve SIF.  
From a numerical point of view, the calculated SIF value ranges of group A and group B were 0-3.108 and 0-17.846 mW\({}^{-2}\)sr\({}^{-1}\)nm\({}^{-1}\), respectively, within the normal range of SIF (0-20 mW\({}^{-2}\)sr\({}^{-1}\)nm\({}^{-1}\)), but there was an obvious difference in the magnitudes. In further comparing the SIF frequency distribution maps through Figure 4, the SIF value map obtained by using the R-NIR regions was concentrated around 0.7 mW\({}^{-2}\)sr\({}^{-1}\)nm\({}^{-1}\), while the SIF values obtained by the NIR only region was focused around 5 mW\({}^{-2}\)sr\({}^{-1}\)nm\({}^{-1}\). This indicates that R-NIR inversion is obviously less superior. Existing studies have shown that the near-infrared band can better retrieve SIF, since the influence of vegetation canopy scattering and reabsorption would be lower in this region [37]. The results of our experiment also confirm this position; that is, the NIR region retrieval is closer to the normal range of SIF values and also SFM verification, which will be stated in the following section.  
Figure 2: SIF retrieval values based on principle component analysis with different PC numbers: (**a**) 1 PC; (**b**) 2 PCs; (**c**) 3 PCs; and (**d**) 4 PCs.
# An Improved Visual SLAM Method with Adaptive Feature Extraction
## 3 Our Method
### Improved Feature Extraction Algorithm
#### 3.1.4 AGAST Extract Feature Points  
We extract feature points using the AGAST method [20] from the divided grid image. The AGAST algorithm has added two states \"not brighter\" and \"not darker\" to the configuration space of FAST. The specific state is defined as follows:  
\[S_{n\to x}=\begin{cases}d,I_{n\to x}<I_{n}-t&(\mathit{darker})\\ \bar{d},I_{n\to x}\geq I_{n}-t\wedge S^{{}^{\prime}}_{n\to x}=u&( \mathit{tonddarker})\\ s,I_{n\to x}\geq I_{n}-t\wedge S^{{}^{\prime}}_{n\to x}=\bar{b}&(\mathit{similar}) \\ s,I_{n\to x}\leq I_{n}-t\wedge S^{{}^{\prime}}_{n\to x}=\bar{d}&(\mathit{similar}) \\ \bar{b},I_{n\to x}\leq I_{n}-t\wedge S^{{}^{\prime}}_{n\to x}=u&( \mathit{torb brighter})\\ b,I_{n\to x}>I_{n}+t&(\mathit{brighter})\end{cases}, \tag{6}\]  
where the state of a pixel for kernel \(n\) is denoted as \(S_{n\to x}\), \(S^{{}^{\prime}}_{n\to x}\) denotes the previous state, \(I\) represents the pixel luminance, and \(u\) indicates the unknown state. \(t\) represents the detection threshold; \(d\) indicates that the grayscale of the current point is darker than the center point; \(\bar{d}\) indicates that the current point's grayscale is non-darker than the center points'; \(s\) indicates that the grayscale of the current point is similar to the center point; \(b\) indicates that the current point's grayscale is brighter than the center points'; \(\bar{b}\) indicates that the grayscale of the current point is non-brighter than the center points'.  
The AGAST [20] algorithm improves the Accelerated Segment Test (AST) module at the bottom of FAST. It uses an algorithm similar to reverse induction in the extended configuration space to find an optimal decision tree and combines the decision trees so that corner point detection can automatically adapt to changes in the external environment without having to reacquire the decision tree. AGAST improves the trinomial decision tree in the FAST algorithm into a binary tree, as shown in Figure 2. Once the pixel neighborhood changes, AGAST switches between two (or more) trees. In homogenous pixel neighborhoods, the left tree achieves fewer pixel evaluations (shorter decision paths), whilethe right tree is optimized for texture regions. AGAST dynamically and efficiently assigns decision trees based on the currently processed image information, improving the detection speed of the algorithm.  
In order to ensure that enough feature points are extracted in areas with less texture, the global adaptive threshold obtained in a is first used as the initial value to detect feature points from the picture. The threshold is set to 1/4 of the original value, and feature points continue to be extracted when the obtained image feature points are insufficient.
# Moving Towards Body-to-Body Sensor Networks for Ubiquitous Applications: A Survey
## 4 BBN Design Challenges and Open Issues
### Wireless Channel Propagation  
The propagation of the wireless signal between humans carrying wearable devices still present a non-deterministic research field. Recent propagation models have been established in the 2.45 GHz ISM band [85; 86; 87], based on the human body physical characteristics, such as mobility, body postures; line-of-sight (LOS), non-line-of-sight (NLOS), body shadowing, fading, etc. Indeed, the feasibility of upper layers design relies on the understanding of the physical layer characteristics.  
