esa-sceva/satcom-chunk-collection huggingface dataset:
# Optoelectronic Devices Based on Atomically Thin Transition Metal Dichalcogenides
## 4 Light-Emitters
In another study, the same group demonstrated light emission from a monolayer WSe\({}_{2}\) sheet with enhanced brightness at room temperature (EQE 5%) [77]. This counter-intuitive behavior was explained by a giant conduction band splitting (\(\sim\)30 meV) with opposite spin orientations for the lowest energy transition in tungsten based TMDs, resulting in a highly efficient dark (non-radiative) excitonic recombination channel at low temperatures [21]. Heterostacks are flexible due to the atomically thickness of the used materials, which enables flexible and semi-transparent optoelectronics. Spontaneous emission from a TMD monolayer can be enhanced due to the Purcell effect by coupling to an optical cavity [78; 79]. Enhanced PL emission from MoS\({}_{2}\), placed on top of a microdisc cavity, was shown by [PERSON] _et al._[80] Photonic crystal cavities [81] (Figure 6a) or microdisc cavities [82; 83] (Figure 6b) with even higher quality factors have been used to demonstrate lasing. These studies show that an optically pumped TMD monolayer can provide sufficient optical gain to compensate for the cavity losses. All three devices demonstrated ultra-low lasing threshold and continuous-wave operation with optical pump power as low as 27 nW at 130 K [81]. Typical characteristics of a laser--abrupt change in the slope of the output light intensity and emission linewidth--were observed, but photon statistic measurements yet need to be carried out to get deeper insight into the lasing behavior.
## 3 PAZ Description
### PAZ Imaging Modes  
The PAZ satellite will operate in four nominal imaging modes (Figure 14):  
* Stripmap: The antenna beam is pointed to a fixed angle in elevation and azimuth, resulting in a strip with constant quality in azimuth (azimuth resolution up to 3 m, scene size up to 30 km \(\times\) 50 km). Single (HH, VV) and dual (HH/VV, HH/HV, VV/VH) polarization modes are possible.
* ScanSAR: The electronic antenna elevation steering is used to switch after bursts of pulses between swathes with different incidence angles. This mode has an azimuth resolution up to 18 m, with a scene size of 100 km \(\times\) 150 km. It can only acquire images with single polarization (HH, VV).
* Spotlight: Azimuth phased array beam steering is used to increase the illumination time and the azimuth resolution, at the cost of azimuth scene size (azimuth resolution up to 2 m, scene size of 10 km \(\times\) 10 km). Single (HH, VV) and dual polarization (HH/VV) operating modes are possible.
* High resolution spotlight: this mode has an azimuth resolution up to 1 m, with a scene size of 10 km \(\times\) 5 km and is able to operate with single (HH, VV) and dual polarization (HH/VV).  
New modes developed for TerraSAR-X, such as the staring spotlight and the wide ScanSAR, are foreseeable for PAZ.
