esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of a Vertical FSO Link with Energy Harvesting Strategy
## 2 System Model
### Geometric Spread and Pointing Error Model  
In addition to attenuation and atmospheric turbulence, geometric beam spread and pointing accuracy also affect the performance of these systems. The geometric loss is caused by the divergence of the transmit beam when propagating through the atmosphere, as \(\omega_{z}=\theta_{T}\cdot L\), with \(\omega_{z}\) being the received beam waist, with \(\theta_{T}\) denoting the transmitter divergence angle, whereas \(L\) is the propagation path length. Since the received beam width is usually wider than the lens aperture size, part of the transmitted power cannot be collected, leading to loss. Thus, this geometric loss depends mainly on the ratio between the received beam waist, \(\omega_{z}\), and the receiver aperture radius, \(r\). However, during the designing of a FSO link, it is possible to control the beamwidth produced at a certain distance, and therefore the ratio \(\omega_{z}/r\), by adjusting properly the laser parameters.  
On the other hand, imperfections in the pointing, acquisition, and tracking process between the ground stations and the UAVs can also cause loss. Note that both phenomena are interrelated and, thus, accurate modeling frameworks have been proposed to account for both effects. To this end, in this work, we consider the general model proposed in [44; 45]. According to this model, a Gaussian beam profile is assumed with a beam waist, \(\omega_{z}\), on the receiver plane and a circular aperture receiver of radius \(r\); then, the attenuation due to the geometric spread with pointing error can be approximated as the Gaussian form  
\[h_{p}\approx A_{o}\exp{\left(-\frac{2\rho^{2}}{\omega_{2 eq}^{2}}\right)}, \tag{10}\]  
where \(\rho\), is the radial pointing error, \(A_{o}\) is the fraction of collected power without pointing error, i.e., only due to geometric spread, and \(\omega_{zeq}^{2}\) is the equivalent beam width. Here, \(A_{o}\) and \(\omega_{zeq}^{2}\) are given by \(A_{o}=\text{erf}(\mu)^{2}\) and \(\omega_{zeq}^{2}=\omega_{z}^{2}\sqrt{\tau}\text{erf}(\mu)/\left[2\mu\exp(-\mu^ {2})\right]\), respectively, being \(\text{erf}(\cdot)\) the error function and \(\mu=\sqrt{\tau}r/(\sqrt{2}\omega_{z})\). Moreover, considering independent identical Gaussian distribution for the horizontal \(x\) and \(y\) displacement in the receiver plane, the radial error \(\rho=\sqrt{x^{2}+y^{2}}\) is modeled as Rayleigh distribution with a jitter variance at the receiver \(\sigma_{s}^{2}\). Under these assumptions, the channel attenuation, \(h_{p}\), can be seen as a function of the radial displacement, \(\rho\), which is a RV. Hence, the pdf of \(h_{p}\) can be seen as a random variable transformation problem, which leads to the following expression [45]:  
\[f_{h_{p}}(h_{p})=\frac{\gamma^{2}}{A_{o}^{\gamma^{2}}}h_{p}^{\gamma^{2}-1}, \tag{11}\]  
where \(\gamma=\omega_{zeq}/(2\sigma_{s})\) denotes the ratio between the equivalent beam radius at the receiver and the pointing error displacement standard deviation.
# Tag-to-Tag Interference Suppression Technique Based on Time Division for RFID
## 4 Anti-Collision for RFID Based on TD
### Data Architecture for RFID Anti-Collision Based on TD
#### 4.3.1 Preamble  
In a noisy and interference environment that may prevent us from accurately detecting the desired signal, the preamble helps to prevent performance degradation of the signal detection [43]. Thus, preamble data are used for the channel estimation, SNR estimation, synchronization, etc. At the receiver of the reader, the efficient channel and SNR estimations should be obtained to enable the accurate detection of the transmitted data from the RFID tags and the estimation of the number of tags in the interrogation zone, respectively. In addition, perfect synchronization is required for the proposed RFID approach because the tag transmits data using a specific time slot. In this paper, we assume that the channel estimation, SNR estimation, and synchronization are performed at the receiver of the reader.  
Assuming that there are four tags that each employs unique time slots, Figure 3 shows the data architecture for the proposed anti-collision RFID system. In this system, preamble data in the \(l\)th tag are transmitted using the \(l\)th time slots in the figure before transmitting the payload.