In [15; 87; 16], some experiments were performed at the ISM frequency of 2.45 GHz, to illustrate the human body impact on signal propagation in BBNs through different scenarios. It is assumed that the antennas are positioned on two distant persons. To estimate signal loss over distance, two additional parameters are added to the log-distance path loss model, which are the body shadowing (\(X_{BS}\)) and the small physiological movements (\(X_{SM}\)). Yet the BBN path loss model of the signal propagation in free space, is given by [87]:  
\[P(d)=P_{0}+10 nlog(d/d_{0})+X_{BS}-X_{SM}\ \ \ \ \ (dB),\]  
where \(P_{0}\) represents the path loss measured at a reference distance (\(d_{0}=1\) m), \(n\) is the path loss exponent and \(d\) is the distance between the transmit and receive antennas. The observed results of the signal propagation between two BBN users could be reported for both stationary and mobile scenarios [16]:
## 3 Results and Discussion
### Effect of Peroxone-Pretreated Wastewater on BWTP
This rapid increase observed in the initial phase is believed to be most deeply related with the phenol degradation, which can be confirmed from the \(OUR_{exo}\) profile of pure phenol shown in Figure 5b. A similar rapid \(OUR_{exo}\) increase can also be observed in the test using the cokes wastewater, of which the major pollutant was phenol itself. Although an excessive amount of MDEA (20,548 mg/L) also existed in the reclaimer wastewater, its contribution to the \(OUR_{exo}\) would be trivial considering its extremely low biodegradability. The \(OUR_{exo}\) of the peroxone-pretreated wastewater also increased soon after the sample injection. However, its increasing rate was clearly lower than the other cases, implying that the initial \(OUR_{exo}\) increase of the peroxone-pretreated wastewater was more strongly related with the microbial degradation of other pollutant rather than phenol. The most probable pollutant was formate as can be seen in Figure 5b. In fact, formate became the most abundant pollutant (17,787 mg/L) in the wastewater after the peroxone pretreatment due to the result of partial oxidation of MDEA. In addition, the phenol and thiocyanate concentrations of the peroxone-pretreated wastewater were only 10.7 and 50.4 mg/L, respectively (see Table 2), which means that their contribution to the initial \(OUR_{exo}\) increase would be minor. The respirometer tests using pure chemicals revealed that the biodegradation rates of the involved pollutants were in the order of phenol > formate > thiocyanate > MDEA. It is interesting that formate, a well-known readily biodegradable compound, was degraded more slowly than phenol. This might be because the microbial sludge used in the respirometer tests had been acclimated to the cokes wastewater for a long time so that phenol-degrading microorganisms should be dominant in the sludge. Though the slow  
Figure 5: \(OUR_{exo}\) profiles: (**a**) cokes, raw reclaimer, and peroxone-pretreated wastewaters; (**b**) phenol (1000 mg/L), SCN\({}^{-}\) (2500 mg/L), formate (20,000 mg/L), and MDEA (20,000 mg/L); 3 mL of samples were injected for all cases. biodegradation rate of formate can be a temporary problem during the startup period of the proposed AOP-combined BWTP, we believe that it will be naturally resolved along with continued operation since the microbial community distribution within the sludge will be eventually optimized to the peroxone-pretreated wastewater through a new sludge acclimation process.
# Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI)
## 5 Experimental Study and Comparative Analysis
### Comparative Analysis of Test Results  
If self-gathered data is used for the verification of lane line detection algorithms, it is hard to evaluate in a common standard. The Caltech datasets are widely utilized because they have not only structured paths, but also unstructured roads including shadows and road marks of varying intensities. Therefore, these data are used to evaluate the algorithm in this paper. Some classic roads are shown in Figure 14.  
Figure 13: Effects of positive detection, missing detection and false detection: (**a**) Positive detection; (**b**) Missing detection; (**c**) False detection.  
The Caltech data sets have been used to evaluate various lane line algorithms by numerous researchers around the world. [PERSON] etc. [30] presented a fast lane line detection method based on RANSAC. He etc. [31] put forward an effective lane line detection method based on Hough transform and the Canny algorithm, and a lane marker detection approach based on automobile instantaneous directional enhancement method was proposed by [PERSON] [32]. The proposed method was compared with the above methods using the Caltech data sets, as shown in Table 2.  
As shown in Table 2, the detection performance of the proposed method is better than those of the other methods. The main reasons are as follows: in [30], the lane line vanishing point is not easy to choose and there is an absence of lane line, so this method is unsuitable for worn lane lines. The RANSAC algorithm [29] is unsuitable for lane lines with large curvatures. The method proposed by [PERSON] encounters difficulties improving lane line detection according to the driving direction due to the inverse of the lane line. The detection results of the method presented in this paper are shown in Figure 15.  
In order to further evaluate the algorithm proposed in this paper, other typical road environments including highways, wet roads, and rural roads are tested. Figure 16 shows some typical road conditions used for evaluation:  
Figure 15: Detection results of the proposed method.  
Figure 17: Detection results of other data sets.