# Multiscale Feature-Based Infrared Ship Detection
## 2 Materials and Methods
### Candidate-Region Extraction Based on Grayscale and Edge Features
, \tag{14}\]  
\[I_{Edge}(i,j)=|f(i,j+1)-f(i+1,j)|+|f(i+1,j+1)-f(i,j)| \tag{15}\]  
In order to remove false edges, the image \(I_{Edge}\) needs to be segmented using a threshold, which is calculated by using Equation (16):  
\[T_{e}=\rho\cdot(Mean(I_{Edge})+Std(I_{Edge})), \tag{16}\]  
where \(Mean\) and \(Std\) represent the mean value and standard deviation, respectively. The mean value is used as the basis of the threshold, while the standard deviation is able to characterize the number of edges present in the image, and finally, a control parameter \(\rho\) is employed to achieve adaptive segmentation. The final binary edge feature image can be calculated as:  
\[BW_{Edge}=\left\{ \begin{array}{ll}1,&I_{Edge}(i,j)>T_{e}\\ 0,&otherwise\end{array} \right. \tag{17}\]  
On the basis of the above results, we define here edge intensity \(E_{n}\) as a feature quantity to measure the edge features in the local area: the window of \(r\times r\) size is moved in steps of  
Figure 6: Process of extracting the CRs and the results. (**a**) Input preprocessed image. (**b**) Coarse-segmented image. (**c**) Binary edge feature map \(BW_{MC}\). (**d**) ROI map. (**e**) CRs map. \(r/2\) across the binary edge feature map \(BW_{Edge}\), from the top left to the bottom right, and the original image can be decomposed into image patches. Then, the number of non-zero pixels in each image block is counted as the edge intensity \(E_{n}\) according to Equation (19):  
\[BW_{Edge}=[BW_{1},BW_{2},\cdots,BW_{n}\cdot\cdots,BW_{N}], \tag{18}\]  
\[E_{n}=\#\Big{\{}(i,j)\in BW_{n}\Big{|}BW_{Edge}(i,j)=1\Big{\}}, \tag{19}\]  
where \(BW_{n}\) is the \(nth\) local image patch and \(\#\{|\}\) represents a counting operation. Compared with the isolated noise points and residual sea surface clutter, ships tend to exhibit distinct edge contour characteristics, that is, they have large values of \(E_{n}\). Therefore, the value of the edge intensity can be used to determine whether there is a candidate target in the local image patch:  
\[BW_{n}=\left\{ \begin{array}{ll}1,&E_{n}>\xi\\ 0,&\text{otherwise}\end{array} \right. \tag{20}\]  
After judging the edge intensity of all local image patches, those marked as 1 are clustered and remapped into the image matrix to obtain ROI images:  
\[I_{ROI}(i,j)=\left\{ \begin{array}{ll}1,&\forall(i,j)\in(BW_{n}=1)\\ 0,&\text{otherwise}\end{array} \right. \tag{21}\]  
The ROI images and the coarse-segmented images (based on Equation (13)) are then logically combined to obtain the final CRs:  
\[I_{CR}(i,j)=BW_{MC}\ \&\&\ I_{ROI}(i,j) \tag{22}\]
## 1 Introduction
Furthermore, the remaining differences between the point clouds from different scanners, due to the imperfect boresight calibration of the upward looking scanner, could be corrected during post processing. Under degraded GNSS conditions, they reported a georeferencing error of up to 1 m for the horizontal position. They also reported that despite the limited absolute accuracy, 3D point measurements during bad GNSS conditions are still useful, especially if the purpose is mainly to exploit their relative positions. As an example, they presented that the standard deviation of such data is only 5 cm if the points from two scanners are combined and 2.6 cm if the points are separated for each scanner. Thus, such data are feasible for the extraction of the features of windows or passages if a certain error as to their absolute position is acceptable. Since the best laser systems in MLS are capable of estimating the range with an accuracy of 2 mm, and as direct georeferencing dominates in error propagation, an improvement is needed in the of georeferencing solution. The options in improving the georeferencing solution include more accurate calibration of the relative orientation of the MLS system components, automatic/manual detection of those objects (the position of which is known) from the road sides that may be used to improve georeferencing, and development of new data fusion approaches for MLS. The most in-depth analysis of MLS quality thus far is that presented by [PERSON] _et al_. [7]. This paper concentrates on evaluating the geometrical properties of laser point clouds collected by various commercial and research-based MLS systems in good GNSS conditions on an established urban test field.
