esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Interventions in Fuel Management Zones Using Remote Sensing
## 2 Materials and Methods
### Approach Overview  
To detect an intervention in an FMZ, this work proposes comparing the vegetation inside the FMZs and the vegetation in the outer areas adjacent to the FMZs, by creating buffers adjacent to the FMZs boundaries. FMZs have different dimensions (they can vary between 10 and 125 m in width), have a format that varies according to the type of FMZ and also, it is necessary to guarantee that these buffers are located on vegetation areas. The materials that are used as a base for the analysis are satellite images and the FMZs vector information.  
The satellite imagery is pre-processed, and the areas of interest were cut to obtain just the data from the FMZs areas that are being analyzed. The FMZs vector information is also pre-processed and used to cut the satellite imagery. From the resulting image, several metrics are extracted and then fed into the machine- learning algorithms to make the predictions. The algorithms with the best results in the literature were used to assess which works best for the presented problem. The used algorithms are Support vector machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-nearest neighbors (KNN). The performance of these algorithms will be mainly analyzed through the F1-score (Equation (1)), which reaches its max value with perfect precision and recall, and the Kappa score.  
\[\text{F1-score}=2*\frac{precision*recall}{precision+recall} \tag{1}\]  
To optimize the learning algorithms the input data was split into two sets, train and test with a 70% and 30% split. 70% of the data (train set) was used in conjunction with 5-fold cross-validation to find the best parameters for all the algorithms via random search of the algorithm's parameters.  
Figure 1: Study area and some Fuel Management Zones (FMZs) in vector format in the zone of Cardigos, Macão. Please note that the images are not in scale.  
Figure 2: Fuel Management Zones (FMZs) along roads and powerlines, in the north of Macão, which were treated in 2018.
## 4 Conclusions  
In this paper, an all-solid DCC ultra BDC-MSF is presented. The influence of MSF parameters on \(\lambda_{\mathrm{P}}\), dispersion, and \(K\) value was studied by making use of the mode coupling mechanism and control variable method. When the parameters are set as \(n_{1}\) = 1.467, \(n_{2}\) = 1.382, \(n_{3}\) = 1.326, \(d_{1}\) = 2.15 \(\upmu\)m, \(d_{2}\) = 0.51 \(\upmu\)m, \(d_{3}\) = 0.84 \(\upmu\)m, and \(\Lambda\) = 1.55 \(\upmu\)m, the designed MSF has very wide compensation bandwidth of up to 190 nm. The ADCM of the designed BDC-MSF to standard SMF is 18.45 times at 1385\(\sim\)1575 nm. At the same time, the \(\mathrm{D_{R}}\) is within \(\pm\)0.72 ps/(nm\(\cdot\)km), and the splicing loss with standard SMF can be controlled under 5 dB. What's more, the \(\mathrm{A_{eff}}\) at 1550 nm is 13.40 \(\upmu\)m\({}^{2}\). The presented SMF has much wider bandwidth and larger compensation multiple than the literature has previously reported. Compared with the air hole-quartz structure BDC-MSF, the designed MSF reduces the difficulty of fiber drawing and is much easier to fuse with standard SMF. This provides a new basis for the construction of an all-optical network.  
[PERSON] wrote the manuscript, and all authors contributed to the completion of the manuscript.  
This research received no external funding.  
Supported by the National Basic Research Program of China (No. 2010 CB327604), National Science Foundation of China (Nos. 61405173, 61405172), National Science Foundation of Hebei Province (Nos. F2018105036, F2018203346, F2019105108), Science and Technology Research Project of College and University in Hebei Province (No. BJ2017108), Science and Technology Project of Tangshan City (Nos. 15130263a, 17130257a, 17110220a, 17110221a), PhD Innovation Fund Project of Tangshan University, and Tangshan Key Laboratory of Indoor Location Technology.  
The authors declare no conflict of interest.