###### Abstract  
This paper proposes an Adaptive Neural Fuzzy Inference System (ANFIS) model for diagnosis of combined Inter Turn Short Circuit (ITSC) and Broken Rotor Bar (BRB) faults in a Squirrel Cage Induction Motor (SC-IM). The signal of the stator current is obtained from a really healthy and faulty SC-IM. Experimental tests have been set up using a 1.5 Hp/380 V three-phase SC-IM with different combined ITSC and BRB faults under different loading conditions. Before entering the model, the Discrete Wavelet Transform (DWT) pre-processes the stator current signal. The DWT generates data sets in order to evaluate the proposed technique. ANFIS based on DWT is used successfully to diagnose the most relevant faults very effectively. In addition, ANFIS based on the DWT method has been compared to ANFIS and ANFIS based on an auto-regressive model, finding that the proposed method achieves higher efficiency than the previous one. The proposed ANFIS based on the DWT model classifies entirely different states of combined ITSC and BRB faults with high accuracy.  
ANFIS; broken rotor bar fault; DWT; feature extraction; inter turn short circuit fault  
1  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Diagnostic Modelling for Induction Motor Faults via ANFIS  
Algorithm and DWT-Based Feature Extraction. _Appl. Sci._**2021**, _11_, 9115. [[https://doi.org/10.3390/app11199115](https://doi.org/10.3390/app11199115)]([https://doi.org/10.3390/app11199115](https://doi.org/10.3390/app11199115))
# Automatic Lip Reading System Based on a Fusion Lightweight Neural Network with Raspberry Pi
###### Abstract  
Virtual Reality (VR) is a kind of interactive experience technology. Human vision, hearing, expression, voice and even touch can be added to the interaction between humans and machine. Lip reading recognition is a new technology in the field of human-computer interaction, which has a broad development prospect. It is particularly important in a noisy environment and within the hearing- impaired population and is obtained by means of visual information from a video to make up for the deficiency of voice information. This information is a visual language that benefits from Augmented Reality (AR). The purpose is to establish an efficient and convenient way of communication. However, the traditional lip reading recognition system has high requirements of running speed and performance of the equipment because of its long recognition process and large number of parameters, so it is difficult to meet the requirements of practical application. In this paper, the mobile end lip-reading recognition system based on Raspberry Pi is implemented for the first time, and the recognition application has reached the latest level of our research. Our mobile lip-reading recognition system can be divided into three stages: First, we extract key frames from our own independent database, and then use a multi-task cascade convolution network (MTCNN) to correct the face, so as to improve the accuracy of lip extraction. In the second stage, we use MobileNets to extract lip image features and long short-term memory (LSTM) to extract sequence information between key frames. Finally, we compare three lip reading models: (1) The fusion model of Bi-LSTM and AlexNet. (2) A fusion model with attention mechanism. (3) The LSTM and MobileNets hybrid network model proposed by us. The results show that our model has fewer parameters and lower complexity. The accuracy of the model in the test dataset is 86.5%. Therefore, our mobile lip reading system is simpler and smaller than other PC platforms and saves computing resources and memory space.  
mobile lip reading system; lightweight neural network; face correction; virtual reality (VR)
# Optimal Design Parameters for Hybrid DC Circuit Breakers Using a Multi-Objective Genetic Algorithm
## 2 HCB Topology and Problem Statement  
### HCB Topology  
#### 2.1.1 Configuration of the HCB  
The configuration of an HCB, which consists of five main branches in series with a residual switch S1 and DC inductor Lde, is shown in Figure 1. The nominal current branch (1) uses a fast mechanical switch S1, which conducts the current under normal conditions. When a fault occurs on the load side, the switch S1 is opened, which leads to the establishment of an arc between two contacts. To extinguish the arc in switch S1, the commutation current branch (2), including an inductor Lp and a precharged capacitor Cp, injects an inverse current by turning on the pair of thyristors T1a-1b or T2a-2b. When the arc is extinguished, the fault current is commutated into the commutation current branch. Simultaneously, the transient interruption voltage (TIV), which is across the HCB terminals, increases rapidly along with the capacitor voltage. If the TIV is higher than the action level of the energy absorption branch (3), the metal oxide varistor (MOV) conducts the fault current and dissipates energy until the fault current reduces to zero. The residual switch S1 is opened and the fault is cleared. Since the capacitor voltage is higher than the system voltage after clearing the fault, the discharging branch (4) consisting of a resistor R and a high-speed switch S3 is used to reduce the capacitor voltage to the system voltage. Finally, the reversing branch (5) changes the polarity of the capacitor voltage with an inductor L and a thyristor T3 to get the HCB ready for the next interruption.