## 5 Empirical Results
### Descriptive Statistics
The results are further validated by the frequency and probability density distribution of ship flow at the port in Figures 2-4. In addition, we investigated the stationarity of the above ship flow series with different time scales. In general, the ADF and KPSS tests are complementarily used to evaluate the stationarity in time series models. Therefore, both methods were used in this study to test the stationarity of the ship flow time series with the intention of obtaining more precise results. The null hypothesis of the ADF test was the presence of a unit root, indicating the nonstationarity; the null hypothesis of the KPSS test was the absence of unit root, indicating stability. Table 1 showed that the \(p\) values of the ADF test of all the ship flow sequences were greater than 0.05; so, the null hypothesis is accepted--that is, the sequences are nonstationary time series. Similarly, the KPSS test results indicated a \(p\) value less than 0.05, rejecting the null hypothesis. Therefore, all the ship flow sequences of three ports are nonstationary time series. Figure 2: Frequency and probability density distribution of daily ship flow sequences. Figure 3: Frequency and probability density distribution of weekly ship flow sequences.
# Spatiotemporal Dynamics and Obstacles of the Multi-Functionality of Land Use in Xiangxi, China
## 3 Results
### The Analysis of Obstacle Factors
#### 3.3.3 The Production Type of Obstacle  
The geographical location of Xiangxi is in a remote region that restricts the efficient development and utilization of the land. Fenghuang, Huaguan, Baojing, Yongshun, and Longshan all suffer from production obstacles. The agriculture and animal husbandry production function (PF1), economic growth function (PF2), transportation support function (PF3), and environmental purification function (EF3) are all hindered by significant obstacles. The agricultural and animal husbandry production of Baojing, Yongshun, and Longshan have certain advantages, especially the agricultural output per capita and animal husbandry output per capita of Longshan and Yongshun in the last five years ranking in the top 3. However, the development of regional agricultural production is extensive, and is dominated by traditional agriculture and animal husbandry. The the level of productivity is low. The Wuling mountain area is high terrain, covering a large area with few people. It is limited by the influence of the surrounding economic influence, which limits the development of the economy and transport in the five areas. For example, in 2017, the sum of economic density of the five areas was 1.26 times that of Jishou. The sum of road area per capita was 1.04 times that of Jishou, and the sum of freight turnover was only 0.31 times that of Jishou. Although the land use intensity of Xiangxi is low, the ecological function has certain advantages. However, in the last five years, the large input of agricultural chemicals in these five areas (in 2017, the average amount of fertilizer applied in the five areas increased by 1.52 times that of 2013) led to an enhancement of human disturbances to the land and an increase in the environmental purification dysfunction degree of the land.  
As a whole, the obstacle factors for land use in Xiangxi are part of the dynamic change in spatial and temporal dimensions. Although there are spatial and temporal differences in the types and degrees of obstacle factors, the main type of obstacles against efficient land use in Xiangxi are due to production, agriculture and animal husbandry (PF1), economic growth (PF2), transportation (PF3), and environmental purity (EF3), which are still the main factors restricting the overall land use function in Xiangxi.
# A Microservices-Based Control Plane for Time-Sensitive Networking
## 4 Tests and Discussion of Results
### Scheduling Solution Inspection  
This subsection aims to verify that the results obtained by the combination between the Dijkstra algorithm output and the ILP scheduling are correct. As we have specified in the previous section, the main objective of the \(\mu\)TSN-CP is to be able to perform all the necessary calculations to find a specific and viable solution for a set of flows, taking into consideration that each stream will have an origin and destination, as well as its own characteristics in terms of bandwidth and maximum latency. We implemented a scheduling Solution Visualizer included in the ILP microservice to verify this goal, which includes the following (see Figure 9 as an example):  
1. Network topology: Specifies the network nodes and the links between them. It is located in the upper-left corner of the viewer.
2. Description of the stream matrix: It includes a set of parameters such as the matrix of network links, the number of frames in each stream, the period of the flows, and an specification of the order of the links to be used according to the Dijkstra algorithm for each stream. These are written in two-dimensional vectors and dictionaries; the order in the array is the stream identifier. The parameters are in the upper-right corner of the diagram in red rectangles.