###### Abstract
We calculate the correlation results, obtain the temporary maximum, and compare it with the threshold. If the threshold is exceeded, only two times more calculations are needed neighboring the provisional maximum on the Doppler axis. The maximum among the results is the final decision. To prevent repeated search and ensure that the points are taken differently each time, we use the modern Fisher-Yates shuffle algorithm [32; 33; 34] to traverse the search results randomly. When the maximum number of iterations is reached, all points in the range are taken. The Fisher-Yates shuffle is an algorithm for generating random arrangements of finite linear arrays. It is unbiased, so the probability of each point being randomly selected is the same. The modern version is more computationally efficient and does not require additional storage space. In the third step, we assume that the Doppler factor at the symbol level follows the Markov process [35]. Only the first symbol needs to be searched in the whole expected frequency domain. Then, the Doppler range of the following symbol is determined by the previous one, which dramatically reduces the search range of the two-dimensional grid. In this way, the calculation is further reduced. The rest of this paper is organized as follows. In Section 2, we introduce the transmitter structure, UWA channel modeling, and the receiver structure of the MSS communication system. In Section 3, a detailed description of the receiving and processing optimization scheme is given, as are some simulation results. In Section 4, we provide a sea trial and data analysis. Section 5 summarizes this paper.
# Estimation of Sedimentary Rock Porosity Using a Digital Image Analysis
###### Abstract  
Pore space characterisation is important in assessing the strength and hydraulic characteristics of rock. This paper proposes a new relationship to estimate the porosity of natural rocks using the data from a scanning electron microscope (SEM) and a pore and crack analysis system (PCAS). The obtained results were compared with the data obtained from a series of conventional mercury intrusion porosimetry (MIP) experiments. Three different rocks, namely siltstone, sandstone, and coal, collected from a depth below 400 m, were investigated in this study. The results indicated that the proposed method of digital analysis could accurately identify the pore size and porosity values, which were in agreement with the experimental data. The relationship between the two-dimensional porosity estimated from the digital analysis and the three-dimensional porosity obtained from laboratory experiments was established. A few limitations on the use of the proposed method have also been identified and discussed.  
porosity; pore size distribution; digital image analysis; sandstone; sedimentary rock; PCAS
## 5 Conclusions  
This paper focuses on the dimensional error compensation in CNC batch grinding. A data-driven sliding mode terminal iterative learning error compensation method is proposed to solve the problem. Through the above research, the following conclusions can be achieved:  
1. Based on the theory of sliding mode terminal iterative learning control, the compensation method is presented. This method only needs to measure the terminal dimension using a touch probe, without using other sensors to detect all kinds of error sources. Moreover, there is no need of model information, which is data-driven;
2. Step and impulse input were introduced into the compensation module to simulate errors. Through many simulations, the values of compensation parameters were defined, and the simulation results showed that the compensation effectiveness was obvious;
3. In order to verify the compensation performance in an actual industrial environment, two groups of experiment were carried out on the machine tool. Based on the comparisons, the compensation parameters defined by simulation performed well and the performance effectiveness of this proposed compensation method was obvious. In the experiment, for 68 pieces of indexable inserts, the qualified rate increased from 48.5% to 95%.  
Figure 11: Dimensional variation before and after error compensation.  
Using this compensation method, the machined indexable inserts will remain within tolerance in successive machining. This method is appropriate for batch manufacturing, which is much more economical and universality.  
Conceptualization, T.C. and X.T.; methodology, T.C.; software, T.C.; validation, T.C.; formal analysis, T.C.; investigation, T.C. and X.T.; resources, X.T.; data curation, T.C.; writing--original draft preparation, T.C.; writing--review and editing, T.C.; visualization, T.C.; supervision, X.T.; project administration, X.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results and Discussion  
### Mesh Convergence  
In order to guarantee that mesh-size effects are not significant in the final results, a convergence study has been performed using the 50% relative density 3D model. Based on the results of this study (Figure 8), all subsequent analyses were performed using 10-noded quadratic tetrahedral elements. These elements have an average size that is 2% of the cell size. Average size is specified here because the models for different relative densities all have different numbers of elements, but the average size remains constant in all of the models.  
Figure 6: Gyroid prismatic specimen in the compression fixture.  
Figure 7: Digital image correlation (DIC) generated transverse-strain field (overlaying the capture image).