## 4 Conclusions  
In this study, the accuracy of the geoid heights derived from the recently released Earth gravity model based on gravity data calculated from the Gravity Recovery and Climate Experiment (GRACE) and the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) satellites was evaluated by comparing it with the geoid heights derived from the Global Navigation Satellite System (GNSS)/leveling on the 1182 unified control points (UCPs) installed by the National Geographic Information Institute (NGII) all over South Korea. The following conclusions were obtained in this study:  
**First** The geoid heights derived from the three high-degree global geopotential models (GGMs) and the three Korean National Geoid (KNGeoid) models presented similar distributions ranging from 17 to 33 m around South Korea.  
**Second** The EGM2008 model showed a rather stable result for the root-mean-square-error (RMSE) of the residuals \(\left(N_{i}^{GNSS/leveling}-N_{i}^{Model}\right)\) that were considered in this study in terms of relative geoid heights \(\left(\Delta N_{i}\right)\) accuracy. Thus, the EGM2008 model is a more suitable model than the GECO and EIGEN-6C4 models as compared with the GNSS/leveling geoid heights all over South Korea. As a result, the EGM2008 model could be selected as the suitable GGM from among the three GGMs for determining a gravimetric geoid model for South Korea.  
**Third** Among the three KNGeoid models, the most recently developed KNGeoid18 model showed better results as a gravimetric geoid model all over South Korea than the KNGeoid13 and KNGeoid14 models as compared with the GNSS/leveling-derived geoid heights.  
For further study of the new gravimetric geoid model, it is necessary to perform an accuracy assessment of the geoid model all over South Korea by adding gravity data and by collocating the GNSS/leveling-derived geoid heights.  
**Author Contributions:** Original idea and methodology, writing, and data analysis, K.B.K.; project administration and supervision, H.S.Y.; review and editing, H.J.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by a National Research Foundation of Korea (NRF) grant funded by the South Korean government (MSIT) (NRF-2019R1C1C1005590).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 7: Histograms of the differences between the geoid heights \(\left(N_{GNSS/leveling}\right)\) derived from GNSS/leveling on the 1182 UCPs and the geoid heights \(\left(N_{KNGeoid}\right)\) derived from the three KNGeoid models. (**a**) KNGeoid13; (**b**) KNGeoid14; and (**c**) KNGeoid18.
# A Multi-Customer Vehicle Scheduling Optimization Method for Coal Intelligent Loading System
## 5 Case Verification
### Case Background  
A coal mining enterprise is a coal supplier. Coal products are traded through auctions. After the transaction is completed, customers are notified to send vehicles to load coal.  
Figure 5: Reverse mutation method.  
Figure 6: Flow chart of SSA.  
The enterprise has a total of 12 coal bunkers, including 2 original coal bunkers, 6 coal storage bunkers, 1 gangue bunker, and 3 loading bunkers for customers to load coal. In the morning, a total of seven customers received order information, which included customer number, vehicle number, customer vehicle number, load capacity, expected arrival loading time window, and loading operation time. As 66 groups have too many data points, only a portion of the customer information is displayed in Table 5; the complete information is shown in the Supplementary Materials.  
When the relevant personnel receive the customer information, they arrange the vehicles based on their experience. The loading plan before optimization is depicted in Table 6.  
In Table 6, the loading scheduling plan for the three loading bunkers of the enterprise can be observed. The specific plan is represented as follows: loading bunker 1 starts at 8:00 a.m. and first loads 4-4 with a loading time of 10 min, ending loading at 8:10 a.m., and then loads 7-3. The expected arrival loading time for 1-8 is between 10:20 and 10:50, but the end time of coal loading is 11:05, which is 15 min later than the expected arrival loading time, resulting in penalty costs. According to the loading plan mentioned above, the loading operation notification table is obtained. Due to the large amount of data, let us consider the loading operation notification for the nine vehicles of customer 1, as shown in Table 7.  
Table 7 specifies that the loading operation started at 8:49 under loading bunker 2 for 1-1. After 12 min of loading coal, the operation was completed, and the vehicle left at 9:01.  
It can be seen that this plan resulted in a total delay of 70 min in loading for all customers, meaning that customer satisfaction was low. Therefore, it is essential to utilize the MCVS optimization method proposed in this paper to schedule the vehicles in order to generate a rational and efficient scheduling plan.