3. Gantt diagram: This shows the used transmission slots, representing each link with a specific color; the horizontal axis depicts the time in milliseconds, while the vertical axis represents the position of the frames of each stream in each link. The notation employed on the vertical axis is as follows: 'S' is the frame stream number, 'L' is the link number through which the frame is traversing, and 'F' is the frame order within the stream. This graph is located at the bottom of the viewer.  
Figure 9: First scheduler problem.  
We now describe three examples of the output provided by the visualizer after finding a solution to the scheduling problem. These examples were tested theoretically and using an auxiliary microservice that generates networking and TSN scheduling problems randomly.
# Topp-Leone Modified Weibull Model: Theory and Applications to Medical and Engineering Data
## 7 Conclusions  
In this paper, a four-parameter family of distributions called the Topp-Leone modified Weibull distribution (\(TLMW\)) is introduced. Some of its statistical and reliability features are derived. The \(TLMW\) density function can decreasing, uni-modal and right skewed but, its hazard rate function can be decreasing, increasing, J-shaped, U-shaped and bathtub. Therefore, it can be used quite effectively in analyzing lifetime data. Three different methods of estimation as; maximum likelihood, maximum product spacing and Bayesian methods are used to estimate the model parameters. Additionally, the \(TLMW\) model can be used as a substitute for many known distributions and in some cases it can provide better results (with respect to model fitting) than the models considered in the paper. The flexibility of this distribution is evaluated by applying it to four real data sets and comparing it to various other distributions. The findings of the tables and figures show that the suggested model regularly outperforms other competitor models for these four data sets as COVID-19 data, survival rates Guinea Pigs, Waiting times of service of 100 bank customers, and Tensile strength for single carbon fibres (in GPa). Finally, the \(TLMW\) distribution will attract a broader range of applications, including medical, engineering, and social sciences, among others.  
These authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University for funding this work through Research Group no. RG-21-09-15.  
Datasets can be found under the Section 6.  
The authors declare no conflict of interest.
## 4 Experiment and Analyses
### Computational Efficiency  
In this subsection, the computational efficiency of the above six fusion methods, that is, the execution time of the corresponding methods in the computer, is compared, as listed in Table 4. Because of the high computational complexity of the sparse representation model, the proposed method spent most of its time on calculating the local gradient sparse descriptor of the source images. However, in the above six fusion methods, the proposed method had great efficiency improvement, compared to Ma18.  
Figure 13: Performance comparison of fusion results between without recursive filter and with recursive filter. (**a–c**) multi-exposure source images; (**d–f**) weight map without recursive filter; (**g–i**) weight map with recursive filter; (**j**) fused image without recursive filter; and (**k**) fused image with recursive filter.
# The Basis for Reliability-Based Mechanical Properties of Structural Aluminium Alloys
## 2 Scope of Analysis
### Important Mechanical Properties  
Minimum (guaranteed) values of relevant mechanical properties of aluminium at room temperatures for all types of alloys and tempers are specified in EN 755-2 [27] for extruded rods, bars, tubes, and profiles and in EN 485-2 [28] for sheets, strips, and plates. Various mechanical properties such as 0.2% proof strength, ultimate tensile strength, elongation at failure, and hardness are prescribed for all types of alloys, tempers, and ranges of thickness. Values related to the reduction of mechanical properties in the heat affected zone (HAZ) for MIG and TIG welding methods are given in EN 1999-1-1 [15], related to nominal values via a reduction factor. Even though a unified designation system should ensure that prescribed values of mechanical properties are the same for all the alloys in the norms worldwide, certain differences between countries are possible [5].  
The main mechanical property for the reliability assessment of aluminium structures is the 0.2% proof strength as it is required for all the design calculations of cross-sectional and member resistances. Ultimate tensile strength is required for the verification of connections or tension failure of the net cross-section. Young's modulus is important in stability verification and the serviceability design of members. Poisson's ratio is needed for the calculation of shear modulus used in lateral-torsional buckling stability verification and various other torsional failure modes. In accordance, the first stage of the research focuses on the statistical analysis of these four mechanical properties of aluminium. Other mechanical properties such as elongation after fracture were not considered in this study. Additionally, properties regarding HAZ were not considered as these need to be analysed after the establishment of a base for non-HAZ values is completed, along with the many parameters regarding welding that are often not reported but are highly influential in terms of mechanical properties.