## 4 Simulation Result and Discussion
### Data Generation  
To gather voltage magnitude data, the TDS is conducted in IEEE 9 Bus and New England 39 Bus test systems utilizing DIgSILENT power factory software, which considers multiple contingencies under various operating settings. The simulation should capture all the system's behavior and as many different operating situations as feasible to represent the practical operation condition of the power system. For this, the loading of each bus is varied between 0.8 and 1.2 of its power rating. Similarly, to simulate the uncertainty of RESs, the power generation from the solar and wind farm is varied based on their probability distributions as given in [42; 43]. The complex load model in DIgSILENT power factory software, which represents the combination of various load component (static and dynamic loads) is considered for simulation. Here, the complex load model is adapted to represent the dynamics of induction motor loads, which is the primary driving factor for the short-term voltage instability in a power system. The three phase short-circuit faults are assumed to occur at each transmission line. The faults last for 0.1 s and are cleared without the loss of power system elements in this study. The configuration of the data set generation that can resemble the overall operation condition of the power system is outlined in Table 3. For each operating scenario, the simulation is run for 5 s to correctly label the STVS state of the power system. For the IEEE 9 Bus system, a total of 19,450 labeled samples are generated under this condition. Similarly, 20,500 labeled samples are generated for the New England 39 Bus system. To monitor the STVS status of the system, all post-fault voltage samples of time 0.25 s are retrieved from voltage trajectories that resemble the OTW. In this study, the OTW length of 0.25 s is assumed to resemble the longest possible time for the protective device (such as the under-voltage load shedding device) to respond to the post-disturbance condition. The number of sampling points is calculated as the ratio of OTW length to the sampling per time step. Here, the sampling time of 0.01 s is taken, so in total, there are 25 sampling points which resembles the post-fault voltage magnitude at each time step. Therefore, the raw data are an input matrix of size \(25\times 9\) for each sample for the IEEE 9 Bus and \(25\times 39\) for the New England 39 Bus systems. Each sample has 25 input time series of voltage magnitude and nine variables for the IEEE 9 Bus test system. Similarly, the samples in the New England 39 Bus system have 25 input time series of  
Figure 10: New England 39 Bus System.  
voltage trajectories and 39 variables representing each bus. Finally, 80% of the data set was randomly selected for training, while 20% was used for validation and testing in this study.
# Comparative Analysis of 5G Mobile Communication Network Architectures
## 4 Simulation Model
### HCC Offloading Decision Module  
The HCC offloading decision module is specifically designed for the HCC architecture. The role of the HCC offloading decision module is to execute offloading decisions for individual tasks entering the edge cloud to minimize end-to-end latency from user perspectives. Since the CCC and MEC architectures do not need to make task offloading decisions, the HCC offloading decision module is utilized when the network model is simulating the HCC architecture. When a specific task enters the edge cloud, it compares the time it takes to process the task directly at the edge cloud (\(t_{EC}\)) to the time it takes to process by offloading it to the central cloud (\(t_{CC}\)). Here, \(t_{EC}\) can be expressed as  
\[t_{EC}=t_{EC}^{queue}+t_{EC}^{process} \tag{1}\]  
where \(t_{EC}^{queue}\) is delay time in the queue and \(t_{EC}^{process}\) is the actual task processing time. Additionally, the task processing time through the central cloud, \(t_{CC}\), can be expressed as  
\[t_{CC}=t_{CC}^{queue}+t_{CC}^{process}+2t_{EC}^{transport} \tag{2}\]  
where \(t_{CC}^{queue}\) is delay time in the queue, \(t_{CC}^{process}\) is the actual task processing time, and \(2t_{EC}^{transport}\) is the round trip time for the task from the edge cloud to central cloud and back.  