# Region Adjacency Graph Approach for Acral Melanocytic Lesion Segmentation
## 2 Methods
### Oversegmentation  
The purpose of the first step of the segmentation algorithm is to group local regions by oversegmenting the image. In our solution, we use the simple linear iterative clustering algorithm (SLIC) which performs k-means in the 5d space defined by both the \(L^{*}\), \(a^{*}\), \(b^{*}\) values of the CIELAB colorspace and \(x\), \(y\) coordinates of the pixels to create superpixels [35; 36]. Superpixel algorithms group pixels into not only perceptual, but semantic meanings of an image. The mapping from pixel grids to superpixels holds the desirable properties of computational efficiency, perceptual meaningfulness and oversegmentation, which means that most of important boundaries in the image are found. To perform the SLIC operation the following parameters must be adjusted: \(K\)--the number of segments (superpixels), \(\sigma\)--the width of Gaussian smoothing kernel, and compactness--a parameter that balances the color-space proximity with image space-proximity. As the number of superpixel is given, each superpixel will have approximately \(N/K\) pixels. For each superpixel, at regular grid intervals, cluster centers are chosen based on the following equation:  
\[c_{k}=[l_{k},a_{k},b_{k},x_{k},y_{k}] \tag{2}\]  
As the Euclidean distance is not suitable for bigger distances in CIELAB colorspace, the following distance, \(D_{s}\), has been applied:  
\[d_{\text{LAB}}=\sqrt{(l_{k}-l_{i})^{2}+(a_{k}-a_{i})^{2}+(b_{k}-b_{i})^{2}}, \tag{3}\]  
\[d_{xy}=\sqrt{(x_{k}-x_{i})^{2}+(y_{k}-y_{i})^{2}}, \tag{4}\]  
\[D_{s}=d_{\text{LAB}}+\frac{m}{S}d_{xy}. \tag{5}\]  
The size of each superpixel is \(N/K\) where \(N\) is the size of the image and \(K\) refers to the number of superpixels. The center of the superpixel is defined as \(S=\sqrt{N/K}\). Parameter \(m\) is responsible for the compactness of the superpixel and was set to 10. At the beginning, the algorithm samples \(K\) regularly spaced cluster centers and moves them to seed locations based on the lowest gradient position in a \(3\times 3\) neighborhood. Each pixel is assigned to its nearest cluster center whose search area overlaps this pixel. After all the pixels are combined with the nearest cluster center, a new center is computed [36].  
Figure 3 shows how similar regions of the image are grouped in superpixels depending on the number of segments and compactness.
## 4 Discussion
### Environmental Effects on the Abilities of the PRI-Based and SIF-Based Models in Estimating GPP  
In our study, RH was the most limiting factor for the PRI-based LUE model in estimating GPP of maize (Figure 6). Previous studies have reported that PRI has the highest and most common correlation with LUE under cloudless or slightly cloudy conditions, rather than water-limited conditions [19; 58]. Our study demonstrated that the incident radiation (PAR) and sky condition (Q) were not influenced the performance of the PRI-based LUE model in estimating GPP (Figure 7a,b) because the APAR included in the model partly account for the sky radiation. The relationship between LUE\({}_{\text{PRI}}\times\)APAR and GPP\({}_{\text{EC}}\) had a lower correlation when RH was lower than 60% (Figure 7c), which was consistent with the VPD-limited conditions in [PERSON] et al. [58]. However, in the cropland with irrigation, SWC was not a limitation. We infer that the PRI-based LUE model was more sensitive to the atmospheric water deficit (low RH) than the soil water deficit (low SWC) because the former had a direct effect on opening and closing of the leaf stomata and then the photosynthesis, not the reflectance of the leaf and canopy. Soil temperature also had a positive effect on the PRI-based LUE model (Figures 6a and 7f). The high T\({}_{\text{s}}\) occurred at the late V stages (DOY 195 to 207) with synchronously high RH (Figure 5). Thus, the maize rapid grown at this stage had a higher photosynthetic rate and linear energy distribution, leading to the good performance of the PRI-based LUE model.  
Different from the PRI-based LUE model, the relationship between SIF\({}_{\text{can}}\) and GPP\({}_{\text{EC}}\) was influenced by the light-related factors, i.e., PAR and Q. It was found that incident radiation played dominant roles in SIF changes among environmental variables by sensitivity tests on simulated SIF data by Soil Canopy Observation Photosynthesis Energy (SCOPE) model [65]. Photosynthesis and chlorophyll fluorescence were highly correlated when vegetation was exposed to excess incident radiation [38]. The incident radiation absorbed by chlorophyll was a key driver of both GPP and SIF, but limited or saturated light conditions could result in different coupling patterns between the efficiency of photosynthesis and fluorescence [66; 67]. Due to the effect of diffuse radiation, the clearer the sky showed the stronger correlation between SIF and GPP for maize canopy in our study (Figure 7). This finding was in agreement with [PERSON] et al. [66], but differed from [PERSON] et al. [46], [PERSON] et al. [68], and [PERSON] et al. [50].