# Projected Augmented Reality Intelligent Model of a City Area with Path Optimization
## 2 Model Proposed
### Obtaining and Preparing Georeferenced Information  
The georeferenced data to create the model was obtained from OpenStreetMap. A region of interest was defined, around the area of the School of Engineering of the Polytechnic Institute of Coimbra. Figure 10 shows the area, after being processed and modeled in Unity. The area modeled contains the School of Engineering (red in the figure), a large residential and commercial area around the school, some green areas, as well as a small portion of the Mondego river and the bridge to the other margin. The urban area covered is approximately \(2\times 1\) km.  
The dataset obtained from OSM contains information about the routes and also partial information on the buildings. It contains the location of most of the buildings and the height of some of them. The information was imported using Blender add-on blender-osm, in March 2019.  
At the time of importing, OSM did not provide accurate information about all the buildings, namely their height was missing. So the height of the buildings for the physical model had to be estimated. Existing buildings can be grouped by height, in a number of categories, manually defined in blender-osm. Three categories were manually defined for the current model. Hence, the buildings in the physical map have three different heights. The buildings that occupy just a small area are given the lowest height. Buildings with no height information in OSM are included in this first category, so even a flat building will receive a small height in the model. This includes most of the buildings, since there is not much information in OSM for buildings of the area being modeled. Average buildings are given average height, and large buildings are given larger height.  
Figure 2 shows the terrain, imported from OSM, as a Blender object, with the buildings on top. Figure 3 shows the solid ready to slice and export to create the physical model.  
Figure 2: Terrain and buildings as Blender objects.
# Design and Investigation of Capsule Networks for Sentence Classification
## 3 Proposed Model
### Convolution Stage  
The convolution operation is mainly used to extract features in the given input. As mentioned above, since one row represents one word, we need to apply the convolution operation to the entire column i.e., we must use a sliding window of kernel size--\(h\times d\) (see Figure 3). Here, \(h\) is the number of words we want to use in a single convolution step (N-gram word). Using the convolution operation this way has two advantages: the sequential order of words is kept, and the total number of convolution operations is reduced. This reduces the total number of parameters which in turn will result in increased system speed.  
In this convolution operation, a feature \(f_{i}\) is generated for a given filter \(K_{h,d}\in\mathbf{R}^{h\times d}\) as:  
\[f_{i}=\varphi(\sum_{h=1}^{h}\sum_{d=1}^{d}K_{h,d}X_{i+h,d}+b_{i}), \tag{1}\]  
where \(f_{i}\), for \(i=1,2,3,...,n\), is the generated feature, \(\varphi\) is a nonlinear activation function (ReLU used here), \(K_{h,d}\) is the filter (we have \(m\) set of those filters), \(X_{i}\), for \(i=1,2,3,...,n\) is the input word vector and \(b_{i}\), for \(i=1,2,3,...,n\) is a bias term. The concatenation of features \(f_{i}\) forms a column feature vector \(F\) in the first stage convolution.  
As can be seen from Figure 1, a parallel set of convolution operations with varying kernel sizes is utilized. The main reason for using this parallel set of convolution layers is to capture possible word combinations which will form as many specific features as possible.  
The results of the second stage parallel convolution operations are then concatenated to produce \(m\) column vectors, where \(m\) is the number of filters. This output is then made to pass through other two stages of convolution, with dropout in between them, before it is processed in the Primary Caps layer. It should be noted that, in addition to extracting features, the convolution stages help in reducing the dimensionality of the input data. This is desirable as CapsNets work better with low dimensional input.