Equations (1) and (2) are further expanded by introducing new parameters: \(w\), \(R_{EC}\), and \(R_{CC}\). Parameter \(w\) is the actual work load of an individual task. \(R_{RC}\) and \(R_{CC}\) are the computing resources for the edge cloud and the central cloud, respectively, expressed in CPU cycle frequency in units of megahertz (MHz). With these parameters, the time required to process a specific task with work load \(w\) for the edge cloud and the central cloud can be expressed, respectively, as follows:  
\[t_{EC}=t_{EC}^{queue}+\frac{w}{R_{EC}} \tag{3}\]  
\[t_{CC}=t_{CC}^{queue}+\frac{w}{R_{CC}}+2t_{EC}^{transport} \tag{4}\]  
In addition to the parameters defined, the decision variable x need to be defined. If we set \(x=0\), then it is assumed that \(t_{EC}\leq t_{CC}\) and the task is processed at the edge cloud. If we set \(x=1\) then the task is offloaded to the central cloud (\(t_{EC}>t_{CC}\)). The overall latency (\(t_{HC}\)), due to the task offloading decision, can be expressed as  
\[t_{HC}=(1-x)t_{EC}+xt_{CC} \tag{5}\]
## 3 Principals of Routing Protocols and Their Breakdowns
### Energy Aware Multi-Path Routing Protocols
#### 3.1.14. Edorq  
In this paper [37], the authors proposed the first depth-based opportunistic algorithm based on Q-learning (EDOEQ). The algorithm has two-phase, (1) the candidate set selection phase and (2) the candidate set coordination phase. The candidate set selection phase is divided into (a) greedy and void recovery modes. The greedy mode is used to select forwarding candidates, and the void recovery mode is used when the greedy mode encounters a void in the system. The maximum reward is awarded when the forwarder selects a node with a minimum distance from the sink and the most residual energy. A holding time is assigned based on the Q-values of the nodes to reduce overhead. The candidate with larger Q-values has a shorter holding time. If a sender does not overhear the same packet from the candidate node after maximum holding time, it is sent to recovery mode. The algorithm simulation was performed using NS2, and the Aqua sim was used as an extension. Their results show that it performed better than DBR, VBF, and QLEAR algorithms regarding energy efficiency, delivery ratio, and network overhead.
## 2 Materials and Methods
### Controller Node Failure-Recovery
#### 2.7.3 Load Criterion  
The second selection criterion is the prospective computational load of the controller candidate after it has taken over the new control task. Taking the prospective load into account in control assignment optimizes the control task distribution for an even load among all prospective candidates. This should ensure that a node is not overburdened with the new task, therefore satisfying the QoS objective AO1 and at the same time avoiding single-point of failures serving objective AO2.  
The prospective load on a node mainly depends on the following factors:  
1. The way in which the operating system running on the node schedules the individual tasks
2. The characteristics and run times of other tasks already running on the node
3. The hardware processing capability of the node  
The middle-ware/operating system we are using in our experiments is RODOS, which utilizes a round-robin task scheduling approach. Using the measured average runtime share \(L_{idle}\in[0,1]\) of the idle task running on the node for a fixed time \(\Delta Tl\) and the empirical determined estimated runtime \(\hat{T}_{ctrl}\) of the new control task we can estimate the expected runtime share of the idle task \(L_{idle,+}\) after the acquisition of the new control task:  
\[L_{idle,+}=L_{idle}-\hat{T}_{ctrl}\,f_{ctrl} \tag{12}\]  
With \(f_{ctrl}=\frac{1}{18}\) as the task frequency of the control task. The estimated task runtime \(\hat{T}_{ctrl}\) is visualized for different MPC control horizon lengths in Figure 8.  
Figure 7: Network hop count between two nodes \(n1\), \(n2\) to distance cost mapping for the bid with \(\beta_{d}=2\).  
If \(L_{idle,+}\) is less than zero the node would be overburdened by the new control task and the bid is sent back marked as infeasible. Otherwise the appropriately scaled cost of computational load \(Cl\) is then computed by:  
\[Cl=e^{15\,(1-L_{idle,+})-13.5}-e^{-15\,(1-L_{idle,+})}+1 \tag{13}\]  
Equation (13) maps the occupied computational resources after the new task acquisition denoted by \(1-L_{idle,+}\) to the load cost by exponentially penalizing high loads (\(L_{idle,+}<0.2\)) and keeping costs on an almost even level for idle loads which have a similar effect on the task performance (\(0.2\geq L_{idle,+}<0.8\)). The effect of this mapping is visualized in Figure 9.  
Figure 8: Empirically determined average runtime \(\hat{T}_{ctrl}\) of the MPC control task for different control horizon lengths running on the STM32F4 node.  