## 3 Results and Discussion
### Mechanical Properties
#### 3.3.3 Tensile Strength  
Figure 9 shows the tensile strength of AFRP composites by ZnCl\({}_{2}\) and PDA treatment. The tensile strength of the untreated condition was 409.39 MPa, and the highest tensile strength was obtained in the 6Z condition (423.74 MPa). This is a 3.51% improvement over the untreated 409.39 MPa. Similar tensile strength improvements were obtained under all treatment conditions. The highest tensile strength was obtained under 6Z conditions, and the lowest tensile strength was obtained under 10 ZP conditions. Additionally, the difference between these two conditions is less than 5%. Tensile strength is related to the performance of the fabric itself. Lewis acid treatment weakens the fabric itself by etching the fabric surface, reducing the performance of the composites. However, etching can increase the surface roughness of the fabric and improve the interfacial adhesion between the fabric and the epoxy resin, thereby improving the performance of the composites. It is judged that there is no change in tensile strength as the two phenomena are canceled out.  
From Figures 8 and 9, it can be seen that the tensile and bending strength exhibit different trends, respectively. The overall trend of tensile strength is not very obvious  
Figure 9: Tensile strength and stress-strain curve of the 6-layer aramid fabric under various conditions. (a) Tensile strength, (b) Stress-strain.  
because the properties of the fabric itself are the key to the tensile strength. Therefore, the variation of tensile strength is within 5% for all conditions. However, in bending strength, it can be seen that the properties of the Lewis-acid-treated conditions are all inferior to the PDA-treated conditions. As explained before, although etching helps to improve the bonding ability with the resin, it reduces the properties of the fabric itself. Thus, the Lewis-acid-treated condition did not perform as well as the PDA-only condition.  
It can be seen from the stress-strain curves of the 6-layer aramid fabric under various conditions in Figure 9b that there is a decrease in elongation at 6Z and 10Z for the two conditions treated with ZnCl\({}_{2}\) compared to the untreated condition. This may be due to the disruption of the polyamide mechanism in the fabric structure by the ZnCl\({}_{2}\) treatment. It can also be seen that all PDA-treated conditions effectively improved elongation. Because the PDA treatment promotes the activation of the fabric surface to enhance the interfacial bonding between the fabric and the epoxy resin. It can be concluded that ZnCl\({}_{2}\) and PDA treatment can reasonably compensate for each other's effects on elongation, thus ensuring the mechanical properties of AFRP.
# Globally Optimal Facility Locations for Continuous-Space Facility Location Problems
## 3 Location Estimation Based on the Manhattan Distance
### Determination of the Globally Optimal Facility Locations
#### 3.5.1 Model for the Uncapacitated Multi-Facility Problem  
With the number of demand points \(n\), we have \(E\) candidate facility locations. Let \(I\) be the set of demand points, which is indexed by \(i\) with \(i\in I\). Let \(L\) be the set of candidate facility locations, which is indexed by \(l\) with \(l\in L\). Then, the distance from the candidate facility location \(l\) to the demand point \(i\) is known, which is denoted by \  
this sense, the number of facilities is also a decision variable. Then, the mathematical model \(\mathcal{M}2\) is given by  
\[\text{Min Obj}_{2}=c\sum_{l=1}^{\text{E}}\sum_{i=1}^{n}\omega_{i}D_{ll}t_{il}+ \text{F}\sum_{l=1}^{\text{E}}g_{l} \tag{45}\]  
subject to  
\[\sum_{l=1}^{\text{E}}g_{l}\leq n \tag{46}\]  
\[\sum_{l=1}^{\text{E}}t_{il}=1\ \forall\ i\in I \tag{47}\]  
\[t_{il}\leq g_{l}\ \forall\ i\in I\ \text{and}\ \forall\ l\in L \tag{48}\]  
The objective function in (45) minimizes the total cost including the weighted travel cost and fixed cost. The first constraint in (46) means that the number of facilities cannot be more than \(n\). Constraints (47) and (48) are the same as the Constraints (43) and (44).