Figure 9: Mapping from free computing load after task acquisition \(L_{idle,+}\) to bid load cost \(Cl\).  
Finally both the distance cost \(Cd_{kk+m}\) and the load cost \(Cl\) are fused to form the bid **Cg** of a candidate node:  
\[\textbf{Cg}=Cl\,\textbf{Cd} \tag{14}\]  
With \(\textbf{Cd}\in\mathbb{R}_{1\times m}\) as the distance cost sequence of length \(m\) in row vector representation.
# A Survey of GIS and IoT Integration: Applications and Architecture
## 4 Integrated IoT and GIS Architectures
### GIS and IoT Integration Architecture
#### 4.3.1 GIS in Processing Layer
The location, dimension, and orientation of these objects can lead to many spatial relationships between them [182]. With the great functionalities of GIS, analyzing these relationships can be helpful to address the challenges we are facing in IoT-based systems. These functionalities of GIS are discussed in [183] and are categorized into six different analytical methods of geospatial analysis namely, Geometric Measures, Data Mining, Basic Analytical Operations, Basic Analytical Methods, Network Analysis, Surface Analysis, and Geostatistics. With the rapid tendency to move to urban areas [35] and an over-utilization of land and sea resources, the quality of water plays a prominent role in people's appropriate areas for living. Using IoT, GIS and remote sensing technology, Reference [92] proposes a water quality monitoring system. GIS is used to divide the Pacific Island Countries into different regions which would be involved in identifying the specific location. The Studies [78; 79] mentioned in Section 3.3 use GIS functionalities to process their information.
# Effect of Different Acoustic Parameters on NOx Emissions of Partially Premixed Flame
## 4 Conclusions  
The NOx emission of partially premixed flames under acoustic parameters of 0-400 Hz/0-1400 Pa were investigated experimentally. The relationship between frequency, amplitude, and EINOx was discussed based on flame length, flame shape, and vortex. The main conclusions are as follows:  
(1) The critical frequency \(f_{c}\), \(f_{\mathrm{c}}\in[170,190]\), occurs in the changing EINOx of partially premixed flame with the increase of amplitude and frequency. A similar phenomenon is also observed in the change of flame length with acoustic parameters. EINOx decreases with the increase of amplitude when \(f<f_{c}\); there is a linear relationship between EINOx and amplitude, and the slope range is from \(-0.009\) to \(-0.003\); EINOx increases with the increase of frequency in a fixed amplitude, and there is also a linear relationship between EINOx and frequency, where the slope range is from 0.01 to 0.03. However, EINOx increases with an increase of amplitude when \(f>f_{c}\), and there is a linear relationship between EINOx and amplitude, where the slope ranges from 0.001 to 0.006.  
Figure 10: Mie scattering images at different acoustic parameters.  
Figure 9: Mie scattering images at different frequency.  
(2) The flame length decreases with the increase of amplitude when \(f<f_{c}\); the reason is that an increasing acoustic effect makes the flame wrinkle and deform, and the mixing effect of the flow field is enhanced. The flame length increases with the increase of amplitude when \(f>f_{c}\); the reason is that the influence of acoustics on the flame becomes weakened in this frequency range; flame diameter is decreased, and fuel gas velocity is increased with the increase of amplitude. This is the dominant factor for the decrease of EINOx.  
(3) Interaction between flame and vortex influences the NOx emission. When acoustic amplitude increases at \(f>f_{c}\), the vortex moves to the upstream zone of the partially premixed flame, there is enhanced interaction at the flame base, and shortened flame length; therefore, NOx emissions decrease.  
Conceptualization, M.W. and Z.S.; methodology, Z.S.; software, Z.S.; validation, M.W., Z.S., and K.D.; formal analysis, M.W.; investigation, Z.S.; resources, K.D.; data curation, M.W. and Z.S.; writing--original draft preparation, M.W. and Z.S.; writing--review and editing, K.D.; visualization, Y.H.; supervision, K.D.; project administration, Y.Z.; funding acquisition, K.D.  
Thanks to the reviewers and editor for manuscript improvements. This research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LY18E060012, National Natural Science Foundation of China under Grant No. 51106139, and the China Scholarship Council under Grant No. 201708330386.  
The authors declare no conflict of interest.